COVID-19 Pandemic in Rajasthan: Mathematical Modelling and Social Distancing

Abstract

Background: Mathematical modelling of epidemics and pandemics serves as an input to policymakers and health planners for preparedness and planning for the containment of infectious diseases and their progression in the population. The susceptible–exposed–infectious/asymptomatic–recovered social distancing (SEIAR-SD) model, an extended application of the original Kermack–McKendrick and Fred Brauer models, was developed to predict the incidence of the COVID-19 pandemic and its progression and duration in the state of Rajasthan, India. Objective: The study aimed at developing a mathematical model, the SEIAR-SD model, of the COVID-19 pandemic in the state of Rajasthan, for predicting the number of cases, progression of the pandemic and its duration. Materials and methods: The SEIAR-SD model was applied for different values of population proportion, symptomatic and asymptomatic cases and social distancing parameters to evaluate the effect of variations in the number of infected persons, size of the pandemic and its duration, with value of other parameters fixed in the model. Actual reported cases were plotted and juxtaposed on the prediction models for comparison. Results: Social distancing was the crucial determinant of the magnitude of COVID-19 cases, the progression of the pandemic and its duration. In the absence of any proven treatment or vaccine, effective social distancing would reduce the number of infections and shorten the peak and duration of the pandemic. Loosening social distancing will increase the number of cases and lead to a heightened peak and prolonged duration of the pandemic. Conclusions: In the absence of an effective treatment or a vaccine against COVID-19, social distancing (lockdown) and public health interventions—case detection with testing and isolation, contact tracing and quarantining—will be crucial for the prevention of the spread of the pandemic and for saving lives.