Global dynamic spatiotemporal pattern of seasonal influenza since 2009 influenza pandemic
Identifieur interne : 000244 ( Pmc/Corpus ); précédent : 000243; suivant : 000245Global dynamic spatiotemporal pattern of seasonal influenza since 2009 influenza pandemic
Auteurs : Zhi-Wei Xu ; Zhong-Jie Li ; Wen-Biao HuSource :
- Infectious Diseases of Poverty [ 2095-5162 ] ; 2020.
Abstract
Understanding the global spatiotemporal pattern of seasonal influenza is essential for influenza control and prevention. Available data on the updated global spatiotemporal pattern of seasonal influenza are scarce. This study aimed to assess the spatiotemporal pattern of seasonal influenza after the 2009 influenza pandemic.
Weekly influenza surveillance data in 86 countries from 2010 to 2017 were obtained from FluNet. First, the proportion of influenza A in total influenza viruses (PA) was calculated. Second, weekly numbers of influenza positive virus (A and B) were divided by the total number of samples processed to get weekly positive rates of influenza A (RWA) and influenza B (RWB). Third, the average positive rates of influenza A (RA) and influenza B (RB) for each country were calculated by averaging RWA, and RWB of 52 weeks. A Kruskal-Wallis test was conducted to examine if the year-to-year change in PA in all countries were significant, and a universal kriging method with linear semivariogram model was used to extrapolate RA and RB in all countries.
PA ranged from 0.43 in Zambia to 0.98 in Belarus, and PA in countries with higher income was greater than those countries with lower income. The spatial patterns of high RB were the highest in sub-Saharan Africa, Asia-Pacific region and South America. RWA peaked in early weeks in temperate countries, and the peak of RWB occurred a bit later. There were some temperate countries with non-distinct influenza seasonality (e.g., Mauritius and Maldives) and some tropical/subtropical countries with distinct influenza seasonality (e.g., Chile and South Africa).
Influenza seasonality is not predictable in some temperate countries, and it is distinct in Chile, Argentina and South Africa, implying that the optimal timing for influenza vaccination needs to be chosen with caution in these unpredictable countries.
Url:
DOI: 10.1186/s40249-019-0618-5
PubMed: 31900215
PubMed Central: 6942408
Links to Exploration step
PMC:6942408Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Global dynamic spatiotemporal pattern of seasonal influenza since 2009 influenza pandemic</title><author><name sortKey="Xu, Zhi Wei" sort="Xu, Zhi Wei" uniqKey="Xu Z" first="Zhi-Wei" last="Xu">Zhi-Wei Xu</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000000089150953</institution-id><institution-id institution-id-type="GRID">grid.1024.7</institution-id><institution>School of Public Health and Social Work & Institute of Health and Biomedical Innovation,</institution><institution>Queensland University of Technology,</institution></institution-wrap>Brisbane, Australia</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000000089150953</institution-id><institution-id institution-id-type="GRID">grid.1024.7</institution-id><institution>Institute of Health and Biomedical Innovation,</institution><institution>Queensland University of Technology,</institution></institution-wrap>Brisbane, Australia</nlm:aff></affiliation><affiliation><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="ISNI">0000 0000 9320 7537</institution-id><institution-id institution-id-type="GRID">grid.1003.2</institution-id><institution>School of Public Health, Faculty of Medicine,</institution><institution>University of Queensland,</institution></institution-wrap>Brisbane, Australia</nlm:aff></affiliation></author><author><name sortKey="Li, Zhong Jie" sort="Li, Zhong Jie" uniqKey="Li Z" first="Zhong-Jie" last="Li">Zhong-Jie Li</name><affiliation><nlm:aff id="Aff4"><institution-wrap><institution-id institution-id-type="ISNI">0000 0000 8803 2373</institution-id><institution-id institution-id-type="GRID">grid.198530.6</institution-id><institution>Division of Infectious Disease,</institution><institution>Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention,</institution></institution-wrap>Beijing, China</nlm:aff></affiliation></author><author><name sortKey="Hu, Wen Biao" sort="Hu, Wen Biao" uniqKey="Hu W" first="Wen-Biao" last="Hu">Wen-Biao Hu</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000000089150953</institution-id><institution-id institution-id-type="GRID">grid.1024.7</institution-id><institution>School of Public Health and Social Work & Institute of Health and Biomedical Innovation,</institution><institution>Queensland University of Technology,</institution></institution-wrap>Brisbane, Australia</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000000089150953</institution-id><institution-id institution-id-type="GRID">grid.1024.7</institution-id><institution>Institute of Health and Biomedical Innovation,</institution><institution>Queensland University of Technology,</institution></institution-wrap>Brisbane, Australia</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">31900215</idno><idno type="pmc">6942408</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6942408</idno><idno type="RBID">PMC:6942408</idno><idno type="doi">10.1186/s40249-019-0618-5</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">000244</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000244</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Global dynamic spatiotemporal pattern of seasonal influenza since 2009 influenza pandemic</title><author><name sortKey="Xu, Zhi Wei" sort="Xu, Zhi Wei" uniqKey="Xu Z" first="Zhi-Wei" last="Xu">Zhi-Wei Xu</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000000089150953</institution-id><institution-id institution-id-type="GRID">grid.1024.7</institution-id><institution>School of Public Health and Social Work & Institute of Health and Biomedical Innovation,</institution><institution>Queensland University of Technology,</institution></institution-wrap>Brisbane, Australia</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000000089150953</institution-id><institution-id institution-id-type="GRID">grid.1024.7</institution-id><institution>Institute