Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus
Identifieur interne : 000181 ( Pmc/Corpus ); précédent : 000180; suivant : 000182Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus
Auteurs : Ka Chun Chong ; Benny Chung Ying ZeeSource :
- BMC Infectious Diseases [ 1471-2334 ] ; 2012.
Abstract
During the early stages of a new influenza pandemic, travel restriction is an immediate and non-pharmaceutical means of retarding incidence growth. It extends the time frame of effective mitigation, especially when the characteristics of the emerging virus are unknown. In the present study, we used the 2009 influenza A pandemic as a case study to evaluate the impact of regulating air, sea, and land transport. Other government strategies, namely, antivirals and hospitalizations, were also evaluated.
Hong Kong arrivals from 44 countries via air, sea, and land transports were imported into a discrete stochastic Susceptible, Exposed, Infectious and Recovered (SEIR) host-flow model. The model allowed a number of latent and infectious cases to pass the border, which constitutes a source of local disease transmission. We also modeled antiviral and hospitalization prevention strategies to compare the effectiveness of these control measures. Baseline reproduction rate was estimated from routine surveillance data.
Regarding air travel, the main route connected to the influenza source area should be targeted for travel restrictions; imposing a 99% air travel restriction delayed the epidemic peak by up to two weeks. Once the pandemic was established in China, the strong land connection between Hong Kong and China rendered Hong Kong vulnerable. Antivirals and hospitalization were found to be more effective on attack rate reductions than travel restrictions. Combined strategies (with 99% restriction on all transport modes) deferred the peak for long enough to establish a vaccination program.
The findings will assist policy-makers with decisions on handling similar future pandemics. We also suggest regulating the extent of restriction and the transport mode, once restriction has been deemed necessary for pandemic control. Although travel restrictions have yet to gain social acceptance, they allow time for mitigation response when a new and highly intrusive virus emerges.
The online version of this article (doi:10.1186/1471-2334-12-309) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/1471-2334-12-309
PubMed: 23157818
PubMed Central: 3577649
Links to Exploration step
PMC:3577649Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus</title><author><name sortKey="Chong, Ka Chun" sort="Chong, Ka Chun" uniqKey="Chong K" first="Ka Chun" last="Chong">Ka Chun Chong</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Ying Zee, Benny Chung" sort="Ying Zee, Benny Chung" uniqKey="Ying Zee B" first="Benny Chung" last="Ying Zee">Benny Chung Ying Zee</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">23157818</idno><idno type="pmc">3577649</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3577649</idno><idno type="RBID">PMC:3577649</idno><idno type="doi">10.1186/1471-2334-12-309</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000181</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000181</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus</title><author><name sortKey="Chong, Ka Chun" sort="Chong, Ka Chun" uniqKey="Chong K" first="Ka Chun" last="Chong">Ka Chun Chong</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Ying Zee, Benny Chung" sort="Ying Zee, Benny Chung" uniqKey="Ying Zee B" first="Benny Chung" last="Ying Zee">Benny Chung Ying Zee</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">BMC Infectious Diseases</title><idno type="eISSN">1471-2334</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>During the early stages of a new influenza pandemic, travel restriction is an immediate and non-pharmaceutical means of retarding incidence growth. It extends the time frame of effective mitigation, especially when the characteristics of the emerging virus are unknown. In the present study, we used the 2009 influenza A pandemic as a case study to evaluate the impact of regulating air, sea, and land transport. Other government strategies, namely, antivirals and hospitalizations, were also evaluated.</p></sec><sec><title>Methods</title><p>Hong Kong arrivals from 44 countries via air, sea, and land transports were imported into a discrete stochastic Susceptible, Exposed, Infectious and Recovered (SEIR) host-flow model. The model allowed a number of latent and infectious cases to pass the border, which constitutes a source of local disease transmission. We also modeled antiviral and hospitalization prevention strategies to compare the effectiveness of these control measures. Baseline reproduction rate was estimated from routine surveillance data.</p></sec><sec><title>Results</title><p>Regarding air travel, the main route connected to the influenza source area should be targeted for travel restrictions; imposing a 99% air travel restriction delayed the epidemic peak by up to two weeks. Once the pandemic was established in China, the strong land connection between Hong Kong and China rendered Hong Kong vulnerable. Antivirals and hospitalization were found to be more effective on attack rate reductions than travel restrictions. Combined strategies (with 99% restriction on all transport modes) deferred the peak for long enough to establish a vaccination program.</p></sec><sec><title>Conclusion</title><p>The findings will assist policy-makers with decisions on handling similar future pandemics. We also suggest regulating the extent of restriction and the transport mode, once restriction has been deemed necessary for pandemic control. Although travel restrictions have yet to gain social acceptance, they allow time for mitigation response when a new and highly intrusive virus emerges.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/1471-2334-12-309) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Dawood, Fs" uniqKey="Dawood F">FS Dawood</name></author><author><name sortKey="Iuliano, Ad" uniqKey="Iuliano A">AD Iuliano</name></author><author><name sortKey="Reed, C" uniqKey="Reed C">C Reed</name></author><author><name sortKey="Meltzer, Mi" uniqKey="Meltzer M">MI Meltzer</name></author><author><name sortKey="Shay, Dk" uniqKey="Shay D">DK Shay</name></author><author><name sortKey="Cheng, Py" uniqKey="Cheng P">PY Cheng</name></author><author><name sortKey="Bandaranayake, D" uniqKey="Bandaranayake D">D Bandaranayake</name></author><author><name sortKey="Breiman, Rf" uniqKey="Breiman R">RF Breiman</name></author><author><name sortKey="Brooks, Wa" uniqKey="Brooks W">WA Brooks</name></author><author><name sortKey="Buchy, P" uniqKey="Buchy P">P Buchy</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Grais, Rf" uniqKey="Grais R">RF Grais</name></author><author><name sortKey="Hugh, G" uniqKey="Hugh G">G Hugh</name></author><author><name sortKey="Glass, Ge" uniqKey="Glass G">GE Glass</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Colizza, V" uniqKey="Colizza V">V Colizza</name></author><author><name sortKey="Barrat, A" uniqKey="Barrat A">A Barrat</name></author><author><name sortKey="Barthelemy, M" uniqKey="Barthelemy M">M Barthelemy</name></author><author><name sortKey="Valleron, Aj" uniqKey="Valleron A">AJ Valleron</name></author><author><name sortKey="Vespignani, A" uniqKey="Vespignani A">A Vespignani</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hollingsworth, Td" uniqKey="Hollingsworth T">TD Hollingsworth</name></author><author><name sortKey="Ferguson, N" uniqKey="Ferguson N">N Ferguson</name></author><author><name sortKey="Anderson, R" uniqKey="Anderson R">R Anderson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cooper, Bs" uniqKey="Cooper B">BS Cooper</name></author><author><name sortKey="Pitman, Rj" uniqKey="Pitman R">RJ Pitman</name></author><author><name sortKey="Edmunds, Wj" uniqKey="Edmunds W">WJ Edmunds</name></author><author><name sortKey="Gay, Nj" uniqKey="Gay N">NJ Gay</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Flahault, A" uniqKey="Flahault A">A Flahault</name></author><author><name sortKey="Valleron, Aj" uniqKey="Valleron A">AJ Valleron</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hufnagel, L" uniqKey="Hufnagel L">L Hufnagel</name></author><author><name sortKey="Brockmann, D" uniqKey="Brockmann D">D Brockmann</name></author><author><name sortKey="Geisel, T" uniqKey="Geisel T">T Geisel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Flahault, A" uniqKey="Flahault A">A Flahault</name></author><author><name sortKey="Vergu, E" uniqKey="Vergu E">E Vergu</name></author><author><name sortKey="Boelle, Py" uniqKey="Boelle P">PY Boelle</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Brownstein, Js" uniqKey="Brownstein J">JS Brownstein</name></author><author><name sortKey="Wolfe, Cj" uniqKey="Wolfe C">CJ Wolfe</name></author><author><name sortKey="Mandl, Kd" uniqKey="Mandl K">KD Mandl</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Tomba, Gs" uniqKey="Tomba G">GS Tomba</name></author><author><name sortKey="Wallinga, J" uniqKey="Wallinga J">J Wallinga</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ciofi Degli Atti, Ml" uniqKey="Ciofi Degli Atti M">ML Ciofi degli Atti</name></author><author><name sortKey="Merler, S" uniqKey="Merler S">S Merler</name></author><author><name sortKey="Rizzo, C" uniqKey="Rizzo C">C Rizzo</name></author><author><name sortKey="Ajelli, M" uniqKey="Ajelli M">M Ajelli</name></author><author><name sortKey="Massari, M" uniqKey="Massari M">M Massari</name></author><author><name sortKey="Manfredi, P" uniqKey="Manfredi P">P Manfredi</name></author><author><name sortKey="Furlanello, C" uniqKey="Furlanello C">C Furlanello</name></author><author><name sortKey="Scalia Tomba, G" uniqKey="Scalia Tomba G">G Scalia Tomba</name></author><author><name sortKey="Iannelli, M" uniqKey="Iannelli M">M Iannelli</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Cowling, Bj" uniqKey="Cowling B">BJ Cowling</name></author><author><name sortKey="Lau, L" uniqKey="Lau L">L Lau</name></author><author><name sortKey="Wu, P" uniqKey="Wu P">P Wu</name></author><author><name sortKey="Wong, H" uniqKey="Wong H">H Wong</name></author><author><name sortKey="Fang, V" uniqKey="Fang V">V Fang</name></author><author><name sortKey="Riley, S" uniqKey="Riley S">S Riley</name></author><author><name sortKey="Nishiura, H" uniqKey="Nishiura H">H Nishiura</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pitman, Rj" uniqKey="Pitman R">RJ Pitman</name></author><author><name sortKey="Cooper, Bs" uniqKey="Cooper B">BS Cooper</name></author><author><name sortKey="Trotter, Cl" uniqKey="Trotter C">CL Trotter</name></author><author><name sortKey="Gay, Nj" uniqKey="Gay N">NJ Gay</name></author><author><name sortKey="Edmunds, Wj" uniqKey="Edmunds W">WJ Edmunds</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bettencourt, Lma" uniqKey="Bettencourt L">LMA Bettencourt</name></author><author><name sortKey="Ribeiro, Rm" uniqKey="Ribeiro R">RM Ribeiro</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Stohr, K" uniqKey="Stohr K">K Stohr</name></author><author><name sortKey="Esveld, M" uniqKey="Esveld M">M Esveld</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Liao, Q" uniqKey="Liao Q">Q Liao</name></author><author><name sortKey="Cowling, Bj" uniqKey="Cowling B">BJ Cowling</name></author><author><name sortKey="Lam, Wwt" uniqKey="Lam W">WWT Lam</name></author><author><name sortKey="Fielding, R" uniqKey="Fielding R">R Fielding</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wu, Jt" uniqKey="Wu J">JT Wu</name></author><author><name sortKey="Riley, S" uniqKey="Riley S">S Riley</name></author><author><name sortKey="Fraser, C" uniqKey="Fraser C">C Fraser</name></author><author><name sortKey="Leung, Gm" uniqKey="Leung G">GM Leung</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ferguson, N" uniqKey="Ferguson N">N Ferguson</name></author><author><name sortKey="Cummings, D" uniqKey="Cummings D">D Cummings</name></author><author><name sortKey="Cauchemez, S" uniqKey="Cauchemez S">S Cauchemez</name></author><author><name sortKey="Fraser, C" uniqKey="Fraser C">C Fraser</name></author><author><name sortKey="Riley, S" uniqKey="Riley S">S Riley</name></author><author><name sortKey="Meeyai, A" uniqKey="Meeyai A">A Meeyai</name></author><author><name sortKey="Iamsirithaworn, S" uniqKey="Iamsirithaworn S">S Iamsirithaworn</name></author><author><name sortKey="Burke, D" uniqKey="Burke D">D Burke</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gani, R" uniqKey="Gani R">R Gani</name></author><author><name sortKey="Hughes, H" uniqKey="Hughes H">H Hughes</name></author><author><name sortKey="Fleming, D" uniqKey="Fleming D">D Fleming</name></author><author><name sortKey="Griffin, T" uniqKey="Griffin T">T Griffin</name></author><author><name sortKey="Medlock, J" uniqKey="Medlock J">J Medlock</name></author><author><name sortKey="Leach, S" uniqKey="Leach S">S Leach</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Longini, Ij" uniqKey="Longini I">IJ Longini</name></author><author><name sortKey="Halloran, M" uniqKey="Halloran M">M Halloran</name></author><author><name sortKey="Nizam, A" uniqKey="Nizam A">A Nizam</name></author><author><name sortKey="Yang, Y" uniqKey="Yang Y">Y Yang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Vynnycky, E" uniqKey="Vynnycky E">E Vynnycky</name></author><author><name sortKey="Edmunds, W" uniqKey="Edmunds W">W Edmunds</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wu, Jt" uniqKey="Wu J">JT Wu</name></author><author><name sortKey="Cowling, Bj" uniqKey="Cowling B">BJ Cowling</name></author><author><name sortKey="Lau, Eh" uniqKey="Lau E">EH Lau</name></author><author><name sortKey="Ip, Dk" uniqKey="Ip D">DK Ip</name></author><author><name sortKey="Ho, Lm" uniqKey="Ho L">LM Ho</name></author><author><name sortKey="Tsang, T" uniqKey="Tsang T">T Tsang</name></author><author><name sortKey="Chuang, Sk" uniqKey="Chuang S">SK Chuang</name></author><author><name sortKey="Leung, Py" uniqKey="Leung P">PY Leung</name></author><author><name sortKey="Lo, Sv" uniqKey="Lo S">SV Lo</name></author><author><name sortKey="Liu, Sh" uniqKey="Liu S">SH Liu</name></author><author><name sortKey="Riley, S" uniqKey="Riley S">S Riley</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Riley, S" uniqKey="Riley S">S Riley</name></author><author><name sortKey="Wu, Jt" uniqKey="Wu J">JT Wu</name></author><author><name sortKey="Leung, Gm" uniqKey="Leung G">GM Leung</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Medlock, J" uniqKey="Medlock J">J Medlock</name></author><author><name sortKey="Galvani, Ap" uniqKey="Galvani A">AP Galvani</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tracht, Sm" uniqKey="Tracht S">SM Tracht</name></author><author><name sortKey="Del Valle, Sy" uniqKey="Del Valle S">SY Del Valle</name></author><author><name sortKey="Hyman, Jm" uniqKey="Hyman J">JM Hyman</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Greenwood, M" uniqKey="Greenwood M">M