Framework for studying testing strategies for COVID-19

Team Lead: Aditya Gopalan, Himanshu Tyagi


The number of confirmed cases of COVID-19 is often used as a proxy for the actual number of 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 aim to develop an agent-based simulation framework to evaluate and compare various testing policies, such as random symptomatic testing, contact tracing and spatially aware sampling of hotspots, as well as interventions such as lockdowns based on their output. We hope that the framework will be useful in designing and evaluating testing strategies for both inference and intervention purposes.

Project webpage and codebase:

Reports: A preliminary report on our investigations is at

In a different thread, we have been working closely with the BBMP COVID war-room to mathematically model testing strategies for containment zones and evaluate the performance of BBMP’s software frameworks. Specifically, we prepared a software model for Padarayanapura containment zone (Ward No 135), taking into account the demographics of this area and carefully modeling how people interact on the streets. Such models help fill gaps in understanding of disease progression in smaller geographical areas. We used this model to propose a Stratified Sampling Strategy for sample collection and testing, a version of which was deployed on ground by BBMP.  Furthermore, we were able to evaluate and compare many strategies suggested by BBMP using this framework.

As a part of the same collaboration, on request from BBMP COVID war-room, we evaluated the performance of the Index App used by BBMP to collate and share data on COVID cases with different agents and organizations on ground. We analysed the improvement in response-time for intervention since the introduction of this software framework, and further, evaluated the effect on reduction in death numbers.

These works provide instances of our ongoing collaboration with BBMP. Our support in data analytics and systematic evaluation of performance has been appreciated by BBMP, and many further efforts are underway. For instance, we are currently working on a prioritization strategy for different tests based on a patients personal attributes such as age  and presence of comorbidities.


  • Aditya Gopalan (
  • Himanshu Tyagi (

Scroll Up