Multilevel Integrated Model with a Novel Systems Approach (MIMANSA) for Simulating the Spread of COVID-19

Arpita Welling, Abhilasha Patel, Padmaj Kulkarni, Vinay Vaidya

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Received date: 1st October 2020

Existing mathematical models for COVID-19 are inadequate to simulate scenarios such as lockdown, quarantining, and exposure rate variations. In this paper, we describe a new mathematical model using the systems approach and mimic the virus spread in the physical world. To understand COVID-19 spread, we divided the in-person social interactions of an individual into three areas: home, workplace, and public places. We built a model to represent a small unit to track these interactions and followed the virus spread. When a person is infected and becomes a silent carrier, the model builds a new layer in the network, tracks patients, and expands automatically. MIMANSA has four control mechanisms, namely the exposure rate, infection rate, lockdown, and quarantining. MIMANSA differentiates between virus-infected patients, silent carriers, and healthy carriers. MIMANSA can simulate scenarios to study the impact of many different conditions simultaneously. MIMANSA is trained and validated using the data from India. The model forecasts the number of COVID-19 cases in India within a 3% margin of error. Although MIMANSA is originally developed for the SARS-CoV-2, it can be modified to study the spread of any virus. Thus, MIMANSA has the capability of making a large impact in the field of epidemiology.

Read in full at medRxiv.

This is an abstract of a preprint hosted on a preprint server, which is currently undergoing peer review at Scientific Reports. The findings have yet to be thoroughly evaluated, nor has a decision on ultimate publication been made. Therefore, the results reported should not be considered conclusive, and these findings should not be used to inform clinical practice, or public health policy, or be promoted as verified information.

Scientific Reports

Nature Research, Springer Nature