Abstract
Introduction: PCR testing for COVID-19 is not done at random but selectively on suspected cases. This paper presents a method to estimate a “genuine Virus Prevalence” by quantifying and removing the bias related to selective testing.Methods: The data used was from nine (9) neighbouring countries in Western Europe that recorded similar epidemic trends despite differences in Testing Rate. Regression analysis was used to establish a relationship of declining Test Positivity with increased Testing Rate. By extrapolating this trend to an “infinitely complete” Testing Rate, an unbiased Test Positivity or “genuine Virus Prevalence” was computed. Via pairing of “genuine Virus Prevalence” with Excess-Deaths, a “genuine Infection Fatality Rate” (IFR) was also derived.Results: Peak levels of “genuine Virus Prevalence” were around 0.5 to 2% during the 1st epidemic “wave” (week 10 to week 20) and are approaching similar levels in the ongoing 2nd “wave” (week 34 onward). “Genuine Virus Prevalence” estimates are relatively close to reported Seroprevalence in the studied countries with a correlation coefficient of 0.54. “Genuine” IFR is found comparable to closed-community model IFR. Finally, results of community mass-testing in Slovakia are within the estimated range of “genuine Virus Prevalence”.Conclusions: Estimates of “genuine Virus Prevalence” benchmark favourably to other indications of virus prevalence suggesting the estimation method is robust and potentially deployable beyond this initial dataset of countries. “Genuine Virus Prevalence” curves suggest that during the 1st epidemic “wave”, curve flattening and waning happened at very modest levels of infection spread, either naturally or facilitated by government measures.
Highlights
PCR testing for COVID-19 is not done at random but selectively on suspected cases
The concept this paper exploits is that epidemic trajectories across a set of neighbouring countries might be sufficiently similar to observe the impact of testing rate on reported test positivity, as the testing rate in different countries was stepped up over time but with different pace and timing
All of the nine European countries recorded a similar two “waves” epidemic trajectory comprising a 1st “wave” in week 10 to week 20 and a 2nd “wave” from about week 34 onward. This is evident in both the number of reported Cases as well as in the percentage (%) of positive test outcomes (“Test Positivity Rate”)
Summary
This paper presents a method to estimate a “genuine virus prevalence” by quantifying and removing the bias related to selective testing. The testing was not deployed at random but primarily performed on suspected cases especially early in the epidemic when testing capacity was very limited. Because of this biased deployment of testing, the reported percentage of positive tests (the so-called “test positivity”) is almost certainly higher than the true percentage of infected members of the population (called “genuine virus prevalence” throughout this paper). Estimates of “genuine virus prevalence” are made by analysing the relationship between testing rate (number of tests per week per capita) and test positivity over time, for a number of countries within the same geographic realm and subject to similar trends in virus prevalence
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