Towards the use of genetic programming in the ecological modelling of mosquito population dynamics
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Towards the use of genetic programming in the ecological modelling of mosquito population dynamics Irene Azzali1 · Leonardo Vanneschi2 · Andrea Mosca3 · Luigi Bertolotti1 · Mario Giacobini1 Received: 5 September 2019 / Revised: 6 December 2019 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Predictive algorithms are powerful tools to support infection surveillance plans based on the monitoring of vector abundance. In this article, we explore the use of genetic programming (GP) to build a predictive model of mosquito abundance based on environmental and climatic variables. We claim, in fact, that the heterogeneity and complexity of this kind of dataset demands algorithms capable of discovering complex relationships among variables. For this reason, we benchmarked GP performance with state of the art machine learning predictive algorithms. In order to provide a real exploitable model of mosquito abundance, we trained GP and the other algorithms on mosquito collections from 2002 to 2005 and we tested the predictive ability in 2006 collections. Results reveal that, among the studied methods, GP has the best performance in terms of accuracy and generalization ability. Moreover, the intrinsic feature selection and readability of the solution provided by GP offer the possibility of a biological interpretation of the model which highlights known or new behaviours responsible for mosquito abundance. GP, therefore, reveals to be a promising tool in the field of ecological modelling, opening the way to the use of a vector based GP approach (VE-GP) which may be more appropriate and beneficial for the problems in analysis. Keywords Ecological modelling · Genetic programming · Machine learning · Regression
1 Introduction West Nile Virus (WNV) is an infectious disease, transmitted to people by the bite of an infected mosquito called the vector of the disease [1]. The virus causes neurological illnesses and death and no human vaccine is available [2]. * Irene Azzali [email protected] Extended author information available on the last page of the article
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Genetic Programming and Evolvable Machines
It is commonly found in Africa, the Middle East, North America and also in Europe. The first outbreak in Italy dates back to 1998, when 14 horses located in Tuscany were confirmed for WNV infection by laboratory analyses [3]. Later, in 2008, besides the largest outbreak, the first human case of WNV neuro-invasive infection in Italy was observed [4]. Since then, a constant circulation of the virus has been highlighted, and a national surveillance plan was established [5]. The plan aims to quantify vector abundance in order to predict the emergence and the amplification of the virus. Therefore, understanding the relationship between vector dynamics and environmental and climatic variables facilitates the adaptation of control or eradication strategies by far. Predictive models of vector spread and abundance are valuable tools to fulfil this goal. A generalized linear mixed
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