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1. Climate change, extreme events, and increased risk of salmonellosis: foodborne diseases active surveillance network (FoodNet), 2004-2014

   https://doi.org/10.1186/s12940-021-00787-y  

   Morgado, Michele E. ; Jiang, Chengsheng ; Zambrana, Jordan ; Upperman, Crystal Romeo ; Mitchell, Clifford ; Boyle, Michelle ; Sapkota, Amy R. ; Sapkota, Amir ( September 2021 , Environmental Health)

   Infections with nontyphoidalSalmonellacause an estimated 19,336 hospitalizations each year in the United States. Sources of infection can vary by state and include animal and plant-based foods, as well as environmental reservoirs. Several studies have recognized the importance of increased ambient temperature and precipitation in the spread and persistence ofSalmonellain soil and food. However, the impact of extreme weather events onSalmonellainfection rates among the most prevalent serovars, has not been fully evaluated across distinct U.S. regions.

   To address this knowledge gap, we obtainedSalmonellacase data forS.Enteriditis,S.Typhimurium,S.Newport, andS.Javiana (2004-2014; n = 32,951) from the Foodborne Diseases Active Surveillance Network (FoodNet), and weather data from the National Climatic Data Center (1960-2014). Extreme heat and precipitation events for the study period (2004-2014) were identified using location and calendar day specific 95^thpercentile thresholds derived using a 30-year baseline (1960-1989). Negative binomial generalized estimating equations were used to evaluate the association between exposure to extreme events and salmonellosis rates.

   We observed that extreme heat exposure was associated with increased rates of infection withS.Newport in Maryland (Incidence Rate Ratio (IRR): 1.07, 95% Confidence Interval (CI): 1.01, 1.14), and Tennessee (IRR: 1.06, 95% CI: 1.04, 1.09), both FoodNet sites with high densities of animal feeding operations (e.g., broilermore »chickens and cattle). Extreme precipitation events were also associated with increased rates ofS.Javiana infections, by 22% in Connecticut (IRR: 1.22, 95% CI: 1.10, 1.35) and by 5% in Georgia (IRR: 1.05, 95% CI: 1.01, 1.08), respectively. In addition, there was an 11% (IRR: 1.11, 95% CI: 1.04-1.18) increased rate ofS. Newport infections in Maryland associated with extreme precipitation events.

   Overall, our study suggests a stronger association between extreme precipitation events, compared to extreme heat, and salmonellosis across multiple U.S. regions. In addition, the rates of infection withSalmonellaserovars that persist in environmental or plant-based reservoirs, such asS.Javiana andS.Newport, appear to be of particular significance regarding increased heat and rainfall events.

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2. Quantifying the economic impact of disasters on businesses using human mobility data: a Bayesian causal inference approach

   https://doi.org/10.1140/epjds/s13688-020-00255-6  

   Yabe, Takahiro ; Zhang, Yunchang ; Ukkusuri, Satish V. ( December 2020 , EPJ Data Science)

   Abstract In recent years, extreme shocks, such as natural disasters, are increasing in both frequency and intensity, causing significant economic loss to many cities around the world. Quantifying the economic cost of local businesses after extreme shocks is important for post-disaster assessment and pre-disaster planning. Conventionally, surveys have been the primary source of data used to quantify damages inflicted on businesses by disasters. However, surveys often suffer from high cost and long time for implementation, spatio-temporal sparsity in observations, and limitations in scalability. Recently, large scale human mobility data (e.g. mobile phone GPS) have been used to observe and analyze human mobility patterns in an unprecedented spatio-temporal granularity and scale. In this work, we use location data collected from mobile phones to estimate and analyze the causal impact of hurricanes on business performance. To quantify the causal impact of the disaster, we use a Bayesian structural time series model to predict the counterfactual performances of affected businesses ( what if the disaster did not occur? ), which may use performances of other businesses outside the disaster areas as covariates. The method is tested to quantify the resilience of 635 businesses across 9 categories in Puerto Rico after Hurricane Maria. Furthermore,more »hierarchical Bayesian models are used to reveal the effect of business characteristics such as location and category on the long-term resilience of businesses. The study presents a novel and more efficient method to quantify business resilience, which could assist policy makers in disaster preparation and relief processes.« less
3. Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

   https://doi.org/10.3390/hydrology8030104  

   Mejia Manrique, Said A. ; Harmsen, Eric W. ; Khanbilvardi, Reza M. ; González, Jorge E. ( September 2021 , Hydrology)

   Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph frommore »the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events.« less
4. Disentangling the impacts of human and environmental change on catchment response during Hurricane Harvey

   https://doi.org/10.1088/1748-9326/ab5234  

   Sebastian, Antonia ; Gori, Avantika ; Blessing, Russell B. ; van der Wiel, Karin ; Bass, Benjamin ( November 2019 , Environmental Research Letters)

   Flooding is a function of hydrologic, climatologic, and land use characteristics. However, the relative contribution of these factors to flood risk over the long-term is uncertain. In response to this knowledge gap, this study quantifies how urbanization and climatological trends influenced flooding in the greater Houston region during Hurricane Harvey. The region—characterized by extreme precipitation events, low topographic relief, and clay-dominated soils—is naturally flood prone, but it is also one of the fastest growing urban areas in the United States. This rapid growth has contributed to increased runoff volumes and rates in areas where anthropogenic climate changes has also been shown to be contributing to extreme precipitation. To disentangle the relative contributions of urban development and climatic changes on flooding during Hurricane Harvey, we simulate catchment response using a spatially-distributed hydrologic model under 1900 and 2017 conditions. This approach provides insight into how timing, volume, and peak discharge in response to Harvey-like events have evolved over more than a century. Results suggest that over the past century, urban development and climate change have had a large impact on peak discharge at stream gauges in the Houston region, where development alone has increased peak discharges by 54% (±28%) and climatemore »change has increased peak discharge by about 20% (±3%). When combined, urban development and climate change nearly doubled peak discharge (84% ±35%) in the Houston area during Harvey compared to a similar event in 1900, suggesting that land use change has magnified the effects of climate change on catchment response. The findings support a precautionary approach to flood risk management that explicitly considers how current land use decisions may impact future conditions under varying climate trends, particularly in low-lying coastal cities.

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5. Community-Connect: COVID-19 Small Business Marketplace with Automated Regulation Matching and Company Lead Retrieval

   https://doi.org/10.1109/IRI51335.2021.00014  

   Coltey, Erik ; Vassigh, Shahin ; Chen, Shu-Ching ( August 2021 , IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI))

   Abstract—Periods of unique economic distress such as the COVID-19 pandemic can be quite difficult for small businesses. Challenges acquiring the supplies necessary to adhere to safety regulations created in the wake of such events can introduce stress on these businesses. This is further exacerbated when supply chains have slowed down, leading to global shortages from most large suppliers. This paper proposes a platform to aid such businesses in procuring COVID-19 related supplies such as Personal Protective Equipment (PPE) from one another, leveraging advanced data acquisition, integration, and Natural Language Processing (NLP) methods. With the pandemic end in sight, the platform described in this paper can be reused for other emergencies such as hurricanes, floods, among others. The proposed platform supports business transactions within a Buyer’s Club (BC), keyword-based sourcing of new businesses to join the platform, and matching products to relevant regulations using greater-than-word length encoding, helping businesses comply with the ever-changing regulatory landscape. Index Terms—COVID-19, Disaster, Natural Language Processing, Data Acquisition, Data Retrieval, User Interfaces