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Predictive Modeling of Seasonal Mosquito Population Patterns with Neural Networks
Petros Damos * 1, 2 , José Tuells 1 , Pablo Caballero 1
1  Department of Community Nursing, Preventive Medicine and Public Health and History of Science, Faculty of Health Science, University of Alicante, ap. De correos, 99, 03080, Alicante, Spain
2  Department of Pharmacy, University General Hospital of Thessaloniki, AHEPA, Aristotle University of Thessaloniki, Kiriakidi 1, Thessaloniki 546 21, Greece
Academic Editor: Andre Wilke

10.3390/IECE-10500 (registering DOI)

Mosquito species are considered important vectors of many diseases in humans, companion animals, and livestock. There is a great need to understand their dynamics and to develop methods for predicting their abundances. However, the population dynamics of mosquitoes are often complex displaying non-linear dynamics and thus, making it difficult to be modeled using linear statistical approaches. In this project, we explored the seasonal population patterns of mosquito populations in a Mediterranean environment in Northern Greece using straightforward machine learning techniques such as Artificial Neural Networks (ANNs). To train, validate and test the network model we have used 2 years weekly counts of adult mosquito data including Culex sp., a major vector of the West Nile virus and related encephalitis diseases. The model training was performed in an open-loop (i.e., parallel series network architecture), including the validation and testing step and later on, after training, it was transformed to a closed-loop for the needs of a multistep-ahead mosquito abundance prediction. Determined by the autocorrelation function, one of the final models is using as inputs one week lagged values of mosquito abundances and was able to capture the adult seasonal mosquito patterns in most cases at acceptable levels. We conclude that ANNs suggest an important candidate for modeling and predicting the seasonal abundance of mosquito data since it is suitable for modeling noisy and incomplete ecological data, with no specific assumptions to be made about the underlying relationships and which are solely determined through data mining. However, we are also looking forward to improving the particular model performance using new data sets since it is of fundamental importance to choose an appropriate training set size and to provide representative coverage of all possible conditions to capture accurately the patterns of ecological time series. Nevertheless, despite the limitations of the current study, this work contributes to knowledge of the seasonal functioning of arthropod vector dynamics and contributes towards the development of decision tools to be used in the preventive management of the transmission cycle of vector-borne diseases.

Keywords: Culex sp.; prediction model; ecological time series; public health