of Health and Biomedical Innovation,</institution><institution>Queensland University of Technology,</institution></institution-wrap>Brisbane, Australia</nlm:aff></affiliation><affiliation><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="ISNI">0000 0000 9320 7537</institution-id><institution-id institution-id-type="GRID">grid.1003.2</institution-id><institution>School of Public Health, Faculty of Medicine,</institution><institution>University of Queensland,</institution></institution-wrap>Brisbane, Australia</nlm:aff></affiliation></author><author><name sortKey="Li, Zhong Jie" sort="Li, Zhong Jie" uniqKey="Li Z" first="Zhong-Jie" last="Li">Zhong-Jie Li</name><affiliation><nlm:aff id="Aff4"><institution-wrap><institution-id institution-id-type="ISNI">0000 0000 8803 2373</institution-id><institution-id institution-id-type="GRID">grid.198530.6</institution-id><institution>Division of Infectious Disease,</institution><institution>Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention,</institution></institution-wrap>Beijing, China</nlm:aff></affiliation></author><author><name sortKey="Hu, Wen Biao" sort="Hu, Wen Biao" uniqKey="Hu W" first="Wen-Biao" last="Hu">Wen-Biao Hu</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000000089150953</institution-id><institution-id institution-id-type="GRID">grid.1024.7</institution-id><institution>School of Public Health and Social Work & Institute of Health and Biomedical Innovation,</institution><institution>Queensland University of Technology,</institution></institution-wrap>Brisbane, Australia</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000000089150953</institution-id><institution-id institution-id-type="GRID">grid.1024.7</institution-id><institution>Institute of Health and Biomedical Innovation,</institution><institution>Queensland University of Technology,</institution></institution-wrap>Brisbane, Australia</nlm:aff></affiliation></author></analytic><series><title level="j">Infectious Diseases of Poverty</title><idno type="ISSN">2095-5162</idno><idno type="eISSN">2049-9957</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p id="Par1">Understanding the global spatiotemporal pattern of seasonal influenza is essential for influenza control and prevention. Available data on the updated global spatiotemporal pattern of seasonal influenza are scarce. This study aimed to assess the spatiotemporal pattern of seasonal influenza after the 2009 influenza pandemic.</p></sec><sec><title>Methods</title><p id="Par2">Weekly influenza surveillance data in 86 countries from 2010 to 2017 were obtained from FluNet. First, the proportion of influenza A in total influenza viruses (P<sub>A</sub>) was calculated. Second, weekly numbers of influenza positive virus (A and B) were divided by the total number of samples processed to get weekly positive rates of influenza A (RW<sub>A</sub>) and influenza B (RW<sub>B</sub>). Third, the average positive rates of influenza A (R<sub>A</sub>) and influenza B (R<sub>B</sub>) for each country were calculated by averaging RW<sub>A</sub>, and RW<sub>B</sub> of 52 weeks. A Kruskal-Wallis test was conducted to examine if the year-to-year change in P<sub>A</sub> in all countries were significant, and a universal kriging method with linear semivariogram model was used to extrapolate R<sub>A</sub> and R<sub>B</sub> in all countries.</p></sec><sec><title>Results</title><p id="Par3">P<sub>A</sub> ranged from 0.43 in Zambia to 0.98 in Belarus, and P<sub>A</sub> in countries with higher income was greater than those countries with lower income. The spatial patterns of high R<sub>B</sub> were the highest in sub-Saharan Africa, Asia-Pacific region and South America. RW<sub>A</sub> peaked in early weeks in temperate countries, and the peak of RW<sub>B</sub> occurred a bit later. There were some temperate countries with non-distinct influenza seasonality (e.g., Mauritius and Maldives) and some tropical/subtropical countries with distinct influenza seasonality (e.g., Chile and South Africa).</p></sec><sec><title>Conclusions</title><p id="Par4">Influenza seasonality is not predictable in some temperate countries, and it is distinct in Chile, Argentina and South Africa, implying that the optimal timing for influenza vaccination needs to be chosen with caution in these unpredictable countries.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Iuliano, Ad" uniqKey="Iuliano A">AD Iuliano</name></author><author><name sortKey="Roguski, Km" uniqKey="Roguski K">KM Roguski</name></author><author><name sortKey="Chang, Hh" uniqKey="Chang H">HH Chang</name></author><author><name sortKey="Muscatello, Dj" uniqKey="Muscatello D">DJ Muscatello</name></author><author><name sortKey="Palekar, R" uniqKey="Palekar R">R 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Infect Dis Poverty</journal-id><journal-id journal-id-type="iso-abbrev">Infect Dis Poverty</journal-id><journal-title-group><journal-title>Infectious Diseases of Poverty</journal-title></journal-title-group><issn pub-type="ppub">2095-5162</issn><issn pub-type="epub">2049-9957</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">31900215</article-id><article-id pub-id-type="pmc">6942408</article-id><article-id pub-id-type="publisher-id">618</article-id><article-id pub-id-type="doi">10.1186/s40249-019-0618-5</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>Global dynamic spatiotemporal pattern of seasonal influenza since 2009 influenza pandemic</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Xu</surname><given-names>Zhi-Wei</given-names></name><xref ref-type="aff" rid="Aff1">1</xref><xref ref-type="aff" rid="Aff2">2</xref><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Li</surname><given-names>Zhong-Jie</given-names></name><xref ref-type="aff" rid="Aff4">4</xref></contrib><contrib contrib-type="author" corresp="yes"><name><surname>Hu</surname><given-names>Wen-Biao</given-names></name><address><email>w2.hu@qut.edu.au</email></address><xref ref-type="aff" rid="Aff1">1</xref><xref ref-type="aff" rid="Aff2">2</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="ISNI">0000000089150953</institution-id><institution-id institution-id-type="GRID">grid.1024.7</institution-id><institution>School of Public Health and