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

Sep 30, 2026

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Routine epidemiological surveillance data provides continuous, near real-time information during epidemics, informing forecasting calibration and public health decisions such as lockdown and quarantine policies. Because such data arises from testing and contact tracing rather than randomized sampling, its representativeness for the true epidemic trajectory is uncertain. Using the BharatSim simulation framework, an agent-based model recreates Pune city COVID-19 testing and tracing strategies to generate synthetic surveillance data, evaluate data fidelity, and assess how strategy choices and tracing/testing efficiency shape both surveillance metrics and epidemic dynamics.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Surveillance data in epidemics

Challenges and representativeness

Simulations to assess surveillance quality

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Interpreting epidemiological surveillance data: a modelling study based on Pune city Prathith Bhargav1,5􀀍, Soumil Kelkar1,3,5, Joy Merwin Monteiro1 & Philip Cherian2,4 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 lockdownsand 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. Routine epidemiological surveillance data is generated by public and private entities as part of efforts to diagnose, treat and contain any outbreak1. For instance, during COVID-19 outbreaks in Pune city, daily surveillance data2 included (1) the number of tests conducted,(2) the number of individuals who tested positive and their demographic information,(3) the number of people hospitalised,(4) the number of deaths, and (5) contacts of identified positive cases. Such surveillance data has been used as input for several epidemiological forecasting models3–9 which have tried to estimate the course of the epidemic in India. Even though these forecasting models have their merits in assisting decision-making and policies, they come with their own set of challenges10–12. Similarly, surveillance data represents a noisy estimate of the actual epidemic and it is therefore important to understand its limitations. Some analytical results have been obtained to model the introduction of delays and under-reporting13, but it is unlikely that the complexities of the public health response itself, such as testing, contact tracing and quarantining as well and resource constraints (such as number of testing kits available) can be easily modelled in an analytical framework. Therefore, simulations appear to be a useful tool to help understand the relationship between surveillance data and the true epidemic. In addition to forecast models, metrics derived from surveillance data such as the Test Positivity Rate (TPR), the Case Fatality Rate (CFR) and the Reproduction Number (Rt ) have themselves also been used to inform public health interventions. The World Health Organization (WHO) advocated the use ofTPR as a metric to indicate whether the epidemic is controlled14. While this recommendation was not prescriptive or data-driven, India, like many other countries, used TPR to gauge the true

FAQs

Why might routine epidemiological surveillance data be unrepresentative of the true epidemic?

Surveillance data is generated through testing and contact tracing rather than randomized sampling, and the process itself can alter epidemic dynamics (e.g., quarantining identified infected persons). This makes it unclear whether observed trends match the actual trends.

What approach does the study use to evaluate surveillance data quality?

The study uses epidemiological simulations with the BharatSim simulation framework to build an agent-based model. The model includes detailed testing and contact tracing strategies based on Pune city practices during COVID-19.

Which factors are examined for their impact on surveillance data and epidemic progression?

The study runs extensive simulations to assess how different public health strategies, as well as test availability and contact tracing efficiency, influence both the resulting surveillance data and the course of the epidemic.

Sep 30, 2026

Upload by Riley