Modeling Health System Infectious Disease Data

描述（由申请人提供）：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暴发对公共卫生非常重要，以便及时实施控制措施。常规收集的自动化卫生服务数据，包括微生物实验室测试、门诊护理和急诊就诊、住院情况、诊断测试和处方药数据，可能对疾病爆发检测非常有用。但是，只要监测下的结果已经存在于一些不需要采取行动的基线水平上，就需要数学、计算和统计模型来执行这种系统。例如，如果有适当的信号检测方法，通过识别数量异常的咳嗽和发烧求医的病人，可能会加快对炭疽生物恐怖主义袭击的识别。

DESCRIPTION (provided by applicant): 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.