Real-time predictive modeling for public health departments to control infectious diseases

项目总结 公共卫生部门越来越多地使用预测建模来指导决策和资源分配，以控制美国的传染病，特别是在新冠肺炎大流行期间。这些新的预测模型有望更好地识别高危人群，以准确部署疫苗接种等干预措施，但关于公共卫生部门如何使用这些模型，以及它们是否转化为减少传染病的政策，证据有限。我寻求解决的主要科学问题是，确定预测模型能否以及在多大程度上可以纳入公共卫生实践，并由公共卫生部门转化为政策，以改善对传染病的控制。通过利用与加州公共卫生部(CDPH)的关键合作和丰富的流行病学数据来源，我将解决一个关键的公共卫生挑战，即如何最佳地分配有限的资源，用于针对百日咳、季节性流感和甲型肝炎的定向疫苗接种。目标是针对最高风险地点和人群的疫苗，以减少爆发和感染的数量。我的假设是，公共卫生部门可以有效地将疫苗接种最佳目标的预测性数学模型纳入其政策决策。我将利用我在预测建模和传染病方面的专业知识，开发开源的预测建模工具，供县公共卫生部门为高危人群分配有针对性的疫苗接种，并研究这些模型在公共卫生使用中的逐步实施。我的远大目标是评估使用预测模型来指导公共卫生部门的疫苗接种决策对公共健康的因果影响。在目标1中，我将开发和验证预测模型，以最佳地针对百日咳、季节性流感和甲型肝炎的高危地点和人群(年龄、人口和风险因素)接种疫苗。该模型将提供各种有针对性的疫苗接种策略的比较有效性和成本，以及针对该县和传染病的总体疫苗推荐。在目标2中，我将应用实施科学的方法来优化公共卫生官员的用户体验，以最大限度地提高可用性、沟通和对基于模型的疫苗建议的接受。在目标3中，我将在加州公共卫生部门实施定向疫苗接种的预测模型，并在一项试点研究中衡量实施结果。这项工作将为未来的CDPH创新试验提供基础，该试验将县公共卫生部门随机化，并评估使用基于模型的最优疫苗分配预测是否可以因果地减少病例和疫情。这项拟议的工作有可能开启新的科学方向，将预测模型转化为公共卫生中的常见做法，然后将其应用于许多传染病。

PROJECT SUMMARY Public health departments increasingly use predictive modeling to guide decisions and resource allocation for the control of infectious diseases in the United States, especially during the COVID-19 pandemic. These novel predictive models offer promise to better identify high-risk populations to precisely deploy interventions such as vaccination, yet there is limited evidence on how these models are used by public health departments and whether they translate into policy that reduces infectious diseases. The major scientific problem I seek to address is to identify whether, and to what degree, predictive models can be incorporated into public health practice and translated into policy by public health departments to improve the control of infectious diseases. By leveraging a key collaboration with the California Department of Public Health (CDPH) and rich epidemiologic data sources, I will address a key public health challenge of how to optimally allocate limited resources for targeted vaccination against pertussis, seasonal influenza, and hepatitis A. The goal is to target vaccines to the highest-risk locations and populations to reduce the number of outbreaks and infections. My hypothesis is that public health departments can effectively incorporate predictive mathematical models on optimal targeting of vaccination into their policy decisions. I will apply my expertise in predictive modeling and infectious diseases to develop open-source, predictive modeling tools for county public health departments to allocate targeted vaccination to the highest-risk populations, and study the step-by-step implementation of these models in public health use. My broad, long-range goal is to evaluate the causal public health impact of using predictive models to guide decisions on vaccination in public health departments. In Aim 1, I will develop and validate predictive models to optimally target vaccines to high-risk locations and populations (age, demographic and risk factor) for pertussis, seasonal influenza, and hepatitis A. The model will provide comparative effectiveness and costs of various targeted vaccination strategies, and an overall vaccine recommendation specific to the county and infectious disease. In Aim 2, I will apply methods from implementation science to optimize the user experience for public health officials to maximize usability, communication, and uptake of model-based vaccine recommendations. In Aim 3, I will implement the predictive models of targeted vaccination in California public health departments and measure implementation outcomes in a pilot study. This work will provide the foundation for a future innovative trial with CDPH that randomizes county public health departments and evaluates whether using model-based predictions on optimal vaccine allocation can causally reduce cases and outbreaks. This proposed work has the potential to unlock new scientific directions of translating predictive models into common practice in public health, which can then be applied across many infectious diseases.