How Reliable are Test Numbers for Revealing the COVID‑19 Ground Truth and Applying Interventions?

Abstract

The number of confirmed cases of COVID-19 is often used
as a proxy for the actual number of ground truth COVID-19-infected
cases in both public discourse and policy making. However, the number
of confirmed cases depends on the testing policy, and it is important
to understand how the number of positive cases obtained using different
testing policies reveals the unknown ground truth. We develop an
agent-based simulation framework in Python that can simulate various
testing policies as well as interventions such as lockdown based on
them. The interaction between the agents can take into account various
communities and mobility patterns. A distinguishing feature of our framework
is the presence of another ‘flu’-like illness with symptoms similar
to COVID-19, that allows us to model the noise in selecting the pool of
patients to be tested. We instantiate our model for the city of Bengaluru
in India, using census data to distribute agents geographically, and
traffic flow mobility data to model long-distance interactions and mixing.
We use the simulation framework to compare the performance of three
testing policies: Random Symptomatic Testing (RST), Contact Tracing
(CT), and a new Location-Based Testing policy (LBT). We observe that
if a sufficient fraction of symptomatic patients come out for testing, then
RST can capture the ground truth quite closely even with very few daily
tests. However, CT consistently captures more positive cases. Interestingly,
our new LBT, which is operationally less intensive than CT, gives
performance that is comparable with CT. In another direction, we compare
the efficacy of these three testing policies in enabling lockdown,
and observe that CT flattens the ground truth curve maximally, followed
closely by LBT, and significantly better than RST.