Greenwood</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Abbey, H" uniqKey="Abbey H">H Abbey</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Phenyo, E" uniqKey="Phenyo E">E Phenyo</name></author><author><name sortKey="Barbel, F" uniqKey="Barbel F">F Barbel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Flahault, A" uniqKey="Flahault A">A Flahault</name></author><author><name sortKey="Deguen, S" uniqKey="Deguen S">S Deguen</name></author><author><name sortKey="Valleron, Aj" uniqKey="Valleron A">AJ Valleron</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Flahault, A" uniqKey="Flahault A">A Flahault</name></author><author><name sortKey="Letrait, S" uniqKey="Letrait S">S Letrait</name></author><author><name sortKey="Blin, P" uniqKey="Blin P">P Blin</name></author><author><name sortKey="Hazout, S" uniqKey="Hazout S">S Hazout</name></author><author><name sortKey="Menares, J" uniqKey="Menares J">J Ménarés</name></author><author><name sortKey="Valleron, Aj" uniqKey="Valleron A">AJ Valleron</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rvachev, La" uniqKey="Rvachev L">LA Rvachev</name></author><author><name sortKey="Longini, Imj" uniqKey="Longini I">IMJ Longini</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Longini, Im" uniqKey="Longini I">IM Longini</name></author><author><name sortKey="Fine, Pem" uniqKey="Fine P">PEM Fine</name></author><author><name sortKey="Thacker, Sb" uniqKey="Thacker S">SB Thacker</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Longini, Imj" uniqKey="Longini I">IMJ Longini</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Boelle, P" uniqKey="Boelle P">P Boelle</name></author><author><name sortKey="Bernillon, P" uniqKey="Bernillon P">P Bernillon</name></author><author><name sortKey="Desenclos, J" uniqKey="Desenclos J">J Desenclos</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chowell, G" uniqKey="Chowell G">G Chowell</name></author><author><name sortKey="Hengartnerb, N" uniqKey="Hengartnerb N">N Hengartnerb</name></author><author><name sortKey="Castillo Chaveza, C" uniqKey="Castillo Chaveza C">C Castillo-Chaveza</name></author><author><name sortKey="Fenimorea, P" uniqKey="Fenimorea P">P Fenimorea</name></author><author><name sortKey="Hyman, J" uniqKey="Hyman J">J Hyman</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Ferguson, Nm" uniqKey="Ferguson N">NM Ferguson</name></author><author><name sortKey="Cummings, Dat" uniqKey="Cummings D">DAT Cummings</name></author><author><name sortKey="Fraser, C" uniqKey="Fraser C">C Fraser</name></author><author><name sortKey="Cajka, Jc" uniqKey="Cajka J">JC Cajka</name></author><author><name sortKey="Cooley, Pc" uniqKey="Cooley P">PC Cooley</name></author><author><name sortKey="Burke, Ds" uniqKey="Burke D">DS Burke</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Fraser, C" uniqKey="Fraser C">C Fraser</name></author><author><name sortKey="Donnelly, Ca" uniqKey="Donnelly C">CA Donnelly</name></author><author><name sortKey="Cauchemez, S" uniqKey="Cauchemez S">S Cauchemez</name></author><author><name sortKey="Hanage, Wp" uniqKey="Hanage W">WP Hanage</name></author><author><name sortKey="Van Kerkhove, Md" uniqKey="Van Kerkhove M">MD Van Kerkhove</name></author><author><name sortKey="Hollingsworth, Td" uniqKey="Hollingsworth T">TD Hollingsworth</name></author><author><name sortKey="Griffin, J" uniqKey="Griffin J">J Griffin</name></author><author><name sortKey="Baggaley, Rf" uniqKey="Baggaley R">RF Baggaley</name></author><author><name sortKey="Jenkins, He" uniqKey="Jenkins H">HE Jenkins</name></author><author><name sortKey="Lyons, Ej" uniqKey="Lyons E">EJ Lyons</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Yang, Y" uniqKey="Yang Y">Y Yang</name></author><author><name sortKey="Sugimoto, Jd" uniqKey="Sugimoto J">JD Sugimoto</name></author><author><name sortKey="Halloran, Me" uniqKey="Halloran M">ME Halloran</name></author><author><name sortKey="Basta, Ne" uniqKey="Basta N">NE Basta</name></author><author><name sortKey="Chao, Dl" uniqKey="Chao D">DL Chao</name></author><author><name sortKey="Matrajt, L" uniqKey="Matrajt L">L Matrajt</name></author><author><name sortKey="Potter, G" uniqKey="Potter G">G Potter</name></author><author><name sortKey="Kenah, E" uniqKey="Kenah E">E Kenah</name></author><author><name sortKey="Longini, J" uniqKey="Longini J">J Longini</name></author><author><name sortKey="Ira, M" uniqKey="Ira M">M Ira</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Longini, Im" uniqKey="Longini I">IM Longini</name></author><author><name sortKey="Nizam, A" uniqKey="Nizam A">A Nizam</name></author><author><name sortKey="Xu, S" uniqKey="Xu S">S Xu</name></author><author><name sortKey="Ungchusak, K" uniqKey="Ungchusak K">K Ungchusak</name></author><author><name sortKey="Hanshaoworakul, W" uniqKey="Hanshaoworakul W">W Hanshaoworakul</name></author><author><name sortKey="Cummings, Dat" uniqKey="Cummings D">DAT Cummings</name></author><author><name sortKey="Halloran, Me" uniqKey="Halloran M">ME Halloran</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Caley, P" uniqKey="Caley P">P Caley</name></author><author><name sortKey="Becker, Ng" uniqKey="Becker N">NG Becker</name></author><author><name sortKey="Philp, Dj" uniqKey="Philp D">DJ Philp</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bell, Dm" uniqKey="Bell D">DM Bell</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cowling, Bj" uniqKey="Cowling B">BJ Cowling</name></author><author><name sortKey="Lau, Eh" uniqKey="Lau E">EH Lau</name></author><author><name sortKey="Lam, Cl" uniqKey="Lam C">CL Lam</name></author><author><name sortKey="Cheng, Ck" uniqKey="Cheng C">CK Cheng</name></author><author><name sortKey="Kovar, J" uniqKey="Kovar J">J Kovar</name></author><author><name sortKey="Chan, Kh" uniqKey="Chan K">KH Chan</name></author><author><name sortKey="Peiris, Jm" uniqKey="Peiris J">JM Peiris</name></author><author><name sortKey="Leung, Gm" uniqKey="Leung G">GM Leung</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Epstein, Jm" uniqKey="Epstein J">JM Epstein</name></author><author><name sortKey="Goedecke, Dm" uniqKey="Goedecke D">DM Goedecke</name></author><author><name sortKey="Yu, F" uniqKey="Yu F">F Yu</name></author><author><name sortKey="Morris, Rj" uniqKey="Morris R">RJ Morris</name></author><author><name sortKey="Wagener, Dk" uniqKey="Wagener D">DK Wagener</name></author><author><name sortKey="Bobashev, Gv" uniqKey="Bobashev G">GV Bobashev</name></author></analytic></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Infect Dis</journal-id><journal-id journal-id-type="iso-abbrev">BMC Infect. Dis</journal-id><journal-title-group><journal-title>BMC Infectious Diseases</journal-title></journal-title-group><issn pub-type="epub">1471-2334</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">23157818</article-id><article-id pub-id-type="pmc">3577649</article-id><article-id pub-id-type="publisher-id">2271</article-id><article-id pub-id-type="doi">10.1186/1471-2334-12-309</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Chong</surname><given-names>Ka Chun</given-names></name><address><email>marc@cct.cuhk.edu.hk</email></address><xref ref-type="aff" rid="Aff1"></xref></contrib><contrib contrib-type="author"><name><surname>Ying Zee</surname><given-names>Benny Chung</given-names></name><address><email>bzee@cct.cuhk.edu.hk</email></address><xref ref-type="aff" rid="Aff1"></xref></contrib><aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.10784.3a</institution-id><institution-id institution-id-type="ISNI">0000 0004 1937 0482</institution-id><institution>Division of Biostatistics, The Jockey Club School of Public Health and Primary Care,</institution><institution>The Chinese University of Hong Kong,</institution></institution-wrap>Hong Kong SAR, China</aff></contrib-group><pub-date pub-type="epub"><day>19</day><month>11</month><year>2012</year></pub-date><pub-date pub-type="pmc-release"><day>19</day><month>11</month><year>2012</year></pub-date><pub-date pub-type="collection"><year>2012</year></pub-date><volume>12</volume><elocation-id>309</elocation-id><history><date date-type="received"><day>9</day><month>11</month><year>2011</year></date><date date-type="accepted"><day>15</day><month>11</month><year>2012</year></date></history><permissions><copyright-statement>© Chong and Zee; licensee BioMed Central Ltd. 2012</copyright-statement><license license-type="OpenAccess"><license-p>This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0">http://creativecommons.org/licenses/by/2.0</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p>During the early stages of a new influenza pandemic, travel restriction is an immediate and non-pharmaceutical means of retarding incidence growth. It extends the time frame of effective mitigation, especially when the characteristics of the emerging virus are unknown. In the present study, we used the 2009 influenza A pandemic as a case study to evaluate the impact of regulating air, sea, and land transport. Other government strategies, namely, antivirals and hospitalizations, were also evaluated.</p></sec><sec><title>Methods</title><p>Hong Kong arrivals from 44 countries via air, sea, and land transports were imported into a discrete stochastic Susceptible, Exposed, Infectious and Recovered (SEIR) host-flow model. The model allowed a number of latent and infectious cases to pass the border, which constitutes a source of local disease transmission. We also modeled antiviral and hospitalization prevention strategies to compare the effectiveness of these control measures. Baseline reproduction rate was estimated from routine surveillance data.</p></sec><sec><title>Results</title><p>Regarding air travel, the main route connected to the influenza source area should be targeted for travel restrictions; imposing a 99% air travel restriction delayed the epidemic peak by up to two weeks. Once the pandemic was established in China, the strong land connection between Hong Kong and China rendered Hong Kong vulnerable. Antivirals and hospitalization were found to be more effective on attack rate reductions than travel restrictions. Combined strategies (with 99% restriction on all transport modes) deferred the peak for long enough to establish a vaccination program.</p></sec><sec><title>Conclusion</title><p>The findings will assist policy-makers with decisions on handling similar future pandemics. We also suggest regulating the extent of restriction and the transport mode, once restriction has been deemed necessary for pandemic control. Although travel restrictions have yet to gain social acceptance, they allow time for mitigation response when a new and highly intrusive virus emerges.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/1471-2334-12-309) contains supplementary material, which is available to authorized users.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Influenza</kwd><kwd>Severe Acute Respiratory Syndrome</kwd><kwd>Influenza Pandemic</kwd><kwd>Transport Mode</kwd><kwd>Severe Acute Respiratory Syndrome</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2012</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="Sec1"><title>Background</title><p>When an emerging influenza virus is introduced to human populations, the pandemic potential of the virus becomes a public concern. Policy makers consider different interventions to contain and mitigate incipient pandemic growth. However, pharmaceutical interventions such as vaccines are not usually available in the early stage of pandemics. Public health measures such as travel restrictions then become essential in controlling pandemic spread.</p><p>Novel influenza A (H1N1), also called swine flu, is a novel influenza virus that caused its first illness in Mexico in 2009. Because of insufficient information regarding this particular infectious agent, the World Health Organization (WHO) declared the event the first global H1N1 influenza pandemic (H1N1pdm) on June 11, 2009. In a recent study, an estimated 284,500 deaths have been associated with H1N1pdm [<xref ref-type="bibr" rid="CR1">1</xref>]. The high transmissibility of the virus has heightened public awareness of disease control measures. Hong Kong’s large-scale international travel pattern and high population density renders the Hong Kong population especially vulnerable. Nearly 300 severe H1N1 cases and 80 fatal H1N1 cases had been reported in Hong Kong at the end of the 2010 flu season [<xref ref-type="bibr" rid="CR2">2</xref>]. The virus has been widely circulated locally, and lessening the disease burden now depends on implementing effective control measures.</p><p>The earliest applied H1N1pdm control measure imposed by the Hong Kong Government was travel restriction via travel advice and entry screening [<xref ref-type="bibr" rid="CR3">3</xref>]. For highly-transmittable infectious diseases such as influenza, the traveling patterns of individuals play an essential role in geographical disease spread. Travel restrictions, a type of social control measure, have been evaluated in several epidemics including influenza [<xref ref-type="bibr" rid="CR4">4</xref>–<xref ref-type="bibr" rid="CR7">7</xref>], human immunodeficiency virus (HIV) [<xref ref-type="bibr" rid="CR8">8</xref>], severe acute respiratory syndrome (SARS) [<xref ref-type="bibr" rid="CR6">6</xref>, <xref ref-type="bibr" rid="CR9">9</xref>], and, recently, the 2009 H1N1pdm [<xref ref-type="bibr" rid="CR10">10</xref>]. Empirical statistics indicate that the influenza season was delayed following reduced flying activity caused by the US 9/11 incident [<xref ref-type="bibr" rid="CR11">11</xref>]. Hufnagel <italic>et al</italic>. [<xref ref-type="bibr" rid="CR9">9</xref>] further demonstrated that isolating a mere 2% of the largest cities was enough to halt the SARS outbreak. Nevertheless, the WHO considers travel restriction to be impractical in the majority of countries [<xref ref-type="bibr" rid="CR12">12</xref>]. In addition, some studies have disputed the value of air travel restrictions in epidemic control [<xref ref-type="bibr" rid="CR6">6</xref>, <xref ref-type="bibr" rid="CR7">7</xref>, <xref ref-type="bibr" rid="CR13">13</xref>]. Cooper <italic>et al</italic>. regarded that benefits accrued from suspending air travels is limited by the short serial interval of influenza. Hollingsworth <italic>et al</italic>. [<xref ref-type="bibr" rid="CR6">6</xref>] concluded that containment of a pandemic influenza strain requires rigorous travel restrictions and small numbers of local infectious inhabitants. In Hong Kong, because the magnitude of travel restrictions imposed by travel advice and entry screening was small, its effectiveness in pandemic delay is disputable.