Short-range Early Phase COVID-19 Forecasting R-Project and Data

Published: 13 October 2023| Version 3 | DOI: 10.17632/cytrb8p42g.3
Christopher Lynch,


This R-Project and its data files are provided in support of ongoing research efforts for forecasting COVID-19 cumulative case growth at varied geographic levels. All code and data files are provided to facilitate reproducibility of current research findings. Seven forecasting methods are evaluated with respect to their effectiveness at forecasting one-, three-, and seven-day cumulative COVID-19 cases, including: (1) a Naïve approach; (2) Holt-Winters exponential smoothing; (3) growth rate; (4) moving average (MA); (5) autoregressive (AR); (6) autoregressive moving average (ARMA); and (7) autoregressive integrated moving average (ARIMA). This package is developed to be directly opened and run in RStudio through the provided RProject file. Code developed using R version 3.6.3. This software generates the findings of the article entitled "Short-range forecasting of coronavirus disease 2019 (COVID-19) during early onset at county, health district, and state geographic levels: Comparative forecasting approach using seven forecasting methods" using cumulative case counts reported by The New York Times up to April 22, 2020. This package provides two avenues for reproducing results: 1) Regenerate the forecasts from scratch using the provided code and data files and then run the analyses; or 2) Load the saved forecast data and run the analyses on the existing data Two related publications listed in "Related Links". License info can be viewed in "License Info.txt". The "RProject" folder contains the RProject file which opens the project in RStudio with the desired working directory set. README files in each sub-folder provide additional detail on each folders' contents. Copyright (c) 2020 Christopher J. Lynch and Ross Gore Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Except as contained in this notice, the name(s) of the above copyright holders shall not be used in advertising or otherwise to promote the sale, use, or other dealings in this Software without prior written authorization.


Steps to reproduce

1) Using RStudio (created using RStudio version 1.2.5033 and R version 3.6.3) , open the provided RProject file entitled "short-range-early-onset-covid-19-forecasting". This will set your workspace to the location of your folder. All needed data files are included within this package and the file paths are relative to the location of the RProject location. No changes (e.g. path name updates) are required to get started. 2) The file entitled "forecasting_article_code.R" is the primary file within the project. This file has been heavily annotated to provide a walk-through orientation on what is being forecast, how the forecasts are being generated, and all of the analysis steps. 3) The code used to create the figures that are contained within the article is the same as provided in the code. Therefore, the code produces the same figures and this allows for reproducibility of the study results. 4) Forecast creation is time-consuming and resource-intensive dependent upon device. Therefore, copies of the files created by the code are also included within the package. These files can be loaded into the project (thus removing the need to generate the forecasts) and used to run through the analysis code. The code annotations provide the leadership needed to achieve this within the code. 5) Readme files are included within each folder for further guidance.


Old Dominion University


Forecasting, Coronavirus, Autoregressive Integrated Moving Average Model, Autoregressive Moving Average Model, COVID-19