BharatSim: An agent-based modelling framework for India

BharatSim is an open-source agent-based modelling framework for the Indian population. It can simulate populations at multiple scales, from small communities to states. BharatSim uses a synthetic population created by applying statistical methods and machine learning algorithms to survey data from multiple sources, including the Census of India, the India Human Development Survey, the National Sample Survey, and the Gridded Population of the World. This synthetic population defines individual agents with multiple attributes, among them age, gender, home and work locations, pre-existing health conditions, and socio-economic and employment status. BharatSim’s domain-specific language provides a framework for the simulation of diverse models. Its computational core, coded in Scala , supports simulations of a large number of individual agents, up to 50 million. Here, we describe the design and implementation of BharatSim, using it to address three questions motivated by the COVID-19 pandemic in India: (i) When can schools be safely reopened given specified levels of hybrid immunity?, (ii) How do new variants alter disease dynamics in the background of prior infections and vaccinations? and (iii) How can the effects of varied non-pharmaceutical interventions (NPIs) be quantified for a model Indian city? Through its India-specific synthetic population, BharatSim allows disease modellers to address questions unique to this country. It should also find use in the computational social sciences, potentially providing new insights into emergent patterns in social behaviour. Author summary Agent-based simulations provide granular ways of describing the dynamics of each individual in a population. Such models are especially useful in describing the spread of an infectious disease, since they can be used to incorporate individual-level heterogeneity in behaviour and susceptibilities, as well as spatio-temporal information. BharatSim is such an agent-based modelling framework for India. It can simulate populations at multiple scales, from a few hundreds to several millions. It creates and uses a predefined synthetic population for India, assimilating it into a simulation framework. The synthetic population defines individuals with multiple attributes, among them age, sex, home, and work locations. We demonstrate the use of BharatSim in three contexts related to the COVID-19 pandemic in India. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement GIM and PC acknowledge support from the World Health Organization SAGE Working Group on Vaccines (APW Contract \#202706833). GIM acknowledges support of the Bill and Melinda Gates Foundation, Grant No: R/BMG/PHY/GMN/20 ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript or available from the authors upon request.