Data-based analysis, modelling and forecasting of the COVID-19 outbreak
Since the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly av...
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| Vydáno v: | PloS one Ročník 15; číslo 3; s. e0230405 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Public Library of Science
31.03.2020
Public Library of Science (PLoS) |
| Témata: | |
| ISSN: | 1932-6203, 1932-6203 |
| On-line přístup: | Získat plný text |
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| Abstract | Since the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, we provide estimates of the main epidemiological parameters. In particular, we provide an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves. On the basis of a Susceptible-Infectious-Recovered-Dead (SIDR) model, we provide estimations of the basic reproduction number (R0), and the per day infection mortality and recovery rates. By calibrating the parameters of the SIRD model to the reported data, we also attempt to forecast the evolution of the outbreak at the epicenter three weeks ahead, i.e. until February 29. As the number of infected individuals, especially of those with asymptomatic or mild courses, is suspected to be much higher than the official numbers, which can be considered only as a subset of the actual numbers of infected and recovered cases in the total population, we have repeated the calculations under a second scenario that considers twenty times the number of confirmed infected cases and forty times the number of recovered, leaving the number of deaths unchanged. Based on the reported data, the expected value of R0 as computed considering the period from the 11th of January until the 18th of January, using the official counts of confirmed cases was found to be ∼4.6, while the one computed under the second scenario was found to be ∼3.2. Thus, based on the SIRD simulations, the estimated average value of R0 was found to be ∼2.6 based on confirmed cases and ∼2 based on the second scenario. Our forecasting flashes a note of caution for the presently unfolding outbreak in China. Based on the official counts for confirmed cases, the simulations suggest that the cumulative number of infected could reach 180,000 (with a lower bound of 45,000) by February 29. Regarding the number of deaths, simulations forecast that on the basis of the up to the 10th of February reported data, the death toll might exceed 2,700 (as a lower bound) by February 29. Our analysis further reveals a significant decline of the case fatality ratio from January 26 to which various factors may have contributed, such as the severe control measures taken in Hubei, China (e.g. quarantine and hospitalization of infected individuals), but mainly because of the fact that the actual cumulative numbers of infected and recovered cases in the population most likely are much higher than the reported ones. Thus, in a scenario where we have taken twenty times the confirmed number of infected and forty times the confirmed number of recovered cases, the case fatality ratio is around ∼0.15% in the total population. Importantly, based on this scenario, simulations suggest a slow down of the outbreak in Hubei at the end of February. |
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| AbstractList | Since the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, we provide estimates of the main epidemiological parameters. In particular, we provide an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves. On the basis of a Susceptible-Infectious-Recovered-Dead (SIDR) model, we provide estimations of the basic reproduction number (R0), and the per day infection mortality and recovery rates. By calibrating the parameters of the SIRD model to the reported data, we also attempt to forecast the evolution of the outbreak at the epicenter three weeks ahead, i.e. until February 29. As the number of infected individuals, especially of those with asymptomatic or mild courses, is suspected to be much higher than the official numbers, which can be considered only as a subset of the actual numbers of infected and recovered cases in the total population, we have repeated the calculations under a second scenario that considers twenty times the number of confirmed infected cases and forty times the number of recovered, leaving the number of deaths unchanged. Based on the reported data, the expected value of R0 as computed considering the period from the 11th of January until the 18th of January, using the official counts of confirmed cases was found to be ∼4.6, while the one computed under the second scenario was found to be ∼3.2. Thus, based on the SIRD simulations, the estimated average value of R0 was found to be ∼2.6 based on confirmed cases and ∼2 based on the second scenario. Our forecasting flashes a note of caution for the presently unfolding outbreak in China. Based on the official counts for confirmed cases, the simulations suggest that the cumulative number of infected could reach 180,000 (with a lower bound of 45,000) by February 29. Regarding the