Modeling Health System Infectious Disease Data

早期发现-由新出现的病原体引起的生物恐怖主义和传染病爆发， 这对公众健康十分重要，以便迅速实施控制措施。收集的数据， 自动化医疗服务数据，包括微生物学实验室检测、流动护理和急诊 部门访问，住院，诊断测试和处方药数据可能非常重要。 用于疾病爆发检测。然而，数学，计算和统计模型是 只要监测结果已经存在，就需要实施这种系统。 不需要采取行动的基线水平。例如，如果适当的信号检测方法是 现有的，确定炭疽生物恐怖主义袭击可能会加快通过承认一个 因咳嗽和发烧而寻求治疗的病人数量异常。 在这个项目中，我们将开发用于早期发现传染病爆发和 在发现疫情后进行监测。这包括（i）描述自然时间的模型 利用感兴趣的保健服务的人数的地域差异，以便调整 对于例如季节和星期几效应，以及（ii）不同的时空畸变检测模型， 在爆发时发出信号。这些模型将应用于不同的地理位置， 从单个医院的单个病房到整个国家，以及不同的数据特异性 从发烧等非常普遍的症状到特定的微生物疾病菌株和抗菌素耐药性， 从一个细菌物种迁移到另一个细菌物种。我们将开发和测试新方法， 两个健康计划（马萨诸塞州的哈佛朝圣者医疗保健和北方的凯撒医疗保健）中的模式 加州），覆盖超过400万人，一个大型的美国转诊医院（布里格姆和妇女）， 全州（马萨诸塞州）MRSA登记处，以及由55家医院组成的国家（阿根廷）联盟， 监测抗生素耐药性。模型和方法将使用历史数据进行评估， 这些卫生系统和基于不同传染病传播动态的模拟数据 模型

Early detection- of bioterrorism and infectious disease outbreaks caused by emerging pathogens is very important for public health, to allow prompt implementation of control measures. Routinely collected, automated health services data, including microbiology laboratory tests, ambulatory care and emergency department visits, hospitalizations, diagnostic tests, and prescription drug data could potentially be very useful for disease outbreak detection. However, mathematical, computational and statistical models are needed to implement such systems whenever the outcomes under surveillance already exist at some baseline level that does not require action. For example, if appropriate signal detection methods were available, identification of an anthrax bioterrorism attack might be accelerated through recognition of an unusual number of patients seeking care for cough and fever.; In this project, we will develop models for the early detection of infectious disease outbreaks and for monitoring an outbreak after it has been detected. This includes (i) models describing the natural temporal and geographical variation in the number of people utilizing the health services of interest, in order to adjust for e.g. seasonal and day-of-week effects and (ii) different space-time aberration detection models that will generate a signal when an outbreak have occurred. These models will be applied at different geographical scales, from individual wards of a single hospital to a whole country, as well as for different data specificity from very general symptoms such as fever to specific microbial disease strains and antimicrobial resistance profiles that migrate from one bacterial species to another. We will develop and test our new methods and models in two health plans (Harvard Pilgrim Health Care in Massachusetts, and Kaiser Permanente Northern California) that cover over 4 million people, a single large US referral hospital (Brigham and Women's), a statewide (Massachusetts) registry of MRSA, and a national (Argentine) consortium of 55 hospitals that monitors antibiotic resistance. The models and methods will be evaluated using both historical data from these health systems and simulated data based on different infectious disease transmission dynamics models.