Analyzing COVID-19 pandemic with a new growth model for population ecology

TL;DR

This paper introduces a new time-dependent growth model for population ecology, applied to epidemic data, and demonstrates its effectiveness in estimating epidemic sizes and predicting COVID-19 case trajectories.

Contribution

The paper proposes a novel growth rate formulation for population dynamics, validated with SARS data, and applied to COVID-19 to improve epidemic modeling accuracy.

Findings

Accurately estimated the final size of SARS epidemic.

Effectively modeled COVID-19 case trajectories in multiple countries.

Predicted epidemic turning points with reasonable accuracy.

Abstract

We have proposed a new form of growth rate for population ecology. Generally, the growth rate is dependent on the size of the population at that particular epoch. We have introduced an alternative time-dependent form of growth rate. This form satisfies essential conditions to represent population growth and can be an alternative form for growth models to analyze population dynamics. We have employed the generalized Richards model as a guideline to compare our results. Further, we have applied our model in the case of epidemics. To check the efficacy of our model, we have verified the 2003 SARS data. This model has estimated the final epidemic size with good accuracy. Thereafter, we intend to describe the present COVID-2019 pandemic. We have performed our analysis with data for Italy, Spain, and Germany. Following, we have tried to predict the number of COVID-19 cases and the turning point for the USA, UK, and India.