</p><p>Despite these limitations, the impact of travel restrictions requires ongoing investigation. Previous studies focused on air travel restrictions alone [<xref ref-type="bibr" rid="CR4">4</xref>, <xref ref-type="bibr" rid="CR14">14</xref>], but in many cities, including Hong Kong, air travel is a secondary means of transport for arriving and departing travelers. Statistics show that more than half of the passengers arriving in Hong Kong annually enter by sea or land [<xref ref-type="bibr" rid="CR15">15</xref>]. As shown in Figure <xref rid="Fig1" ref-type="fig">1</xref>, over ten million visitors per annum enter Hong Kong via land transport from Asia. Visitors from North America and Europe constitute a relatively high proportion of air transport arrivals. Therefore, to assess the true effectiveness of travel restrictions, air, sea, and land transport must all be incorporated into the evaluation. Additionally, most of the published mathematical models admit only those latent individuals who travel between countries. However, with limited screening sensitivity at the entry border points [<xref ref-type="bibr" rid="CR16">16</xref>, <xref ref-type="bibr" rid="CR17">17</xref>], a large number of infected cases could enter, thereby dramatically increasing the rate of local disease transmission [<xref ref-type="bibr" rid="CR18">18</xref>].<fig id="Fig1"><label>Figure 1</label><caption><p><bold>Total arrival (in millions) by air, sea, and land transport.</bold> Forty-four countries were selected in total which contributed more than 95% of arrivals to Hong Kong.</p></caption><graphic xlink:href="12879_2011_Article_2271_Fig1_HTML" id="d29e333"></graphic></fig></p><p>Whereas travel restrictions can be imposed almost immediately, antiviral drugs require extended time for preparation. In Hong Kong, antiviral and hospitalization strategies were implemented about 3.5 months after the first global H1N1pdm import [<xref ref-type="bibr" rid="CR19">19</xref>]. The main purpose of travel restrictions are to defer the pandemic, whereas antivirals and hospitalizations aim to reduce the transmission rate and severity of disease [<xref ref-type="bibr" rid="CR5">5</xref>]. These strategies have proven useful in many influenza epidemics, including that of the novel H1N1pdm. Vaccination alone effectively mitigates most of the epidemics, by reducing the risk of a susceptible being infected, and thus the possibility of seeding the disease in the community. Nevertheless, vaccine design, development and public administration are lengthy processes. Current manufacturing capacity is insufficient to produce the vaccines within a few months following declaration of an influenza pandemic [<xref ref-type="bibr" rid="CR20">20</xref>]. Hong Kong Government officials implemented the H1N1 vaccination program about nine months following the first global import [<xref ref-type="bibr" rid="CR21">21</xref>], by which time the H1N1pdm had passed its peak. Low public acceptance of vaccine uptake during the H1N1pdm period compounded the issue. In one study, only 7% of subjects reported that they were ‘likely/very likely/certain’ to be vaccinated [<xref ref-type="bibr" rid="CR22">22</xref>].</p><p>Impact of epidemic interventions is usually quantified by mathematical models. Clinical trial design is impractical for assessing the effectiveness of some interventions, such as face masks and isolation, because of ethical considerations relating to epidemics in general. By using mathematical models, the epidemic dynamics and intervention effectiveness can be determined. Such models can evaluate a range of interventions; isolation [<xref ref-type="bibr" rid="CR23">23</xref>], quarantine [<xref ref-type="bibr" rid="CR24">24</xref>], antiviral drugs [<xref ref-type="bibr" rid="CR25">25</xref>, <xref ref-type="bibr" rid="CR26">26</xref>], school closures [<xref ref-type="bibr" rid="CR27">27</xref>, <xref ref-type="bibr" rid="CR28">28</xref>], vaccinations [<xref ref-type="bibr" rid="CR29">29</xref>, <xref ref-type="bibr" rid="CR30">30</xref>] and face masks [<xref ref-type="bibr" rid="CR31">31</xref>], among others.</p><p>In the study, we use the Hong Kong Governmental response to the 2009 H1N1pdm as a model case study to evaluate the effectiveness of travel restrictions of different magnitudes and transport modes i.e. air, sea, and land, combined with other interventions, namely antivirals and hospitalizations, in the event of a novel influenza virus. The impact is assessed by simulations from an epidemic model. We also investigate the effects of changing important parameters, including reproduction numbers in non-local visitors to Hong Kong, screening sensitivity at entry border points, and date at which travel restrictions are imposed. The results provide valuable information to policy-makers and public health experts in the event of similar future pandemics.</p></sec><sec id="Sec2"><title>Methods</title><sec id="Sec3"><title>Population and transportation</title><p>Population data were extracted from the International Database (IDB), U.S. Census Bureau [<xref ref-type="bibr" rid="CR32">32</xref>]. The individual probability of travel for each country was calculated as the daily travel rate divided by the population size. The arrival data were extracted from visitor arrival statistics provided by the Hong Kong Tourism Board [<xref ref-type="bibr" rid="CR15">15</xref>]. These statistics include the total number of arrivals by country, together with their modes of transport. Forty-four countries, collectively contributing more than 95% of annual arrivals to Hong Kong, were selected for the analysis (Figure <xref rid="Fig1" ref-type="fig">1</xref>). The yearly frequency of departing Hong Kong residents by different transport modes were collected from the Census and Statistics Department, Hong Kong [<xref ref-type="bibr" rid="CR33">33</xref>]. The data are listed in Additional file <xref rid="MOESM1" ref-type="media">1</xref> and are assumed to be uniformly distributed on a daily scale.</p></sec><sec id="Sec4"><title>Disease transmission model</title><p>We extended the discrete stochastic <italic>SEIR</italic> model [<xref ref-type="bibr" rid="CR34">34</xref>–<xref ref-type="bibr" rid="CR36">36</xref>] to study the H1N1pdm dynamics and the impacts of local interventions. The stochastic approach differs from that of deterministic models [<xref ref-type="bibr" rid="CR4">4</xref>, <xref ref-type="bibr" rid="CR37">37</xref>–<xref ref-type="bibr" rid="CR41">41</xref>]. In our model, foreign virus arriving by air, land, and sea transport adapts and establishes in a local community with inherent uncertainty. Introducing this chance effect into the epidemic dynamics enhances the realism of the model. The model outputs are the deferred time period and the illness attack rate (AR) (defined as the number of new infected cases per head of population during a given time period).</p><p>All individuals in the local population were assumed to be susceptible, and the average latent and infectious periods were set to 1.45 and 2.9 days, respectively [<xref ref-type="bibr" rid="CR10">10</xref>, <xref ref-type="bibr" rid="CR42">42</xref>]. The population, <italic>N</italic>, was divided into four classes: susceptible (<italic>S</italic>(<italic>t</italic>)); exposed (<italic>E</italic>(<italic>t</italic>)); infectious (<italic>I</italic>(<italic>t</italic>)); and recovered (<italic>R</italic>(<italic>t</italic>)), at each time point <italic>t</italic>. Because no information was available on cross-immunity from past influenza infections, the initial population was set at 100% susceptible. Once susceptible individuals became infected, they advanced to the latent (non-infectious) stage. Following the latent period, they became infectious and could transmit the disease to other susceptible individuals. A number of individuals moved to the next compartment with a defined probability. The number of individuals advancing to each stage was assumed to follow a binomial distribution.</p><sec id="Sec5"><title>Disease transmission from travelers</title><p>In the disease transmission model, latent (<inline-formula id="IEq1"><alternatives><mml:math id="M1"><mml:mfenced close="" open="" separators=""><mml:mrow><mml:mi>I</mml:mi><mml:msubsup><mml:mrow><mml:mi>M</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mi>E</mml:mi></mml:mrow></mml:msubsup><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mfenced></mml:math><inline-graphic xlink:href="12879_2011_Article_2271_IEq1_HTML.gif"></inline-graphic></alternatives></inline-formula>) and infectious (<inline-formula id="IEq2"><alternatives><mml:math id="M2"><mml:mfenced close="" open="" separators=""><mml:mrow><mml:mi>I</mml:mi><mml:msubsup><mml:mrow><mml:mi>M</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mi>I</mml:mi></mml:mrow></mml:msubsup><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mfenced></mml:math><inline-graphic xlink:href="12879_2011_Article_2271_IEq2_HTML.gif"></inline-graphic></alternatives></inline-formula>) travelers arrived from 44 foreign countries by transport <italic>k</italic>-th and were assigned to compartments <italic>E</italic>(<italic>t</italic>) and <italic>I</italic>(<italic>t</italic>), respectively. Because the screening sensitivity at the border points of entry was limited, a proportion (1−<italic>ν</italic>) of infectious cases were imported to Hong Kong, an approach not considered in other global patch models [<xref ref-type="bibr" rid="CR7">7</xref>, <xref ref-type="bibr" rid="CR10">10</xref>]. The number of cases imported to the local city was also assumed to be binomially distributed, with a probability equal to the chance of travel via the specified transport mode.</p><p>To allow for spatial heterogeneities between non-local countries, case numbers for each country were generated from discrete time SEIR models assigned independent reproduction numbers (<italic>R</italic><sub>0</sub>), defined as the average number of secondary infections induced by a typical infectious individual in a wholly susceptible population. The magnitude of <italic>R</italic><sub>0</sub>depends on the individual contact rate, disease transmissibility, and the duration of infectiousness; hence, <italic>R</italic><sub>0</sub> is expected to differ between countries. In this paper, the <italic>R</italic><sub>0</sub>of foreign countries were estimated by the initial exponential growth rate method [<xref ref-type="bibr" rid="CR43">43</xref>] assuming no intervention during the early stage of H1N1pdm. They were fitted by daily counts of laboratory-confirmed infected cases in each country, obtained from pandemic H1N1 situation updates archived in the World Health Organization (WHO) [<xref ref-type="bibr" rid="CR44">44</xref>] and the European Centre for Disease Prevention and Control (ECDC) [<xref ref-type="bibr" rid="CR45">45</xref>]. Several local exposed (<italic>E</italic><italic>X</italic><sup><italic>E</italic></sup>(<italic>t</italic>)) and infectious (<italic>E</italic><italic>X</italic><sup><italic>I</italic></sup>(<italic>t</italic>)) cases were removed from the compartments based on the proportion of travel by the specified means of transport. Simulation was started from the day of initial global import. The effect of varying the <italic>R</italic><sub>0</sub>s of specified foreign countries by 20% was performed. The details of disease transmission from travelers are provided in Additional file <xref rid="MOESM1" ref-type="media">1</xref>.</p></sec><sec id="Sec6"><title>Control measures</title><p>The mathematical model assesses the effectiveness of: (i) travel restrictions (for different transport modes) and (ii) local antiviral and hospitalization interventions. Travel restrictions were supposed to take effect on the day following the first global onset case. Different start dates were tested in a sensitivity analysis. The antiviral and the hospitalization strategies were implemented locally 3.5 months following the first global onset case, echoing the strategies employed by the Department of Health, Hong Kong [<xref ref-type="bibr" rid="CR19">19</xref>].</p><sec id="Sec7"><title>Travel restrictions relating to sea, land, and air travel</title><p>We imposed 90% and 99% travel restrictions (<italic>f</italic><sub><italic>k</italic></sub>), on different transport modes <italic>k</italic>. The term ‘travel restriction by <italic>f</italic><sub><italic>k</italic></sub>’ meant allowing only a fraction of (1−<italic>f</italic><sub><italic>k</italic></sub>) import individuals to be transported to Hong Kong from overseas through transportation <italic>k</italic>. We also considered only one-third (<italic>ν</italic>) of those (1−<italic>f</italic><sub><italic>k</italic></sub>) imported infectious cases as successfully identified positive cases at the entry borders in the baseline scenario [<xref ref-type="bibr" rid="CR16">16</xref>]. The screened positive individuals entering Hong Kong were transported to hospital for further examination [<xref ref-type="bibr" rid="CR3">3</xref>]. Confirmed cases were recommended to undertake voluntary quarantine. We assumed that all identified cases accepted voluntary quarantine. Screening sensitivities of 95% and 5%, and travel-restriction start dates of three and five months following the first global import, were also evaluated (Additional file <xref rid="MOESM1" ref-type="media">1</xref>).