Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020
Identifieur interne : 000040 ( Pmc/Checkpoint ); précédent : 000039; suivant : 000041Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020
Auteurs : Kimberlyn Roosa ; Yiseul Lee ; Ruiyan Luo ; Alexander Kirpich ; Richard Rothenberg ; James M. Hyman ; Ping Yan ; Gerardo ChowellSource :
- Journal of Clinical Medicine [ 2077-0383 ] ; 2020.
Abstract
The ongoing COVID-19 epidemic continues to spread within and outside of China, despite several social distancing measures implemented by the Chinese government. Limited epidemiological data are available, and recent changes in case definition and reporting further complicate our understanding of the impact of the epidemic, particularly in the epidemic’s epicenter. Here we use previously validated phenomenological models to generate short-term forecasts of cumulative reported cases in Guangdong and Zhejiang, China. Using daily reported cumulative case data up until 13 February 2020 from the National Health Commission of China, we report 5- and 10-day ahead forecasts of cumulative case reports. Specifically, we generate forecasts using a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model, which have each been previously used to forecast outbreaks due to different infectious diseases. Forecasts from each of the models suggest the outbreaks may be nearing extinction in both Guangdong and Zhejiang; however, the sub-epidemic model predictions also include the potential for further sustained transmission, particularly in Zhejiang. Our 10-day forecasts across the three models predict an additional 65–81 cases (upper bounds: 169–507) in Guangdong and an additional 44–354 (upper bounds: 141–875) cases in Zhejiang by February 23, 2020. In the best-case scenario, current data suggest that transmission in both provinces is slowing down.
Url:
DOI: 10.3390/jcm9020596
PubMed: 32098289
PubMed Central: 7073898
Affiliations:
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PMC:7073898Le document en format XML
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020</title>
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(Y.L.);<email>rluo@gsu.edu</email>
(R.L.);<email>akirpich@gsu.edu</email>
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(R.L.);<email>akirpich@gsu.edu</email>
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(R.R.);<email>gchowell@gsu.edu</email>
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<author><name sortKey="Hyman, James M" sort="Hyman, James M" uniqKey="Hyman J" first="James M." last="Hyman">James M. Hyman</name>
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</nlm:aff>
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<author><name sortKey="Yan, Ping" sort="Yan, Ping" uniqKey="Yan P" first="Ping" last="Yan">Ping Yan</name>
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<author><name sortKey="Chowell, Gerardo" sort="Chowell, Gerardo" uniqKey="Chowell G" first="Gerardo" last="Chowell">Gerardo Chowell</name>
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<front><div type="abstract" xml:lang="en"><p>The ongoing COVID-19 epidemic continues to spread within and outside of China, despite several social distancing measures implemented by the Chinese government. Limited epidemiological data are available, and recent changes in case definition and reporting further complicate our understanding of the impact of the epidemic, particularly in the epidemic’s epicenter. Here we use previously validated phenomenological models to generate short-term forecasts of cumulative reported cases in Guangdong and Zhejiang, China. Using daily reported cumulative case data up until 13 February 2020 from the National Health Commission of China, we report 5- and 10-day ahead forecasts of cumulative case reports. Specifically, we generate forecasts using a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model, which have each been previously used to forecast outbreaks due to different infectious diseases. Forecasts from each of the models suggest the outbreaks may be nearing extinction in both Guangdong and Zhejiang; however, the sub-epidemic model predictions also include the potential for further sustained transmission, particularly in Zhejiang. Our 10-day forecasts across the three models predict an additional 65–81 cases (upper bounds: 169–507) in Guangdong and an additional 44–354 (upper bounds: 141–875) cases in Zhejiang by February 23, 2020. In the best-case scenario, current data suggest that transmission in both provinces is slowing down.</p>