Social Work & Institute of Health and Biomedical Innovation,</institution><institution>Queensland University of Technology,</institution></institution-wrap>Brisbane, Australia</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="ISNI">0000000089150953</institution-id><institution-id institution-id-type="GRID">grid.1024.7</institution-id><institution>Institute of Health and Biomedical Innovation,</institution><institution>Queensland University of Technology,</institution></institution-wrap>Brisbane, Australia</aff><aff id="Aff3"><label>3</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0000 9320 7537</institution-id><institution-id institution-id-type="GRID">grid.1003.2</institution-id><institution>School of Public Health, Faculty of Medicine,</institution><institution>University of Queensland,</institution></institution-wrap>Brisbane, Australia</aff><aff id="Aff4"><label>4</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0000 8803 2373</institution-id><institution-id institution-id-type="GRID">grid.198530.6</institution-id><institution>Division of Infectious Disease,</institution><institution>Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention,</institution></institution-wrap>Beijing, China</aff></contrib-group><pub-date pub-type="epub"><day>3</day><month>1</month><year>2020</year></pub-date><pub-date pub-type="pmc-release"><day>3</day><month>1</month><year>2020</year></pub-date><pub-date pub-type="collection"><year>2020</year></pub-date><volume>9</volume><elocation-id>2</elocation-id><history><date date-type="received"><day>24</day><month>9</month><year>2019</year></date><date date-type="accepted"><day>18</day><month>12</month><year>2019</year></date></history><permissions><copyright-statement>© The Author(s). 2020</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold>This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p id="Par1">Understanding the global spatiotemporal pattern of seasonal influenza is essential for influenza control and prevention. Available data on the updated global spatiotemporal pattern of seasonal influenza are scarce. This study aimed to assess the spatiotemporal pattern of seasonal influenza after the 2009 influenza pandemic.</p></sec><sec><title>Methods</title><p id="Par2">Weekly influenza surveillance data in 86 countries from 2010 to 2017 were obtained from FluNet. First, the proportion of influenza A in total influenza viruses (P<sub>A</sub>) was calculated. Second, weekly numbers of influenza positive virus (A and B) were divided by the total number of samples processed to get weekly positive rates of influenza A (RW<sub>A</sub>) and influenza B (RW<sub>B</sub>). Third, the average positive rates of influenza A (R<sub>A</sub>) and influenza B (R<sub>B</sub>) for each country were calculated by averaging RW<sub>A</sub>, and RW<sub>B</sub> of 52 weeks. A Kruskal-Wallis test was conducted to examine if the year-to-year change in P<sub>A</sub> in all countries were significant, and a universal kriging method with linear semivariogram model was used to extrapolate R<sub>A</sub> and R<sub>B</sub> in all countries.</p></sec><sec><title>Results</title><p id="Par3">P<sub>A</sub> ranged from 0.43 in Zambia to 0.98 in Belarus, and P<sub>A</sub> in countries with higher income was greater than those countries with lower income. The spatial patterns of high R<sub>B</sub> were the highest in sub-Saharan Africa, Asia-Pacific region and South America. RW<sub>A</sub> peaked in early weeks in temperate countries, and the peak of RW<sub>B</sub> occurred a bit later. There were some temperate countries with non-distinct influenza seasonality (e.g., Mauritius and Maldives) and some tropical/subtropical countries with distinct influenza seasonality (e.g., Chile and South Africa).</p></sec><sec><title>Conclusions</title><p id="Par4">Influenza seasonality is not predictable in some temperate countries, and it is distinct in Chile, Argentina and South Africa, implying that the optimal timing for influenza vaccination needs to be chosen with caution in these unpredictable countries.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Influenza a</kwd><kwd>Influenza B</kwd><kwd>Seasonality</kwd><kwd>Spatial pattern</kwd><kwd>Vaccination</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="Sec1"><title>Background</title><p id="Par10">Seasonal influenza caused substantial morbidity and mortality worldwide, especially in elderly population and children aged under five years. It is estimated that, from 1999 to 2015, there were 291 243 to 645 832 seasonal influenza-associated respiratory deaths every year globally [<xref ref-type="bibr" rid="CR1">1</xref>], causing a considerable health burden. For example, Australia witnessed its largest influenza season in 2017 since the 2009 pandemic, posing a substantial burden to primary care and hospitals [<xref ref-type="bibr" rid="CR2">2</xref>]. Unveiling the global spatial pattern of seasonal influenza is essential for national and international decision making on influenza prevention and control.</p><p id="Par11">Vaccination has been widely recognized as the most effective means of seasonal influenza prevention and can largely ease the burden caused by influenza. Identification of the optimal timing for vaccination is of great importance because vaccine-induced immunity wanes quickly after vaccination [<xref ref-type="bibr" rid="CR3">3</xref>], and unfolding influenza seasonality is a crucial step for determining optimal vaccination timing. The widespread consensus in the literature is that influenza seasonality pattern is more ascertained in temperate regions/countries, but remains largely unclear and controversial in tropical and subtropical regions/countries [<xref ref-type="bibr" rid="CR4">4</xref>–<xref ref-type="bibr" rid="CR6">6</xref>]. It has been suggested that although influenza seasonality in tropics and subtropics is complicated, it might still be possible to group countries into similar zones for tailored and timely vaccination [<xref ref-type="bibr" rid="CR7">7</xref>].