</p></sec><sec id="Sec8"><title>Antiviral and hospitalization</title><p>We assumed that 12% (<italic>p</italic><sub><italic>T</italic></sub>) of the infectious subjects were offered antiviral and 6% (<italic>p</italic><sub><italic>H</italic></sub>) of the infectious subjects were hospitalized, based on influenza pandemic records [<xref ref-type="bibr" rid="CR23">23</xref>, <xref ref-type="bibr" rid="CR25">25</xref>]. The remaining 82% (<italic>p</italic><sub><italic>U</italic></sub>) of infectious individuals were untreated. The antiviral reduces infectiousness (<italic>ψ</italic>) of individuals by 60% [<xref ref-type="bibr" rid="CR46">46</xref>]. Either intervention reduce the average infectious period by 1.5 days [<xref ref-type="bibr" rid="CR47">47</xref>]. Compartments for antiviral <italic>T</italic>(<italic>t</italic>) and hospitalization <italic>H</italic>(<italic>t</italic>) were developed separately in the model for individual assessment of the treatments. The stochastic system is <disp-formula id="Equ1"><label>1</label><alternatives><mml:math id="M3"><mml:mtable columnalign="left"><mml:mtr><mml:mtd><mml:mi>S</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mi>Δt</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mi>S</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>−</mml:mo><mml:mi>B</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mi>E</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mi>Δt</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mi>E</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>+</mml:mo><mml:mi>B</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mspace width="5.5em"></mml:mspace><mml:mo>+</mml:mo><mml:munder><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:munder><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>f</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>)</mml:mo><mml:mi>I</mml:mi><mml:msubsup><mml:mrow><mml:mi>M</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mi>E</mml:mi></mml:mrow></mml:msubsup><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>−</mml:mo><mml:mi>E</mml:mi><mml:msup><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>E</mml:mi></mml:mrow></mml:msup><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>−</mml:mo><mml:mi>C</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mi>I</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mi>Δt</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mi>I</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>+</mml:mo><mml:mi>C</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>+</mml:mo><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>ν</mml:mi><mml:mo>)</mml:mo><mml:munder><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:munder><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>f</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>)</mml:mo><mml:mi>I</mml:mi><mml:msubsup><mml:mrow><mml:mi>M</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mi>I</mml:mi></mml:mrow></mml:msubsup><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mspace width="5.5em"></mml:mspace><mml:mo>−</mml:mo><mml:mi>E</mml:mi><mml:msup><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>I</mml:mi></mml:mrow></mml:msup><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>−</mml:mo><mml:mi>D</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>−</mml:mo><mml:mi>M</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>−</mml:mo><mml:mi>N</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mi>T</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mi>Δt</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mi>T</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>+</mml:mo><mml:mi>M</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>−</mml:mo><mml:mi>P</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mi>H</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mi>Δt</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mi>H</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>+</mml:mo><mml:mi>N</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>−</mml:mo><mml:mi>Q</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mi>R</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mi>Δt</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mi>R</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>+</mml:mo><mml:mi>D</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>+</mml:mo><mml:mi>P</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>+</mml:mo><mml:mi>Q</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:math><graphic xlink:href="12879_2011_Article_2271_Equ1_HTML.gif" position="anchor"></graphic></alternatives></disp-formula></p><p>We denote <italic>bin</italic>(<italic>m, n</italic>) as a binomial distribution with probability <italic>m</italic> and number of total individuals <italic>n</italic>. The distributions of the classes are <disp-formula id="Equ2"><label>2</label><alternatives><mml:math id="M4"><mml:mtable columnalign="left"><mml:mtr><mml:mtd><mml:mi>B</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>∼</mml:mo><mml:mtext>bin</mml:mtext><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>exp</mml:mi><mml:mo>[</mml:mo><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:mfrac><mml:mo>[</mml:mo><mml:mi>I</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>+</mml:mo><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>ψ</mml:mi><mml:mo>)</mml:mo><mml:mi>T</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mspace width="5em"></mml:mspace><mml:mo>+</mml:mo><mml:mi>H</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>]</mml:mo><mml:mi>Δt</mml:mi><mml:mo>]</mml:mo><mml:mo>)</mml:mo><mml:mo>,</mml:mo><mml:mi>S</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mi>C</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>∼</mml:mo><mml:mtext>bin</mml:mtext><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mtext>exp</mml:mtext><mml:mo>(</mml:mo><mml:mo>−</mml:mo><mml:mi>αΔt</mml:mi><mml:mo>)</mml:mo><mml:mo>,</mml:mo><mml:mi>E</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mi>M</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>∼</mml:mo><mml:mtext>bin</mml:mtext><mml:mo>(</mml:mo><mml:msub><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>T</mml:mi></mml:mrow></mml:msub><mml:mi>Δt</mml:mi><mml:mo>,</mml:mo><mml:mi>I</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mi>N</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>∼</mml:mo><mml:mtext>bin</mml:mtext><mml:mo>(</mml:mo><mml:msub><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>H</mml:mi></mml:mrow></mml:msub><mml:mi>Δt</mml:mi><mml:mo>,</mml:mo><mml:mi>I</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mi>D</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>∼</mml:mo><mml:mtext>bin</mml:mtext><mml:mo>(</mml:mo><mml:msub><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>U</mml:mi></mml:mrow></mml:msub><mml:mo>[</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mtext>exp</mml:mtext><mml:mo>(</mml:mo><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>γ</mml:mi></mml:mrow><mml:mrow><mml:mi>R</mml:mi></mml:mrow></mml:msub><mml:mi>Δt</mml:mi><mml:mo>)</mml:mo><mml:mo>]</mml:mo><mml:mo>,</mml:mo><mml:mi>I</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mi>P</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>∼</mml:mo><mml:mtext>bin</mml:mtext><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>exp</mml:mi><mml:mo>(</mml:mo><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>γ</mml:mi></mml:mrow><mml:mrow><mml:mi>T</mml:mi></mml:mrow></mml:msub><mml:mi>Δt</mml:mi><mml:mo>)</mml:mo><mml:mo>,</mml:mo><mml:mi>T</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>)</mml:mo></mml:mtd></mml:mtr><mml:mtr columnalign="left"><mml:mtd><mml:mi>Q</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>∼</mml:mo><mml:mtext>bin</mml:mtext><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mtext>exp</mml:mtext><mml:mo>(</mml:mo><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>γ</mml:mi></mml:mrow><mml:mrow><mml:mi>H</mml:mi></mml:mrow></mml:msub><mml:mi>Δt</mml:mi><mml:mo>)</mml:mo><mml:mo>,</mml:mo><mml:mi>H</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>)</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:math><graphic xlink:href="12879_2011_Article_2271_Equ2_HTML.gif" position="anchor"></graphic></alternatives></disp-formula></p><p>where <italic>β</italic> is the disease transmission rate and 1/<italic>α</italic>is the average latent period. The probability of a susceptible person becoming infected is 1−<italic>exp</italic>[−<italic>β</italic>[<italic>I</italic>(<italic>t</italic>) + (1−<italic>ψ</italic>)<italic>T</italic>(<italic>t</italic>) + <italic>H</italic>(<italic>t</italic>)]/<italic>N</italic>] during time step <italic>Δt</italic>. <italic>γ</italic><sub><italic>R</italic></sub>, <italic>γ</italic><sub><italic>T</italic></sub>, and <italic>γ</italic><sub><italic>H</italic></sub> specify the removal rates from the infectious state, the antiviral treatment state, and the hospitalization state, respectively. The details of the mathematical methodology and the simulation are provided in Additional file <xref rid="MOESM1" ref-type="media">1</xref>.</p></sec></sec></sec><sec id="Sec9"><title>Epidemic evolution and baseline scenario</title><p>The H1N1pdm is seeded according to the start dates of each country (listed in Additional file <xref rid="MOESM1" ref-type="media">1</xref>). The earliest epidemic was seeded in Mexico on March 11, 2009 [<xref ref-type="bibr" rid="CR48">48</xref>]. For each country, the number of infected cases was generated from the discrete-time <italic>SEIR</italic> model, based on the estimated reproduction number.</p><p>Since the Hong Kong Government confirmed the first imported case of H1N1pdm on May 1, 2009 [<xref ref-type="bibr" rid="CR49">49</xref>], the initial numbers of latent cases and infectious cases were iteratively estimated, thereby minimizing the difference between the reported date and the simulated first passage time (FPT). Allowing for stochastic variability, the baseline transmission rate was estimated for the first two months following the day of the first local import, in the absence of travel restrictions and intervention. Local daily surveillance of confirmed infected cases (May 1, 2009 to June 30, 2009) was available from press releases on human swine flu, published by the Department of Health, Hong Kong [<xref ref-type="bibr" rid="CR50">50</xref>]. The range of <italic>R</italic><sub>0</sub> values encompassed mild and severe scenarios.</p></sec></sec><sec id="Sec10"><title>Results</title><sec id="Sec11"><title>Scenarios with no interventions</title><p>The local baseline <italic>R</italic><sub>0</sub> was estimated at around 1.4. Values of <italic>R</italic><sub>0</sub> were chosen to simulate mild (<italic>R</italic><sub>0</sub>=1<italic>.</italic>1) and severe (<italic>R</italic><sub>0</sub>=1<italic>.</italic>7) influenza outbreaks in Hong Kong, and were consistent with those reported in previous studies [<xref ref-type="bibr" rid="CR48">48</xref>, <xref ref-type="bibr" rid="CR51">51</xref>]. In foreign countries, <italic>R</italic><sub>0</sub> ranged from 1.1 to 1.9. In the baseline scenario (<italic>R</italic><sub>0</sub>=1<italic>.</italic>4), the median FPT and first one hundred passage time (FHPT) of infected cases entering Hong Kong were 55 and 90 days, respectively (Table <xref rid="Tab1" ref-type="table">1</xref>). Because the H1N1pdm was initiated in the Americas, the primary means by which the infected cases arrived in Hong Kong during the fourth month was air travel (Figure <xref rid="Fig2" ref-type="fig">2</xref>). The number of cases imported by air transport exceeded that of land transport during the first six months. Thereafter, because the emerging virus had circulated to most of the Asian countries, including China, the number of cases imported by land transport increased exponentially. Because ships constitute a minor transport mode to Hong Kong, they delivered few cases throughout the pandemic period (Figure <xref rid="Fig2" ref-type="fig">2</xref>).<fig id="Fig2"><label>Figure 2</label><caption><p><bold>Number of imported cases to Hong Kong by different transports vs. days with no travel restriction.</bold> Day one was taken to be March 11, 2009 (the time of the first global case onset). The solid lines represent the average cases; the dotted lines represent the corresponding lower and upper bounds of the 95% non-parametric confidence intervals.</p></caption><graphic xlink:href="12879_2011_Article_2271_Fig2_HTML" id="d29e1761"></graphic></fig></p><table-wrap id="Tab1"><label>Table 1</label><caption><p><bold>Median FPTs and FHPTs (in days) with confidence intervals (CI) at the baseline scenario</bold></p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left">Control measure</th><th align="left">Transportation</th><th align="left">FPT (95% CI)</th><th align="left">FHPT (95% CI)</th></tr></thead><tbody><tr><td align="justify">No travel restriction</td><td align="left"></td><td align="left">55 (35, 67)</td><td align="left">90 (89, 92)</td></tr><tr><td align="justify">90% travel restriction</td><td align="left">Air</td><td align="left">62 (42, 72)</td><td align="left">99 (97, 100)</td></tr><tr><td align="justify"></td><td align="left">Sea</td><td align="left">56 (34, 67)</td><td align="left">92 (90, 93)</td></tr><tr><td align="justify"></td><td align="left">Land</td><td align="left">58 (44, 69)</td><td align="left">93 (91, 95)</td></tr><tr><td align="justify"></td><td align="left">Air, Sea</td><td align="left">66 (51, 77)</td><td align="left">102 (101, 104)</td></tr><tr><td align="justify"></td><td align="left">Air, Land</td><td align="left">69 (45, 81)</td><td align="left">106 (104, 107)</td></tr><tr><td align="justify"></td><td align="left">Sea, Land</td><td align="left">58 (30, 69)</td><td align="left">95 (93, 96)</td></tr><tr><td align="justify"></td><td align="left">All transports</td><td align="left">94 (88, 98)</td><td align="left">114 (114, 115)</td></tr><tr><td align="justify">99% travel restriction</td><td align="left">Air</td><td align="left">61 (37, 72)</td><td align="left">99 (97,101)</td></tr><tr><td align="justify"></td><td align="left">Sea</td><td align="left">57 (28, 68)</td><td align="left">92 (90, 94)</td></tr><tr><td align="justify"></td><td align="left">Land</td><td align="left">59 (38, 69)</td><td align="left">93 (92, 95)</td></tr><tr><td align="justify"></td><td align="left">Air, Sea</td><td align="left">65 (39, 78)</td><td align="left">104 (101, 105)</td></tr><tr><td align="justify"></td><td align="left">Air, Land</td><td align="left">68 (49, 82)</td><td align="left">107 (108, 110)</td></tr><tr><td align="justify"></td><td align="left">Sea, Land</td><td align="left">59 (34, 70)</td><td align="left">95 (93, 96)</td></tr><tr><td align="justify"></td><td align="left">All transports</td><td align="left">117 (116, 118)</td><td align="left">138 (138, 139)</td></tr></tbody></table><table-wrap-foot><p>Travel restrictions took effect on the day after the first global case onset. The medians and the non-parametric 95% confidence intervals were obtained from 100 simulation runs.</p></table-wrap-foot></table-wrap><p>In the absence of control measures, and setting Hong Kong <italic>R</italic><sub>0</sub>=1<italic>.</italic>4, the seven months’ cumulative AR was close to that of the final AR (Figure <xref rid="Fig3" ref-type="fig">3</xref>A). In a mild local scenario (<italic>R</italic><sub>0</sub>=1<italic>.</italic>1), the cumulative AR was a mere 2% after five months, and after seven months, had reached just two-thirds the final cumulative AR (Figure <xref rid="Fig4" ref-type="fig">4</xref>A). In a severe local scenario (<italic>R</italic><sub>0</sub>=1<italic>.</italic>7), the H1N1pdm had nearly terminated after seven months, and the cumulative AR exceeded 70% (Figure <xref rid="Fig4" ref-type="fig">4</xref>E).<fig id="Fig3"><label>Figure 3</label><caption><p><bold>Median cumulative attack rates (in %) for different control measures at the baseline scenario.</bold> The absences and the presences of the uses of antiviral and hospitalization are illustrated in the left-hand column (<bold>A</bold> and <bold>C</bold>) and in the right-hand column (<bold>B</bold> and <bold>D</bold>), respectively. The upper panel (<bold>A</bold> and <bold>B</bold>) and the lower panel (<bold>C</bold> and <bold>D</bold>) illustrate the 90% and the 99% restriction rescaling, respectively. The medians were obtained from 100 simulation runs; AH = antiviral and hospitalization.</p></caption><graphic xlink:href="12879_2011_Article_2271_Fig3_HTML" id="d29e2042"></graphic></fig><fig id="Fig4"><label>Figure 4</label><caption><p><bold>Median cumulative attack rates (in %) for different control measures at the mild and the severe scenarios.