Forecasting confirmed cases of the COVID-19 pandemic with a migration-based epidemiological model

TL;DR

This paper introduces the SaucIR model, a novel epidemiological framework that incorporates human mobility and detailed infection compartments to improve COVID-19 case forecasting accuracy.

Contribution

The paper develops a new compartmental model extending SIR by including asymptomatic and unconfirmed infected states, with dynamic population flow modeling for spatial effects.

Findings

High accuracy in predicting COVID-19 confirmed cases in China and other countries.

Model outperforms existing models in forecasting reliability.

Effective integration of geographic spread enhances prediction quality.

Abstract

The unprecedented coronavirus disease 2019 (COVID-19) pandemic is still a worldwide threat to human life since its invasion into the daily lives of the public in the first several months of 2020. Predicting the size of confirmed cases is important for countries and communities to make proper prevention and control policies so as to effectively curb the spread of COVID-19. Different from the 2003 SARS epidemic and the worldwide 2009 H1N1 influenza pandemic, COVID-19 has unique epidemiological characteristics in its infectious and recovered compartments. This drives us to formulate a new infectious dynamic model for forecasting the COVID-19 pandemic within the human mobility network, named the SaucIR-model in the sense that the new compartmental model extends the benchmark SIR model by dividing the flow of people in the infected state into asymptomatic, pathologically infected but unconfirmed, and confirmed. Furthermore, we employ dynamic modeling of population flow in the model in order that spatial effects can be incorporated effectively. We forecast the spread of accumulated confirmed cases in some provinces of mainland China and other countries that experienced severe infection during the time period from late February to early May 2020. The novelty of incorporating the geographic spread of the pandemic leads to a surprisingly good agreement with published confirmed case reports. The numerical analysis validates the high degree of predictability of our proposed SaucIR model compared to existing resemblance. The proposed forecasting SaucIR model is implemented in Python. A web-based application is also developed by Dash (under construction).