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

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.

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Robust estimates of the true (population) infection rate for COVID-19: a backcasting approach

Royal Society Open Science ◽

10.1098/rsos.200909 ◽

2020 ◽

Vol 7 (11) ◽

pp. 200909 ◽

Cited By ~ 1

Author(s):

Steven J. Phipps ◽

R. Quentin Grafton ◽

Tom Kompas

Keyword(s):

Infection Rate ◽

Negative Relationship ◽

Developed Countries ◽

Cumulative Number ◽

Detection Rates ◽

True Number ◽

Significant Negative Relationship ◽

True Detection ◽

Viral Testing ◽

True Population

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 to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 developed countries. Our sample comprised countries with similar levels of medical care and with populations that have similar age distributions. Monte Carlo methods were used to robustly sample parameter uncertainty. We found a strong and statistically significant negative relationship between the proportion of the population who test positive and the implied true detection rate. Despite an overall improvement in detection rates as the pandemic has progressed, our estimates showed that, as at 31 August 2020, the true number of people to have been infected across our sample of 15 countries was 6.2 (95% CI: 4.3–10.9) times greater than the reported number of cases. In individual countries, the true number of cases exceeded the reported figure by factors that range from 2.6 (95% CI: 1.8–4.5) for South Korea to 17.5 (95% CI: 12.2–30.7) for Italy.

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