First two months of the 2019 Coronavirus Disease (COVID-19) epidemic in China: real-time surveillance and evaluation with a second derivative model
Identifieur interne : 000358 ( an2020/Analysis ); précédent : 000357; suivant : 000359First two months of the 2019 Coronavirus Disease (COVID-19) epidemic in China: real-time surveillance and evaluation with a second derivative model
Auteurs : Xinguang Chen [États-Unis] ; Bin Yu [États-Unis]Source :
- Global Health Research and Policy [ 2397-0642 ] ; 2020.
Abstract
Similar to outbreaks of many other infectious diseases, success in controlling the novel 2019 coronavirus infection requires a timely and accurate monitoring of the epidemic, particularly during its early period with rather limited data while the need for information increases explosively.
In this study, we used a second derivative model to characterize the coronavirus epidemic in China with cumulatively diagnosed cases during the first 2 months. The analysis was further enhanced by an exponential model with a close-population assumption. This model was built with the data and used to assess the detection rate during the study period, considering the differences between the true infections, detectable and detected cases.
Results from the second derivative modeling suggest the coronavirus epidemic as nonlinear and chaotic in nature. Although it emerged gradually, the epidemic was highly responsive to massive interventions initiated on January 21, 2020, as indicated by results from both second derivative and exponential modeling analyses. The epidemic started to decelerate immediately after the massive actions. The results derived from our analysis signaled the decline of the epidemic 14 days before it eventually occurred on February 4, 2020. Study findings further signaled an accelerated decline in the epidemic starting in 14 days on February 18, 2020.
The coronavirus epidemic appeared to be nonlinear and chaotic, and was responsive to effective interventions. The methods used in this study can be applied in surveillance to inform and encourage the general public, public health professionals, clinicians and decision-makers to take coordinative and collaborative efforts to control the epidemic.
Url:
DOI: 10.1186/s41256-020-00137-4
PubMed: 32158961
PubMed Central: 7050133
Affiliations:
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xml:lang="en"><sec><title>Background</title><p id="Par1">Similar to outbreaks of many other infectious diseases, success in controlling the novel 2019 coronavirus infection requires a timely and accurate monitoring of the epidemic, particularly during its early period with rather limited data while the need for information increases explosively.</p></sec><sec><title>Methods</title><p id="Par2">In this study, we used a second derivative model to characterize the coronavirus epidemic in China with cumulatively diagnosed cases during the first 2 months. The analysis was further enhanced by an exponential model with a close-population assumption. This model was built with the data and used to assess the detection rate during the study period, considering the differences between the true infections, detectable and detected cases.</p></sec><sec><title>Results</title><p id="Par3">Results from the second derivative modeling suggest the coronavirus epidemic as nonlinear and chaotic in nature. Although it emerged gradually, the epidemic was highly responsive to massive interventions initiated on January 21, 2020, as indicated by results from both second derivative and exponential modeling analyses. The epidemic started to decelerate immediately after the massive actions. The results derived from our analysis signaled the decline of the epidemic 14 days before it eventually occurred on February 4, 2020. Study findings further signaled an accelerated decline in the epidemic starting in 14 days on February 18, 2020.</p></sec><sec><title>Conclusions</title><p id="Par4">The coronavirus epidemic appeared to be nonlinear and chaotic, and was responsive to effective interventions. The methods used in this study can be applied in surveillance to inform and encourage the general public, public health professionals, clinicians and decision-makers to take coordinative and collaborative efforts to control the epidemic.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Christensen, T" uniqKey="Christensen T">T Christensen</name></author><author><name sortKey="Painter, M" uniqKey="Painter M">M Painter</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hui, Dl" uniqKey="Hui D">DL Hui</name></author><author><name sortKey="Ng, Mk" uniqKey="Ng M">MK Ng</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Upadhyay, Ranjit Kumar" uniqKey="Upadhyay R">Ranjit Kumar Upadhyay</name></author><author><name sortKey="Roy, Parimita" uniqKey="Roy P">Parimita Roy</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gonzalez Parra, G" uniqKey="Gonzalez Parra G">G Gonzalez-Parra</name></author><author><name sortKey="Arenas, Aj" uniqKey="Arenas A">AJ Arenas</name></author><author><name sortKey="Chen Charpentier, Bm" uniqKey="Chen Charpentier B">BM Chen-Charpentier</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sharomi, O" uniqKey="Sharomi O">O Sharomi</name></author><author><name sortKey="Podder, Cn" uniqKey="Podder C">CN Podder</name></author><author><name sortKey="Gumel, Ab" uniqKey="Gumel A">AB Gumel</name></author><author><name sortKey="Mahmud, Sm" uniqKey="Mahmud S">SM Mahmud</name></author><author><name sortKey="Rubinstein, E" uniqKey="Rubinstein E">E Rubinstein</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mabrouk, Ms" uniqKey="Mabrouk M">MS Mabrouk</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dalziel, Benjamin D" uniqKey="Dalziel B">Benjamin D. Dalziel</name></author><author><name sortKey="Bj Rnstad, Ottar N" uniqKey="Bj Rnstad O">Ottar N. Bjørnstad</name></author><author><name sortKey="Van Panhuis, Willem G" uniqKey="Van Panhuis W">Willem G. van Panhuis</name></author><author><name sortKey="Burke, Donald S" uniqKey="Burke D">Donald S. Burke</name></author><author><name sortKey="Metcalf, C Jessica E" uniqKey="Metcalf C">C. Jessica E. Metcalf</name></author><author><name sortKey="Grenfell, Bryan T" uniqKey="Grenfell B">Bryan T. Grenfell</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Truscott, James" uniqKey="Truscott J">James Truscott</name></author><author><name sortKey="Fraser, Christophe" uniqKey="Fraser C">Christophe Fraser</name></author><author><name sortKey="Cauchemez, Simon" uniqKey="Cauchemez S">Simon Cauchemez</name></author><author><name sortKey="Meeyai, Aronrag" uniqKey="Meeyai A">Aronrag Meeyai</name></author><author><name sortKey="Hinsley, Wes" uniqKey="Hinsley W">Wes Hinsley</name></author><author><name sortKey="Donnelly, Christl A" uniqKey="Donnelly C">Christl A. Donnelly</name></author><author><name sortKey="Ghani, Azra" uniqKey="Ghani A">Azra Ghani</name></author><author><name sortKey="Ferguson, Neil" uniqKey="Ferguson N">Neil Ferguson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Upadhyay, Ranjit Kumar" uniqKey="Upadhyay R">Ranjit Kumar Upadhyay</name></author><author><name sortKey="Roy, Parimita" uniqKey="Roy P">Parimita Roy</name></author><author><name sortKey="Rai, Vikas" uniqKey="Rai V">Vikas Rai</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Khanna, M" uniqKey="Khanna M">M Khanna</name></author><author><name sortKey="Kumar, B" uniqKey="Kumar B">B Kumar</name></author><author><name sortKey="Gupta, A" uniqKey="Gupta A">A Gupta</name></author><author><name sortKey="Kumar, P" uniqKey="Kumar P">P Kumar</name></author></analytic></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Floride</li></region></list><tree><country name="États-Unis"><region name="Floride"><name sortKey="Chen, Xinguang" sort="Chen, Xinguang" uniqKey="Chen X" first="Xinguang" last="Chen">Xinguang Chen</name></region><name sortKey="Yu, Bin" sort="Yu, Bin" uniqKey="Yu B" first="Bin" last="Yu">Bin Yu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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