ArticleScientific reports2019
Real-Time Forecasting of Hand-Foot-and-Mouth Disease Outbreaks using the Integrating Compartment Model and Assimilation Filtering.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 48 citations in OpenAlex.
- Article
- Temperature-based spatiotemporal heterogeneity of hand, foot, and mouth disease transmission in China.BMC infectious diseases · 2025Article
- Mapping Incidence and Prevalence Peak Data for SIR Modeling Applications.Journal of mathematical biology · 2025Article
- Joint estimation of hand-foot-mouth disease model and prediction in korea using the ensemble kalman filter.PLoS computational biology · 2025Article
- A real-time forecasting and estimating system of West Nile virus: a case study of the 2023 WNV outbreak in Colorado, USA.Royal Society open science · 2024Article
- Machine learning-based diffusion model for prediction of coronavirus-19 outbreak.Neural computing & applications · 2023Article
- Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa.Environmental science and pollution research international · 2023Article
- Predicting the number of COVID-19 infections and deaths in USA.Globalization and health · 2022Article
- Wireless Technologies for Social Distancing in the Time of COVID-19: Literature Review, Open Issues, and Limitations.Sensors (Basel, Switzerland) · 2022Review
- Estimating COVID-19Machine learning with applications · 2021Article
- A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries.Life (Basel, Switzerland) · 2021Article
- COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach.Sustainable cities and society · 2021Article
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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