Scholarly Works, Population Health Sciences
Permanent URI for this collection
Research articles, presentations, and other scholarship
Browse
Browsing Scholarly Works, Population Health Sciences by Content Type "Conference proceeding"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
- Increasing resiliency of integrated food-energy-water systems to viral pandemics: lessons from COVID-19Calder, Ryan S. D.; Grady, Caitlin; Jeuland, Marc; Kirchhoff, Christine J.; Rodgers, Samuel; Hale, Rebecca L.; Muenich, Rebecca L. (2021-12-15)COVID-19 disrupted labor and capital inputs to interdependent food, energy, and water (FEW) systems. We demonstrate how graphical modeling of phenomena realized during COVID-19 can reveal dynamics of FEW systems during viral pandemics. For example, physical distancing slowed COVID-19 spread but led to economic disruption and may have increased COVID-19 susceptibility by exacerbating FEW insecurities among individuals with many comorbidities. We review predictions of pandemic impacts on FEW systems and identify the mechanisms that explain divergences with respect to observed outcomes during the COVID-19 pandemic. For example, supply-side breakdowns were averted, likely due to low morbidity and mortality among working-age people and net declines in overall energy demand. Modern food systems promote viral emergence, and future pandemics are likely to differ from COVID-19 with respect to one or more key variables such as age-specific mortality or viral infectivity. We use the case study of the poultry supply chain to highlight challenges in understanding how future viral pandemics may jeopardize food security. For example, a lack of publicly available data on staffing levels, working conditions, and product throughputs limits the possibility to simulate supply chain breakdowns as a function of outbreaks in meatpacking plants. Workers provide labor inputs to the food system, while the food system exposes them to risks of illness and death; simultaneously, workers face economic pressures to work while sick and face demand-side FEW insecurities that affect viral susceptibility. Labor inputs to industrial food supply chains hinge on such system dynamics for which there is virtually no quantitative modeling capacity. COVID-19 however provides an opportunity to parameterize and evaluate new models for FEW resiliency. We propose near-term data collection priorities that span classic FEW research, such as characterization of materials throughputs, and include social science methods and perspectives, such as accounting for workers’ behavioral responses to competing health and economic pressures.
- Integrated mechanistic and economic modeling of ecosystem services to inform land-use decisions under uncertaintyCalder, Ryan S. D. (2022-12-15)Management of public lands in the United States is guided by site-specific objectives that can be synergistic or competing and that affect stakeholders unequally. Furthermore, actions available to land managers affect outcomes of interest in ways that are often incompletely understood. For example, in the United States, military bases variously serve as habitat for vulnerable or endangered species, provide flood protection to nearby communities, permit hunting and fishing, and sequester carbon from the atmosphere. Military bases and diverse other types of public lands provide other socially and economically relevant services that depend on certain biophysical conditions. Base managers and other policymakers seek tools to improve their understanding of (1) how alternative land-use practices may affect the range of biophysical outcomes of interest on and off the sites they manage; and (2) the total and relative economic importance of changes to those outcomes.
- Spatial Big Data Analytics of Influenza Epidemic in Vellore, IndiaLopez, Daphne; Gunasekaran, M.; Murugan, B. Senthil; Kaur, Harpreet; Abbas, Kaja M. (IEEE, 2014-01-01)The study objective is to develop a big spatial data model to predict the epidemiological impact of influenza in Vellore, India. Large repositories of geospatial and health data provide vital statistics on surveillance and epidemiological metrics, and valuable insight into the spatiotemporal determinants of disease and health. The integration of these big data sources and analytics to assess risk factors and geospatial vulnerability can assist to develop effective prevention and control strategies for influenza epidemics and optimize allocation of limited public health resources. We used the spatial epidemiology data of the HIN1 epidemic collected at the National Informatics Center during 2009-2010 in Vellore. We developed an ecological niche model based on geographically weighted regression for predicting influenza epidemics in Vellore, India during 2013-2014. Data on rainfall, temperature, wind speed, humidity and population are included in the geographically weighted regression analysis. We inferred positive correlations for H1N1 influenza prevalence with rainfall and wind speed, and negative correlations for H1N1 influenza prevalence with temperature and humidity. We evaluated the results of the geographically weighted regression model in predicting the spatial distribution of the influenza epidemic during 2013-2014.
- Water & Health in Rural China & Appalachia(Virginia Tech, 2019-10-04)The goal of this conference was to connect VT faculty and students with researchers and officials from the Chinese Center for Disease Control and Prevention and UC Berkeley in order to share past/present/planned research relevant to low-income settings in rural Appalachia and China. The conference and attendant working sessions (held before and after) also served as a forum for officially expanding the previously named Berkeley/China-CDC Program for Water & Health to Virginia Tech, as well as a planning platform for new collaborative projects.