Title: Estimating the true (population) infection rate for COVID-19: A Backcasting Approach with Monte Carlo Methods

Type Dataset Phipps, Steven John, Grafton, R. Quentin, Kompas, Tom (2020): Estimating the true (population) infection rate for COVID-19: A Backcasting Approach with Monte Carlo Methods. Zenodo. Dataset. https://zenodo.org/record/3821525

Authors: Phipps, Steven John (College of Sciences and Engineering, University of Tasmania, Hobart, Tasmania, Australia) ; Grafton, R. Quentin (Crawford School of Public Policy, Australian National University, Canberra, ACT, Australia) ; Kompas, Tom (Centre of Excellence for Biosecurity Risk Analysis, University of Melbourne, Melbourne, Australia) ;

Links

• Item record in Zenodo
• Digital object URL

Summary

Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country over time, make it difficult to estimate the true (population) infection rate based on the confirmed number of cases obtained through RNA viral testing. We applied a backcasting approach, coupled with Monte Carlo methods, to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 countries where reliable data are available. We find a positive relationship between the testing rate per 1,000 people and the implied true detection rate of COVID-19, and a negative relationship between the proportion who test positive and the implied true detection rate. Our estimates suggest that the true number of people infected across our sample of 15 developed countries is 18.2 (5-95% CI: 11.9-39.0) times greater than the reported number of cases. In individual countries, the true number of cases exceeds the reported figure by factors that range from 1.7 (5-95% CI: 1.1-3.6) for Australia to 35.6 (5-95% CI: 23.2-76.3) for Belgium.

More information

• DOI: 10.5281/zenodo.3821525

Subjects

• COVID-19, SARS-CoV-2, pandemic, public health, infection rate, parameter uncertainty

Dates

• Publication date: 2020
• Issued: May 12, 2020

Rights

• https://creativecommons.org/licenses/by/4.0/legalcode Creative Commons Attribution 4.0 International
• info:eu-repo/semantics/openAccess Open Access

Format

electronic resource

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