</bold> The absences and the presences of the uses of the antiviral and hospitalization are illustrated in the left-hand column (<bold>A</bold>, <bold>C</bold>, <bold>E</bold>, and <bold>G</bold>) and in the right-hand column (<bold>B</bold>, <bold>D</bold>, <bold>F</bold>, and <bold>H</bold>), respectively. The first and the third panels (<bold>A</bold>, <bold>B</bold>, <bold>E</bold>, and <bold>F</bold>), and the second and the forth panels (<bold>C</bold>, <bold>D</bold>, <bold>G</bold>, and <bold>H</bold>) illustrate the 90% and the 99% restriction re-scalings, respectively. The medians were obtained from 100 simulation runs; AH = antiviral and hospitalization.</p></caption><graphic xlink:href="12879_2011_Article_2271_Fig4_HTML" id="d29e2105"></graphic></fig></p></sec><sec id="Sec12"><title>Impact of the interventions</title><p>Among the three kinds of transport, disease spread was most effectively delayed by restriction on air travel. Air travel restriction delayed the FPT and FHPT by one week relative to the no-intervention control case (Table <xref rid="Tab1" ref-type="table">1</xref>). The peak time might have been delayed by two weeks if a single 99% air travel restriction had been imposed (Figure <xref rid="Fig5" ref-type="fig">5</xref>C). The pandemic established in China six months following the first global import to Hong Kong; the strong land connection between the two countries significantly enhanced the number of imported cases. Therefore, suspending both air and land transport could delay the passage time by a further one to two weeks, and the peak by about 3.5 weeks (Figure <xref rid="Fig5" ref-type="fig">5</xref>A and C).<fig id="Fig5"><label>Figure 5</label><caption><p><bold>Daily incidences for different control measures vs. days at the baseline scenario (</bold><bold><italic>R</italic></bold><sub><bold><italic>0</italic></bold></sub><bold><italic>=1.4</italic></bold><bold>).</bold> The absences and the presences of the uses of antiviral and hospitalization are illustrated in the left-hand column (<bold>A</bold> and <bold>C</bold>) and in the right-hand column (<bold>B</bold> and <bold>D</bold>), respectively. The upper panel (<bold>A</bold> and <bold>B</bold>) and the lower panel (<bold>C</bold> and <bold>D</bold>) illustrate the 90% and the 99% restriction rescaling, respectively. Day one was taken to be March 11, 2009 (the time of the first global case onset). The solid lines represent the average cases; the dotted lines represent the corresponding lower and upper bounds of the 95% non-parametric confidence intervals; AH = antiviral and hospitalization.</p></caption><graphic xlink:href="12879_2011_Article_2271_Fig5_HTML" id="d29e2171"></graphic></fig></p><p>Travel restrictions on all transport modes most effectively delayed the spread of the H1N1pdm. As shown in Figure <xref rid="Fig5" ref-type="fig">5</xref>, the difference between 90% and 99% transport reduction was apparent only when all three transport modes were restricted. Once the volume of all transports was reduced by 90%, FPT and FHPT were retarded by one month relative to the control case. 99% travel restriction delayed the FPT and FHPT by an additional two months (Table <xref rid="Tab1" ref-type="table">1</xref>). 90% and 99% restriction of all transport modes deferred the peak for about six weeks (Figure <xref rid="Fig5" ref-type="fig">5</xref>A), and 12 weeks (Figure <xref rid="Fig5" ref-type="fig">5</xref>C), respectively.</p><p>Nevertheless, blocking of sea or land transport alone cannot prevent disease spread; it did not confer any large reduction in the five and seven months’ cumulative ARs. Even with sea transports reduced by 99%, the peak is delayed by only one week, relative to the control case (Figure <xref rid="Fig5" ref-type="fig">5</xref>C).</p><p>In reducing attack rate, antiviral and hospitalization administration (AH) proved more promising than travel restrictions. Neither 90% nor 99% travel restrictions reduced the epidemic magnitude by more than 10%. Implementation of AH on a proportion of infected individuals could halve the peak rate, and reduce the final cumulative ARs (relative to the case of no intervention) from 58% to 37% (Figure <xref rid="Fig5" ref-type="fig">5</xref>B and Figure <xref rid="Fig3" ref-type="fig">3</xref>B). However, the peak time of epidemic was only slightly delayed.</p><p>Combining travel restrictions with AH, the impacts on mitigation are greatly enhanced. Air travel restrictions plus AH delayed the peak time by more than three weeks (Figure <xref rid="Fig5" ref-type="fig">5</xref>B and D). A 99% restriction of both air and land travel delayed the peak time by more than six weeks (Figure <xref rid="Fig5" ref-type="fig">5</xref>D). Imposing AH plus a 99% restriction on all transport modes flattened the epidemic curve more effectively than did AH plus 90% travel restriction. This strict condition greatly repressed the cumulative ARs, limiting them to around 1% (Figure <xref rid="Fig3" ref-type="fig">3</xref>D). Most importantly, the peak was delayed by approximately five months (Figure <xref rid="Fig5" ref-type="fig">5</xref>D). Supplemented by AH, total travel restriction reduced the final cumulative AR to about 14%.</p><p>In a milder local scenario (<italic>R</italic><sub>0</sub>=1<italic>.</italic>1), travel restrictions not only effectively delayed the H1N1pdm, but also flattened the incidence curve. Suspension of air travel remained the best choice among the three transport modes for repressing the cumulative ARs (Figure <xref rid="Fig4" ref-type="fig">4</xref>A and C). Because the disease transmissions were comparatively slow and mild, 90% land import restriction was sufficient to decrease the peak ARs by one-third (Figure <xref rid="Fig6" ref-type="fig">6</xref>A and C). Besides reducing the peak incidence by 25%, 99% restriction of all transport delayed the peak time by one year following the first global import. As shown in Figure <xref rid="Fig6" ref-type="fig">6</xref>B and D, combining AH and travel restrictions resulted in significant peak reduction. Restricting all travel routes as well as administering AH, the spread of the local epidemic was halted; the 99% travel restriction retained the final cumulative AR at around 0.2% (Figure <xref rid="Fig4" ref-type="fig">4</xref>B and D).<fig id="Fig6"><label>Figure 6</label><caption><p><bold>Daily incidences for different control measures vs. days for the mild (</bold><bold><italic>R</italic></bold><sub><bold><italic>0</italic></bold></sub><bold><italic>=1.1</italic></bold><bold>) and the severe (</bold><bold><italic>R</italic></bold><sub><bold><italic>0</italic></bold></sub><bold><italic>=1.7</italic></bold><bold>) scenarios.</bold> The absences and the presences of the uses of the antiviral and hospitalization are illustrated in the left-hand column (<bold>A</bold>, <bold>C</bold>, <bold>E</bold>, and <bold>G</bold>) and in the right-hand column (<bold>B</bold>, <bold>D</bold>, <bold>F</bold>, and <bold>H</bold>), respectively. The first and the third panels (<bold>A</bold>, <bold>B</bold>, <bold>E</bold>, and <bold>F</bold>), and the second and the forth panels (<bold>C</bold>, <bold>D</bold>, <bold>G</bold>, and <bold>H</bold>) illustrate the 90% and the 99% restriction re-scalings, respectively. Day one was taken to be March 11, 2009 (the time of the first global case onset). The solid lines represent the average cases; the dotted lines represent the corresponding lower and upper bounds of the 95% non-parametric confidence intervals; AH = antiviral and hospitalization.</p></caption><graphic xlink:href="12879_2011_Article_2271_Fig6_HTML" id="d29e2327"></graphic></fig></p><p>Incoming travel restrictions became less effective (especially where ARs was concerned) as the contagion level of the influenza virus increased to <italic>R</italic><sub>0</sub> = 1<italic>.</italic>7. The rapid disease transmission rate raised the five months’ cumulative AR to an average of 22% (Figure <xref rid="Fig4" ref-type="fig">4</xref>E and G). Imposing 99% restriction on all transport modes remained sufficient to retard the disease spread, deferring the epidemic peak time by about eight weeks (Figure <xref rid="Fig6" ref-type="fig">6</xref>G). However, under 90% total travel restriction, no significant delay was observed. Supplementation by AH became more important in this scenario (Figure <xref rid="Fig4" ref-type="fig">4</xref>F). Because the incidence growth was now suppressed by AH, travel restrictions more effectively repressed the epidemic. Imposing a 99% restriction on all transport, the seven months’ cumulative AR was restrained to 4% or less (on average; Figure <xref rid="Fig4" ref-type="fig">4</xref>H), with an approximate delay in peak time of 12 weeks (Figure <xref rid="Fig6" ref-type="fig">6</xref>H).</p><sec id="Sec13"><title>Effect of <italic>R</italic><sub>0</sub>from non-local countries</title><p>In our study, we varied the <italic>R</italic><sub>0</sub>s from 44 foreign countries by 20%, and re-evaluated the model outputs. The changes in foreign <italic>R</italic><sub>0</sub>s affected the number of imported cases, implying that growth of a local epidemic depends upon the passage times of the cases. With reductions of <italic>R</italic><sub>0</sub>s, imposing restrictions solely on air travel nearly halved the cumulative ARs. A 99% all-transport restriction was sufficient to halt the local spread (cumulative ARs attain < 0.1%) after seven months (with or without AH administration). It maintained the seven months’ cumulative AR at around 12% even with a 20% increase in <italic>R</italic><sub>0</sub>.</p></sec><sec id="Sec14"><title>Effect of screening sensitivity at entry border points</title><p>Increasing the screening sensitivity at the entry border slightly retards the local epidemic. In most of the travel restriction scenarios, the additional FHPTs delay imposed by the strict 95% screening sensitivity and the relaxed 5% screening sensitivity was, at most, one to two weeks.</p></sec><sec id="Sec15"><title>Effect of implementation date on travel restrictions</title><p>Imposing travel restrictions five months after the arrival of the first global case is ineffective. Even with a total transport reduction of 99%, the reduction in the cumulative ARs was found to be negligibly small. By comparison, allowing a three-month gap between arrival of the first global case and imposition of travel restrictions, the seven-months cumulative AR could be restrained at around 2% by imposing AH plus 99% restriction on all transport modes.</p></sec></sec></sec><sec id="Sec16"><title>Discussion</title><p>Non-pharmaceutical interventions such as travel restrictions are immediate means by which to slow pandemic growth and to extend the time available for vaccine production. Here we collected statistics on arrival numbers in Hong Kong from 44 countries via air, sea, and land transport [<xref ref-type="bibr" rid="CR15">15</xref>]. These data were input to a mathematical model to evaluate the impact of travel restriction on different scales and by different modes, combined with other government strategies (namely, antivirals and hospitalizations), using the 2009 H1N1pdm as an example. From our results, we infer that the main connecting route and transport mode between source and destination (in this instance, air travel from the Americas/Mexico to Hong Kong), should be targeted for travel restrictions in a pandemic. This is in addition to suspending travels from large, high-density cities [<xref ref-type="bibr" rid="CR9">9</xref>]. The emerging 2009 H1N1pdm virus had circulated to most Asian countries, including densely-populated China, six months after the first global case was reported. The number of imported cases from China to Hong Kong by land transport thereafter increased exponentially. Reducing land travel could have significantly lowered the number of import transmissions. In mild cases, such a restriction reduces the proportion of peak incidence and delays the peak time by up to one month. However, suspending travels on a single route only slightly decreases the peak incidence and the final epidemic size. Restricting either sea or land transport, but not both, confers little advantage in terms of disease spread.</p><p>Travel restrictions may not be effective at reducing epidemic size. Based on our results, antivirals and hospitalization lower the disease incidence as well as the final epidemic size, but do not prevent the import of contagious cases or delay the peak time. In most scenarios, imposing AH on a proportion of infected individuals (< 20%) moderately mitigates the severity of the pandemic, reducing the peak incidence by half. Several previous studies have lauded AH as an effective new epidemic control measure [<xref ref-type="bibr" rid="CR5">5</xref>, <xref ref-type="bibr" rid="CR24">24</xref>, <xref ref-type="bibr" rid="CR52">52</xref>]. On the other hand, when AH and travel restrictions are imposed together they supplement each other, further mitigating the pandemic. Since imposing AH suppresses the growth of local transmission, the number of local infected sources is reduced, while travel restrictions prevent the import of fresh infectious sources. Imposing both interventions thus considerably extends the peak time. When rigorous restriction on all transport modes is combined with AH, the delays (peak appearing after the 10th month) are possible to allow vaccine production (i.e. beyond the nine months following the first global import to Hong Kong, during which time a vaccine program was developed and administered to the local public).</p><p>The effectiveness of travel reductions depends upon the rate of epidemic growth in different foreign countries [<xref ref-type="bibr" rid="CR6">6</xref>]. If control measures had been responsible for reduced transmission in foreign countries (modeled by decreasing the <italic>R</italic><sub>0</sub>s by an average of 20%), a 99% restriction on all external transport modes might have halted the local spread. In any case, increasing the screening sensitivity at the entry border points conferred a one to two week delay benefit. In reality, some individuals would refuse to undertake voluntary quarantine despite screening positive at the border. Such refusals would decrease the sensitivity for screening of quarantined symptomatic cases. Although the true screening sensitivity may not match our model settings i.e. 30%, we showed that screening sensitivity exerts only a secondary effect on epidemic delay. In the simulation results, the average maximum number of the screened import cases is 928 (95% confidence interval: 895-961), whereas there are 1400 isolation beds in 14 major hospitals in Hong Kong, which was set by the government after SARS [<xref ref-type="bibr" rid="CR53">53</xref>]. Thus, the control measure would unlikely entail a capacity problem in Hong Kong. Our findings also imply that restrictions be imposed no later than three months following the first infectious global import. Implementing travel restrictions at or beyond the end of the fifth month would be almost useless, because the local epidemic would by then have evolved to a mature stage, in which disease transmission would depend on the local exponential increase in cases, rather than on successive imports.