Interpreting epidemiological surveillance data: A modelling study from Pune City

Abstract

Routine epidemiological surveillance data represents one of the most continuous and comprehensive sources of data during the course of an epidemic. This data is used as inputs to epidemiological forecasting models as well as for public health decision making such as imposition and lifting of lockdowns and quarantine measures. However, such data is generated during testing and contact tracing and not through randomized sampling which makes it unclear how representative such data is of the epidemic itself. Using the BharatSim simulation framework, we build an agent-based epidemiological model with a detailed algorithm of testing and contact tracing representative of actual strategies employed in Pune city to generate synthetic surveillance data. We simulate the impact of different public health strategies, availability of tests and contact tracing efficiencies on the resulting surveillance data as well as on the course of the epidemic. The fidelity of the resulting surveillance data in representing the real-time state of the epidemic and in decision-making is explored in the context of Pune city.