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.

中文翻译：

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在蚊子种群动态的生态建模中使用遗传编程

预测算法是支持基于载体丰度监测的感染监测计划的强大工具。在本文中，我们探讨了使用遗传编程 (GP) 来构建基于环境和气候变量的蚊子丰度预测模型。我们声称，事实上，这种数据集的异质性和复杂性要求算法能够发现变量之间的复杂关系。出于这个原因，我们使用最先进的机器学习预测算法对 GP 性能进行了基准测试。为了提供一个真正可利用的蚊子丰度模型，我们在 2002 年至 2005 年期间针对蚊子收集训练了 GP 和其他算法，并测试了 2006 年收集的预测能力。结果表明，在研究的方法中，GP在准确率和泛化能力方面表现最好。此外，GP 提供的解决方案的内在特征选择和可读性提供了对该模型的生物学解释的可能性，该模型突出了负责蚊子丰度的已知或新行为。因此，GP 被证明是生态建模领域的一个很有前途的工具，为使用基于向量的 GP 方法（VE-GP）开辟了道路，该方法可能更适合和有益于分析中的问题。