</p><p>In the study, we focused on a major city, Hong Kong, as a high-density, well-traveled region especially suited to the assessment of travel restrictions. Travel restrictions reduced the illness rate only in the event of mild local disease transmission intensity. In some rural areas or island countries, the disease transmission intensities as well as the reproduction numbers remain at low levels due to limited human mobility and contacts. In addition, these areas may be infrequently visited by foreign travelers. Such areas may benefit significantly from travel suspension. In some studies [<xref ref-type="bibr" rid="CR54">54</xref>, <xref ref-type="bibr" rid="CR55">55</xref>], beneficial delays in epidemic establishment have been reported, as a result of blocking imported cases. Apart from travel restrictions, there are other public health measures such as regular hand washing, voluntary quarantine, and school closures to reduce the impact of influenza pandemic. Compared with travel restrictions, school closure is easier to implement in a community. Past influenza pandemics have shown a particular focus on disease transmission in children. School closures resulted in a positive effect proven to be effective in reducing the disease transmission during the H1N1pdm [<xref ref-type="bibr" rid="CR28">28</xref>]. Nevertheless, while school closures and antivirals are good for transmission reduction, they may not be for buying more time in epidemic preparation. Closing schools for a long time would induce social and economical impacts, whereas closing schools for a short period of time may not be sufficient to show effects on community transmission [<xref ref-type="bibr" rid="CR56">56</xref>]. Other social distancing measures like cancelling public gatherings or international events raise questions about which sizes of public gatherings would warrant cancelling. These factors could be considered in future research.</p><p>Several limitations are present in our study. Restrictions for inbound travel could be beneficial to the pandemic mitigation but not outbound travel restrictions. Restrictions for outbound travel could lead to a worse situation of a pandemic growth after successive local cases arise. This is because the departure frequency is more than the arrival frequency in Hong Kong (Additional file <xref rid="MOESM1" ref-type="media">1</xref>: Table S1), and the excess proportion of individuals are restricted to stay and infect or transmit influenza virus to others. So there are increases to the attack rates for this scenario. Nevertheless, the restrictions on outbound travel to prevent spreading to other countries is especially beneficial for those with limited resources of pandemic prevention. Outbound travel restrictions would be better imposed during the containment phase in order to prevent a global spread of pandemic virus. As our study does not incorporate the comprehensive traveling network between countries required for a global viewpoint of pandemic spread, we cannot completely determine the value of outbound travel restrictions. Moreover, we were unable to quantify the infection risk for outbound susceptible travelers during their trip abroad because of limited information regarding their contact patterns. Although outbound passengers may become infected during their time abroad, they have nonetheless escaped from local infections. Our estimated <italic>R</italic><sub>0</sub> for Hong Kong was 1.4, close to that of the global median (Additional file <xref rid="MOESM1" ref-type="media">1</xref>: Table S2). The similar disease transmission intensity between countries would unlikely incur large infection-risk differences between outbound and local susceptible individuals, provided that the periods of H1N1pdm in different countries are not widely spaced. In addition, all travelers are assumed to undertake a single-step journey to their destination, and no adjustment for multi-step journeys is admitted in the model. Nevertheless, previously reported reports reveal little quantitative difference between single- and multi-step travel [<xref ref-type="bibr" rid="CR57">57</xref>]. More importantly, enforcing rigorous travel restrictions has been undoubtedly unrealistic to date, since such restrictions would substantially degrade the local economy. In 2009, [<xref ref-type="bibr" rid="CR58">58</xref>], tourism-related activities such as accommodation services, retail trade, transport services, and food and beverage services contributed 2.6% (US$5,200 million) to Hong Kong’s Gross Domestic Product (GDP). Large travel reductions thus incur high economic loss. However, increasingly severe diseases, such as SARS and influenza A (H1N1), have entered our society within recent decades, and have affected wider age groups than have past epidemics. The emergence of a highly lethal virus is feasible in the near future. In mitigating viral pandemics, the benefit to be gained from imposing travel restrictions as an adjunct to other effective control measures must be balanced against potential economic impacts. A comprehensive cost benefit analysis will thus be addressed in our future research.</p></sec><sec id="Sec17"><title>Conclusions</title><p>Our study suggested that air travel restrictions should be priorities for consideration when a new influenza pandemic begins overseas. When the pandemic is initiated in China or other places where there is land travel to Hong Kong, land travel restrictions should also be a priority. If restrictions are able to cover 99% travelers with the use of antiviral and hospitalization, the resulting pandemic delays are possible to allow vaccine production; if the restrictions cannot cover 90% or more travelers, then the peak time will happen sooner. In this case, control measures such as antiviral should be enacted earlier to alleviate the epidemic growth. To date travel restrictions have yet to gain widespread social acceptance, but the benefits may significantly outweigh the costs, especially when a new and highly intrusive virus emerges.</p></sec><sec sec-type="supplementary-material"><title>Electronic supplementary material</title><sec id="Sec18"><p><supplementary-material content-type="local-data" id="MOESM1"><media xlink:href="12879_2011_2271_MOESM1_ESM.pdf"><caption><p>Additional file 1: Technical appendix. Mathematical model formulation, impact of other variations, and sensitivity analysis. (PDF 2 MB)</p></caption></media></supplementary-material></p></sec></sec></body><back><app-group><app id="App1"><sec id="Sec19"><title>Authors’ original submitted files for images</title><p>Below are the links to the authors’ original submitted files for images.<media position="anchor" xlink:href="12879_2011_2271_MOESM2_ESM.png" id="MOESM2"><caption><p>Authors’ original file for figure 1</p></caption></media><media position="anchor" xlink:href="12879_2011_2271_MOESM3_ESM.png" id="MOESM3"><caption><p>Authors’ original file for figure 2</p></caption></media><media position="anchor" xlink:href="12879_2011_2271_MOESM4_ESM.pdf" id="MOESM4"><caption><p>Authors’ original file for figure 3</p></caption></media><media position="anchor" xlink:href="12879_2011_2271_MOESM5_ESM.pdf" id="MOESM5"><caption><p>Authors’ original file for figure 4</p></caption></media><media position="anchor" xlink:href="12879_2011_2271_MOESM6_ESM.png" id="MOESM6"><caption><p>Authors’ original file for figure 5</p></caption></media><media position="anchor" xlink:href="12879_2011_2271_MOESM7_ESM.tiff" id="MOESM7"><caption><p>Authors’ original file for figure 6</p></caption></media></p></sec></app></app-group><fn-group><fn><p><bold>Competing interests</bold></p><p>The authors declare that they have no competing interests.</p></fn><fn><p><bold>Authors’ contributions</bold></p><p>Both authors contributed to the study and performed statistical analysis. They all drafted the manuscript and approved the final version.</p></fn></fn-group><ack><title>Acknowledgements</title><p>The authors would like to thank Dr. David Wilmshurst and Dr. Hildy Fong for helpful comments on editing; and Dr. Leonie Zandra Pipe for editing the final manuscript. The authors also thank two reviewers for their valuable and constructive comments.</p></ack><ref-list id="Bib1"><title>References</title><ref id="CR1"><label>1.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Dawood</surname><given-names>FS</given-names></name><name><surname>Iuliano</surname><given-names>AD</given-names></name><name><surname>Reed</surname><given-names>C</given-names></name><name><surname>Meltzer</surname><given-names>MI</given-names></name><name><surname>Shay</surname><given-names>DK</given-names></name><name><surname>Cheng</surname><given-names>PY</given-names></name><name><surname>Bandaranayake</surname><given-names>D</given-names></name><name><surname>Breiman</surname><given-names>RF</given-names></name><name><surname>Brooks</surname><given-names>WA</given-names></name><name><surname>Buchy</surname><given-names>P</given-names></name><etal></etal></person-group><article-title>Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: a modelling study</article-title><source>Lancet Infect Dis</source><year>2012</year><volume>12</volume><issue>9</issue><fpage>687</fpage><lpage>695</lpage><pub-id pub-id-type="doi">10.1016/S1473-3099(12)70121-4</pub-id><pub-id pub-id-type="pmid">22738893</pub-id></element-citation></ref><ref id="CR2"><label>2.</label><mixed-citation publication-type="other">Centre for Health Protection, HKSAR. Swine and seasonal flu monitor (volume 2, number 40) week 40. [<ext-link ext-link-type="uri" xlink:href="http://www.chp.gov.hk/files/pdf/revised_SSFM_7_10_2010.pdf">http://www.chp.gov.hk/files/pdf/revised_SSFM_7_10_2010.pdf</ext-link>]</mixed-citation></ref><ref id="CR3"><label>3.</label><mixed-citation publication-type="other">Centre for Health Protection, HKSAR. Press releases on April 30, 2009. [<ext-link ext-link-type="uri" xlink:href="http://www.chp.gov.hk/en/content/116/16770.html">http://www.chp.gov.hk/en/content/116/16770.html</ext-link>]</mixed-citation></ref><ref id="CR4"><label>4.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Grais</surname><given-names>RF</given-names></name><name><surname>Hugh</surname><given-names>G</given-names></name><name><surname>Glass</surname><given-names>GE</given-names></name></person-group><article-title>Assessing the impact of airline travel on the geographic spread of pandemic influenza</article-title><source>Eur J Epidemiol</source><year>2003</year><volume>18</volume><issue>11</issue><fpage>1065</fpage><lpage>1072</lpage><pub-id pub-id-type="doi">10.1023/A:1026140019146</pub-id><pub-id pub-id-type="pmid">14620941</pub-id></element-citation></ref><ref id="CR5"><label>5.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Colizza</surname><given-names>V</given-names></name><name><surname>Barrat</surname><given-names>A</given-names></name><name><surname>Barthelemy</surname><given-names>M</given-names></name><name><surname>Valleron</surname><given-names>AJ</given-names></name><name><surname>Vespignani</surname><given-names>A</given-names></name></person-group><article-title>Modeling the worldwide spread of pandemic influenza: Baseline case and containment interventions</article-title><source>PLoS Med</source><year>2007</year><volume>4</volume><fpage>e13</fpage><pub-id pub-id-type="doi">10.1371/journal.pmed.0040013</pub-id><pub-id pub-id-type="pmid">17253899</pub-id></element-citation></ref><ref id="CR6"><label>6.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hollingsworth</surname><given-names>TD</given-names></name><name><surname>Ferguson</surname><given-names>N</given-names></name><name><surname>Anderson</surname><given-names>R</given-names></name></person-group><article-title>Will travel restrictions control the international spread of pandemic influenza?</article-title><source>Nat Med</source><year>2006</year><volume>12</volume><fpage>497</fpage><lpage>499</lpage><pub-id pub-id-type="doi">10.1038/nm0506-497</pub-id><pub-id pub-id-type="pmid">16675989</pub-id></element-citation></ref><ref id="CR7"><label>7.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Cooper</surname><given-names>BS</given-names></name><name><surname>Pitman</surname><given-names>RJ</given-names></name><name><surname>Edmunds</surname><given-names>WJ</given-names></name><name><surname>Gay</surname><given-names>NJ</given-names></name></person-group><article-title>Delaying the international spread of pandemic influenza</article-title><source>PLoS Med</source><year>2006</year><volume>3</volume><issue>6</issue><fpage>e212</fpage><pub-id pub-id-type="doi">10.1371/journal.pmed.0030212</pub-id><pub-id pub-id-type="pmid">16640458</pub-id></element-citation></ref><ref id="CR8"><label>8.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Flahault</surname><given-names>A</given-names></name><name><surname>Valleron</surname><given-names>AJ</given-names></name></person-group><article-title>A method for assessing the global spread of HIV-1 infection based on air travel</article-title><source>Math Pop Stud</source><year>1992</year><volume>3</volume><fpage>161</fpage><lpage>171</lpage><pub-id pub-id-type="doi">10.1080/08898489209525336</pub-id></element-citation></ref><ref id="CR9"><label>9.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hufnagel</surname><given-names>L</given-names></name><name><surname>Brockmann</surname><given-names>D</given-names></name><name><surname>Geisel</surname><given-names>T</given-names></name></person-group><article-title>Forecast and control of epidemics in a globalized world</article-title><source>Proc Natl Acad Sci USA</source><year>2004</year><volume>101</volume><issue>42</issue><fpage>15124</fpage><lpage>15129</lpage><pub-id pub-id-type="doi">10.1073/pnas.0308344101</pub-id><pub-id pub-id-type="pmid">15477600</pub-id></element-citation></ref><ref id="CR10"><label>10.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Flahault</surname><given-names>A</given-names></name><name><surname>Vergu</surname><given-names>E</given-names></name><name><surname>Boelle</surname><given-names>PY</given-names></name></person-group><article-title>Potential for a global dynamic of influenza A (H1N1)</article-title><source>BMC Infect Dis</source><year>2009</year><volume>9</volume><fpage>129</fpage><pub-id pub-id-type="doi">10.1186/1471-2334-9-129</pub-id><pub-id pub-id-type="pmid">19674455</pub-id></element-citation></ref><ref id="CR11"><label>11.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Brownstein</surname><given-names>JS</given-names></name><name><surname>Wolfe</surname><given-names>CJ</given-names></name><name><surname>Mandl</surname><given-names>KD</given-names></name></person-group><article-title>Empirical evidence for the effect of airline travel on inter-regional influenza spread in the United States</article-title><source>PLoS Med</source><year>2006</year><volume>3</volume><issue>10</issue><fpage>e401</fpage><pub-id pub-id-type="doi">10.1371/journal.pmed.0030401</pub-id><pub-id pub-id-type="pmid">16968115</pub-id></element-citation></ref><ref id="CR12"><label>12.