The Signicance of the Detection Ratio for Predictions on the Outcome of an Epidemic - A Message from Mathematical Modelers

Abstract

In attempting to predict the further course of the novel coronavirus (COVID-19) pandemic caused by SARS-CoV-2, mathematical models of different types are frequently employed and calibrated to reported case numbers. Among the major challenges in interpreting these data is the uncertainty about the amount of undetected infections, or conversely: the detection ratio. As a result, some models include assumptions about the percentage of detected cases among total infections while others completely neglect undetected cases. Here, we illustrate how model projections about case and fatality numbers vary significantly under varying assumptions on the detection ratio. Uncertainties in model predictions can be significantly reduced by representative testing, both for antibodies and active virus RNA, to uncover past and current infections that have gone undetected thus far.

Modeling the spread of COVID-19 in Germany: Early assessment and possible scenarios

“…On the other hand, lower reporting ratios in early April provide a larger margin for improvement by enhanced testing as in our high vigilance scenario. While reporting ratios are notoriously hard to estimate in early phases of an epidemic, and have a potentially enormous impact on the predictions made by any model [46], several models completely neglect the presence of undetected cases [33,45]. The aggressiveness of the virus and hence the mortality among all affected individuals (whether diagnosed or not) is another unknown, but different assumptions about this parameter can be expected to have similar impacts on all the scenarios discussed here.…”

Emerging Polynomial Growth Trends in COVID-19 Pandemic Data and Their Reconciliation with Compartment Based Models

“…For the countries in the PGED regime we also show the best PGED fit to the data. 4 In Figure 1 we present two groups of countries in the early stages of the epidemic: the countries in the EG phase (Afghanistan, Bolivia, Colombia, El Salvador, India) and countries in the PG phase (Argentina, Indonesia, Qatar, Russia, Somalia). To demonstrate an evidence of EG and PG, respectively, we compare the recent data with a straight line in the respective plot.…”

“…• A relation of the reported data to the real (unobserved) number of infected in a population is not understood and the estimates for the ratio of total cases in population to the number of observed cases vary even in the order of magnitude [3,4,5,6,7,8,9,10].…”