Infections Forecasting and Intervention Effect Evaluation for COVID-19 via a Data-Driven Markov Process and Heterogeneous Simulation

TL;DR

This paper models COVID-19 transmission using a Markov process and heterogeneous simulation to forecast spread, evaluate intervention effects, and assist in developing control strategies across different regions.

Contribution

It introduces a novel Markov process model with heterogeneous simulation for COVID-19 transmission and intervention effect evaluation.

Findings

Model closely tracks real COVID-19 transmission data

Discloses complex heterogeneity and explosion in transmission

Assists in developing and comparing control strategies

Abstract

The Coronavirus Disease 2019 (COVID-19) pandemic has caused tremendous amount of deaths and a devastating impact on the economic development all over the world. Thus, it is paramount to control its further transmission, for which purpose it is necessary to find the mechanism of its transmission process and evaluate the effect of different control strategies. To deal with these issues, we describe the transmission of COVID-19 as an explosive Markov process with four parameters. The state transitions of the proposed Markov process can clearly disclose the terrible explosion and complex heterogeneity of COVID-19. Based on this, we further propose a simulation approach with heterogeneous infections. Experimentations show that our approach can closely track the real transmission process of COVID-19, disclose its transmission mechanism, and forecast the transmission under different non-drug intervention strategies. More importantly, our approach can helpfully develop effective strategies for controlling COVID-19 and appropriately compare their control effect in different countries/cities.