Modeling of epidemic spread in Indian urban conditionsTeam Lead: Rajesh Sundaresan
This joint project with TIFR Mumbai aims to model the epidemic spread taking Indian urban conditions into account. The goal is to assist epidemiologists and decision makers with (a) understanding the effectiveness of imposing and lifting various kinds of restrictions (b) anticipating hospital needs (c) devising testing strategies.
The public health threat arising from the worldwide spread of COVID-19 led the Government of India to announce a nation-wide`lockdown’ starting 25 March 2020, an extreme social distancing measure aimed at reducing contact rates in the population and slowing down the transmission of the virus. In this work, we present the outcomes of our city-scale simulation experiments that suggest how the disease may evolve once restrictions are lifted. The idea of modelling a large metropolis is appropriate since the spread in Maharashtra, NCR, Tamil Nadu, etc. is mostly in well connected large cities.
We study the impact of case isolation, home quarantine, social distancing of the elderly, school and college closures, closure of offices, odd-even strategies, etc., as components of various post-lockdown restrictions that might remain in force for some time after the complete lockdown is lifted. More specifically, the post-lockdown scenarios studied, beginning with the most restrictive, are lockdown for an unlimited period, lockdown until 03 May 2020, lockdown until 19 April 2020, with various other restrictions either (1) until 31 May 2020, or (2) until 03 May 2020, or (3) with only case isolation but no other restriction starting from 20 April 2020. In all post-lockdown scenarios, we assume that case isolation will continue to be active with 90% compliance.
Our city-scale study suggests that the infection is likely to have a second wave and the public health threat remains, unless steps are taken to aggressively trace, localise, isolate the cases, and prevent influx of new infections. The new levels and the peaking times for health care demand depend on the levels of infection spreads in each city at the time of relaxation of restrictions. The lockdown has bought us the crucial time needed to do the tracking, isolation, containment and resource mobilisation.
Our estimates in this draft are based on an agent-based city-scale simulator, taking a city’s demographics and interaction spaces into consideration. We use the cities of Bengaluru and Mumbai as examples, but the study could be extended to other cities as well. The agent-based simulator includes households, schools/colleges, workplaces, commute-distance based transport spaces, community spaces, and factors for high density localities. The detailed modelling of such interaction spaces enables a targeted study of the impact of component interventions and their combinations on the epidemic spread, e.g., schools and colleges closure, social distancing of the elderly, odd-even strategies, within-city transportation restrictions, containment zones within the city, etc.
Our framework could potentially be used to inform testing strategies, but at this time we have not incorporated them in our study. There is also a potential for mapping vulnerable zones. We have also not accounted for spontaneous changes in behaviour in the population. While we have assumed Bengaluru’s and Mumbai’s demographics, the case progression in hospitals, though age-stratified and adapted to our demographics, is based on available literature which is still evolving. Availability of more specific case histories from our hospitals will help us get better estimates of the necessary resources for tackling the epidemic. Instantiations of the city-scale simulator for multiple cities where the epidemic is prevalent should help us get better national estimates on the necessary resources for tackling the epidemic.
We emphasise that this report has been prepared to help researchers and public health officials understand the effectiveness of social distancing interventions related to COVID-19. The report should not be used for medical diagnostic, prognostic or treatment purposes or for guidance on personal travel plans.
Simulator for smaller-scale city: https://cni.iisc.ac.in/simulator
- Rajesh Sundaresan (email@example.com, team lead)
- Preetam Patil
- Nihesh Rathod
- A. Y. Sarath
- Sharad Sriram
- Nidhin Koshy Vaidhiyan
- Narendra Dixit
- Aditya Krishna Swamy
The TIFR Mumbai team is headed by Prof. Sandeep Juneja.
Agent-based simulator with several interventions have been implemented for Bengaluru and Mumbai in collaboration with TIFR Mumbai.