</p><p id="Par12">Prior studies have reported that the epidemiology (e.g., seasonality) of influenza A and influenza B may differ from each other [<xref ref-type="bibr" rid="CR8">8</xref>–<xref ref-type="bibr" rid="CR10">10</xref>], and the relative importance of influenza A and influenza B in driving seasonal influenza peak may vary across different countries [<xref ref-type="bibr" rid="CR11">11</xref>]. For the development of strategic seasonal influenza control programs (e.g., using trivalent vaccines or quadrivalent vaccines), it is essential to assess the proportions of influenza A virus and influenza B virus in seasonal influenza virus.</p><p id="Par13">Global, contemporaneous and comparative analysis of influenza data would help focus resources more effectively on areas/populations that need it most [<xref ref-type="bibr" rid="CR12">12</xref>, <xref ref-type="bibr" rid="CR13">13</xref>]. Previous studies have reported the global spatial and temporal patterns of seasonal influenza up to 2015 [<xref ref-type="bibr" rid="CR8">8</xref>, <xref ref-type="bibr" rid="CR14">14</xref>, <xref ref-type="bibr" rid="CR15">15</xref>], but very up-to-date information is not available in existing literature. Our study attempted to characterize the global spatial pattern of seasonal influenza A and B after 2009 influenza pandemic (i.e., from 2010 to 2017), to assess the proportions of influenza A virus and influenza B virus in total influenza virus, and to elucidate the seasonality of seasonal influenza A and B in temperate countries and tropical/subtropical countries. The specific objectives were three-fold: I). what was the proportion of influenza A virus in total influenza positive virus (P<sub>A</sub>) in each country; and whether there were any year-to-year changes in this proportion? II). what were the high risk regions of influenza A and influenza B? and III). what were the global seasonal patterns of influenza A and influenza B?</p></sec><sec id="Sec2"><title>Methods</title><sec id="Sec3"><title>Data collection</title><p id="Par14">Weekly influenza surveillance data from 2010 to 2017 were collected from FluNet, an online database of WHO Global Influenza Surveillance Network for laboratory-confirmed influenza samples [<xref ref-type="bibr" rid="CR6">6</xref>, <xref ref-type="bibr" rid="CR8">8</xref>]. Detailed information on FluNet can be found in WHO website (<ext-link ext-link-type="uri" xlink:href="http://www.who.int/influenza/gisrs_laboratory/flunet/en/">http://www.who.int/influenza/gisrs_laboratory/flunet/en/</ext-link>). FluNet data are real-life data. The diagnostic methods may vary widely between countries because of manpower and training issues, but FluNet data are the most widely available data that can be used by WHO surveillance to design the seasonal influenza vaccines. It is not easy practically (if not impossible) to unify the world’s approach to testing for these influenza viruses due to the resource variability and limitations. Thus, FluNet data are still quite valuable despite its limitations. Specifically, the data extracted in this study included the following variables: total number of influenza positive virus, total number of influenza A virus, total number of influenza B virus, and total number of samples processed. Countries with complete data of at least one year from 2010 to 2017 were selected, and in total there were 86 countries included in this study. The detailed influenza information on the countries selected, including time period(s), total number of samples processed, total number of influenza positive virus, total number of influenza A virus, and total number of influenza B virus, is depicted in Additional file <xref rid="MOESM1" ref-type="media">1</xref>: Table S1. World Bank categorized all countries into four income groups, including low income, lower middle income, upper middle income, and high income. We collected this information for each included country to assess if P<sub>A</sub> varied across different income groups (<ext-link ext-link-type="uri" xlink:href="http://blogs.worldbank.org/opendata/new-country-classifications-income-level-2017-2018">http://blogs.worldbank.org/opendata/new-country-classifications-income-level-2017-2018</ext-link>).</p></sec><sec id="Sec4"><title>Data analysis</title><p id="Par15">There were three analytical approaches corresponding to three objectives. First, for each country, total number of influenza A virus was divided by total number of influenza positive virus to get the proportion of influenza A virus in total influenza positive virus (P<sub>A</sub>). Data on 21 countries with complete data from 2010 to 2017 were used to present the year-to-year change in P<sub>A</sub>. The yearly P<sub>A</sub> data for each country were ratio and were not normally distributed (after normality test), so we conducted a Kruskal-Wallis test to check if the year-to-year changes in P<sub>A</sub> in all countries were statistically significant. Second, weekly numbers of influenza positive virus for influenza A and influenza B, as well as total number of samples processed, across all years were merged into 52 weeks in each country. Weekly numbers of influenza positive virus (influenza A and influenza B) were divided by the total number of samples processed to get weekly positive rates of influenza A (RW<sub>A</sub>), and influenza B (RW<sub>B</sub>). The average positive rates of influenza A (R<sub>A</sub>), and influenza B (R<sub>B</sub>) for each country were calculated by averaging RW<sub>A</sub>, and RW<sub>B</sub> of 52 weeks, and a kriging approach was used to extrapolate the average influenza positive rate in all countries globally. Specifically, we used the “universal” kriging method and the “linear” semivariogram model. Universal kriging is a powerful method which simultaneously estimates a trend and used the resulting errors for kriging. The equations for calculating P<sub>A</sub>, RW<sub>A</sub>, RW<sub>B</sub>, R<sub>A</sub> and R<sub>B</sub>, are presented in Table <xref rid="Tab1" ref-type="table">1</xref>. Third, heat maps were plotted using RW<sub>A</sub> and RW<sub>B</sub> to present the seasonal patterns of influenza A and influenza B in temperate countries and tropical/subtropical countries. A cosinor function combined with Poisson regression was used to quantify the peak time and trough time of influenza A and influenza B [<xref ref-type="bibr" rid="CR16">16</xref>].
