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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

https://doi.org/10.1080/2330443X.2020.1859030

Mohler, George; Short, Martin B.; Schoenberg, Frederic; Sledge, Daniel (January 2021, Statistics and Public Policy)
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The challenges of modeling and forecasting the spread of COVID-19

https://doi.org/10.1073/pnas.2006520117

Bertozzi, Andrea L.; Franco, Elisa; Mohler, George; Short, Martin B.; Sledge, Daniel (January 2020, Proceedings of the National Academy of Sciences)

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.
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From Disaster Response to Community Recovery: Nongovernmental Entities, Government, and Public Health

https://doi.org/10.2105/AJPH.2018.304895

Sledge, Daniel; Thomas, Herschel F. (March 2019, American Journal of Public Health)

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Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis

https://doi.org/10.1016/j.jcrimjus.2020.101692

Mohler, George; Bertozzi, Andrea L.; Carter, Jeremy; Short, Martin B.; Sledge, Daniel; Tita, George E.; Uchida, Craig D.; Brantingham, P. Jeffrey (May 2020, Journal of Criminal Justice)

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