Interpreting epidemiological surveillance data: a modelling study based on Pune city

Abstract

Abstract Routine epidemiological surveillance data represents one of the most continuous and current sources of data during the course of an epidemic. This data is used to calibrate epidemiological forecasting models, as well as for public health decision making such as the imposition and lifting of lockdowns and quarantine measures. However, such data is generated during testing and contact tracing and not through randomized sampling. Furthermore, since the process of generating this data affects the epidemic trajectory itself – identification of infected persons might lead to them being quarantined, for instance – it is unclear how representative such data is of the actual epidemic itself. For example, will the observed rise in infections correspond well with the actual rise in infections? To answer such questions, we employ epidemiological simulations not to study the effectiveness of different public health strategies in controlling the spread of the epidemic , but to study the quality of the resulting surveillance data and derived metrics and their utility for decision making. Using the BharatSim simulation framework, we build an agent-based epidemiological model with a detailed representation of testing and contact tracing strategies based on those employed in Pune city during the COVID-19 pandemic to generate synthetic surveillance data. Infected persons are identified, quarantined and/or hospitalised based on these strategies. We perform extensive simulations to study the impact of different public health strategies and the 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.