number of deaths, simulations forecast that on the basis of the up to the 10th of February reported data, the death toll might exceed 2,700 (as a lower bound) by February 29. Our analysis further reveals a significant decline of the case fatality ratio from January 26 to which various factors may have contributed, such as the severe control measures taken in Hubei, China (e.g. quarantine and hospitalization of infected individuals), but mainly because of the fact that the actual cumulative numbers of infected and recovered cases in the population most likely are much higher than the reported ones. Thus, in a scenario where we have taken twenty times the confirmed number of infected and forty times the confirmed number of recovered cases, the case fatality ratio is around ∼0.15% in the total population. Importantly, based on this scenario, simulations suggest a slow down of the outbreak in Hubei at the end of February. Since the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, we provide estimates of the main epidemiological parameters. In particular, we provide an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves. On the basis of a Susceptible-Infectious-Recovered-Dead (SIDR) model, we provide estimations of the basic reproduction number (R.sub.0 ), and the per day infection mortality and recovery rates. By calibrating the parameters of the SIRD model to the reported data, we also attempt to forecast the evolution of the outbreak at the epicenter three weeks ahead, i.e. until February 29. As the number of infected individuals, especially of those with asymptomatic or mild courses, is suspected to be much higher than the official numbers, which can be considered only as a subset of the actual numbers of infected and recovered cases in the total population, we have repeated the calculations under a second scenario that considers twenty times the number of confirmed infected cases and forty times the number of recovered, leaving the number of deaths unchanged. Based on the reported data, the expected value of R.sub.0 as computed considering the period from the 11th of January until the 18th of January, using the official counts of confirmed cases was found to be ~4.6, while the one computed under the second scenario was found to be ~3.2. Thus, based on the SIRD simulations, the estimated average value of R.sub.0 was found to be ~2.6 based on confirmed cases and ~2 based on the second scenario. Our forecasting flashes a note of caution for the presently unfolding outbreak in China. Based on the official counts for confirmed cases, the simulations suggest that the cumulative number of infected could reach 180,000 (with a lower bound of 45,000) by February 29. Regarding the number of deaths, simulations forecast that on the basis of the up to the 10th of February reported data, the death toll might exceed 2,700 (as a lower bound) by February 29. Our analysis further reveals a significant decline of the case fatality ratio from January 26 to which various factors may have contributed, such as the severe control measures taken in Hubei, China (e.g. quarantine and hospitalization of infected individuals), but mainly because of the fact that the actual cumulative numbers of infected and recovered cases in the population most likely are much higher than the reported ones. Thus, in a scenario where we have taken twenty times the confirmed number of infected and forty times the confirmed number of recovered cases, the case fatality ratio is around ~0.15% in the total population. Importantly, based on this scenario, simulations suggest a slow down of the outbreak in Hubei at the end of February. Since the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, we provide estimates of the main epidemiological parameters. In particular, we provide an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves. On the basis of a Susceptible-Infectious-Recovered-Dead (SIDR) model, we provide estimations of the basic reproduction number (R0), and the per day infection mortality and recovery rates. By calibrating the parameters of the SIRD model to the reported data, we also attempt to forecast the evolution of the outbreak at the epicenter three weeks ahead, i.e. until February 29. As the number of infected individuals, especially of those with asymptomatic or mild courses, is suspected to be much higher than the official numbers, which can be considered only as a subset of the actual numbers of infected and recovered cases in the total population, we have repeated the calculations under a second scenario that considers twenty times the number of confirmed infected cases and forty times the number of recovered, leaving the number of deaths unchanged. Based on the reported data, the expected value of R0 as computed considering the period from the 11th of January until the 18th of January, using the official counts of confirmed cases was found to be ∼4.6, while the one computed under the second scenario was found to be ∼3.2. Thus, based on the SIRD simulations, the estimated average value of R0 was found to be ∼2.6 