During the COVID-19 pandemic, individuals have depended on risk information to make decisions about everyday behaviors and public policy. In this online informational intervention, we assessed whether an interactive website influenced individuals' risk tolerance to support public health goals. We collected data from 10,891 unique users who interacted with the online COVID-19 Event Risk Tool (https://covid19risk.biosci.gatech.edu/), which featured interactive elements (a dynamic risk map, survey questions, and a risk quiz with accuracy feedback). After learning about the risk of COVID-19 exposure, participants reported being less willing to participate in potentially risky events. This increase in risk aversion was most pronounced for large event sizes and for individuals who had underestimated risk. We also uncovered a bias in risk estimation: Participants tended to overestimate the risk of small events, but underestimate the risk of large events. Our results bear implications for risk communication and insights for broader research on risky decision-making.
The localcovid19now R package provides functionality to load, unify and visualize recent COVID-19 case data at subnational scales in order to provide localized situational reports and improve understanding of the scale of local COVID-19 transmission. The package loads data from a variety of data sources and returns the most recent estimate of recorded per capita active COVID-19 infections, the date of the most recent report, and the geometry of each region. These data can then be visualized via mapping documented per capita active infections. We also provide functionality to visualize the risk of exposure to COVID-19, given a particular event size.
The localcovid19now R package provides functionality to load, unify and visualize recent COVID-19 case data at subnational scales in order to provide localized situational reports and improve understanding of the scale of local COVID-19 transmission. The package loads data from a variety of data sources and returns the most recent estimate of recorded per capita active COVID-19 infections, the date of the most recent report, and the geometry of each region. These data can then be visualized via mapping documented per capita active infections. We also provide functionality to visualize the risk of exposure to COVID-19 given a particular event size.
As transit agencies expand, they may outgrow their existing bus storage and service facilities. When selecting a site for an additional facility, an important consideration is the change in bus deadhead time, which affects the agency’s operating costs. Minimizing bus deadhead time is the subject of many studies, though agencies may lack the necessary software or programming skill to implement those methods. This study presents a flexible tool for determination of bus facility location. Using the R dodgr package, it evaluates each candidate site based on a given bus network and existing depots and calculates the network minimum deadhead time for each potential set of facilities. Importantly, the tool could be used by any transit agency, no matter its resources. It runs on open-source software and uses only General Transit Feed Specification (GTFS) and data inputs readily available to transit agencies in the U.S.A., filling the accessibility gap identified in the literature. The tool is demonstrated through a case study with the Metropolitan Atlanta Rapid Transit Authority (MARTA), which is considering a new bus depot as it builds its bus rapid transit network. The case study used current MARTA bus GTFS data, existing depot locations, and vacant properties from Fulton County, Georgia. The tool evaluated 17 candidate sites and found that the winning site would save 29.7 deadhead hours on a typical weekday, which translates to more than $12,000 daily based on operating cost assumptions. The output provides important guidance to transit agencies evaluating sites for a new bus depot.
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