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1. Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus

   https://doi.org/10.3390/land9120519  

   Magliocca, Nicholas R. (December 2020, Land)

   null (Ed.)

   The nexus of food, energy, and water systems (FEWS) has become a salient research topic, as well as a pressing societal and policy challenge. Computational modeling is a key tool in addressing these challenges, and FEWS modeling as a subfield is now established. However, social dimensions of FEWS nexus issues, such as individual or social learning, technology adoption decisions, and adaptive behaviors, remain relatively underdeveloped in FEWS modeling and research. Agent-based models (ABMs) have received increasing usage recently in efforts to better represent and integrate human behavior into FEWS research. A systematic review identified 29 articles in which at least two food, energy, or water sectors were explicitly considered with an ABM and/or ABM-coupled modeling approach. Agent decision-making and behavior ranged from reactive to active, motivated by primarily economic objectives to multi-criteria in nature, and implemented with individual-based to highly aggregated entities. However, a significant proportion of models did not contain agent interactions, or did not base agent decision-making on existing behavioral theories. Model design choices imposed by data limitations, structural requirements for coupling with other simulation models, or spatial and/or temporal scales of application resulted in agent representations lacking explicit decision-making processes or social interactions. In contrast, several methodological innovations were also noted, which were catalyzed by the challenges associated with developing multi-scale, cross-sector models. Several avenues for future research with ABMs in FEWS research are suggested based on these findings. The reviewed ABM applications represent progress, yet many opportunities for more behaviorally rich agent-based modeling in the FEWS context remain. 

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2. Increasing behavioral richness and managing structural uncertainty in social-ecological system agent-based models

   https://doi.org/10.18174/sesmo.18749  

   Magliocca, Nicholas; Pathak, Ruchie; Price, Ashleigh; Tanveer, Hashir; Kumar, Mukesh; Moradkhani, Hamid (February 2024, Socio-Environmental Systems Modelling)

   Responding to the challenges of societal transformation in the face of climate change, efforts to integrate behaviorally rich models of adaptation decision-making into large-scale macroeconomic and Earth system models are growing and agent-based models (ABMs) are an effective tool for doing so. However, behavioral richness in ABMs has been limited to implementations of single decision models for all agents in a simulated population. The main goals of this study were to: 1) implement the ‘building-block processes’ (BBPs) approach for decision model heterogeneity; 2) demonstrate the application of sensitivity and uncertainty analyses to quantify the scope of structural uncertainty produced by alternative decision models under variable price and climate conditions; and 3) apply the Observing System Simulation Experiment (OSSE) approach to validate such a behaviorally rich BBPs model at the level of individual agent decisions. Using an ABM of agricultural producers’ decision-making, we demonstrated that uncertainty in crop and farm management decisions introduced by heterogeneous decision models was equal to and in some instances greater than that due to variable price or precipitation conditions. Unrealistically rapid or stagnant behavioral dynamics were evident in model versions implementing single decision models for all agents. Moreover, interactions among agents with diverse decision models in the same population produced consistently more accurate outcomes and realistic behavioral dynamics. The BBPs framework and accompanying sensitivity and uncertainty analyses demonstrated here offer a path forward for increasing behavioral richness in ABMs, which is key to understanding processes of adaptation central to societal responses to climate change. 

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3. Data-driven mobility models for COVID-19 simulation

   https://doi.org/10.1145/3423455.3430305  

   Pesavento, John; Chen, Andy; Yu, Rayan; Kim, Joon-Seok; Kavak, Hamdi; Anderson, Taylor; Züfle, Andreas (November 2020, Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities (ARIC '20))

   null (Ed.)

