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1. An Interactive Dashboard to Study the Impact of Hurricane Florence on Food Bank Operations

   https://doi.org/10.1109/CSCI51800.2020.00219  

   Enyew, Bantwale ; Odubela, Kehinde ; Jiang, Steven ; Davis, Lauren ( December 2020 , 2020 International Conference on Computational Science and Computational Intelligence (CSCI))

   North Carolina is the third most hurricane-prone states in the US. In 2018, Hurricane Florence caused a lot of damages to households in North Carolina. The Food Bank of Central and Eastern North Carolina (FBCENC) serves 34 counties in North Carolina, and 22 of them were affected by Hurricane Florence. This research aims to investigate the impact of Hurricane Florence on the operations of FBCENC. We developed interactive dashboards to visualize food bank operational data and other relevant data and studied the trends and patterns of food distribution in three key stages: preparedness, response, and recovery. These dashboards enable food bank operations managers to explore and interact with the data with ease to explore the operational data at different stages, at different branch level, and on a different time scale (monthly, weekly, or daily). The impact on the operations of affected service areas vs. not affected areas could be investigated as well. The findings of this research will provide insight into how humanitarian relief agencies can better prepare for, respond to, and recover from the disruptions caused by hurricanes.
2. Proceedings of the 2018 IISE annual conference

   Islam, M. ; Ivy, J. ( May 2018 , Modeling for Efficient Assignment of Multiple Distribution Centers for the Equitable and Effective Distribution of Donated Food)

   Food insecurity affects more than 41 million people annually in the United States. Within the Feeding America network, approximately 200 food banks are working throughout the US to serve people in need with donated food. Satisfying hunger need of food insecure people with limited supply is a challenge for these food banks. A numerical study is performed on data from Food Bank of Central and Eastern North Carolina (FBCENC) to capture the major attributes controlling its food distribution system. FBCENC seeks to distribute donated food equitably so that each service area (county) receives food proportional to its demand while minimizing the undistributed food donations. In addition to seeking equitable and effective food distribution policies, FBCENC wants to identify distribution branches to maximize the accessibility of the counties to donated food. An assignment and distribution model is developed to minimize the cost of maintaining a user-specified cap on the maximum inequity in food distribution. A sensitivity analysis between the user-specified maximum inequity cap and effectiveness shows the effectiveness of donated food distribution can be improved significantly by sacrificing equitable distribution slightly.
3. Demonstrating a systems approach for integrating disparate data streams to inform decisions on children’s environmental health

   https://doi.org/10.1186/s12889-022-12682-3  

   Hubal, Elaine A. Cohen ; DeLuca, Nicole M. ; Mullikin, Ashley ; Slover, Rachel ; Little, John C. ; Reif, David M. ( February 2022 , BMC Public Health)

   The use of systems science methodologies to understand complex environmental and human health relationships is increasing. Requirements for advanced datasets, models, and expertise limit current application of these approaches by many environmental and public health practitioners.

   A conceptual system-of-systems model was applied for children in North Carolina counties that includes example indicators of children’s physical environment (home age, Brownfield sites, Superfund sites), social environment (caregiver’s income, education, insurance), and health (low birthweight, asthma, blood lead levels). The web-based Toxicological Prioritization Index (ToxPi) tool was used to normalize the data, rank the resulting vulnerability index, and visualize impacts from each indicator in a county. Hierarchical clustering was used to sort the 100 North Carolina counties into groups based on similar ToxPi model results. The ToxPi charts for each county were also superimposed over a map of percentage county population under age 5 to visualize spatial distribution of vulnerability clusters across the state.

   Data driven clustering for this systems model suggests 5 groups of counties. One group includes 6 counties with the highest vulnerability scores showing strong influences from all three categories of indicators (social environment, physical environment, and health). A second group contains 15 counties with high vulnerability scores drivenmore »by strong influences from home age in the physical environment and poverty in the social environment. A third group is driven by data on Superfund sites in the physical environment.

   This analysis demonstrated how systems science principles can be used to synthesize holistic insights for decision making using publicly available data and computational tools, focusing on a children’s environmental health example. Where more traditional reductionist approaches can elucidate individual relationships between environmental variables and health, the study of collective, system-wide interactions can enable insights into the factors that contribute to regional vulnerabilities and interventions that better address complex real-world conditions.

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4. Using ABM to Study the Potential of Land Use Change for Mitigation of Food Deserts

   https://doi.org/10.3390/su14159715  

   Gebrehiwot, A.A. ; Hashemi-Beni, L. ; Kurkalova, L.A. ; Liang, C.L. ; Jha, M.K. ( July 2022 , Sustainability)

   Land-use transition is one of the most profound human-induced alterations of the Earth’s system. It can support better land management and decision-making for increasing the yield of food production to fulfill the food needs in a specific area. However, modeling land-use change involves the complexity of human drivers and natural or environmental constraints. This study develops an agent-based model (ABM) for land use transitions using critical indicators that contribute to food deserts. The model’s performance was evaluated using Guilford County, North Carolina, as a case study. The modeling inputs include land covers, climate variability (rainfall and temperature), soil quality, land-use-related policies, and population growth. Studying the interrelationships between these factors can improve the development of effective land-use policies and help responsible agencies and policymakers plan accordingly to improve food security. The agent-based model illustrates how and when individuals or communities could make specific land-cover transitions to fulfill the community’s food needs. The results indicate that the agent-based model could effectively monitor land use and environmental changes to visualize potential risks over time and help the affected communities plan accordingly.
5. Legionella pneumophila occurrence in drinking water supplied by private wells

   https://doi.org/10.1111/lam.13273  

   Mapili, K. ; Pieper, K. J. ; Dai, D. ; Pruden, A. ; Edwards, M. A. ; Tang, M. ; Rhoads, W. J. ( April 2020 , Letters in Applied Microbiology)

   Unregulated private wells are understudied potential sources of community-acquired Legionnaires’ disease. Here we conducted a comprehensive survey of 44 homes supplied by private wells in Wake County, North Carolina, quantifying Legionella spp. DNA, Legionella pneumophila DNA, and total bacterial 16S rRNA genes via real-time polymerase chain reaction in hot and cold drinking water samples, along with culturable L. pneumophila via IDEXX Legiolert in cold drinking water samples. Legionella spp. DNA, L. pneumophila DNA and culturable L. pneumophila were detected in 100, 65·5 and 15·9% of the 44 homes, respectively, and culturable levels were comparable to some municipal surveys applying the same methods. Total coliforms and Escherichia coli were monitored as representative faecal indicators and were found in 20·4 and 0·0% of homes. Within certain sample types, Legionella spp. and L. pneumophila gene copy numbers were positively associated with total bacteria (i.e. total 16S rRNA genes) and water softener use, but were not associated with faecal indicator bacteria, inorganic water parameters or other well characteristics. These findings confirm that occurrence of Legionella and L. pneumophila is highly variable in private wells.

   Legionella is the leading identified cause of waterborne disease outbreaks associated with US municipalmore »water systems. While Legionella is known to occur naturally in groundwater, prior efforts to characterize its occurrence in unregulated private wells are limited to sampling at the wellhead and not in the home plumbing where Legionella can thrive. This work documents much higher levels of Legionella in home plumbing versus water directly from private wells and examines factors associated with higher Legionella occurrence.

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