Simulation-based estimation of the early spread of COVID-19 in Iran: actual versus confirmed cases.
Identifieur interne : 000014 ( Main/Exploration ); précédent : 000013; suivant : 000015Simulation-based estimation of the early spread of COVID-19 in Iran: actual versus confirmed cases.
Auteurs : Navid Ghaffarzadegan ; Hazhir RahmandadSource :
- System dynamics review [ 0883-7066 ]
Abstract
Understanding the state of the COVID-19 pandemic relies on infection and mortality data. Yet official data may underestimate the actual cases due to limited symptoms and testing capacity. We offer a simulation-based approach which combines various sources of data to estimate the magnitude of outbreak. Early in the epidemic we applied the method to Iran's case, an epicenter of the pandemic in winter 2020. Estimates using data up to March 20th, 2020, point to 916,000 (90% UI: 508 K, 1.5 M) cumulative cases and 15,485 (90% UI: 8.4 K, 25.8 K) total deaths, numbers an order of magnitude higher than official statistics. Our projections suggest that absent strong sustaining of contact reductions the epidemic may resurface. We also use data and studies from the succeeding months to reflect on the quality of original estimates. Our proposed approach can be used for similar cases elsewhere to provide a more accurate, early, estimate of outbreak state. © 2020 System Dynamics Society.
DOI: 10.1002/sdr.1655
PubMed: 32834468
PubMed Central: PMC7361282
Affiliations:
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Yet official data may underestimate the actual cases due to limited symptoms and testing capacity. We offer a simulation-based approach which combines various sources of data to estimate the magnitude of outbreak. Early in the epidemic we applied the method to Iran's case, an epicenter of the pandemic in winter 2020. Estimates using data up to March 20th, 2020, point to 916,000 (90% UI: 508 K, 1.5 M) cumulative cases and 15,485 (90% UI: 8.4 K, 25.8 K) total deaths, numbers an order of magnitude higher than official statistics. Our projections suggest that absent strong sustaining of contact reductions the epidemic may resurface. We also use data and studies from the succeeding months to reflect on the quality of original estimates. Our proposed approach can be used for similar cases elsewhere to provide a more accurate, early, estimate of outbreak state. © 2020 System Dynamics Society.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32834468</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>28</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0883-7066</ISSN><JournalIssue CitedMedium="Print"><Volume>36</Volume><Issue>1</Issue><PubDate><MedlineDate>2020 Jan-Mar</MedlineDate></PubDate></JournalIssue><Title>System dynamics review</Title><ISOAbbreviation>Syst Dyn Rev</ISOAbbreviation></Journal><ArticleTitle>Simulation-based estimation of the early spread of COVID-19 in Iran: actual versus confirmed cases.</ArticleTitle><Pagination><MedlinePgn>101-129</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1002/sdr.1655</ELocationID><Abstract><AbstractText>Understanding the state of the COVID-19 pandemic relies on infection and mortality data. Yet official data may underestimate the actual cases due to limited symptoms and testing capacity. We offer a simulation-based approach which combines various sources of data to estimate the magnitude of outbreak. Early in the epidemic we applied the method to Iran's case, an epicenter of the pandemic in winter 2020. Estimates using data up to March 20th, 2020, point to 916,000 (90% UI: 508 K, 1.5 M) cumulative cases and 15,485 (90% UI: 8.4 K, 25.8 K) total deaths, numbers an order of magnitude higher than official statistics. Our projections suggest that absent strong sustaining of contact reductions the epidemic may resurface. We also use data and studies from the succeeding months to reflect on the quality of original estimates. Our proposed approach can be used for similar cases elsewhere to provide a more accurate, early, estimate of outbreak state. © 2020 System Dynamics Society.</AbstractText><CopyrightInformation>© 2020 System Dynamics Society.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ghaffarzadegan</LastName><ForeName>Navid</ForeName><Initials>N</Initials><Identifier Source="ORCID">https://orcid.org/0000-0003-3632-8588</Identifier><AffiliationInfo><Affiliation>Department of Industrial and Systems Engineering Virginia Tech Falls Church VA USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rahmandad</LastName><ForeName>Hazhir</ForeName><Initials>H</Initials><Identifier Source="ORCID">https://orcid.org/0000-0002-2784-9042</Identifier><AffiliationInfo><Affiliation>Sloan School of Management MIT Cambridge MA USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>07</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Syst Dyn Rev</MedlineTA><NlmUniqueID>101648484</NlmUniqueID><ISSNLinking>0883-7066</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID‐19</Keyword><Keyword MajorTopicYN="N">coronavirus</Keyword><Keyword MajorTopicYN="N">data science</Keyword><Keyword MajorTopicYN="N">health care</Keyword><Keyword MajorTopicYN="N">public policy</Keyword><Keyword MajorTopicYN="N">simulation</Keyword><Keyword MajorTopicYN="N">system dynamics</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>04</Month><Day>15</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>05</Month><Day>14</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>05</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>05</Month><Day>19</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32834468</ArticleId><ArticleId IdType="doi">10.1002/sdr.1655</ArticleId><ArticleId IdType="pii">SDR1655</ArticleId><ArticleId IdType="pmc">PMC7361282</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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