<table-wrap id="Tab1"><label>Table 1</label><caption><p>The equations for calculating the indexes</p></caption><table frame="hsides" rules="groups"><thead><tr><th>Index</th><th>Definition of the index</th><th>Equation to calculate the index</th></tr></thead><tbody><tr><td>P<sub>A</sub></td><td>the proportion of influenza A virus in total influenza positive virus</td><td>(total number of influenza A virus) / (total number of influenza positive virus)</td></tr><tr><td>RW<sub>A</sub></td><td>weekly positive rate of influenza A</td><td>(weekly number of influenza A positive virus) / (total number of samples processed)</td></tr><tr><td>RW<sub>B</sub></td><td>weekly positive rate of influenza B</td><td>(weekly number of influenza B positive virus) / (total number of samples processed)</td></tr><tr><td>R<sub>A</sub></td><td>the average positive rate of influenza A</td><td>(RW<sub>Aweek1</sub> + RW<sub>Aweek2</sub>+… + RW<sub>Aweek52</sub>) / 52</td></tr><tr><td>R<sub>B</sub></td><td>the average positive rate of influenza B</td><td>(RW<sub>Bweek1</sub> + RW<sub>Bweek2</sub>+...+RW<sub>Bweek52</sub>) / 52</td></tr></tbody></table></table-wrap></p><p id="Par16">Spatial mapping and kriging were conducted in ArcGIS 10.5 (ESRI Inc., Redlands, CA, USA), and all other analyses were done in R package 3.4.4 (<ext-link ext-link-type="uri" xlink:href="https://www.r-project.org/">https://www.r-project.org/</ext-link>).</p></sec></sec><sec id="Sec5"><title>Results</title><sec id="Sec6"><title>The global spatial pattern of P<sub>A</sub> and its temporal change</title><p id="Par17">The proportion of influenza A virus in total influenza positive virus (P<sub>A</sub>) were illustrated in Fig. <xref rid="Fig1" ref-type="fig">1</xref>. The highest P<sub>A</sub> was in Belarus (upper-middle-income), Ethiopia (low-income), Iraq (upper-middle-income), and Venezuela (upper-middle-income). Specifically, P<sub>A</sub> was greater than 0.5 in all countries except for Zambia and Lebanon. Fig. <xref rid="Fig2" ref-type="fig">2</xref> shows that P<sub>A</sub> was higher in high-income and upper-middle-income countries than low-income and lower-middle-income countries (<italic>P</italic> = 0.0015 in the Kruskal-Wallis test), although one of the four countries with the highest P<sub>A</sub> was a low-income country (i.e., Ethiopia). Fig. <xref rid="Fig3" ref-type="fig">3</xref> shows that there were year-to-year changes in the relative proportions of influenza A virus. The Kruskal-Wallis test indicates that the year-to-year changes globally were not statistically significant (<italic>P</italic> = 0.5271).
<fig id="Fig1"><label>Fig. 1</label><caption><p>Global pattern of influenza A proportion</p></caption><graphic xlink:href="40249_2019_618_Fig1_HTML" id="MO1"></graphic></fig><fig id="Fig2"><label>Fig. 2</label><caption><p>Influenza A proportion in countries from different income groups</p></caption><graphic xlink:href="40249_2019_618_Fig2_HTML" id="MO2"></graphic></fig><fig id="Fig3"><label>Fig 3</label><caption><p>Temporal change in influenza A proportion in 21 countries</p></caption><graphic xlink:href="40249_2019_618_Fig3_HTML" id="MO3"></graphic></fig></p></sec><sec id="Sec7"><title>The global spatial patterns of R<sub>A</sub> and R<sub>B</sub></title><p id="Par18">The global spatial patterns of R<sub>A</sub> and R<sub>B</sub> was shown in Fig. <xref rid="Fig4" ref-type="fig">4</xref>. The highest R<sub>A</sub> distributed in Venezuela, Bolivia, Nepal, Ethiopia and China, and the highest R<sub>B</sub> distributed in sub-Saharan Africa, Asia-Pacific region and South America.
<fig id="Fig4"><label>Fig. 4</label><caption><p>Global pattern of influenza positive rate</p></caption><graphic xlink:href="40249_2019_618_Fig4_HTML" id="MO4"></graphic></fig></p></sec><sec id="Sec8"><title>The seasonal patterns of influenza a and influenza B</title><p id="Par19">The seasonal patterns of RW<sub>A</sub> and RW<sub>B</sub> in temperate countries and tropical/subtropical countries by latitude were presented in Fig. <xref rid="Fig5" ref-type="fig">5</xref>. RW<sub>A</sub> and RW<sub>B</sub> peaked in early weeks of each year in most temperate countries except for Australia, New Caledonia, Mauritius, Maldives and Malaysia, and the peak weeks of RW<sub>A</sub> occurred earlier than influenza RW<sub>B</sub>. For most tropical/subtropical countries, influenza seasonality pattern was diverse (i.e., either having several peaks or occurring all year around). However, influenza seasonality was distinct in Chile, Argentina, and South Africa. The peak time and trough time of influenza in all selected countries quantified by cosinor function are presented in Table <xref rid="Tab2" ref-type="table">2</xref>. The peak time and trough time varied considerably across different countries.
