Digital Detection of Infectious Eye Epidemics

 描述(由申请人提供)：原发眼部感染不会定期通知美国疾病控制中心。在美国，传染性眼病流行没有定期报告、跟踪或预测，尽管跟踪会有明显的好处 给眼睛健康社区。我们假设，几种新的数字监测方法可以改善对眼病流行的监测和检测，首先是一种或更常见的眼部感染模型：结膜炎。由腺病毒(流行性角膜结膜炎或EKC)或细菌引起的严重结膜炎在没有预警的情况下发生，并在地点和时间上零星发生。流行病具有很高的传染性，在美国造成了巨大的成本负担，尽管长期视力障碍是不寻常的。这些和其他眼病流行的风险因素可能正在上升，预算削减可能会进一步减少对传统监测报告和预防的资金。因此，需要新的解决方案来监测眼病流行。最近，网络搜索、社交媒体和其他“来源”的数字“大数据”已被用于追踪传染病。在这里，我们建议(Aim1)使用大数据通过一些统计方法识别候选眼病流行，以及(AIM2)使用医疗记录，并开发用于参与式监测的专家数字哨兵网络，以验证任何候选流行病，以及(Aim3)由于AIM2中的健康记录和其他临床报告也不是完美的黄金标准，我们还将 使用隐藏状态模型为每种方法估计检测真实结膜炎流行的敏感度和特异度(以及其他适当的描述符)。

 DESCRIPTION (provided by applicant): Primary ocular infections are not regularly notifiable to the US Center for Disease Control. Infectious ocular disease epidemics are not regularly reported, tracked, or predicted in the US, despite the fact that tracking would be of clear benefit to the eye health community. We hypothesize that several novel digital surveillance approaches can improve monitoring and detection of ocular disease epidemics, starting with one or the more common model eye infection: conjunctivitis. Severe outbreaks of conjunctivitis caused by adenovirus (epidemic keratoconjunctivitis or EKC) or bacteria occur without warning and sporadically in location and time. Epidemics are highly contagious with a significant burden of cost in the US, although long-term vision impairment is unusual. Risk factors for these and other ocular disease epidemics may be on the rise, and budgetary cuts may further reduce funding for traditional surveillance reporting and prevention. Thus new solutions are needed for monitoring ocular disease epidemics. Recently web searches, social media, and other "sourced" digital "big data" has been used to track infectious diseases. Here, we propose (Aim1) to identify candidate ocular disease epidemics using big data through a number of statistical approaches, as well as to (Aim2) use medical records and also develop a digital sentinel network of specialists for participatory surveillance, in order to validate any candidate epidemics, and (Aim3) Since even the health records and other clinical reports in Aim2 are not a perfect gold standard, we will also use a hidden state model to estimate, for each methodology, the sensitivity and specificity (and other appropriate descriptors) of detection of a true conjunctivitis epidemic.