RAPID: Improving Computational Epidemiology with Higher Fidelity Models of Human Behavior

RAPID：通过更高保真度的人类行为模型改进计算流行病学

• 批准号: 2033390
• 负责人: Peter Pirolli
• 金额: $ 20万
• 依托单位: Florida Institute for Human and Machine Cognition, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033390&HistoricalAwards=false
• 关键词: RAPID; Improving; Computational; Epidemiology; Higher

Forecasts of how the COVID-19 epidemic will progress, in terms of regional rate of infections and deaths, are made by epidemiological models. The projections of these models influence the decisions of public health and other officials, as well as members of the general public. In the absence of a vaccine, it is crucial that epidemiological models accurately predict how the rate of transmission changes in response to non-pharmaceutical interventions such as advisories about social distancing, wearing masks, washing hands, etc. This requires accurate and precise modeling of how people respond both psychologically and behaviorally to this guidance. People in different regions and subgroups may have very different individual mindsets and capabilities so that they respond differently to different guidance, which may change over time, e.g., “shelter-in-place fatigue”. Current epidemiological models are do not incorporate scientifically established computational models of human psychology and behavior change. This project is about developing agents that represent an individual, and populations of agents simulating the human population of a given area to be part of a new kind of epidemiological model for forecasting Covid-19 cases. Individual agents will be built upon prior models of decision-making and behavior-change. This will model relevant individual-level responses and resulting population dynamics for a select set of US regions. Online media and datasets will be used to seed populations of agents to model populations of the selected US regions. New algorithms for cognitive content mining of attitudes, beliefs, intentions, and preferences for a regional population will be developed and validated quantitatively against observed behavior and epidemiological data in a set of US state-level data (four states and their sub-regions) using a mix of statistical modeling and agent-based modeling. Improvements in regional forecasting of Covid-19 incidence rates, estimated transmission rates in response to community guidance, and behavior compliance using cell-phone mobility and non-essential visit data to measure effectiveness of the newly designed agents and enhance the design of messages to contain COVID-19.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

根据区域感染率和死亡率，通过流行病学模型预测COVID-19疫情将如何发展。这些模型的预测影响公共卫生和其他官员以及普通公众的决策。在没有疫苗的情况下，至关重要的是，流行病学模型必须准确预测传播速度如何响应非药物干预措施，如关于保持社交距离、戴口罩、洗手等的建议。这需要对人们如何在心理和行为上对这种指导作出反应进行准确和精确的建模。不同区域和子群体的人可能有非常不同的个人心态和能力，因此他们对不同的指导作出不同的反应，这些指导可能随着时间的推移而改变，例如，“就地避难疲劳”。目前的流行病学模型没有纳入科学建立的人类心理和行为变化的计算模型。该项目旨在开发代表个体的病原体，以及模拟特定地区人口的病原体种群，作为预测Covid-19病例的新型流行病学模型的一部分。个体代理将建立在先前的决策和行为改变模型之上。这将模拟相关的个人层面的反应和由此产生的人口动态为一组选定的美国地区。在线媒体和数据集将用于播种代理种群，以模拟选定美国地区的种群。将开发用于对区域人口的态度、信念、意图和偏好进行认知内容挖掘的新算法，并使用统计建模和基于代理的建模的混合方法，根据一组美国州级数据（四个州及其子区域）中观察到的行为和流行病学数据进行定量验证。改进了对Covid-19发病率的区域预测、根据社区指导估计的传播率，以及利用手机移动性和非必要的访问数据衡量新设计药物的有效性和加强信息设计以遏制Covid-19的行为依从性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Cognitive modeling for computational epidemiology

计算流行病学的认知建模

• 发表时间: 2020
• 期刊: Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SPB-BRIMS 2020
• 作者: Pirolli, P.;Bhatia, A.;Mitsopoulos, K.;Lebiere, C.;Orr, M.
• 通讯作者: Orr, M.

Mining Online Social Media to Drive Psychologically Valid Agent Models of Regional Covid-19 Mask Wearing

挖掘在线社交媒体以推动区域性 Covid-19 口罩佩戴的心理有效代理模型

• 发表时间: 2021
• 期刊: Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SPB-BRIMS 2021
• 作者: Pirolli, P.;Carley, K.M.;Dalton, A.;Dorr, B.J.;Lebiere, C.;Martin, M.K.;Mather, B.;Mitsopoulos, K.;Orr, M.;Strzalkowski, T.
• 通讯作者: Strzalkowski, T.