<fig id="Fig5"><label>Fig. 5</label><caption><p>Seasonality of influenza A and B in temperate and tropical/subtropical countries</p></caption><graphic xlink:href="40249_2019_618_Fig5_HTML" id="MO5"></graphic></fig><table-wrap id="Tab2"><label>Table 2</label><caption><p>Peak time and trough time (week) of influenza (listed by latitude)</p></caption><table frame="hsides" rules="groups"><thead><tr><th>Country</th><th>Peak (A<sup>a</sup>)</th><th>Trough (A)</th><th>Peak (B<sup>a</sup>)</th><th>Trough (B)</th></tr></thead><tbody><tr><td>Chile</td><td>30.5</td><td>4.5</td><td>40.5</td><td>14.5</td></tr><tr><td>Argentina</td><td>29.3</td><td>3.3</td><td>37.6</td><td>11.6</td></tr><tr><td>South Africa</td><td>27.9</td><td>1.9</td><td>35.3</td><td>9.3</td></tr><tr><td>Australia</td><td>33.8</td><td>7.8</td><td>33.7</td><td>7.7</td></tr><tr><td>Paraguay</td><td>36.7</td><td>10.7</td><td>39.6</td><td>13.6</td></tr><tr><td>New Caledonia</td><td>33.4</td><td>7.4</td><td>40.9</td><td>14.9</td></tr><tr><td>Mauritius</td><td>12.4</td><td>38.4</td><td>28.3</td><td>2.3</td></tr><tr><td>Madagascar</td><td>13.4</td><td>39.4</td><td>37.3</td><td>11.3</td></tr><tr><td>Bolivia</td><td>30.8</td><td>4.8</td><td>21.8</td><td>47.8</td></tr><tr><td>Zambia</td><td>31.8</td><td>5.8</td><td>32.3</td><td>6.3</td></tr><tr><td>Brazil</td><td>15.2</td><td>41.2</td><td>40</td><td>14</td></tr><tr><td>Peru</td><td>34.3</td><td>8.3</td><td>37.4</td><td>11.4</td></tr><tr><td>Tanzania</td><td>51.8</td><td>26.8</td><td>8.2</td><td>34.2</td></tr><tr><td>Indonesia</td><td>2.4</td><td>28.4</td><td>15.9</td><td>41.9</td></tr><tr><td>Ecuador</td><td>2.6</td><td>28.6</td><td>24.8</td><td>50.8</td></tr><tr><td>Democratic Republic of the Congo</td><td>2.6</td><td>28.6</td><td>15.4</td><td>41.4</td></tr><tr><td>Uganda</td><td>40.3</td><td>14.3</td><td>37.6</td><td>11.6</td></tr><tr><td>Maldives</td><td>16.3</td><td>42.3</td><td>44.1</td><td>18.1</td></tr><tr><td>Malaysia</td><td>13.4</td><td>39.4</td><td>12.8</td><td>38.8</td></tr><tr><td>Colombia</td><td>33</td><td>7</td><td>45.3</td><td>19.3</td></tr><tr><td>French Guiana</td><td>9.2</td><td>35.2</td><td>22.8</td><td>48.8</td></tr><tr><td>Suriname</td><td>28.4</td><td>2.4</td><td>30.7</td><td>4.7</td></tr><tr><td>Cameroon</td><td>50.4</td><td>24.4</td><td>44.1</td><td>18.1</td></tr><tr><td>Venezuela</td><td>1.9</td><td>27.9</td><td>32.3</td><td>6.3</td></tr><tr><td>Côte d’Ivoire</td><td>25.1</td><td>51.1</td><td>37.8</td><td>11.8</td></tr><tr><td>Sri Lanka</td><td>10</td><td>36</td><td>4.7</td><td>30.7</td></tr><tr><td>Ghana</td><td>18.7</td><td>44.7</td><td>34.8</td><td>8.8</td></tr><tr><td>Panama</td><td>24.2</td><td>50.2</td><td>28.5</td><td>2.5</td></tr><tr><td>Togo</td><td>34</td><td>8</td><td>46.7</td><td>20.7</td></tr><tr><td>Sierra Leone</td><td>40</td><td>14</td><td>25.4</td><td>51.4</td></tr><tr><td>Ethiopia</td><td>52.9</td><td>26.9</td><td>12.8</td><td>38.8</td></tr><tr><td>Nigeria</td><td>39</td><td>13</td><td>47</td><td>21</td></tr><tr><td>Costa Rica</td><td>45</td><td>19</td><td>37.4</td><td>11.4</td></tr><tr><td>Burkina Faso</td><td>51.7</td><td>25.7</td><td>50</td><td>24</td></tr><tr><td>Cambodia</td><td>37</td><td>11</td><td>42.9</td><td>16.9</td></tr><tr><td>Nicaragua</td><td>36.9</td><td>10.9</td><td>33.4</td><td>7.4</td></tr><tr><td>El Salvador</td><td>22.9</td><td>48.9</td><td>34.2</td><td>8.2</td></tr><tr><td>Senegal</td><td>47.4</td><td>21.4</td><td>31.1</td><td>5.1</td></tr><tr><td>Honduras</td><td>24.8</td><td>50.8</td><td>51.7</td><td>25.7</td></tr><tr><td>Thailand</td><td>37.4</td><td>11.4</td><td>5.6</td><td>31.6</td></tr><tr><td>Guatemala</td><td>9.4</td><td>35.4</td><td>40.8</td><td>14.8</td></tr><tr><td>Vietnam</td><td>22.7</td><td>48.7</td><td>4.9</td><td>30.9</td></tr><tr><td>Mali</td><td>3</td><td>29</td><td>4.3</td><td>30.3</td></tr><tr><td>Niger</td><td>2.8</td><td>28.8</td><td>5.4</td><td>31.4</td></tr><tr><td>Jamaica</td><td>47.1</td><td>21.1</td><td>31</td><td>5</td></tr><tr><td>Laos</td><td>38.8</td><td>12.8</td><td>2.1</td><td>28.1</td></tr><tr><td>Deminican Republic</td><td>18.2</td><td>44.2</td><td>31</td><td>5</td></tr><tr><td>Cuba</td><td>25.4</td><td>51.4</td><td>40.8</td><td>14.8</td></tr><tr><td>India</td><td>17.5</td><td>43.5</td><td>39.9</td><td>13.9</td></tr><tr><td>Bangladesh</td><td>25.3</td><td>51.3</td><td>34.7</td><td>8.7</td></tr><tr><td>Mexico</td><td>2.1</td><td>28.1</td><td>6.8</td><td>32.8</td></tr><tr><td>Qatar</td><td>49.6</td><td>23.6</td><td>4.6</td><td>30.6</td></tr><tr><td>Bahrain</td><td>45.2</td><td>19.2</td><td>10.1</td><td>36.1</td></tr><tr><td>Egypt</td><td>51.1</td><td>25.1</td><td>19.8</td><td>45.8</td></tr><tr><td>Bhutan</td><td>24.8</td><td>50.8</td><td>51.4</td><td>25.4</td></tr><tr><td>Nepal</td><td>26.7</td><td>52.7</td><td>41.1</td><td>15.1</td></tr><tr><td>Pakistan</td><td>51.6</td><td>25.6</td><td>5.5</td><td>31.5</td></tr><tr><td>Jordan</td><td>52.7</td><td>26.7</td><td>14.9</td><td>40.9</td></tr><tr><td>Iran</td><td>1</td><td>27</td><td>14.3</td><td>40.3</td></tr><tr><td>Iraq</td><td>1.6</td><td>27.6</td><td>2.7</td><td>28.7</td></tr><tr><td>Afghanistan</td><td>43.4</td><td>17.4</td><td>46.3</td><td>20.3</td></tr><tr><td>Lebanon</td><td>4.4</td><td>30.4</td><td>9.7</td><td>35.7</td></tr><tr><td>Malta</td><td>5.7</td><td>31.7</td><td>8.7</td><td>34.7</td></tr><tr><td>South Korea</td><td>4.8</td><td>30.8</td><td>12.1</td><td>38.1</td></tr><tr><td>China</td><td>4.2</td><td>30.2</td><td>8.4</td><td>34.4</td></tr><tr><td>Greece</td><td>7.1</td><td>33.1</td><td>10.5</td><td>36.5</td></tr><tr><td>Spain</td><td>4.1</td><td>30.1</td><td>9.1</td><td>35.1</td></tr><tr><td>Georgia</td><td>5.7</td><td>31.7</td><td>10.8</td><td>36.8</td></tr><tr><td>United