</label><mixed-citation publication-type="other">World Health Organization. WHO global influenza preparedness plan 2005. [<ext-link ext-link-type="uri" xlink:href="http://www.who.int/csr/resources/publications/influenza/WHO_CDS_CSR_GIP_2005_5.pdf">http://www.who.int/csr/resources/publications/influenza/WHO_CDS_CSR_GIP_2005_5.pdf</ext-link>]</mixed-citation></ref><ref id="CR13"><label>13.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Tomba</surname><given-names>GS</given-names></name><name><surname>Wallinga</surname><given-names>J</given-names></name></person-group><article-title>A simple explanation for the low impact of border control as a countermeasure to the spread of an infectious disease</article-title><source>Mathl Biosci</source><year>2008</year><volume>214</volume><issue>1–2</issue><fpage>70</fpage><lpage>72</lpage><pub-id pub-id-type="doi">10.1016/j.mbs.2008.02.009</pub-id></element-citation></ref><ref id="CR14"><label>14.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ciofi degli Atti</surname><given-names>ML</given-names></name><name><surname>Merler</surname><given-names>S</given-names></name><name><surname>Rizzo</surname><given-names>C</given-names></name><name><surname>Ajelli</surname><given-names>M</given-names></name><name><surname>Massari</surname><given-names>M</given-names></name><name><surname>Manfredi</surname><given-names>P</given-names></name><name><surname>Furlanello</surname><given-names>C</given-names></name><name><surname>Scalia Tomba</surname><given-names>G</given-names></name><name><surname>Iannelli</surname><given-names>M</given-names></name></person-group><article-title>Mitigation measures for pandemic influenza in Italy: an individual based model considering different scenarios</article-title><source>PLoS ONE</source><year>2008</year><volume>3</volume><issue>3</issue><fpage>e1790</fpage><pub-id pub-id-type="doi">10.1371/journal.pone.0001790</pub-id><pub-id pub-id-type="pmid">18335060</pub-id></element-citation></ref><ref id="CR15"><label>15.</label><mixed-citation publication-type="other">Hong Kong Tourism Board. Visitor arrival statistics. [<ext-link ext-link-type="uri" xlink:href="http://partnernet.hktourismboard.com/pnweb/jsp/doc/listDocL.jsp?charset=en&doc_id=107560&filename=VAS+12200">http://partnernet.hktourismboard.com/pnweb/jsp/doc/listDocL.jsp?charset=en&doc_id=107560&filename=VAS+12200</ext-link>]</mixed-citation></ref><ref id="CR16"><label>16.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Cowling</surname><given-names>BJ</given-names></name><name><surname>Lau</surname><given-names>L</given-names></name><name><surname>Wu</surname><given-names>P</given-names></name><name><surname>Wong</surname><given-names>H</given-names></name><name><surname>Fang</surname><given-names>V</given-names></name><name><surname>Riley</surname><given-names>S</given-names></name><name><surname>Nishiura</surname><given-names>H</given-names></name></person-group><article-title>Entry screening to delay local transmission of 2009 pandemic influenza A (H1N1)</article-title><source>BMC Infect Dis</source><year>2010</year><volume>10</volume><fpage>82</fpage><pub-id pub-id-type="doi">10.1186/1471-2334-10-82</pub-id><pub-id pub-id-type="pmid">20353566</pub-id></element-citation></ref><ref id="CR17"><label>17.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Pitman</surname><given-names>RJ</given-names></name><name><surname>Cooper</surname><given-names>BS</given-names></name><name><surname>Trotter</surname><given-names>CL</given-names></name><name><surname>Gay</surname><given-names>NJ</given-names></name><name><surname>Edmunds</surname><given-names>WJ</given-names></name></person-group><article-title>Entry screening for severe acute respiratory syndrome (SARS) or influenza: policy evaluation</article-title><source>BMJ</source><year>2005</year><volume>331</volume><issue>7527</issue><fpage>1242</fpage><lpage>1243</lpage><pub-id pub-id-type="doi">10.1136/bmj.38573.696100.3A</pub-id><pub-id pub-id-type="pmid">16176938</pub-id></element-citation></ref><ref id="CR18"><label>18.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Bettencourt</surname><given-names>LMA</given-names></name><name><surname>Ribeiro</surname><given-names>RM</given-names></name></person-group><article-title>Real time bayesian estimation of the epidemic potential of emerging infectious diseases</article-title><source>PLoS ONE</source><year>2008</year><volume>3</volume><issue>5</issue><fpage>e2185</fpage><pub-id pub-id-type="doi">10.1371/journal.pone.0002185</pub-id><pub-id pub-id-type="pmid">18478118</pub-id></element-citation></ref><ref id="CR19"><label>19.</label><mixed-citation publication-type="other">Hong Kong Information Services Department. News Archives. HKSAR press releases June 27, 2009. New hospital and clinic arrangements for human swine flu patients. [<ext-link ext-link-type="uri" xlink:href="http://www.info.gov.hk/gia/general/200906/27/P200906270251.htm">http://www.info.gov.hk/gia/general/200906/27/P200906270251.htm</ext-link>]</mixed-citation></ref><ref id="CR20"><label>20.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Stohr</surname><given-names>K</given-names></name><name><surname>Esveld</surname><given-names>M</given-names></name></person-group><article-title>Will vaccines be available for the next influenza pandemic?</article-title><source>Science</source><year>2004</year><volume>306</volume><issue>5705</issue><fpage>2195</fpage><lpage>2196</lpage><pub-id pub-id-type="doi">10.1126/science.1108165</pub-id><pub-id pub-id-type="pmid">15618505</pub-id></element-citation></ref><ref id="CR21"><label>21.</label><mixed-citation publication-type="other">Hong Kong Information Services Department. News Archives. HKSAR press releases December 21, 2009. Human swine influenza vaccination programme launched (with photo). [<ext-link ext-link-type="uri" xlink:href="http://www.info.gov.hk/gia/general/200912/21/P200912210180.htm">http://www.info.gov.hk/gia/general/200912/21/P200912210180.htm</ext-link>]</mixed-citation></ref><ref id="CR22"><label>22.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Liao</surname><given-names>Q</given-names></name><name><surname>Cowling</surname><given-names>BJ</given-names></name><name><surname>Lam</surname><given-names>WWT</given-names></name><name><surname>Fielding</surname><given-names>R</given-names></name></person-group><article-title>Factors affecting intention to receive and self-reported receipt of 2009 pandemic (H1N1) vaccine in Hong Kong: A longitudinal study</article-title><source>PLoS ONE</source><year>2011</year><volume>6</volume><issue>3</issue><fpage>e17713</fpage><pub-id pub-id-type="doi">10.1371/journal.pone.0017713</pub-id><pub-id pub-id-type="pmid">21412418</pub-id></element-citation></ref><ref id="CR23"><label>23.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wu</surname><given-names>JT</given-names></name><name><surname>Riley</surname><given-names>S</given-names></name><name><surname>Fraser</surname><given-names>C</given-names></name><name><surname>Leung</surname><given-names>GM</given-names></name></person-group><article-title>Reducing the impact of the next influenza pandemic using household-based public health interventions</article-title><source>PLoS Med</source><year>2006</year><volume>3</volume><issue>9</issue><fpage>e361</fpage><pub-id pub-id-type="doi">10.1371/journal.pmed.0030361</pub-id><pub-id pub-id-type="pmid">16881729</pub-id></element-citation></ref><ref id="CR24"><label>24.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ferguson</surname><given-names>N</given-names></name><name><surname>Cummings</surname><given-names>D</given-names></name><name><surname>Cauchemez</surname><given-names>S</given-names></name><name><surname>Fraser</surname><given-names>C</given-names></name><name><surname>Riley</surname><given-names>S</given-names></name><name><surname>Meeyai</surname><given-names>A</given-names></name><name><surname>Iamsirithaworn</surname><given-names>S</given-names></name><name><surname>Burke</surname><given-names>D</given-names></name></person-group><article-title>Strategies for containing an emerging influenza pandemic in Southeast Asia</article-title><source>Nature</source><year>2005</year><volume>437</volume><issue>7056</issue><fpage>209</fpage><lpage>214</lpage><pub-id pub-id-type="doi">10.1038/nature04017</pub-id><pub-id pub-id-type="pmid">16079797</pub-id></element-citation></ref><ref id="CR25"><label>25.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Gani</surname><given-names>R</given-names></name><name><surname>Hughes</surname><given-names>H</given-names></name><name><surname>Fleming</surname><given-names>D</given-names></name><name><surname>Griffin</surname><given-names>T</given-names></name><name><surname>Medlock</surname><given-names>J</given-names></name><name><surname>Leach</surname><given-names>S</given-names></name></person-group><article-title>Potential impact of antiviral drug use during influenza pandemic</article-title><source>Emerg Infect Dis</source><year>2005</year><volume>11</volume><issue>9</issue><fpage>1355</fpage><lpage>1362</lpage><pub-id pub-id-type="doi">10.3201/eid1209.041344</pub-id><pub-id pub-id-type="pmid">16229762</pub-id></element-citation></ref><ref id="CR26"><label>26.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Longini</surname><given-names>IJ</given-names></name><name><surname>Halloran</surname><given-names>M</given-names></name><name><surname>Nizam</surname><given-names>A</given-names></name><name><surname>Yang</surname><given-names>Y</given-names></name></person-group><article-title>Containing pandemic influenza with antiviral agents</article-title><source>Am J Epidemiol</source><year>2004</year><volume>159</volume><fpage>623</fpage><lpage>633</lpage><pub-id pub-id-type="doi">10.1093/aje/kwh092</pub-id><pub-id pub-id-type="pmid">15033640</pub-id></element-citation></ref><ref id="CR27"><label>27.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Vynnycky</surname><given-names>E</given-names></name><name><surname>Edmunds</surname><given-names>W</given-names></name></person-group><article-title>Analyses of the 1957 (Asian) influenza pandemic in the United Kingdom and the impact of school closures</article-title><source>Epidemiol Infect</source><year>2008</year><volume>136</volume><fpage>166</fpage><lpage>179</lpage><pub-id pub-id-type="pmid">17445311</pub-id></element-citation></ref><ref id="CR28"><label>28.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wu</surname><given-names>JT</given-names></name><name><surname>Cowling</surname><given-names>BJ</given-names></name><name><surname>Lau</surname><given-names>EH</given-names></name><name><surname>Ip</surname><given-names>DK</given-names></name><name><surname>Ho</surname><given-names>LM</given-names></name><name><surname>Tsang</surname><given-names>T</given-names></name><name><surname>Chuang</surname><given-names>SK</given-names></name><name><surname>Leung</surname><given-names>PY</given-names></name><name><surname>Lo</surname><given-names>SV</given-names></name><name><surname>Liu</surname><given-names>SH</given-names></name><name><surname>Riley</surname><given-names>S</given-names></name></person-group><article-title>School closure and mitigation of pandemic (H1N1) 2009, Hong Kong</article-title><source>Emerg Infect Dis</source><year>2010</year><volume>16</volume><issue>3</issue><fpage>538</fpage><lpage>541</lpage><pub-id pub-id-type="doi">10.3201/eid1603.091216</pub-id><pub-id pub-id-type="pmid">20202441</pub-id></element-citation></ref><ref id="CR29"><label>29.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Riley</surname><given-names>S</given-names></name><name><surname>Wu</surname><given-names>JT</given-names></name><name><surname>Leung</surname><given-names>GM</given-names></name></person-group><article-title>Optimizing the dose of pre-pandemic influenza vaccines to reduce the infection attack rate</article-title><source>PLoS Med</source><year>2007</year><volume>4</volume><issue>6</issue><fpage>e218</fpage><pub-id pub-id-type="doi">10.1371/journal.pmed.0040218</pub-id><pub-id pub-id-type="pmid">17579511</pub-id></element-citation></ref><ref id="CR30"><label>30.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Medlock</surname><given-names>J</given-names></name><name><surname>Galvani</surname><given-names>AP</given-names></name></person-group><article-title>Optimizing influenza vaccine distribution</article-title><source>Science</source><year>2009</year><volume>325</volume><issue>5948</issue><fpage>1705</fpage><lpage>1708</lpage><pub-id pub-id-type="doi">10.1126/science.1175570</pub-id><pub-id pub-id-type="pmid">19696313</pub-id></element-citation></ref><ref id="CR31"><label>31.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Tracht</surname><given-names>SM</given-names></name><name><surname>Del Valle</surname><given-names>SY</given-names></name><name><surname>Hyman</surname><given-names>JM</given-names></name></person-group><article-title>Mathematical modeling of the effectiveness of facemasks in reducing the spread of novelinfluenza A (H1N1)</article-title><source>PLoS ONE</source><year>2010</year><volume>5</volume><issue>2</issue><fpage>e9018</fpage><pub-id pub-id-type="doi">10.1371/journal.pone.0009018</pub-id><pub-id pub-id-type="pmid">20161764</pub-id></element-citation></ref><ref id="CR32"><label>32.</label><mixed-citation publication-type="other">U.S. Census Bureau. International Data base (IDB). Total midyear population by world. [<ext-link ext-link-type="uri" xlink:href="http://www.census.gov/population/international/data/idb/informationGateway.php">http://www.census.gov/population/international/data/idb/informationGateway.php</ext-link>]</mixed-citation></ref><ref id="CR33"><label>33.</label><mixed-citation publication-type="other">Census and Statistics Department, HKSAR. Transport, communications and tourism statistics. Hong Kong resident departures by control point. [<ext-link ext-link-type="uri" xlink:href="http://www.censtatd.gov.hk/FileManager/EN/Content_807/transport.pdf">http://www.censtatd.gov.hk/FileManager/EN/Content_807/transport.pdf</ext-link>]</mixed-citation></ref><ref id="CR34"><label>34.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Greenwood</surname><given-names>M</given-names></name></person-group><article-title>On the statistical measure of infectiousness</article-title><source>J Hyg (Lond)</source><year>1931</year><volume>31</volume><issue>3</issue><fpage>336</fpage><lpage>351</lpage><pub-id pub-id-type="doi">10.1017/S002217240001086X</pub-id><pub-id pub-id-type="pmid">20475096</pub-id></element-citation></ref><ref id="CR35"><label>35.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Abbey</surname><given-names>H</given-names></name></person-group><article-title>An examination of the Reed-Frost theory of epidemics</article-title><source>Hum Biol</source><year>1952</year><volume>24</volume><issue>3</issue><fpage>201</fpage><lpage>33</lpage><pub-id pub-id-type="pmid">12990130</pub-id></element-citation></ref><ref id="CR36"><label>36.