@article{vizanko_kadinski_cummings_ostfeld_berglund_2024, title={Modeling prevention behaviors during the COVID-19 pandemic using Bayesian belief networks and protection motivation theory}, volume={3}, ISSN={["1539-6924"]}, url={https://doi.org/10.1111/risa.14287}, DOI={10.1111/risa.14287}, abstractNote={Abstract}, journal={RISK ANALYSIS}, author={Vizanko, Brent and Kadinski, Leonid and Cummings, Christopher and Ostfeld, Avi and Berglund, Emily Zechman}, year={2024}, month={Mar} }
 @article{vizanko_kadinski_ostfeld_berglund_2024, title={Social distancing, water demand changes, and quality of drinking water during the COVID-19 pandemic}, volume={102}, ISSN={["2210-6715"]}, url={https://doi.org/10.1016/j.scs.2024.105210}, DOI={10.1016/j.scs.2024.105210}, abstractNote={The COVID-19 pandemic changed daily routines for people around the globe due to the adoption of social distancing measures, such as working from home and restricted travel. Changes in daily routines created new water demand patterns, and the spatial redistribution of water demands in urban water distribution systems affected water quality. A range of factors can influence individual decisions to social distance, including demographics, risk perceptions, and prior experience with infectious disease. This research develops an agent-based modeling framework to simulate decisions to social distance, the effect of social distancing on water demands, and effects on the performance of water infrastructure and the quality of delivered drinking water. This framework couples a hydraulic model, a COVID-19 transmission model, and Bayesian belief network (BBN) driven decision-making models within an agent-based modeling framework. The model is applied for a virtual city, Micropolis, to explore the effects of social distancing decisions on water age. Results demonstrate an increase in average water age and changes to the expected flow directions in pipes under scenarios of increasing social distancing. Nodes near industrial areas experience higher degradation of water quality. This research provides a new framework to develop and evaluate water infrastructure management strategies during pandemics.}, journal={SUSTAINABLE CITIES AND SOCIETY}, author={Vizanko, Brent and Kadinski, Leonid and Ostfeld, Avi and Berglund, Emily Zechman}, year={2024}, month={Mar} }
 @inproceedings{vizanko_kadinski_ostfeld_berglund_cummings_2022, title={Coupling Agent-based Modeling with Water Distribution System Models to Simulate Social Distancing and Water Infrastructure Performance during COVID-19}, url={https://doi.org/10.4995/WDSA-CCWI2022.2022.14750}, DOI={10.4995/WDSA-CCWI2022.2022.14750}, abstractNote={Beside the immense impacts on public health, the COVID-19 pandemic also disrupted daily routines for people around the globe due to the adoption of social distancing measures, such as working from home and restricted travel in order to minimize viral exposure and transmission. Changes in daily routines created new water demand patterns, and the spatial redistribution of water demands in urban water distribution system networks affects water age, nodal pressures, and energy consumption. A range of factors influence individuals’ social distancing decisions including demographics, risk perceptions, and prior experience with infectious disease. This presentation reports a comprehensive modeling framework to capture decisions to social distance, the effect of social distancing on water demands, and the effects on the performance of water infrastructure. First, new Bayesian Belief Network (BBN) models are developed to simulate social distancing decision-making based on publicly available survey data describing COVID-19 risk perception, social distancing behaviors, and demographics. Data were collected in March and April of 2020 and included over N=6,991 participants from 11 countries in North America, Europe, and Asia. Feature sets are developed from participant characteristics using forward selection and Naïve Bayes classifiers to predict behaviors, including working from home. BBN model output is used within an agent-based modeling (ABM) framework to simulate how individuals interact within a community and dynamically adopt social distancing behaviors based on communication and transmission of infection. Agents represent individuals who transmit COVID-19, communicate with each other, decide to social distance, and exert water demands at residential and non-residential locations. COVID-19 transmission among agents is modelled using a susceptible-exposed-infected-removed (SEIR) model. Finally, the ABM is coupled with a water distribution model to simulate how changes in the location of demands affect water distribution metrics. The model is applied for a virtual city, Micropolis, to explore how varying population characteristics can affect water infrastructure. This research provides a new framework to develop and evaluate water infrastructure management strategies during pandemics. }, author={Vizanko, Brent and Kadinski, Leonid and Ostfeld, Avi and Berglund, Emily and Cummings, Christopher L}, year={2022}, month={Jul} }