States</td><td>4.1</td><td>30.1</td><td>12.2</td><td>38.2</td></tr><tr><td>Slovenia</td><td>5.9</td><td>31.9</td><td>10.7</td><td>36.7</td></tr><tr><td>France</td><td>6.2</td><td>32.2</td><td>7.2</td><td>33.2</td></tr><tr><td>Mongolia</td><td>4.5</td><td>30.5</td><td>10</td><td>36</td></tr><tr><td>Austria</td><td>7</td><td>33</td><td>10.8</td><td>36.8</td></tr><tr><td>Kazakhstan</td><td>6.5</td><td>32.5</td><td>9.6</td><td>35.6</td></tr><tr><td>Germany</td><td>6.7</td><td>32.7</td><td>12.2</td><td>38.2</td></tr><tr><td>Poland</td><td>6</td><td>32</td><td>10.7</td><td>36.7</td></tr><tr><td>Netherlands</td><td>6.7</td><td>32.7</td><td>12.7</td><td>38.7</td></tr><tr><td>Ireland</td><td>5.8</td><td>31.8</td><td>8</td><td>34</td></tr><tr><td>Belarus</td><td>14.3</td><td>40.3</td><td>12.4</td><td>38.4</td></tr><tr><td>United Kingdom</td><td>5.4</td><td>31.4</td><td>9.1</td><td>35.1</td></tr><tr><td>Denmark</td><td>5.4</td><td>31.4</td><td>9.3</td><td>35.3</td></tr><tr><td>Canada</td><td>4</td><td>30</td><td>14</td><td>40</td></tr><tr><td>Russia</td><td>7.2</td><td>33.2</td><td>12.6</td><td>38.6</td></tr><tr><td>Norway</td><td>5.8</td><td>31.8</td><td>11.2</td><td>37.2</td></tr><tr><td>Finland</td><td>52.8</td><td>26.8</td><td>9.5</td><td>35.5</td></tr><tr><td>Iceland</td><td>8.3</td><td>34.3</td><td>15.4</td><td>41.4</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>A: influenza A; B: influenza B</p></table-wrap-foot></table-wrap></p><p id="Par20">The seasonal patterns of RW<sub>A</sub> and RW<sub>B</sub> in 21 countries with complete data from 2010 to 2017 were shown in Additional file <xref rid="MOESM2" ref-type="media">2</xref>: Figure S1. Influenza seasonality in the two tropical/subtropical countries, i.e., Chile and Argentina, was steadily distinct across different years.</p></sec></sec><sec id="Sec9"><title>Discussion</title><p id="Par21">This study used data up to 2017 and kriging approach to unravel the updated global spatial pattern of seasonal influenza. It quantified the proportion of influenza A virus in total influenza virus and modeled the peak times of influenza A and influenza B in each country from 2010 to 2017. Three findings are note-worthy. First, the highest P<sub>A</sub> was observed in Belarus, Ethiopia, Iraq, and Venezuela, and P<sub>A</sub> changed from year to year. Second, for influenza B, high risk regions distributed in sub-Saharan Africa, Asia-Pacific region and South America. Third, influenza seasonality was distinct in most temperate countries but there were some exceptions (e.g., Mauritius and Maldives), and influenza seasonality was surprisingly distinct in some tropical/subtropical countries, including Chile, Argentina and South Africa.</p><p id="Par22">Unsurprisingly, we observed that influenza A was the dominant subtype in almost all countries (except for Zambia and Lebanon). Notwithstanding, we found that the proportion of influenza B was greater than the proportion of influenza A in certain years in Malaysia, Nicaragua, Panama, Egypt, and Norway. Iuliano et al. estimated the global burden of influenza-associated respiratory deaths and reported that the highest mortality rate was found in sub-Saharan Africa and southeast Asia, and among those who are aged 75 years or older [<xref ref-type="bibr" rid="CR1">1</xref>]. The high positive rate of influenza B that we observed in sub-Saharan Africa suggested that not only mortality but also morbidity in this region were high, calling for more influenza prevention resources to be allocated to this socioeconomically-disadvantaged region. Previous studies have also highlighted the necessity of building comprehensive influenza surveillance system in sub-Saharan Africa [<xref ref-type="bibr" rid="CR17">17</xref>–<xref ref-type="bibr" rid="CR19">19</xref>]. Regarding the influenza situation in Asia-Pacific region, some countries such as Australia and China had a high influenza positive rate (finding of the present study) but low/moderate mortality rate [<xref ref-type="bibr" rid="CR1">1</xref>], implying a relatively good healthcare system in these countries but also suggesting a strong need to identify the national, regional, and local optimal vaccination timing for cost-effective influenza prevention (especially for China as it has wide latitude spans) [<xref ref-type="bibr" rid="CR10">10</xref>], and to build up influenza early warning system which gives warning in a timely manner (e.g., internet-based early warning tools incorporating information collected through traditional surveillance system) [<xref ref-type="bibr" rid="CR20">20</xref>]. Our prior works have suggested that early warning of infectious diseases using data from search engine (e.g., Google and Baidu) may shed some new light on infectious disease control [<xref ref-type="bibr" rid="CR21">21</xref>, <xref ref-type="bibr" rid="CR22">22</xref>]. The constrains and barriers for influenza control and prevention in Asia-Pacific region are multifaceted (e.g., logistic and resourcing issues) [<xref ref-type="bibr" rid="CR23">23</xref>], and preventing people in this region from influenza attacks requires concerted efforts from policy makers, public health officials, healthcare workers, and scientists.