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<journal-id journal-id-type="iso-abbrev">J Clin Med</journal-id>
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<title-group><article-title>Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020</article-title>
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<contrib-group><contrib contrib-type="author"><name><surname>Roosa</surname>
<given-names>Kimberlyn</given-names>
</name>
<xref ref-type="aff" rid="af1-jcm-09-00596">1</xref>
<xref rid="c1-jcm-09-00596" ref-type="corresp">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Lee</surname>
<given-names>Yiseul</given-names>
</name>
<xref ref-type="aff" rid="af1-jcm-09-00596">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Luo</surname>
<given-names>Ruiyan</given-names>
</name>
<xref ref-type="aff" rid="af1-jcm-09-00596">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Kirpich</surname>
<given-names>Alexander</given-names>
</name>
<xref ref-type="aff" rid="af1-jcm-09-00596">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Rothenberg</surname>
<given-names>Richard</given-names>
</name>
<xref ref-type="aff" rid="af1-jcm-09-00596">1</xref>
</contrib>
<contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0001-5247-5794</contrib-id>
<name><surname>Hyman</surname>
<given-names>James M.</given-names>
</name>
<xref ref-type="aff" rid="af2-jcm-09-00596">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Yan</surname>
<given-names>Ping</given-names>
</name>
<xref ref-type="aff" rid="af3-jcm-09-00596">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chowell</surname>
<given-names>Gerardo</given-names>
</name>
<xref ref-type="aff" rid="af1-jcm-09-00596">1</xref>
</contrib>
</contrib-group>
<aff id="af1-jcm-09-00596"><label>1</label>
Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30302, USA;<email>ylee97@gsu.edu</email>
(Y.L.);<email>rluo@gsu.edu</email>
(R.L.);<email>akirpich@gsu.edu</email>
(A.K.);<email>rrothenberg@gsu.edu</email>
(R.R.);<email>gchowell@gsu.edu</email>
(G.C.)</aff>
<aff id="af2-jcm-09-00596"><label>2</label>
Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, LA 70118, USA;<email>mhyman@tulane.edu</email>
</aff>
<aff id="af3-jcm-09-00596"><label>3</label>
Infectious Disease Prevention and Control Branch, Public Health Agency of Canada, Ottawa, ON K1A 0K9, Canada;<email>ping.yan@canada.ca</email>
</aff>
<author-notes><corresp id="c1-jcm-09-00596"><label>*</label>
Correspondence: <email>kroosa1@student.gsu.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="epub"><day>22</day>
<month>2</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="collection"><month>2</month>
<year>2020</year>
</pub-date>
<volume>9</volume>
<issue>2</issue>
<elocation-id>596</elocation-id>
<history><date date-type="received"><day>14</day>
<month>2</month>
<year>2020</year>
</date>
<date date-type="accepted"><day>19</day>
<month>2</month>
<year>2020</year>
</date>
</history>
<permissions><copyright-statement>© 2020 by the authors.</copyright-statement>
<copyright-year>2020</copyright-year>
<license license-type="open-access"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) 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>
).</license-p>
</license>
</permissions>
<abstract><p>The ongoing COVID-19 epidemic continues to spread within and outside of China, despite several social distancing measures implemented by the Chinese government. Limited epidemiological data are available, and recent changes in case definition and reporting further complicate our understanding of the impact of the epidemic, particularly in the epidemic’s epicenter. Here we use previously validated phenomenological models to generate short-term forecasts of cumulative reported cases in Guangdong and Zhejiang, China. Using daily reported cumulative case data up until 13 February 2020 from the National Health Commission of China, we report 5- and 10-day ahead forecasts of cumulative case reports. Specifically, we generate forecasts using a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model, which have each been previously used to forecast outbreaks due to different infectious diseases. Forecasts from each of the models suggest the outbreaks may be nearing extinction in both Guangdong and Zhejiang; however, the sub-epidemic model predictions also include the potential for further sustained transmission, particularly in Zhejiang. Our 10-day forecasts across the three models predict an additional 65–81 cases (upper bounds: 169–507) in Guangdong and an additional 44–354 (upper bounds: 141–875) cases in Zhejiang by February 23, 2020. In the best-case scenario, current data suggest that transmission in both provinces is slowing down.</p>
