Abstract

Compartmental models in epidemiology characterize the spread of an infectious disease by formulating ordinary differential equations to quantify the rate of disease progression through subpopulations defined by the Susceptible-Infectious-Removed (SIR) scheme. The classic rate law central to the SIR compartmental models assumes that the rate of transmission is first order regarding the infectious agent. The current study demonstrates that this assumption does not always hold and provides a theoretical rationale for a more general rate law, inspired by mixed-order chemical reaction kinetics, leading to a modified mathematical model for non-first-order kinetics. Using observed data from 127 countries during the initial phase of the COVID-19 pandemic, we demonstrated that the modified epidemic model is more realistic than the classic, first-order-kinetics based model. We discuss two coefficients associated with the modified epidemic model: transmission rate constant k and transmission reaction order n. While k finds utility in evaluating the effectiveness of control measures due to its responsiveness to external factors, n is more closely related to the intrinsic properties of the epidemic agent, including reproductive ability. The rate law for the modified compartmental SIR model is generally applicable to mixed-kinetics disease transmission with heterogeneous transmission mechanisms. By analyzing early-stage epidemic data, this modified epidemic model may be instrumental in providing timely insight into a new epidemic and developing control measures at the beginning of an outbreak.

Highlights

  • IntroductionSpeedy action guided by the knowledge of pathogens is crucial

  • In epidemic control, speedy action guided by the knowledge of pathogens is crucial

  • We provide a theoretical rationale for the modified rate law by drawing from chemical kinetics and demonstrate the modified model by analyzing observed data from 127 countries during the initial phase of the COVID-19 pandemic

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Summary

Introduction

Speedy action guided by the knowledge of pathogens is crucial. Mathematical models have become essential tools for understanding infectious diseases since the early 20th century [1]. A critical challenge in modeling epidemics is how to gain insight into the intrinsic mechanism of disease transmission during the early stages of epidemics when there are limited data [2,3]. The Susceptible-Infectious-Removed (SIR) model is based on a scheme that compartmentalizes the population into susceptible (S), infectious (I), and removed (R) subpopulations [4]. Coefficients and ordinary differential equations are used to quantify the transformation of subjects.

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