</p><p id="Par23">The noticeable change of influenza seasonality in the included countries that we observed in this study, to some extent, indicates that there is no one-size-suits-all vaccination timing for tropical/subtropical countries and some temperate countries. Grouping tropical/subtropical countries into several zones for influenza vaccination might need much more detailed works (e.g., identifying the fundamental determinants behind the year-to-year change in seasonality etc.). A prior study investigating the global environmental drivers found that absolute humidity and temperature drive the outbreaks of seasonal influenza [<xref ref-type="bibr" rid="CR24">24</xref>], and the season of influenza in Vietnam has been found coinciding with the rainy seasons [<xref ref-type="bibr" rid="CR4">4</xref>], implying that future endeavors aiming to look at the relationships between climatic factors and influenza season in a regional or local scale in tropical and subtropical countries are warranted.</p><p id="Par24">This study has two strengths. First, it unfolded the global spatial pattern of influenza positive rate, which may aid policy making in influenza control and prevention. Second, it identified some temperate countries with non-distinct influenza seasonality and some tropical/subtropical countries with distinct influenza seasonality. Five limitations of this study need to be acknowledged. First, there was sampling bias due to overall bias of case data being reported between different countries. Second, the time periods for all selected countries were not consistent, although they were all within the range of 2010 to 2017. It would benefit the influenza surveillance a lot if some countries with data covering a short period of time (e.g., Mauritius) had more resources injection. Third, the country-level data restricted us to explore the socioecological drivers of influenza seasonality. Fourth, different subtypes of influenza A have different and complex transmission routes, and the results of this study only present a global figure on all influenza A subtypes. Fifth, there were large amount of missing data on the subtypes of influenza A in the FluNet data, which restricted us to distinguish and study the patterns of different influenza viruses.</p></sec><sec id="Sec10"><title>Conclusions</title><p id="Par25">Influenza control and prevention attention can predominantly be paid to influenza A in countries such as Venezuela. Sub-Saharan Africa needs more influenza control resources, and efficient influenza prevention programs in Asia-Pacific region call for state-of-the-art internet-based influenza early warning system incorporating traditional surveillance data. Future attempts using spatiotemporal approaches to explore the drivers (e.g., socio-ecological factors) behind the influenza seasonality of tropical and subtropical countries are warranted.</p></sec><sec sec-type="supplementary-material"><title>Supplementary information</title><sec id="Sec11"><p><supplementary-material content-type="local-data" id="MOESM1"><media xlink:href="40249_2019_618_MOESM1_ESM.docx"><caption><p><bold>Additional file 1: Table S1.</bold> Detailed information on influenza in the selected countries (listed by latitude).</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="MOESM2"><media xlink:href="40249_2019_618_MOESM2_ESM.docx"><caption><p><bold>Additional file 2: Figure S1.</bold> Seasonality patterns of influenza A and influenza B in countries of temperate climate and of tropical or subtropical climate, from 2010 to 2017.</p></caption></media></supplementary-material></p></sec></sec></body><back><glossary><title>Abbreviations</title><def-list><def-item><term>P<sub>A</sub></term><def><p id="Par5">The proportion of influenza A virus in total influenza positive virus;</p></def></def-item><def-item><term>R<sub>A</sub></term><def><p id="Par6">The average positive rate of influenza A</p></def></def-item><def-item><term>R<sub>B</sub></term><def><p id="Par7">The average positive rate of influenza B</p></def></def-item><def-item><term>RW<sub>A</sub></term><def><p id="Par8">Weekly positive rate of influenza A</p></def></def-item><def-item><term>RW<sub>B</sub></term><def><p id="Par9">Weekly positive rate of influenza B</p></def></def-item></def-list></glossary><sec><title>Supplementary information</title><p><bold>Supplementary information</bold> accompanies this paper at 10.1186/s40249-019-0618-5.</p></sec><ack><title>Acknowledgements</title><p>We’d like to thank World Health Organization for making the FluNet data publicly available. Dr. Wenbiao Hu was supported by an Australian Research Council Future Fellowship (award number FT140101216).</p></ack><notes notes-type="author-contribution"><title>Authors’ contributions</title><p>WH conceived the study. ZX and WH analyzed the data. ZX drafted the manuscript. WH, ZX and ZL interpreted the results and revised the manuscript. All authors read the approved the final manuscript.</p></notes><notes notes-type="funding-information"><title>Funding</title><p>No funding.</p></notes><notes notes-type="data-availability"><title>Availability of data and materials</title><p>The data used in this study are publicly available data, and can be accessed from WHO FluNet (<ext-link ext-link-type="uri" xlink:href="https://www.who.int/influenza/gisrs_laboratory/flunet/en/">https://www.who.int/influenza/gisrs_laboratory/flunet/en/</ext-link>).</p></notes><notes><title>Ethics approval and consent to participate</title><p id="Par26">Not applicable.</p></notes><notes><title>Consent for publication</title><p id="Par27">Not applicable.</p></notes><notes notes-type="COI-statement"><title>Competing interests</title><p id="Par28">The authors declare that they have no competing interests.</p></notes><ref-list id="Bib1"><title>References</title><ref id="CR1"><label>1.</label><element-citation publication-type="journal"><person-group 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