Cascade SEIRD: Forecasting the Spread of COVID-19 with Dynamic Parameters Update

Abstract

The SEIR model is widely used in simulating the spread of infectious diseases. COVID-19 virus is a very severe infectious disease. Some studies leverage the SEIR or SEIRD model to simulate the spread and estimate the number of infected and recovered people as time goes on. However, these models suffer from two key deficiencies: (i) conventional SEIRD does not update its model parameters w.r.t. time; (ii) it focuses on predicting the trend, instead of the actual number of infections in the future. In this paper, we propose a cascade SEIRD model. The model learns and updates its parameters every day. Moreover, it is able to predict the number of infection cases, recovered cases and deaths. Specifically, we leverage a machine learning like approach to dynamically estimate the parameters of infection rate, incubation rate, recovery rate and death rate, which can be updated by gradient descent algorithm. Once the nature of the parameters w.r.t. time is determined, ARIMA model is adopted to characterize the dynamics of the parameters and predict their future changes. To validate the effectiveness of the proposed cascade SEIRD model, we conduct experiments on five data sets of different scales of regions (China, Hubei, Wuhan, Shenzhen, US). Experimental results show that the proposed cascade SEIRD achieves the most accurate prediction and outperforms state-of-the-art techniques.