based on confirmed cases and ∼2 based on the second scenario. Our forecasting flashes a note of caution for the presently unfolding outbreak in China. Based on the official counts for confirmed cases, the simulations suggest that the cumulative number of infected could reach 180,000 (with a lower bound of 45,000) by February 29. Regarding the number of deaths, simulations forecast that on the basis of the up to the 10th of February reported data, the death toll might exceed 2,700 (as a lower bound) by February 29. Our analysis further reveals a significant decline of the case fatality ratio from January 26 to which various factors may have contributed, such as the severe control measures taken in Hubei, China (e.g. quarantine and hospitalization of infected individuals), but mainly because of the fact that the actual cumulative numbers of infected and recovered cases in the population most likely are much higher than the reported ones. Thus, in a scenario where we have taken twenty times the confirmed number of infected and forty times the confirmed number of recovered cases, the case fatality ratio is around ∼0.15% in the total population. Importantly, based on this scenario, simulations suggest a slow down of the outbreak in Hubei at the end of February.Since the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, we provide estimates of the main epidemiological parameters. In particular, we provide an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves. On the basis of a Susceptible-Infectious-Recovered-Dead (SIDR) model, we provide estimations of the basic reproduction number (R0), and the per day infection mortality and recovery rates. By calibrating the parameters of the SIRD model to the reported data, we also attempt to forecast the evolution of the outbreak at the epicenter three weeks ahead, i.e. until February 29. As the number of infected individuals, especially of those with asymptomatic or mild courses, is suspected to be much higher than the official numbers, which can be considered only as a subset of the actual numbers of infected and recovered cases in the total population, we have repeated the calculations under a second scenario that considers twenty times the number of confirmed infected cases and forty times the number of recovered, leaving the number of deaths unchanged. Based on the reported data, the expected value of R0 as computed considering the period from the 11th of January until the 18th of January, using the official counts of confirmed cases was found to be ∼4.6, while the one computed under the second scenario was found to be ∼3.2. Thus, based on the SIRD simulations, the estimated average value of R0 was found to be ∼2.6 based on confirmed cases and ∼2 based on the second scenario. Our forecasting flashes a note of caution for the presently unfolding outbreak in China. Based on the official counts for confirmed cases, the simulations suggest that the cumulative number of infected could reach 180,000 (with a lower bound of 45,000) by February 29. Regarding the number of deaths, simulations forecast that on the basis of the up to the 10th of February reported data, the death toll might exceed 2,700 (as a lower bound) by February 29. Our analysis further reveals a significant decline of the case fatality ratio from January 26 to which various factors may have contributed, such as the severe control measures taken in Hubei, China (e.g. quarantine and hospitalization of infected individuals), but mainly because of the fact that the actual cumulative numbers of infected and recovered cases in the population most likely are much higher than the reported ones. Thus, in a scenario where we have taken twenty times the confirmed number of infected and forty times the confirmed number of recovered cases, the case fatality ratio is around ∼0.15% in the total population. Importantly, based on this scenario, simulations suggest a slow down of the outbreak in Hubei at the end of February. |
| Audience | Academic |
| Author | Siettos, Constantinos Anastassopoulou, Cleo Tsakris, Athanasios Russo, Lucia |
| AuthorAffiliation | 2 Consiglio Nazionale delle Ricerche, Science and Technology for Energy and Sustainable Mobility, Napoli, Italy Center for Disease control and Prevention, UNITED STATES 3 Dipartimento di Matematica e Applicazioni “Renato Caccioppoli”, Università degli Studi di Napoli Federico II, Napoli, Italy 1 Department of Microbiology, Medical School, University of Athens, Athens, Greece |
| AuthorAffiliation_xml | – name: 1 Department of Microbiology, Medical School, University of Athens, Athens, Greece – name: Center for Disease control and Prevention, UNITED STATES – name: 2 Consiglio Nazionale delle Ricerche, Science and Technology for Energy and Sustainable Mobility, Napoli, Italy – name: 3 Dipartimento di Matematica e Applicazioni “Renato Caccioppoli”, Università degli Studi di Napoli Federico II, Napoli, Italy |
