Spatial Big Data Analytics of Influenza Epidemic in Vellore, India.

Daphne Lopez, M Gunasekaran, B Senthil Murugan, Harpreet Kaur, Kaja M Abbas
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Abstract

The study objective is to develop a big spatial data model to predict the epidemiological impact of influenza in Vellore, India. Large repositories of geospatial and health data provide vital statistics on surveillance and epidemiological metrics, and valuable insight into the spatiotemporal determinants of disease and health. The integration of these big data sources and analytics to assess risk factors and geospatial vulnerability can assist to develop effective prevention and control strategies for influenza epidemics and optimize allocation of limited public health resources. We used the spatial epidemiology data of the HIN1 epidemic collected at the National Informatics Center during 2009-2010 in Vellore. We developed an ecological niche model based on geographically weighted regression for predicting influenza epidemics in Vellore, India during 2013-2014. Data on rainfall, temperature, wind speed, humidity and population are included in the geographically weighted regression analysis. We inferred positive correlations for H1N1 influenza prevalence with rainfall and wind speed, and negative correlations for H1N1 influenza prevalence with temperature and humidity. We evaluated the results of the geographically weighted regression model in predicting the spatial distribution of the influenza epidemic during 2013-2014.

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印度Vellore流感流行的空间大数据分析。
该研究的目标是开发一个大的空间数据模型来预测流感在印度Vellore的流行病学影响。庞大的地理空间和卫生数据库提供了关于监测和流行病学指标的重要统计数据,以及对疾病和健康的时空决定因素的宝贵见解。将这些大数据源和分析整合起来,以评估风险因素和地理空间脆弱性,有助于制定有效的流感疫情防控战略,优化有限的公共卫生资源配置。利用2009-2010年国家信息学中心收集的甲型h1n1流感流行病学空间流行病学数据。我们开发了一个基于地理加权回归的生态位模型,用于预测2013-2014年印度Vellore的流感流行。降雨量、气温、风速、湿度和人口数据均纳入地理加权回归分析。我们推断H1N1流感流行与降雨量和风速呈正相关,与温度和湿度呈负相关。对地理加权回归模型预测2013-2014年流感流行空间分布的结果进行了评价。
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