Correcting under-reported COVID-19 case numbers: estimating the true scale of the pandemic

Abstract

The COVID-19 virus has spread worldwide in a matter of a few months, while healthcare systems struggle to monitor and report current cases. Testing results have struggled with the relative capabilities, testing policies and preparedness of each affected country, making their comparison a non-trivial task. Since severe cases, which more likely lead to fatal outcomes, are detected at a higher rate than mild cases, the reported virus mortality is likely inflated in most countries. Lockdowns and changes in human behavior modulate the underlying growth rate of the virus. Under-sampling of infection cases may lead to the under-estimation of total cases, resulting in systematic mortality estimation biases. For healthcare systems worldwide it is important to know the expected number of cases that will need treatment. In this manuscript, we identify a generalizable growth rate decay reflecting behavioral change. We propose a method to correct the reported COVID-19 cases and death numbers by using a benchmark country (South Korea) with nearoptimal testing coverage, with considerations on population demographics. We extrapolate expected deaths and hospitalizations with respect to observations in countries that passed the exponential growth curve. By applying our correction, we predict that the number of cases is highly under-reported in most countries and a significant burden on worldwide hospital capacity.

Importance of untested infectious individuals for the suppression of COVID-19 epidemics

“…At the other end of the testing spectrum, we estimate that for each infected individual tested in Germany, between 0.2 and 2 individuals might have not been tested at the beginning of the epidemic. A higher testing rate for Germany is in qualitative agreement with estimates given elsewhere 22,26 . Our estimates for the reporting rate, however, tend to be higher than those obtained by Jagodnik et al 26 and the differences we found between countries are not as extreme as those given by Chicci et al 22 Assuming that the testing rate remains constant during the course of epidemics and no control interventions are implemented, our model predicts that the number of tested and untested infected individuals would evolve in parallel in all the studied outbreaks which would last for around 12 weeks in all cases (see Figure 3).…”

“…A higher testing rate for Germany is in qualitative agreement with estimates given elsewhere 22,26 . Our estimates for the reporting rate, however, tend to be higher than those obtained by Jagodnik et al 26 and the differences we found between countries are not as extreme as those given by Chicci et al 22 Assuming that the testing rate remains constant during the course of epidemics and no control interventions are implemented, our model predicts that the number of tested and untested infected individuals would evolve in parallel in all the studied outbreaks which would last for around 12 weeks in all cases (see Figure 3). We see, however, that the epidemic in Germany would belong to a different class in the sense that the number of untested infected individuals remains smaller than the number of tested individuals during the whole epidemic.…”

“…Mathematical modelling has been very successful in epidemiology [16][17][18] and there is an ongoing effort to propose models to describe the dynamics of COVID-19 epidemics 3,11,14,15,[19][20][21][22][23][24][25] . Unreported infectious individuals have been included in some models 3,11,22,26 but their influence on control strategies has not been analysed.…”

Immuno-epidemiological model of two-stage epidemic growth

“…These data should be taken with caution because they can be strongly influenced by the number of effectuated tests. According to some media reports, there is a shortage of testing facility and/or shortage of testing kits in many countries, and the accurate number of infected individuals can be much larger [7]. Dynamics of the number of infected individuals can be influenced by various factors, in particular, by their spatial distribution and nature of social mixing.…”

Severe underestimation of COVID-19 case numbers: effect of epidemic growth rate and test restrictions

“…Multiple studies have shown that the reported numbers of confirmed cases may underestimate the true number of cases by a factor of 10 or even more. [4][5][6] While such studies can provide useful insights, they often cannot explain the observed change in the CFR during an epidemic.…”