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Phenyo</surname><given-names>E</given-names></name><name><surname>Barbel</surname><given-names>F</given-names></name><collab>Lekone Finkenstadt</collab></person-group><article-title>Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study</article-title><source>Biometrics</source><year>2006</year><volume>62</volume><issue>4</issue><fpage>1170</fpage><lpage>1177</lpage><pub-id pub-id-type="doi">10.1111/j.1541-0420.2006.00609.x</pub-id><pub-id pub-id-type="pmid">17156292</pub-id></element-citation></ref><ref id="CR37"><label>37.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Flahault</surname><given-names>A</given-names></name><name><surname>Deguen</surname><given-names>S</given-names></name><name><surname>Valleron</surname><given-names>AJ</given-names></name></person-group><article-title>A mathematical model for the european spread of influenza</article-title><source>Eur J of Epidemiol</source><year>1994</year><volume>10</volume><issue>4</issue><fpage>471</fpage><lpage>474</lpage><pub-id pub-id-type="doi">10.1007/BF01719679</pub-id><pub-id pub-id-type="pmid">7843359</pub-id></element-citation></ref><ref id="CR38"><label>38.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Flahault</surname><given-names>A</given-names></name><name><surname>Letrait</surname><given-names>S</given-names></name><name><surname>Blin</surname><given-names>P</given-names></name><name><surname>Hazout</surname><given-names>S</given-names></name><name><surname>Ménarés</surname><given-names>J</given-names></name><name><surname>Valleron</surname><given-names>AJ</given-names></name></person-group><article-title>Modelling the 1985 influenza epidemic in France</article-title><source>Stat in Med</source><year>1988</year><volume>7</volume><issue>11</issue><fpage>1147</fpage><lpage>1155</lpage><pub-id pub-id-type="doi">10.1002/sim.4780071107</pub-id><pub-id pub-id-type="pmid">3201040</pub-id></element-citation></ref><ref id="CR39"><label>39.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Rvachev</surname><given-names>LA</given-names></name><name><surname>Longini</surname><given-names>IMJ</given-names></name></person-group><article-title>A mathematical model for the global spread of influenza</article-title><source>Math Biosci</source><year>1985</year><volume>75</volume><fpage>3</fpage><lpage>22</lpage><pub-id pub-id-type="doi">10.1016/0025-5564(85)90064-1</pub-id></element-citation></ref><ref id="CR40"><label>40.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Longini</surname><given-names>IM</given-names></name><name><surname>Fine</surname><given-names>PEM</given-names></name><name><surname>Thacker</surname><given-names>SB</given-names></name></person-group><article-title>Predicting the global spread of new infectious agents</article-title><source>Am J Epidemiol</source><year>1986</year><volume>123</volume><issue>3</issue><fpage>383</fpage><lpage>391</lpage><pub-id pub-id-type="pmid">3946385</pub-id></element-citation></ref><ref id="CR41"><label>41.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Longini</surname><given-names>IMJ</given-names></name></person-group><article-title>A mathematical model for predicting the geographic spread of new infectious agents</article-title><source>Math Biosci</source><year>1988</year><volume>90</volume><issue>1–2</issue><fpage>367</fpage><lpage>383</lpage><pub-id pub-id-type="doi">10.1016/0025-5564(88)90075-2</pub-id></element-citation></ref><ref id="CR42"><label>42.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Boelle</surname><given-names>P</given-names></name><name><surname>Bernillon</surname><given-names>P</given-names></name><name><surname>Desenclos</surname><given-names>J</given-names></name></person-group><article-title>A preliminary estimation of the reproduction ratio for new influenza A (H1N1) from the outbreak in Mexico, March-April 2009</article-title><source>Euro Surveill</source><year>2009</year><volume>14</volume><issue>19</issue><fpage>19205</fpage><pub-id pub-id-type="pmid">19442402</pub-id></element-citation></ref><ref id="CR43"><label>43.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Chowell</surname><given-names>G</given-names></name><name><surname>Hengartnerb</surname><given-names>N</given-names></name><name><surname>Castillo-Chaveza</surname><given-names>C</given-names></name><name><surname>Fenimorea</surname><given-names>P</given-names></name><name><surname>Hyman</surname><given-names>J</given-names></name></person-group><article-title>The basic reproductive number of Ebola and the effects of public health measures: The cases of Congo and Uganda</article-title><source>J Theo Bio</source><year>2004</year><volume>229</volume><fpage>119</fpage><lpage>126</lpage><pub-id pub-id-type="doi">10.1016/j.jtbi.2004.03.006</pub-id></element-citation></ref><ref id="CR44"><label>44.</label><mixed-citation publication-type="other">World Health Organization. Situation updates - Pandemic (H1N1) 2009. [<ext-link ext-link-type="uri" xlink:href="http://www.who.int/csr/disease/swineflu/updates/en/index.html">http://www.who.int/csr/disease/swineflu/updates/en/index.html</ext-link>]</mixed-citation></ref><ref id="CR45"><label>45.</label><mixed-citation publication-type="other">European Centre for Disease Prevention and Control. Daily update on the 2009 influenza A (H1N1) pandemic. [<ext-link ext-link-type="uri" xlink:href="http://www.ecdc.europa.eu/en/healthtopics/h1n1/pages/daily_update.aspx">http://www.ecdc.europa.eu/en/healthtopics/h1n1/pages/daily_update.aspx</ext-link>]</mixed-citation></ref><ref id="CR46"><label>46.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ferguson</surname><given-names>NM</given-names></name><name><surname>Cummings</surname><given-names>DAT</given-names></name><name><surname>Fraser</surname><given-names>C</given-names></name><name><surname>Cajka</surname><given-names>JC</given-names></name><name><surname>Cooley</surname><given-names>PC</given-names></name><name><surname>Burke</surname><given-names>DS</given-names></name></person-group><article-title>Strategies for mitigating an influenza pandemic</article-title><source>Nature</source><year>2006</year><volume>442</volume><issue>7101</issue><fpage>448</fpage><lpage>452</lpage><pub-id pub-id-type="doi">10.1038/nature04795</pub-id><pub-id pub-id-type="pmid">16642006</pub-id></element-citation></ref><ref id="CR47"><label>47.</label><mixed-citation publication-type="other">Centers for Disease Control and Prevention. Questions and answers: Antiviral drugs, 2009-2010 flu Season. [<ext-link ext-link-type="uri" xlink:href="http://www.cdc.gov/H1N1flu/antiviral.htm">http://www.cdc.gov/H1N1flu/antiviral.htm</ext-link>]</mixed-citation></ref><ref id="CR48"><label>48.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Fraser</surname><given-names>C</given-names></name><name><surname>Donnelly</surname><given-names>CA</given-names></name><name><surname>Cauchemez</surname><given-names>S</given-names></name><name><surname>Hanage</surname><given-names>WP</given-names></name><name><surname>Van Kerkhove</surname><given-names>MD</given-names></name><name><surname>Hollingsworth</surname><given-names>TD</given-names></name><name><surname>Griffin</surname><given-names>J</given-names></name><name><surname>Baggaley</surname><given-names>RF</given-names></name><name><surname>Jenkins</surname><given-names>HE</given-names></name><name><surname>Lyons</surname><given-names>EJ</given-names></name><etal></etal></person-group><article-title>Pandemic potential of a strain of influenza A (H1N1): early findings</article-title><source>Science</source><year>2009</year><volume>324</volume><issue>5934</issue><fpage>1557</fpage><lpage>1561</lpage><pub-id pub-id-type="doi">10.1126/science.1176062</pub-id><pub-id pub-id-type="pmid">19433588</pub-id></element-citation></ref><ref id="CR49"><label>49.</label><mixed-citation publication-type="other">Hong Kong Information Services Department. News Archives. HKSAR Press Releases on May 2, 2009. Press releases on human swine flu. [<ext-link ext-link-type="uri" xlink:href="http://www.info.gov.hk/gia/general/200905/02/P200905020242.htm">http://www.info.gov.hk/gia/general/200905/02/P200905020242.htm</ext-link>]</mixed-citation></ref><ref id="CR50"><label>50.</label><mixed-citation publication-type="other">Hong Kong Information Services Department. News Archives. HKSAR press releases from 2009 May 1 to 2009 Dec 31. Press releases on human swine flu. [<ext-link ext-link-type="uri" xlink:href="http://www.isd.gov.hk/pr/eng/">http://www.isd.gov.hk/pr/eng/</ext-link>]</mixed-citation></ref><ref id="CR51"><label>51.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Yang</surname><given-names>Y</given-names></name><name><surname>Sugimoto</surname><given-names>JD</given-names></name><name><surname>Halloran</surname><given-names>ME</given-names></name><name><surname>Basta</surname><given-names>NE</given-names></name><name><surname>Chao</surname><given-names>DL</given-names></name><name><surname>Matrajt</surname><given-names>L</given-names></name><name><surname>Potter</surname><given-names>G</given-names></name><name><surname>Kenah</surname><given-names>E</given-names></name><name><surname>Longini</surname><given-names>J</given-names></name><name><surname>Ira</surname><given-names>M</given-names></name></person-group><article-title>The transmissibility and control of pandemic influenza A (H1N1) Virus</article-title><source>Science</source><year>2009</year><volume>326</volume><issue>5953</issue><fpage>729</fpage><lpage>733</lpage><pub-id pub-id-type="doi">10.1126/science.1177373</pub-id><pub-id pub-id-type="pmid">19745114</pub-id></element-citation></ref><ref id="CR52"><label>52.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Longini</surname><given-names>IM</given-names></name><name><surname>Nizam</surname><given-names>A</given-names></name><name><surname>Xu</surname><given-names>S</given-names></name><name><surname>Ungchusak</surname><given-names>K</given-names></name><name><surname>Hanshaoworakul</surname><given-names>W</given-names></name><name><surname>Cummings</surname><given-names>DAT</given-names></name><name><surname>Halloran</surname><given-names>ME</given-names></name></person-group><article-title>Containing pandemic influenza at the source</article-title><source>Science</source><year>2005</year><volume>309</volume><issue>5737</issue><fpage>1083</fpage><lpage>1087</lpage><pub-id pub-id-type="doi">10.1126/science.1115717</pub-id><pub-id pub-id-type="pmid">16079251</pub-id></element-citation></ref><ref id="CR53"><label>53.</label><mixed-citation publication-type="other">Food and Health Bureau, HKSAR. Report on Hong Kong’s Anti-SARS Measures. [<ext-link ext-link-type="uri" xlink:href="http://www.fhb.gov.hk/download/services/events/040623_sars-report/dh_ha.pdf">http://www.fhb.gov.hk/download/services/events/040623_sars-report/dh_ha.pdf</ext-link>]</mixed-citation></ref><ref id="CR54"><label>54.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Caley</surname><given-names>P</given-names></name><name><surname>Becker</surname><given-names>NG</given-names></name><name><surname>Philp</surname><given-names>DJ</given-names></name></person-group><article-title>The waiting time for inter-country spread of pandemic influenza</article-title><source>PLoS ONE</source><year>2007</year><volume>2</volume><fpage>e143</fpage><pub-id pub-id-type="doi">10.1371/journal.pone.0000143</pub-id><pub-id pub-id-type="pmid">17206278</pub-id></element-citation></ref><ref id="CR55"><label>55.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Bell</surname><given-names>DM</given-names></name></person-group><article-title>Non-pharmaceutical interventions for pandemic influenza, international measures</article-title><source>Emerg Infect Dis</source><year>2006</year><volume>12</volume><fpage>81</fpage><lpage>87</lpage><pub-id pub-id-type="doi">10.3201/eid1208.060129</pub-id><pub-id pub-id-type="pmid">16494722</pub-id></element-citation></ref><ref id="CR56"><label>56.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Cowling</surname><given-names>BJ</given-names></name><name><surname>Lau</surname><given-names>EH</given-names></name><name><surname>Lam</surname><given-names>CL</given-names></name><name><surname>Cheng</surname><given-names>CK</given-names></name><name><surname>Kovar</surname><given-names>J</given-names></name><name><surname>Chan</surname><given-names>KH</given-names></name><name><surname>Peiris</surname><given-names>JM</given-names></name><name><surname>Leung</surname><given-names>GM</given-names></name></person-group><article-title>School closure and mitigation of pandemic (H1N1) 2009, Hong Kong</article-title><source>Emerg Infect Dis</source><year>2008</year><volume>14</volume><issue>10</issue><fpage>1660</fpage><lpage>1662</lpage><pub-id pub-id-type="doi">10.3201/eid1410.080646</pub-id><pub-id pub-id-type="pmid">18826841</pub-id></element-citation></ref><ref id="CR57"><label>57.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Epstein</surname><given-names>JM</given-names></name><name><surname>Goedecke</surname><given-names>DM</given-names></name><name><surname>Yu</surname><given-names>F</given-names></name><name><surname>Morris</surname><given-names>RJ</given-names></name><name><surname>Wagener</surname><given-names>DK</given-names></name><name><surname>Bobashev</surname><given-names>GV</given-names></name></person-group><article-title>Controlling pandemic flu: the value of international air travel restrictions</article-title><source>PLoS ONE</source><year>2007</year><volume>2</volume><fpage>e401</fpage><pub-id pub-id-type="doi">10.1371/journal.pone.0000401</pub-id><pub-id pub-id-type="pmid">17476323</pub-id></element-citation></ref><ref id="CR58"><label>58.</label><mixed-citation publication-type="other">Census and Statistics Department, HKSAR. Tourism value added by industry, 2005, 2007 and 2009. Hong Kong monthly digest of statistics. [<ext-link ext-link-type="uri" xlink:href="http://www.censtatd.gov.hk/hkstat/sub/sp80.jsp?subjectID=80&tableID=188&ID=0&productType=8">http://www.censtatd.gov.hk/hkstat/sub/sp80.jsp?subjectID=80&tableID=188&ID=0&productType=8</ext-link>]</mixed-citation></ref><ref-list id="BSec1"><title>Pre-publication history</title><ref id="CR59"><mixed-citation publication-type="other">The pre-publication history for this paper can be accessed here:<ext-link ext-link-type="uri" xlink:href="http://www.biomedcentral.com/1471-2334/12/309/prepub">http://www.biomedcentral.com/1471-2334/12/309/prepub</ext-link></mixed-citation></ref></ref-list></ref-list></back></pmc></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/PandemieGrippaleV1/Data/Pmc/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000181 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Pmc/Corpus/biblio.hfd -nk 000181 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= PandemieGrippaleV1
|flux= Pmc
|étape= Corpus
|type= RBID
|clé= PMC:3577649
|texte= Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Pmc/Corpus/RBID.i -Sk "pubmed:23157818" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Pmc/Corpus/biblio.hfd \
| NlmPubMed2Wicri -a PandemieGrippaleV1
| This area was generated with Dilib version V0.6.34. |  |