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Current Bioinformatics


ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Review Article

A Review of Epidemic Models Related to Meteorological Factors

Author(s): Yuewu Liu , Yan Peng*, Qingfan Li and Xiangquan Xiong

Volume 13 , Issue 4 , 2018

Page: [360 - 366] Pages: 7

DOI: 10.2174/1574893612666170619083048

Price: $65


Background: An epidemic can spread rapidly among a large number of people in a community within a short period of time. Some infectious diseases, including influenza, hand, foot and mouth disease, dengue and meningitis, are temporally limited by variations in the meteorological factors, such as sunshine, temperature, humidity, rainfall, atmospheric pressure, wind speed and so on. Therefore, it is necessary to predict the behavior of outbreak of these infectious diseases based on meteorological factors.

Objective: Review various epidemic models related to meteorological factors.

Results: We discuss two kinds of epidemic models: deterministic models and stochastic models. The deterministic models include switched SIR model, seasonal SIR model, periodic SEIR system and seasonal SEIQR model. And the stochastic models involve multiple regression models, auto-regressive moving average model, autoregressive distributed lag model, time series Poisson regression models and generalized additive models. Furthermore, we introduce the latest applications of these models, respectively.

Conclusion: In our work, these deterministic models and stochastic models can successfully predict the diseases outbreak using meteorological factors, and they all are now widely used in the field. However, few meteorological factors are used in these models. With the development of Meteorological Science, large amounts of Meteorological factor data will be obtained. More key Meteorological factors causing an epidemic will be identified. Therefore, in the future, more key meteorological factors will be considered in models and they will further improve the accuracy of the forecast.

Keywords: Infectious disease, weather factor, meteorological factor, deterministic models, stochastic models.

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