Real-Time Forecasting of Hand-Foot-and-Mouth Disease Outbreaks using the Integrating Compartment Model and Assimilation Filtering

Zhicheng Zhan,Peng Yang,Quanyi Wang,Weihua Dong,Yongmei Lu,Peng Jia

doi:10.1038/s41598-019-38930-y

Abstract

Hand-foot-and-mouth disease (HFMD) is a highly contagious viral infection, and real-time predicting of HFMD outbreaks will facilitate the timely implementation of appropriate control measures. By integrating a susceptible-exposed-infectious-recovered (SEIR) model and an ensemble Kalman filter (EnKF) assimilation method, we developed an integrated compartment model and assimilation filtering forecast model for real-time forecasting of HFMD. When applied to HFMD outbreak data collected for 2008–11 in Beijing, China, our model successfully predicted the peak week of an outbreak three weeks before the actual arrival of the peak, with a predicted maximum infection rate of 85% or greater than the observed rate. Moreover, dominant virus types enterovirus 71 (EV-71) and coxsackievirus A16 (CV-A16) may account for the different patterns of HFMD transmission and recovery observed. The results of this study can be used to inform agencies responsible for public health management of tailored strategies for disease control efforts during HFMD outbreak seasons.

Highlights

Previous studies have examined the factors influencing HFMD11–16, the characteristics and transmission patterns of Hand-foot-and-mouth disease (HFMD) vary across different regions and seasons, and the prediction of HFMD outbreaks remains a daunting task
Www.nature.com/scientificreports traditional compartment model relies on a set of static conditions and model parameters that are difficult to estimate for forecasting the HFMD outbreak due to the interactions of many uncertain factors, such as weather conditions and measures to control for social interaction
Conditions contributing to an HFMD outbreak are usually dynamic, which rarely meets the presumption of a traditional compartment model

Summary

Introduction

Previous studies have examined the factors influencing HFMD11–16, the characteristics and transmission patterns of HFMD vary across different regions and seasons, and the prediction of HFMD outbreaks remains a daunting task. Prior forecasting employs the SEIR model and the current week’s variable measurements to forecast the infection rate of the week, while the posterior analysis produces assimilation results by adjusting prior forecasts for observed data. As more observed data were fed into the model, the ensemble of variables and parameters were updated, and the peak magnitude accuracy continued to improve and approached 100% (i.e., zero offset).

Results

Conclusion