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

Due to the spread of the coronavirus, public health officials grapple with multiple issues such as recommending a lockdown, contact tracing, promoting the use of masks, social distancing, frequent handwashing, as well as quarantining. It is even more challenging to find the optimal combination of these factors without the use of a suitable mathematical model.

In this paper, we discuss a novel systems approach to building a model for simulating the spread of COVID-19. The model, MIMANSA, divides an individual’s in-person social interactions into three areas, namely home, workplace, and public places. The model tracks the in-person interactions and follows the virus spread. When a new silent carrier is created, the model automatically expands and builds a new layer in the network.

MIMANSA has four control mechanisms, namely the exposure, infection rate, lockdown, and quarantining. MIMANSA differentiates between virus-infected patients, silent carriers, and healthy carriers. It can consider variations in virus activity levels of asymptomatic patients, varying the exposure to the virus, and varying the infection rate depending on the person’s immunity. MIMANSA can simulate scenarios to study the impact of many different conditions simultaneously. It could assist public health officials in complex decision making, enable scientists in projecting the SARS-CoV-2 virus spread and aid hospital administrators in the management of beds and equipment.

MIMANSA is trained and validated using the data from the USA and India. Our results show that MIMANSA forecasts the number of COVID-19 cases in the USA, and India within a 3% margin of error.