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Agent-based models (ABMs) are powerful tools used for better understanding, predicting, and responding to diseases. ABMs are well-suited to represent human health behaviors, a key driver of disease spread. However, many existing ABMs of infectious respiratory disease spread oversimplify or ignore behavioral aspects due to limited data and the variety of behavioral theories available. Therefore, this study aims to develop and implement a data-driven framework for agent decision-making related to health behaviors in geospatial ABMs of infectious disease spread. The agent decision-making framework uses a logistic regression model expressed in the form of odds ratios to calculate the probability of adopting a behavior. The framework is integrated into a geospatial ABM that simulates the spread of COVID-19 and mask usage among the student population at George Mason University in Fall 2021. The framework leverages odds ratios, which can be derived from surveys or open data, and can be modified to incorporate variables identified by behavioral theories. This advancement will offer the public and decision-makers greater insight into disease transmission, accurate predictions on disease outcomes, and preparation for future infectious disease outbreaks. 

This paper proposes a data fusion framework that seeks to investigate joint mobility signals around wildfires in relation to geographic scale of analysis (level of spatial aggregation), as well as spatial and temporal extents (i.e. distance to the event and duration of the observation period). We highlight the usefulness of our framework using intra-urban mobility data from Mapbox and SafeGraph for two wildfires in California: Lake Fire (August-September 2020, Los Angeles County) and Silverado Fire (October-November 2020, Orange County). We identify two distinct patterns of mobility behavior: one associated with the wildfire event and another one - with the routine daily mobility of the nearby urban core. Using the combination of data fusion and tensor decomposition, the framework allows us to capture additional insights from the data, that were otherwise unavailable in raw mobility data. 

Given a set S of spatial feature types, its feature instances, a study area, and a neighbor relationship, the goal is to find pairs such that C is a statistically significant regional-colocation pattern in r_{g}. This problem is important for applications in various domains including ecology, economics, and sociology. The problem is computationally challenging due to the exponential number of regional colocation patterns and candidate regions. Previously, we proposed a miner [Subhankar et. al, 2022] that finds statistically significant regional colocation patterns. However, the numerous simultaneous statistical inferences raise the risk of false discoveries (also known as the multiple comparisons problem) and carry a high computational cost. We propose a novel algorithm, namely, multiple comparisons regional colocation miner (MultComp-RCM) which uses a Bonferroni correction. Theoretical analysis, experimental evaluation, and case study results show that the proposed method reduces both the false discovery rate and computational cost.