Elevated and prolonged exposure to extreme heat is an important cause of excess summertime mortality and morbidity. To protect people from health threats, some governments are currently operating syndromic surveillance systems. However, A lack of resources to support time- and labor- intensive diagnostic and reporting processes make it difficult establishing region-specific surveillance systems. Big data created by social media and web search may improve upon the current syndromic surveillance systems by directly capturing people’s individual and subjective thoughts and feelings during heat waves. This study aims to investigate the relationship between heat-related web searches, social media messages, and heat-related health outcomes. We collected Twitter messages that mentioned “air conditioning (AC)” and “heat” and Google search data that included weather, medical, recreational, and adaptation information from May 7 to November 3, 2014, focusing on the state of Florida, U.S. We separately associated web data against two different sources of health outcomes (emergency department (ED) and hospital admissions) and five disease categories (cardiovascular disease, dehydration, heat-related illness, renal disease, and respiratory disease). Seasonal and subseasonal temporal cycles were controlled using autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) and generalized linear model (GLM). The results show that the number of heat-related illness and dehydration cases exhibited a significant positive relationship with web data. Specifically, heat-related illness cases showed positive associations with messages (heat, AC) and web searches (drink, heat stroke, park, swim, and tired). In addition, terms such as park, pool, swim, and water tended to show a consistent positive relationship with dehydration cases. However, we found inconsistent relationships between renal illness and web data. Web data also did not improve the models for cardiovascular and respiratory illness cases. Our findings suggest web data created by social medias and search engines could improve the current syndromic surveillance systems. In particular, heat-related illness and dehydration cases were positively related with web data. This paper also shows that activity patterns for reducing heat stress are associated with several health outcomes. Based on the results, we believe web data could benefit both regions without the systems and persistently hot and humid climates where excess heat early warning systems may be less effective.

中文翻译：

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使用网络数据改善对热敏感健康结果的监测

长时间长时间暴露在高温下是导致夏季死亡率和发病率增加的重要原因。为了保护人们免受健康威胁，一些政府目前正在运行症状监测系统。但是，由于缺乏资源来支持耗时费力的诊断和报告过程，因此难以建立针对特定地区的监视系统。社交媒体和网络搜索创建的大数据可以通过直接捕捉人们在热浪中的个人和主观想法和感受，来改善当前的症状监测系统。这项研究旨在调查与热相关的网络搜索，社交媒体消息和与热相关的健康结果之间的关系。我们收集了提及“空调（AC）”和“热量”的Twitter消息，以及2014年5月7日至11月3日的Google搜索数据，其中包括天气，医疗，娱乐和适应信息，重点是美国佛罗里达州。针对两种不同的健康结果来源（急诊科（ED）和医院入院）和五种疾病类别（心血管疾病，脱水，与热有关的疾病，肾脏疾病和呼吸道疾病）分别关联的网络数据。使用自回归移动平均广义自回归条件异方差（ARMA-GARCH）和广义线性模型（GLM）控制季节性和亚季节时间周期。结果表明，与热有关的疾病和脱水病例的数量与网络数据呈显着正相关。具体而言，与热相关的疾病病例与消息（热，AC）和网络搜索（饮酒，中暑，公园，游泳和疲倦）呈正相关。此外，公园，游泳池，游泳和水等术语往往与脱水病例呈一致的正相关关系。但是，我们发现肾脏疾病和网络数据之间的关系不一致。网络数据也没有改善心血管和呼吸系统疾病病例的模型。我们的发现表明，社交媒体和搜索引擎创建的Web数据可以改善当前的症状监测系统。特别是与热有关的疾病和脱水病例与网络数据成正相关。本文还表明，减少热应激的活动方式与几种健康结果有关。根据结果​​，