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Simulation-based estimation of the early spread of COVID-19 in Iran: actual versus confirmed cases.

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.

SUBMITTER: Ghaffarzadegan N

PROVIDER: S-EPMC7361282 | biostudies-literature | 2020 Jan-Mar

REPOSITORIES: biostudies-literature

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Simulation-based estimation of the early spread of COVID-19 in Iran: actual versus confirmed cases.

Ghaffarzadegan Navid N Rahmandad Hazhir H

System dynamics review 20200101 1

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) cumulat ...[more]

PMID: 32834468

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Project description:Background:A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. Objective:To estimate the length of the incubation period of COVID-19 and describe its public health implications. Design:Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. Setting:News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. Participants:Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. Measurements:Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. Results:There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. Limitation:Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. Conclusion:This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases. Primary Funding Source:U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.

Dataset Information

Simulation-based estimation of the early spread of COVID-19 in Iran: actual versus confirmed cases.

Publications

Simulation-based estimation of the early spread of COVID-19 in Iran: actual versus confirmed cases.

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