Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data
Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data
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DOI：
10.1186/s12879-019-3874-x
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发表时间：
2019-03-21
影响因子：
3.7
通讯作者：
Prendinger, Helmut
中科院分区：
文献类型：
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作者：
Jain, Raghvendra;Sontisirikit, Sra;Prendinger, Helmut
背景本研究的目标是创建一个系统，可以使用有关登革热传播因素的可用相关信息；利用它来预测某个地理区域内登革热的发生，以便公共卫生专家能够准备、管理和控制该流行病。我们的研究为我们对健康、疾病和医疗保健系统的理解和管理提供了新的地理空间见解。方法我们提出了一种基于机器学习的方法，能够利用多个数据源的数据提供泰国五十个地区中每个地区登革热预测的预测估计。使用一组预测变量，我们通过预测变量的最佳组合提高了模型的预测准确性，其中包括：气象数据、临床数据、疾病监测的滞后变量、社会经济数据和编码登革热传播空间依赖性的数据。我们使用广义加性模型（GAM），使用2008年至2012年的数据来拟合预测因子（滞后一个月）与登革出血热（DHF）临床数据之间的关系。使用2013年至2015年的数据和一组比较预测模型，我们根据RMSE和SRMSE以及调整的R平方值、偏差来评估拟合模型的预测能力AIC.Results 中的解释和变化该模型允许组合不同的预测变量，以一个月的提前期进行预测，并描述用于表征预测的变量的统计显着性。根据曼谷特定恒定阈值和 WHO 流行病移动阈值，从特异性、敏感性、阳性预测值 (PPV) 和阴性预测值 (NPV) 方面评估最终模型的区分能力。 结论 样本外验证的结果比样本内验证的结果差，但它显示出提前一个月检测疫情的能力。我们还确定，对于预测一个地区内的登革热疫情，周边地区的登革热发病率和社会经济数据的影响具有统计显着性。这验证了人员流动模式和人类活动的空间异质性对疫情传播的影响。
BackgroundThe goal of this research is to create a system that can use the available relevant information about the factors responsible for the spread of dengue and; use it to predict the occurrence of dengue within a geographical region, so that public health experts can prepare for, manage and control the epidemic. Our study presents new geospatial insights into our understanding and management of health, disease and health-care systems.MethodsWe present a machine learning-based methodology capable of providing forecast estimates of dengue prediction in each of the fifty districts of Thailand by leveraging data from multiple data sources. Using a set of prediction variables, we show an increase in prediction accuracy of the model with an optimal combination of predictors which include: meteorological data, clinical data, lag variables of disease surveillance, socioeconomic data and the data encoding spatial dependence on dengue transmission. We use Generalized Additive Models (GAMs) to fit the relationships between the predictors (with a lag of one month) and the clinical data of Dengue hemorrhagic fever (DHF) using the data from 2008 to 2012. Using the data from 2013 to 2015 and a comparative set of prediction models, we evaluate the predictive ability of the fitted models according to RMSE and SRMSE as well as using adjusted R-squared value, deviance explained and change in AIC.ResultsThe model allows for combining different predictors to make forecasts with a lead time of one month and also describe the statistical significance of the variables used to characterize the forecast. The discriminating ability of the final model was evaluated against Bangkok specific constant threshold and WHO moving threshold of the epidemic in terms of specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).ConclusionsThe out-of-sample validation showed poorer results than the in-sample validation, however it demonstrated ability in detecting outbreaks up-to one month ahead. We also determine that for the predicting dengue outbreaks within a district, the influence of dengue incidences and socioeconomic data from the surrounding districts is statistically significant. This validates the influence of movement patterns of people and spatial heterogeneity of human activities on the spread of the epidemic.