Browsing by Author "Sledge, Daniel"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Analyzing the Impacts of Public Policy on COVID-19 Transmission: A Case Study of the Role of Model and Dataset Selection Using Data from Indiana
    (Taylor & Francis, 2021) Mohler, George; Short, Martin B.; Schoenberg, Frederic; Sledge, Daniel; Computer and Information Science, School of Science
    Dynamic estimation of the reproduction number of COVID-19 is important for assessing the impact of public health measures on virus transmission. State and local decisions about whether to relax or strengthen mitigation measures are being made in part based on whether the reproduction number, Rt , falls below the self-sustaining value of 1. Employing branching point process models and COVID-19 data from Indiana as a case study, we show that estimates of the current value of Rt , and whether it is above or below 1, depend critically on choices about data selection and model specification and estimation. In particular, we find a range of Rt values from 0.47 to 1.20 as we vary the type of estimator and input dataset. We present methods for model comparison and evaluation and then discuss the policy implications of our findings.
  • Thumbnail Image
    Item
    The challenges of modeling and forecasting the spread of COVID-19
    (National Academy of Sciences, 2020-07-02) Bertozzi, Andrea L.; Franco, Elisa; Mohler, George; Short, Martin B.; Sledge, Daniel; Computer and Information Science, School of Science
    The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.
  • Thumbnail Image
    Item
    Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis
    (Elsevier, 2020-05-01) Mohler, George; Bertozzi, Andrea L.; Carter, Jeremy; Short, Martin B.; Sledge, Daniel; Tita, George E.; Uchida, Craig D.; Brantingham, P. Jeffrey; Computer and Information Science, School of Science
    Governments have implemented social distancing measures to address the ongoing COVID-19 pandemic. The measures include instructions that individuals maintain social distance when in public, school closures, limitations on gatherings and business operations, and instructions to remain at home. Social distancing may have an impact on the volume and distribution of crime. Crimes such as residential burglary may decrease as a byproduct of increased guardianship over personal space and property. Crimes such as domestic violence may increase because of extended periods of contact between potential offenders and victims. Understanding the impact of social distancing on crime is critical for ensuring the safety of police and government capacity to deal with the evolving crisis. Understanding how social distancing policies impact crime may also provide insights into whether people are complying with public health measures. Examination of the most recently available data from both Los Angeles, CA, and Indianapolis, IN, shows that social distancing has had a statistically significant impact on a few specific crime types. However, the overall effect is notably less than might be expected given the scale of the disruption to social and economic life.