Spatiotemporal forecasting for dengue, chikungunya fever and Zika using machine learning and artificial expert committees based on meta-heuristics
Spatiotemporal forecasting for dengue, chikungunya fever and Zika using machine learning and artificial expert committees based on meta-heuristics
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DOI：
10.1007/s42600-022-00202-6
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发表时间：
2022-02
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通讯作者：
Cecilia Cordeiro da Silva;Clarisse Lins de Lima;Ana Clara Gomes da Silva;G. Moreno;A. Musah;A. Aldosery;L. Dutra;T. Ambrizzi;I. V. G. Borges;M. Tunali;S. Basibuyuk;O. Yenigün;T. Massoni;Kate Jones;Luiza Campos;P. Kostkova;A. G. da Silva Filho;W. P. dos Santos
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作者：
Cecilia Cordeiro da Silva;Clarisse Lins de Lima;Ana Clara Gomes da Silva;G. Moreno;A. Musah;A. Aldosery;L. Dutra;T. Ambrizzi;I. V. G. Borges;M. Tunali;S. Basibuyuk;O. Yenigün;T. Massoni;Kate Jones;Luiza Campos;P. Kostkova;A. G. da Silva Filho;W. P. dos Santos
目的登革热被认为是近几十年来最大的公共卫生问题之一。气候和人口的变化、城市的无序发展和国际贸易带来了基孔肯雅热和寨卡病毒等新的虫媒病毒。虫媒病毒的控制依赖于媒介埃及伊蚊的控制。目的在这项工作中，我们提出了一种基于机器学习的疾病预测方法，该方法能够预测感染病例和感染地点。我们还提出了一个基于元启发式方法的人工专家委员会来检测最相关的风险因素。方法以巴西累西腓2013-2016年的数据为例，应用该方法对登革热、基孔肯雅热和寨卡病毒进行预测。我们使用虫媒病毒病例数据以及气候和环境信息：风速、温度和降水量。结果10树随机森林回归预测效果最好，皮尔逊相关系数>0.99，RMSE(%)<6%。此外，人工专家委员会能够在每两个月的时间段内提供与预测病例最相关的因素。结论时空预测结果显示了虫媒病毒的演变，指出主要集中在城市绿地较富裕的地区和供水不规范的低收入社区。确定与预测最相关的因素以及病例的空间分布，可有助于规划和执行旨在改善卫生基础设施的公共政策以及规划和控制病媒。
PurposeDengue is considered one of the biggest public health problems in recent decades. Climate and demographic changes, the disorderly growth of cities and international trade have brought new arboviruses such as chikungunya and Zika. Control of arboviruses depends on control of the vector: the Aedes aegypti mosquito.ObjectiveIn this work, we propose a methodology for building disease predictors capable of predicting infected cases and locations based on machine learning. We also propose an artificial experts committee based on meta-heuristic methods to detect the most relevant risk factors. MethodAs a case study, we applied the methodology to forecast dengue, chikungunya and Zika, with data from the City of Recife, Brazil, from 2013 to 2016. We used arboviruses cases data and climatic and environmental information: wind speeds, temperatures and precipitation. ResultsThe best prediction results were obtained with 10-tree Random Forest regression, with Pearson’s correlation above 0.99 and RMSE (%) below 6%. Additionally, the artificial experts committee was able to present the most relevant factors for predicting cases in each two-month period.ConclusionThe spatiotemporal prediction results showed the evolution of arboviruses, pointing out as major focuses on both regions richer in urban green areas and low-income neighborhood with irregular water supply. Determining the most relevant factors for prediction, as well as the spatial distribution of cases, can be useful for the planning and execution of public policies aimed at improving the health infrastructure and planning and controlling the vector.