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


  • Aditya Gopalan (
  • Himanshu Tyagi (

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