Forecasting methods and models of disease spread

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The number of papers addressing the forecasting of the infectious disease morbidity is rapidly growing due to accumulation of available statistical data. This article surveys the major approaches for the shortterm and the long-term morbidity forecasting. Their limitations and the practical application possibilities are pointed out. The paper presents the conventional time series analysis methods — regression and autoregressive models; machine learning-based approaches — Bayesian networks and artificial neural networks; case-based reasoning; filtration-based techniques. The most known mathematical models of infectious diseases are mentioned: classical equation-based models (deterministic and stochastic), modern simulation models (network and agent-based).

Keywords: morbidity forecasting, point-to-point estimates, regression models, ARIMA, hidden Markov models, method of analogues, exponential smoothing, SIR, Rvachev–Baroyan model, cellular automata, populationbased models, agent-based models
Citation in English: Kondratyev M.A. Forecasting methods and models of disease spread // Computer Research and Modeling, 2013, vol. 5, no. 5, pp. 863-882
Citation in English: Kondratyev M.A. Forecasting methods and models of disease spread // Computer Research and Modeling, 2013, vol. 5, no. 5, pp. 863-882
DOI: 10.20537/2076-7633-2013-5-5-863-882
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