PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Karthik Sankaran Co-author(s): Bineh Ndefru (bndefru@ucla.edu), Theresa Stewart (theresa@risksciences.ucla.edu), Prof. Ali Mosleh (mosleh@g.ucla.edu), Arjun Earthperson (aarjun@ncsu.edu), Natalie Zawalick (nataliez@ucla.edu)
| Risk-Informed Decision-Making Tool for COVID-19 Community Behavior and Intervention Scenario Assessment |
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| The spread of the COVID-19 pandemic across the world has presented a unique problem to researchers and policymakers alike. In addition to uncertainty around the nature of the virus itself, the impact of rapidly changing policy decisions on the spread of the virus has been difficult to predict. Using an epidemiological SIRD model as a basis, this paper presents a methodology developed to address the wide variety of uncertain factors impacting disease spread, and ultimately to understand how a policy decision may impact the community long term. The model being presented, named the COVID-19 Decision Support (CoviDeS) tool, is an agent-based time simulation model which uses Bayesian networks to determine state changes of each individual. The CoviDeS model has a level of interpretability more extensive than many of the existing models, allowing for insights to be drawn regarding the relationships between various inputs and the transmission of the disease. Test cases will be presented for different scenarios that demonstrate relative differences in transmission resulting from different policy decisions. Further, we will demonstrate the model’s ability to support decisions for a smaller sub-community that is contained in a larger population center (e.g. a university within a city). For example, one might question if a university like UCLA should reopen with the emergence of new variants of the disease, or how media coverage might influence spread, or why masks continue to be mandated even after vaccines have been administered to large portions of the population. This paper details an approach for modelling a complex, dynamic problem such as COVID-19 that allows modelers to answer these and other difficult questions. Results of simulations for the city of Los Angeles are presented to demonstrate the use of the model for parametric analysis that could give insight to real world scenarios of interest. Though improvements can be made in the model’s accuracy relative to real case data, the methods presented offer value for future use either as a predictive tool or as a decision-making tool for COVID-19 or future pandemic scenarios. |