ArticleInfectious diseases of poverty2016
Stochastic modelling of infectious diseases for heterogeneous populations.
Article in Infectious diseases of poverty, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Bayesian inference for the onset time and epidemiological characteristics of emerging infectious diseases.Frontiers in public health · 2024Article
- Monte Carlo simulation of COVID-19 pandemic using Planck's probability distribution.Bio Systems · 2022Article
- Effect of non-pharmacological interventions on the COVID-19 epidemic in Saudi Arabia.Epidemiology and infection · 2021Article
- A primer on using mathematics to understand COVID-19 dynamics: Modeling, analysis and simulations.Infectious Disease Modelling · 2021Article
- Interdisciplinary Approaches to COVID-19.Advances in experimental medicine and biology · 2021Article
- Viral Pandemics of the Last Four Decades: Pathophysiology, Health Impacts and Perspectives.International journal of environmental research and public health · 2020Review
- Outbreak minimization v.s. influence maximization: an optimization framework.BMC medical informatics and decision making · 2020Article
- The urgent need for integrated science to fight COVID-19 pandemic and beyond.Journal of translational medicine · 2020Review
- Leveraging Computational Modeling to Understand Infectious Diseases.Current pathobiology reports · 2020Review
- Erratum to: Stochastic modelling of infectious diseases for heterogeneous populations.Infectious diseases of poverty · 2017Article
- Estimation of sickness absenteeism among Italian healthcare workers during seasonal influenza epidemics.PloS one · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInfectious diseases such as SARS and H1N1 can significantly impact people's lives and cause severe social and economic damages. Recent outbreaks have stressed the urgency of effective research on the dynamics of infectious disease spread. However, it is difficult to predict when and where outbreaks may emerge and how infectious diseases spread because many factors affect their transmission, and some of them may be unknown.
methodsOne feasible means to promptly detect an outbreak and track the progress of disease spread is to implement surveillance systems in regional or national health and medical centres. The accumulated surveillance data, including temporal, spatial, clinical, and demographic information can provide valuable information that can be exploited to better understand and model the dynamics of infectious disease spread. The aim of this work is to develop and empirically evaluate a stochastic model that allows the investigation of transmission patterns of infectious diseases in heterogeneous populations.
resultsWe test the proposed model on simulation data and apply it to the surveillance data from the 2009 H1N1 pandemic in Hong Kong. In the simulation experiment, our model achieves high accuracy in parameter estimation (less than 10.0 % mean absolute percentage error). In terms of the forward prediction of case incidence, the mean absolute percentage errors are 17.3 % for the simulation experiment and 20.0 % for the experiment on the real surveillance data.
conclusionWe propose a stochastic model to study the dynamics of infectious disease spread in heterogeneous populations from temporal-spatial surveillance data. The proposed model is evaluated using both simulated data and the real data from the 2009 H1N1 epidemic in Hong Kong and achieves acceptable prediction accuracy. We believe that our model can provide valuable insights for public health authorities to predict the effect of disease spread and analyse its underlying factors and to guide new control efforts.
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