Calculation Method and Application of Time-Varying Transmission Rate via Data-Driven Approach

Abstract

Most research about compartmental models of infection disease often consider the transmission rate as a constant, which is not ideal for the dynamic surveillance of infectious diseases. This study fully utilized continuously updated real-time epidemiological data and proposed a SEAIUHR model incorporating asymptomatic and symptomatic infectiousness, reported and unreported cases, inpatient and non-inpatient cases, and vaccine inoculation. This study proposed a novel approach based on our model to calculate the time-varying transmission rate with an under-report rate, vaccination efficiency, and relaxation of social distancing behavior. The proposed method was evaluated based on epidemiological data from the United States. The results suggest that using this approach to combine epidemiological data can provide a clearer understanding of the spread rule of epidemic, offering data support for subsequent related research.