Modeling and Analysis in Support of Organizational Decisions During the COVID-19 Pandemic
AbstractThe 2019 coronavirus disease (COVID-19) disrupted economic and social systems on an unprecedented scale. Organizational leaders faced unstructured problems that required novel analysis and evidenced-based decision-making approaches. This paper explains several analytical tools and problem-solving methodologies used at the United States Military Academy at West Point to support decision-making related to operational activities and future planning. While many of the tools apply basic analytical methods, the novelty of this paper lies in the unique application of the tools, visual presentation of data analytics, and the explanation of the contextual circumstances that prompted the development of these tools.
Adams, D. (2020). Modeling the pandemic: the simulations driving the world’s response to COVID-19. Nature, 580, 316-318.
Bates, A., Z. Bell, A. Mountford, and P. Evangelista (2015). Military resource allocation as a set covering problem. Industrial and Systems Engineering Review, 3(1), 1-6.
Bernard, H. R., P. D. Killworth, E. C. Johnsen, G. A. Shelley, & C. McCarty (2001). Estimating the ripple effect of a disaster. Connections, 24(2), 18-22.
Centers for Disease Control and Prevention (CDC) (2021, January 5th). Table 1 - Deaths involving coronavirus disease 2019 (COVID-19), pneumonia, and influenza reported to NCHS by sex and age group - United States - Week ending 2/1/2020 to 12/26/2020, Retrieved January 5th, 2021, from from cdc.gov/nchs/nvss/vsrr/covid19/index.htm.
Gu, Youyang (2020, October 1st). COVID-19 Projections Using Machine Learning. Retrieved October 1st, 2020, from covid19-projections.com
Harrell, C., B.K. Ghosh, and R.O. Bowden (2012). Simulation Using ProModel, 3rd ed. New York: McGraw Hill.
Hijmans, Robert J. (2020). raster: Geographic Data Analysis and Modeling. R package version 3.1-5. https://CRAN.R-project.org/package=raster
IHME COVID-19 Forecasting Team (2020). Modeling COVID-19 scenarios for the United States. Nature Medicine, 23. -https://doi.org/10.1038/s41591-020-1132-9.
Los Alamos National Laboratory (2020, September 1st). LANL COVID-19 Cases and Deaths Forecasts. Retrieved September 1st, 2020, from https://covid-19.bsvgateway.org/.
Matthews, T. J., and B. E. Hamilton (2002, December 11th). Mean Age of Mother, 1970–2000. National Vital Statistics Report, 51(1), 1-13.
McCormick, T. H., M. J. Salganik, & T. Zheng (2010). How many people do you know?: Efficiently estimating personal network size. Journal of the American Statistical Association, 105(489), 59-70. https://doi.org/10.1198/jasa.2009.ap08518
Meyerowitz-Katz, G. and L. Merone (2020). A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates. International Journal of Infectious Diseases 101, 138-148. https://doi.org/10.1016/j.ijid.2020.09.1464.
Moody, J. (2005). Fighting a hydra: A note on the network embeddedness of the war on terror. Structure and Dynamics, 1(2).
Nishiura, H. and G. Chowell (2009). The Effective Reproduction Number as a Prelude to Statistical Estimation of Time-Dependent Epidemic Trends. In G. Chowell et al. (Eds.), Mathematical and Statistical Estimation Approaches, 103-121. Dordrecht: Springer.
Russell, T.W., Golding, N., Hellewell, J. et al. (2020). Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections. BMC Med, 18(332). https://doi.org/10.1186/s12916-020-01790-9.
USAFacts (2021, January 5th). US Coronavirus Cases & Deaths by State. Retrieved January 5th, 2021, from usafacts.org/visualizations/coronavirus-covid-19-spread-map/.
Wickham, Hadley. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
The copyediting stage is intended to improve the flow, clarity, grammar, wording, and formatting of the article. It represents the last chance for the author to make any substantial changes to the text because the next stage is restricted to typos and formatting corrections. The file to be copyedited is in Word or .rtf format and therefore can easily be edited as a word processing document. The set of instructions displayed here proposes two approaches to copyediting. One is based on Microsoft Word's Track Changes feature and requires that the copy editor, editor, and author have access to this program. A second system, which is software independent, has been borrowed, with permission, from the Harvard Educational Review. The journal editor is in a position to modify these instructions, so suggestions can be made to improve the process for this journal.