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  1. Joshua Gilman
  2. Bryant Grant
  3. Megan Seeley
  4. Xin Wang

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Description: Mollalo et al. (2020) investigated county-level variations of COVID-19 incidence across the continental United States using spatial lag and spatial error models to investigate spatial dependence as well as geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The original analyses are retrospective and use observational data collected from federal and other public sources. Although not publicly available, we were able to obtain the original data based on the authors's description. However, the analysis code was not made available. At the outset of a larger project on reproducibility and replicability in the human-environment and geographic sciences, we chose this study to reproduce on account of its application of spatial statistical methods common in spatial epidemiology and its compatibility with graduate student training in spatial statistics (e.g., spatial regression and pattern analysis) as well as its data and code availability. As a reproduction study, we aimed to independently generate identical results from the original publication.

License: CC-By Attribution 4.0 International


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