   Agent-based models (ABM) play a prominent role in guiding critical decision-making and supporting the development of effective policies for better urban resilience and response to the COVID-19 pandemic. However, many ABMs lack realistic representations of human mobility, a key process that leads to physical interaction and subsequent spread of disease. Therefore, we propose the application of Latent Dirichlet Allocation (LDA), a topic modeling technique, to foot-traffic data to develop a realistic model of human mobility in an ABM that simulates the spread of COVID-19. In our novel approach, LDA treats POIs as "words" and agent home census block groups (CBGs) as "documents" to extract "topics" of POIs that frequently appear together in CBG visits. These topics allow us to simulate agent mobility based on the LDA topic distribution of their home CBG. We compare the LDA based mobility model with competitor approaches including a naive mobility model that assumes visits to POIs are random. We find that the naive mobility model is unable to facilitate the spread of COVID-19 at all. Using the LDA informed mobility model, we simulate the spread of COVID-19 and test the effect of changes to the number of topics, various parameters, and public health interventions. By examining the simulated number of cases over time, we find that the number of topics does indeed impact disease spread dynamics, but only in terms of the outbreak's timing. Further analysis of simulation results is needed to better understand the impact of topics on simulated COVID-19 spread. This study contributes to strengthening human mobility representations in ABMs of disease spread. 

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4. Interpreting County-Level COVID-19 Infections using Transformer and Deep Learning Time Series Models

   https://doi.org/10.1109/ICDH60066.2023.00046  

   Islam, Md Khairul; Liu, Yingzheng; Erkelens, Andrej; Daniello, Nick; Marathe, Aparna; Fox, Judy (July 2023, IEEE)

   Deep Learning for Time-series plays a key role in AI for healthcare. To predict the progress of infectious disease outbreaks and demonstrate clear population-level impact, more granular analyses are urgently needed that control for important and potentially confounding county-level socioeconomic and health factors. We forecast US county-level COVID-19 infections using the Temporal Fusion Transformer (TFT). We focus on heterogeneous time-series deep learning model prediction while interpreting the complex spatiotemporal features learned from the data. The significance of the work is grounded in a real-world COVID-19 infection prediction with highly non-stationary, finely granular, and heterogeneous data. 1) Our model can capture the detailed daily changes of temporal and spatial model behaviors and achieves better prediction performance compared to other time-series models. 2) We analyzed the attention patterns from TFT to interpret the temporal and spatial patterns learned by the model. 3) We collected around 2.5 years of socioeconomic and health features for 3142 US counties, such as observed cases, and a number of static (age distribution and health disparity) and dynamic features (vaccination, disease spread, transmissible cases, and social distancing). Using the proposed framework, we have shown that our model can learn complex interactions. Interpreting different impacts at the county level would be crucial for understanding the infection process that can help effective public health decision-making. 

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5. Interpreting County-Level COVID-19 Infections using Transformer and Deep Learning Time Series Models

   https://doi.org/10.1109/ICDH60066.2023.00046  

   Islam, K; Liu, Y.; Erkelens, A.; Daniello, N.; Marathe, A.; Fox, J. (February 2023, IEEE International Conference on Digital Health (ICDH))

   Deep Learning for Time-series plays a key role in AI for healthcare. To predict the progress of infectious disease outbreaks and demonstrate clear population-level impact, more granular analyses are urgently needed that control for important and potentially confounding county-level socioeconomic and health factors. We forecast US county-level COVID-19 infections using the Temporal Fusion Transformer (TFT). We focus on heterogeneous time-series deep learning model prediction while interpreting the complex spatiotemporal features learned from the data. The significance of the work is grounded in a real-world COVID-19 infection prediction with highly non-stationary, finely granular, and heterogeneous data. 1) Our model can capture the detailed daily changes of temporal and spatial model behaviors and achieves better prediction performance compared to other time-series models. 2) We analyzed the attention patterns from TFT to interpret the temporal and spatial patterns learned by the model. 3) We collected around 2.5 years of socioeconomic and health features for 3142 US counties, such as observed cases, and a number of static (age distribution and health disparity) and dynamic features (vaccination, disease spread, transmissible cases, and social distancing). Using the proposed framework, we have shown that our model can learn complex interactions. Interpreting different impacts at the county level would be crucial for understanding the infection process that can help effective public health decision-making. 

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