PanCommunity
Leveraging Data and Models for Understanding and Improving Community Response in Pandemics
The goal of this integrative NSF Smart and Connected Communities research effort is to enhance the understanding of the complex relationships characterizing pandemics and interventions under crisis.
The global-scale response to the COVID-19 pandemic triggered drastic measures including economic shutdowns, travel bans, stay-home orders, and even complete lockdowns of entire cities, regions, and countries. The need to effectively produce and deliver PPE, testing and vaccines has affected different communities of stakeholders in different ways, requiring coordination at family/business units, counties/states to federal level entities. This project, therefore, considers communities at local, federal, and international (US and Japan) scales and investigate impact of testing, preventative measures and vaccines, when used in combination, to improve community and inter-agency response at the different scales. The impacts of this research includes technologies to help save lives, restore basic services and community functionality, and establish a platform that supports core capabilities including planning, public information, and warning.
The project organizes an interdisciplinary community, bringing together (a) computer/data scientists, (b) domain and social scientists and policy experts, (c) federal, state, local governments, (d) industry and nonprofits, and (e) educators, to serve as a nexus for major research collaborations that will: overcome key research barriers and explore and catalyze new paradigms and practices in cross-community response to pandemics; enable development and sharing of sustainable and reusable technologies, coupled with extensive broader dissemination activities; act as a resource for public policy guidance on relevant strategies and regulations; and provide education, broadening participation, and workforce development at all levels (K12 to postgraduate) for the next generation of scientists, engineers, and practitioners.
The project involves a close collaboration between Arizona State University in the United States, and Kyoto University in Japan. The project involves interfaces with community partners in Tempe, Arizona and Kyoto, as well as national-level civic organizations in both the U.S. and Japan.
You can also visit the PanCommunity Project @ NSF Smart and Connected Communities Virtual Organization
We are developing
new data and model informed methods, brought together in PanCommunity, to develop testing and vaccination policies, considering coordination, collaboration, and competition across communities at multiple scales.
novel coupled simulation and optimization frameworks that account not only for economical but also social costs in supporting decision making.
Motivation
When making decisions impacting public utility and encouraging and/or enforcing (possibly unpopular) behavioral rules, public administrators need to also rely on data and knowledge supporting their choices, which can be used to better inform those citizens who will be affected by such decisions. Often times, decision makers would need to conduct hypothetical reasoning, to estimate the implications of actions they are considering, or the impacts of new laws (such as enforcing some prohibition, or setting new thresholds in the definition of allowed activities) which they are planning to enact. Yet, the dynamics of complex interconnected systems under pandemic and other disaster/emergency scenarios are extremely difficult and improving the resilience of communities brings forth various challenges that are difficult, or even out-right impossible, with the technologies available today.
The nature of the observational data these systems produce (e.g., transmissibility of the disease, level of spread across spatially and socially distributed communities) collected from diverse sources is highly dynamical and heterogeneous. Also, these data are inherently sparse due to expensive, inconsistent, or infeasible sensing in the real-world, and the impossibility to collect data from scenarios that have not been observed, but are central to decision making. While synthetic data from models potentially address these challenges, methods in model description and assessment, simulation ensemble generation, and high-performance model composition are far from being readily usable. Moreover, to allow contextually-relevant decision making, there is need for participatory systems which allows user and communities to provide their own domain knowledge, and support combining existing data and local community resources and constraints.