ArticleIEEE journal of biomedical and health informatics2015
Temporal and spatial monitoring and prediction of epidemic outbreaks.
Article in IEEE journal of biomedical and health informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Proposal of a novel Artificial Intelligence Distribution Service platform for healthcare.F1000Research · 2021Pooled it
- Forecasting the transmission trends of respiratory infectious diseases with an exposure-risk-based model at the microscopic level.Environmental research · 2022Article
- Internet of Things Framework for Oxygen Saturation Monitoring in COVID-19 Environment.IEEE internet of things journal · 2022Article
- Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method.BMC bioinformatics · 2021Article
- Artificial Intelligence Transforms the Future of Health Care.The American journal of medicine · 2019Review
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper introduces a nonlinear dynamic model to study spatial and temporal dynamics of epidemics of susceptible-infected-removed type. It involves modeling the respective collections of epidemic states and syndromic observations as random finite sets. Each epidemic state consists of the number of infected individuals in an isolated population system and the corresponding partially known parameters of the epidemic model. The infectious disease could spread between population systems with known probabilities based on prior knowledge of ecological and biological features of the environment. The problem is then formulated in the context of Bayesian framework and estimated via a probability hypothesis density filter. Each population system under surveillance is assumed to be homogenous and fixed, with daily reports on the number of infected people available for monitoring and prediction. When model parameters are partially known, results of numerical studies indicate that the proposed approach can help early prediction of the epidemic in terms of peak and duration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.