Informatics for zoonotic disease surveillance: combining animal and human data

描述（由申请人提供）： 影响人类的大多数新出现的传染病是人畜共患的;可在动物和人类之间传播的疾病。动物的健康可以是人类动物传染病的一个哨兵。不幸的是，大多数地方和州卫生部门的流行病学家无法自动访问这些数据。利用动物健康数据预测人类患人畜共患病的风险，可以让流行病学家更快地发现公共卫生威胁。早期发现意味着早期干预，可以降低发病率和死亡率。该职业奖将通过以下方式研究这一问题：了解地方和国家行政层面人畜共患病监测的数据和技术需求（目标1），将这些需求应用于开发一个整合动物和人类健康数据的试点“动物-人类”监测系统（目标2），并评估这种新型系统用于人畜共患病监测的潜力（目标3）。完成这三个步骤的过程将建立一个整合动物和人类健康数据的框架。 目标1将通过一个混合模型设计来解决，该模型使用应用人畜共患病监测的定性观察，以及一项电子调查来评估这一过程中所涉及的数据和技术需求。定性部分将包括对康涅狄格州卫生部门和诊断实验室进行人畜共患病监测的个人的观察和访谈。目标2将需要根据目标1所确定的需要，开发一个动物-人畜共患病监测试点系统，其中包括一个经过可用性测试的界面，用于分析人类疾病趋势。最终目标（3）将通过对“动物-人类”系统与“仅人类”（仅包含人类公共卫生数据的系统）进行受试者间比较评价，评估动物-人类人畜共患病监测系统的潜力。这两个系统将由康涅狄格州目前和未来的专业人员（研究生）进行评估，以分析人类不同人畜共患病的趋势。这项工作将为整合动物和人类数据提供一个框架，并展示这种协同作用在监测人畜共患病方面的潜力。这将有望导致地方和州卫生部门建立强大的监测系统。

DESCRIPTION (provided by applicant): The majority of emerging infectious diseases that affect humans are zoonotic; diseases that are transmittable between animals and humans. The health of animals can be a sentinel for zoonotic diseases in humans. Unfortunately, most local and state health department epidemiologists do not have automated access to this data. Using data on animal health to predict risk of zoonotic diseases in humans could allow epidemiologists to detect public health threats sooner. Earlier detection means earlier intervention which could lead to less morbidity and mortality. This career award will study this problem by: gaining an understanding of the data and technology needs for zoonotic disease surveillance at the local and state administrative level (Aim 1), applying these needs to the development of a pilot 'animal-human' surveillance system that integrates health data of animals and humans (Aim 2), and evaluating the potential of this novel system for zoonotic disease surveillance (Aim 3). The completion of this 3-step process will establish a framework for integrating health data of animals and humans. Aim 1 will be addressed through a mixed model design using qualitative observation of applied zoonotic surveillance, and an electronic survey to asses the data and technology needs involved in this process. The qualitative portion will consist of observation and interviews of individuals who practice zoonotic surveillance at health departments and diagnostic laboratories in Connecticut. Aim 2 will entail the development of a pilot animal-human zoonotic surveillance system, based on the identified needs from Aim 1, that contains a usability-tested interface for the analysis of disease trends in humans. The final Aim (3) will serve to asses the potential of an animal-human zoonotic surveillance system by conducting a between subjects comparative evaluation of the 'animal-human' system vs. a 'human-only' (a system containing only human public health data). The two systems will be evaluated by current and future professionals (graduate students) in Connecticut for analyzing trends of different zoonotic diseases in humans. This work will provide a framework for integrating animal and human data and demonstrate the potential of this synergy in surveillance of zoonotic disease. It will hopefully lead to the development of powerful surveillance systems in local and state health departments.