| Author_xml | – sequence: 1 givenname: Cleo surname: Anastassopoulou fullname: Anastassopoulou, Cleo – sequence: 2 givenname: Lucia surname: Russo fullname: Russo, Lucia – sequence: 3 givenname: Athanasios surname: Tsakris fullname: Tsakris, Athanasios – sequence: 4 givenname: Constantinos orcidid: 0000-0002-9568-3355 surname: Siettos fullname: Siettos, Constantinos |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32231374$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.4161/viru.24041 10.1093/aje/kwi230 |
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| References | P Zhou (pone.0230405.ref005) 2020 JT Wu (pone.0230405.ref012) 2020 CI Siettos (pone.0230405.ref013) 2013; 4 P Wu (pone.0230405.ref017) 2020; 25 nCoV CDC Response Team (pone.0230405.ref007) 2020 pone.0230405.ref018 C Hunag (pone.0230405.ref008) 2020 E Dong (pone.0230405.ref016) 2020 N Imai (pone.0230405.ref011) 2019 pone.0230405.ref021 N Chen (pone.0230405.ref006) 2020 pone.0230405.ref001 pone.0230405.ref002 S Zhao (pone.0230405.ref010) 2020 pone.0230405.ref003 pone.0230405.ref014 pone.0230405.ref015 R Lu (pone.0230405.ref004) 2020 AC Ghani (pone.0230405.ref019) 2005; 162 D Wang (pone.0230405.ref009) 2020 M Battegay (pone.0230405.ref022) 2020 pone.0230405.ref020 |
| References_xml | – ident: pone.0230405.ref003 – ident: pone.0230405.ref002 – ident: pone.0230405.ref001 – year: 2020 ident: pone.0230405.ref008 article-title: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China publication-title: Lancet – volume: 25 issue: 3 year: 2020 ident: pone.0230405.ref017 article-title: Real-time tentative assessment of the epidemiological characteristics of novel coronavirus infections in Wuhan, China, as at 22 January 2020 publication-title: Eurosurveillance – volume: 4 start-page: 295 issue: 4 year: 2013 ident: pone.0230405.ref013 article-title: Mathematical modeling of infectious disease dynamics publication-title: Virulence doi: 10.4161/viru.24041 – ident: pone.0230405.ref021 – ident: pone.0230405.ref020 – year: 2020 ident: pone.0230405.ref005 article-title: A pneumonia outbreak associated with a new coronavirus of probable bat origin publication-title: Nature – volume: 162 start-page: 479 issue: 5 year: 2005 ident: pone.0230405.ref019 article-title: Methods for Estimating the Case Fatality Ratio for a Novel, Emerging Infectious Disease publication-title: American Journal of Epidemiology doi: 10.1093/aje/kwi230 – year: 2020 ident: pone.0230405.ref009 article-title: Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China publication-title: JAMA – year: 2020 ident: pone.0230405.ref016 article-title: An interactive web-based dashboard to track COVID-19 in real time publication-title: The Lancet Infectious Diseases – ident: pone.0230405.ref015 – year: 2020 ident: pone.0230405.ref010 article-title: Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak publication-title: Int J Infect Dis – year: 2020 ident: pone.0230405.ref022 article-title: 2019-novel Coronavirus (2019-nCoV): estimating the case fatality rate—a word of caution publication-title: Swiss Medical Weekly – year: 2020 ident: pone.0230405.ref007 article-title: Initial Public Health Response and Interim Clinical Guidance for the 2019 Novel Coronavirus Outbreak—United States, December 31, 2019-February 4, 2020 publication-title: MMWR Morb Mortal Wkly Rep – year: 2020 ident: pone.0230405.ref012 article-title: Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study publication-title: The Lancet – ident: pone.0230405.ref018 – year: 2020 ident: pone.0230405.ref004 article-title: Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding publication-title: The Lancet – year: 2019 ident: pone.0230405.ref011 article-title: Report 3: Transmissibility of 2019-nCoV publication-title: Int J Infect Dis – year: 2020 ident: pone.0230405.ref006 article-title: Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study publication-title: The Lancet – ident: pone.0230405.ref014 |
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| SubjectTerms | Analysis Asymptomatic Basic Reproduction Number Betacoronavirus Biology and Life Sciences China China - epidemiology Computation Computer simulation Confidence intervals Coronaviridae Coronavirus Infections - mortality Coronaviruses COVID-19 Data Interpretation, Statistical Disease Outbreaks Epidemiologic Measurements Epidemiology Fatalities Forecasting Health aspects Humans Infection Control Lower bounds Mathematical models Medicine and Health Sciences Middle East respiratory syndrome Models, Statistical Mortality Outbreaks Pandemics Parameter estimation People and Places Physical Sciences Pneumonia, Viral - mortality Quarantine Recovery Research and Analysis Methods SARS-CoV-2 Viral diseases |
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| Title | Data-based analysis, modelling and forecasting of the COVID-19 outbreak |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/32231374 https://www.proquest.com/docview/2384797032 https://www.proquest.com/docview/2385274729 https://pubmed.ncbi.nlm.nih.gov/PMC7108749 https://doaj.org/article/965e89f7e3f9462ab289362c6ce386c1 http://dx.doi.org/10.1371/journal.pone.0230405 |
| Volume | 15 |
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