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

公共卫生部门控制传染病的实时预测模型

基本信息

批准号：
10878316
负责人：
Nathan Lo
金额：
$ 46.34万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10878316
关键词：
Address Age Age Factors COVID-19 pandemic California Collaborations Communicable Diseases Communication County Data Data Sources Decision Making Demographic Factors Disease Disease Outbreaks Epidemiology Equilibrium Evaluation Evolution Feasibility Studies Foundations Future Goals Hepatitis A Heterogeneity Individual Infection Influenza A virus Intervention Interview Investigation Location Measures Methods Modeling Online Systems Outcome Pertussis Pilot Projects Policies Population Public Health Public Health Practice Public Policy Randomized Recommendation Research Resource Allocation Resources Risk Factors Seasons Standardization Surveys Testing Time Translating United States Vaccination Vaccines Validation Work comparative effectiveness cost design epidemiologic data experience experimental study high risk high risk population implementation measures implementation outcomes implementation science improved infection risk innovation insight mathematical model model development novel open source post-doctoral training predictive modeling predictive tools randomized trial routine practice seasonal influenza surveillance data tool uptake usability vaccination strategy web-based tool

项目摘要

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.

项目摘要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，尤其是在Covid-19-19大流行期间。这些新颖的预测模型可以更好地识别高风险人群来精确部署诸如疫苗接种之类的干预措施，但是关于公共卫生部门如何使用这些模型的证据有限，以及它们是否转化为减少感染性疾病的政策。我寻求解决的主要科学问题是确定是否可以将预测模型纳入公共卫生实践中，并由公共卫生部门转化为政策，以改善对传染病的控制。通过利用与加利福尼亚公共卫生部（CDPH）和丰富的流行病学数据来源的关键合作，我将解决如何最佳地分配有限的资源来针对百日咳，季节性流感和乙型肝炎的主要资源挑战。该目标是将靶向疫苗靶向最高风险的位置和人口销售和销售数量的销售数量，并降低了销售数量的销售数量，并降低了数量的销售。我的假设是，公共卫生部门可以有效地将预测性数学模型纳入其最佳靶向疫苗接种中。我将在预测性建模和传染病中运用我的专业知识，以开发为县公共卫生部门开发的，预测的建模工具，以将有针对性的疫苗接种分配给最高风险的人群，并研究这些模型在公共卫生使用中的逐步实施。我广泛的远程目标是评估使用预测模型指导公共卫生部门疫苗接种决策的因果公共卫生的影响。在AIM 1中，我将开发和验证预测模型，以最佳的疫苗为疫苗针对百日咳，季节性流感和肝炎A的高风险地点和人群（年龄，人口统计学和风险因素）。该模型将提供各种靶向疫苗接种策略的比较有效性和成本，以及对县和县的整体疫苗建议和感染性疾病的建议。在AIM 2中，我将采用实施科学的方法来优化公共卫生官员的用户体验，以最大程度地提高可用性，沟通和基于模型的疫苗建议。在AIM 3中，我将在加利福尼亚公共卫生部门实施针对性疫苗接种的预测模型，并在一项试点研究中衡量实施结果。这项工作将为与CDPH的未来创新试验奠定基础，该试验将县公共卫生部门随机化，并评估对最佳疫苗分配的基于模型的预测是否可以因果减少病例和爆发。这项拟议的工作有可能解锁将预测模型转化为公共卫生中常见实践的新科学方向，然后可以在许多传染病中应用。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Determining Herd Immunity Thresholds for Hepatitis A Virus Transmission to Inform Vaccination Strategies Among People Who Inject Drugs in 16 US States.

确定甲型肝炎病毒传播的群体免疫阈值，为美国 16 个州注射毒品者的疫苗接种策略提供信息。

DOI：
10.1093/cid/ciad552
发表时间：
2024
期刊：
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
影响因子：
0
作者：
Yang,Judy;Lo,NathanC;Dankwa,EmmanuelleA;Donnelly,ChristlA;Gupta,Ribhav;Montgomery,MarthaP;Weng,MarkK;Martin,NatashaK
通讯作者：
Martin,NatashaK

Comparison of model predictions of typhoid conjugate vaccine public health impact and cost-effectiveness.

DOI：
10.1016/j.vaccine.2022.12.032
发表时间：
2023-01-23
期刊：
VACCINE
影响因子：
5.5
作者：
Burrows, Holly;Antillon, Marina;Gauld, Jillian S.;Kim, Jong-Hoon;Mogasale, Vittal;Ryckman, Theresa;Andrews, Jason R.;Lo, Nathan C.;Pitzer, Virginia E.
通讯作者：
Pitzer, Virginia E.

Predicting the public health impact of bivalent vaccines and nirmatrelvir-ritonavir against COVID-19.

预测二价疫苗和 nirmatrelvir-ritonavir 针对 COVID-19 的公共卫生影响。

DOI：
10.1101/2023.05.18.23289533
发表时间：
2023
期刊：
medRxiv : the preprint server for health sciences
影响因子：
0
作者：
Park,HaileyJ;Tan,SophiaT;León,TomásM;Jain,Seema;Schechter,Robert;Lo,NathanC
通讯作者：
Lo,NathanC

Predicting the Public Health Impact of Bivalent Vaccines and Nirmatrelvir-Ritonavir Against Coronavirus Disease 2019.

DOI：
10.1093/ofid/ofad415
发表时间：
2023-09
期刊：
Open forum infectious diseases
影响因子：
4.2
作者：
通讯作者：

Infectiousness of SARS-CoV-2 breakthrough infections and reinfections during the Omicron wave.

DOI：
10.1038/s41591-022-02138-x
发表时间：
2023-02
期刊：
NATURE MEDICINE
影响因子：
82.9
作者：
Tan, Sophia T.;Kwan, Ada T.;Rodriguez-Barraquer, Isabel;Singer, Benjamin J.;Park, Hailey J.;Lewnard, Joseph A.;Sears, David;Lo, Nathan C.
通讯作者：
Lo, Nathan C.