A Generalized Epidemiological Model for COVID-19 with Dynamic and Asymptomatic Population

TL;DR

This paper introduces a comprehensive epidemiological model for COVID-19 that accounts for asymptomatic transmission and population movement, providing better predictions and informing public health strategies.

Contribution

It extends standard models to include asymptomatic carriers and mobility, improving predictive accuracy and policy guidance for COVID-19 and other epidemics.

Findings

Testing asymptomatic populations is crucial for controlling the pandemic.

The model outperforms existing epidemiological models in prediction accuracy.

Mobility significantly influences disease spread and should be incorporated in policies.

Abstract

In this paper, we develop an extension of standard epidemiological models, suitable for COVID-19. This extension incorporates the transmission due to pre-symptomatic or asymptomatic carriers of the virus. Furthermore, this model also captures the spread of the disease due to the movement of people to/from different administrative boundaries within a country. The model describes the probabilistic rise in the number of confirmed cases due to the concomitant effects of (incipient) human transmission and multiple compartments. The associated parameters in the model can help architect the public health policy and operational management of the pandemic. For instance, this model demonstrates that increasing the testing for symptomatic patients does not have any major effect on the progression of the pandemic, but testing rate of the asymptomatic population has an extremely crucial role to play. The model is executed using the data obtained for the state of Chhattisgarh in the Republic of India. The model is shown to have significantly better predictive capability than the other epidemiological models. This model can be readily applied to any administrative boundary (state or country). Moreover, this model can be applied for any other epidemic as well.