</abstract>
<kwd-group><kwd>COVID-19</kwd>
<kwd>coronavirus</kwd>
<kwd>China</kwd>
<kwd>real-time forecasts</kwd>
<kwd>phenomenological models</kwd>
<kwd>sub-epidemic model</kwd>
</kwd-group>
</article-meta>
</front>
<floats-group><fig id="jcm-09-00596-f001" orientation="portrait" position="float"><label>Figure 1</label>
<caption><p>Forecasting results of 5- and 10-day ahead estimates of cumulative reported case counts for Guangdong, China, generated on 13 February 2020. The dots are the mean estimates for each model, and the hinge lines represent the 95% prediction intervals.</p>
</caption>
<graphic xlink:href="jcm-09-00596-g001"></graphic>
</fig>
<fig id="jcm-09-00596-f002" orientation="portrait" position="float"><label>Figure 2</label>
<caption><p>Forecasting results of 5- and 10-day ahead estimates of cumulative reported case counts for Zhejiang, China, generated on 13 February 2020. The dots are the mean estimates for each model, and the hinge lines represent the 95% prediction intervals.</p>
</caption>
<graphic xlink:href="jcm-09-00596-g002"></graphic>
</fig>
<fig id="jcm-09-00596-f003" orientation="portrait" position="float"><label>Figure 3</label>
<caption><p>Ten-day ahead logistic growth model forecasts of cumulative reported COVID-19 cases in Guangdong and Zhejiang, China, generated on 13 February 2020. The blue circles correspond to the cumulative cases reported up until 13 February 2020; the solid red lines correspond to the mean model solution; the dashed red lines depict the 95% prediction intervals; and the black vertical dashed line separates the calibration and forecasting periods.</p>
</caption>
<graphic xlink:href="jcm-09-00596-g003"></graphic>
</fig>
<fig id="jcm-09-00596-f004" orientation="portrait" position="float"><label>Figure 4</label>
<caption><p>Ten-day ahead Richards model forecasts of cumulative reported COVID-19 cases in Guangdong and Zhejiang, China, generated on 13 February 2020. The blue circles correspond to the cumulative cases reported up until 13 February 2020; the solid red lines correspond to the mean model solution; the dashed red lines depict the 95% prediction intervals; and the black vertical dashed line separates the calibration and forecasting periods.</p>
</caption>
<graphic xlink:href="jcm-09-00596-g004"></graphic>
</fig>
<fig id="jcm-09-00596-f005" orientation="portrait" position="float"><label>Figure 5</label>
<caption><p>Ten-day ahead sub-epidemic model forecasts of cumulative reported COVID-19 cases in Guangdong and Zhejiang, China, generated on 13 February 2020. The blue circles correspond to the cumulative cases reported up until 13 February 2020; the solid red lines correspond to the mean model solution; the dashed red lines depict the 95% prediction intervals; and the black vertical dashed line separates the calibration and forecasting periods.</p>
</caption>
<graphic xlink:href="jcm-09-00596-g005"></graphic>
</fig>
</floats-group>
</pmc>
<affiliations><list></list>
<tree><noCountry><name sortKey="Chowell, Gerardo" sort="Chowell, Gerardo" uniqKey="Chowell G" first="Gerardo" last="Chowell">Gerardo Chowell</name>
<name sortKey="Hyman, James M" sort="Hyman, James M" uniqKey="Hyman J" first="James M." last="Hyman">James M. Hyman</name>
<name sortKey="Kirpich, Alexander" sort="Kirpich, Alexander" uniqKey="Kirpich A" first="Alexander" last="Kirpich">Alexander Kirpich</name>
<name sortKey="Lee, Yiseul" sort="Lee, Yiseul" uniqKey="Lee Y" first="Yiseul" last="Lee">Yiseul Lee</name>
<name sortKey="Luo, Ruiyan" sort="Luo, Ruiyan" uniqKey="Luo R" first="Ruiyan" last="Luo">Ruiyan Luo</name>
<name sortKey="Roosa, Kimberlyn" sort="Roosa, Kimberlyn" uniqKey="Roosa K" first="Kimberlyn" last="Roosa">Kimberlyn Roosa</name>
<name sortKey="Rothenberg, Richard" sort="Rothenberg, Richard" uniqKey="Rothenberg R" first="Richard" last="Rothenberg">Richard Rothenberg</name>
<name sortKey="Yan, Ping" sort="Yan, Ping" uniqKey="Yan P" first="Ping" last="Yan">Ping Yan</name>
</noCountry>
</tree>
</affiliations>
</record>
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