Al-Mamun, Mohammad A

Asare, Ernest O

English

VCU Scholars Compass

Masthead LogoVirginia Commonwealth UniversityVCU Scholars CompassBiology and Medicine Through MathematicsConference 2019May 15th, 12:00 PMMachine learning methods to forecast temporalpattern of Aedes mosquito species usingmeteorological variables in Ontario, CanadaMohammad A. Al-MamunYale University, mamun19871987@gmail.comErnest O. AsareYale University, ernest.asare@yale.eduFollow this and additional works at: https://scholarscompass.vcu.edu/bammPart of the Epidemiology Commons, Life Sciences Commons, and the Physical Sciences andMathematics CommonsThis Event is brought to you for free and open access by the Dept. of Mathematics and Applied Mathematics at VCU Scholars Compass. It has beenaccepted for inclusion in Biology and Medicine Through Mathematics Conference by an authorized administrator of VCU Scholars Compass. Formore information, please contact libcompass@vcu.edu.https://scholarscompass.vcu.edu/bamm/2019/wed/9Machine learning methods to forecast temporal pattern of Aedes mosquito species using meteorological variables in Ontario, Canada  Mohammad A. Al-Mamun and Ernest O. Asare   Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA  Recent outbreaks of mosquito-borne diseases warrant us to understand better how different mosquito species survive and transmit infectious pathogens in different ecological settings. Aedes (Ae) species such as Ae. aegypti and Ae. albopictus are the principal vectors of dengue, chikungunya, yellow fever, and Zika viruses. Despite this, there is a significant knowledge gap exists on how these species survive, maintain their abundance and transmit pathogens under different climatic conditions. In this study, we developed time series models and machine learning method to predict the Aedes species in different medical units in Ontario, Canada. The observed mosquito trap data have obtained from the Department of Public Health, Ontario, Canada from 2003 to 2016. We developed a time series model- Autoregressive integrated moving average (ARIMA) and ensemble machine learning method- Random Forest (RF) to forecast Aedes abundance. We accounted for two meteorological variables: temperature and rainfall impact on the Aedes abundance in the different medical units. Our results suggest that RF outperforms over ARIMA model in predicting the temporal Aedes abundance. The implication of developing forecasting methods for Aedes species is three folds, first, these models predict how different meteorological variables changes the abundance within a temperate region, second, the forecast of the models will explain the outbreaks of different mosquito-borne diseases in the Ontario region, and third, these models inform targeted mosquito control efforts based on the temporal mosquito abundance prediction.   

Machine learning methods to forecast temporal pattern of Aedes mosquito species using meteorological variables in Ontario, Canada

https://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=1342&amp;context=bamm

Machine learning methods to forecast temporal pattern of Aedes mosquito species using meteorological variables in Ontario, Canada

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