About our COVID-19 Forecast Models

We are not epidemiologists.  We are retail leaders and retail data scientists.  As retailers, we have been struggling to understand the impact of Covid-19 on our businesses and we decided to develop COVID-19 projections as a means of determining when we maybe able to open for business again.  To develop these forecasts we have studied the methodologies being utilized by many of the experts in the epidemiological field. In this regard, the following resources have been instrumental in our model development and we recommend them as additional sources of information: 

- Imperial College
- Health Data
- Johns Hopkins

We are directly, and only, using the data published by Johns Hopkins for purposes of developing our forecasts. 

These forecasts assume that the recent growth rate of daily cases continues.  Fortunately, as we have been building the models we have observed that the daily case growth rates are slowing down in areas where mitigation efforts are strong.  This gives hope that our projections may be closer to worse case.  This being said,you can see that areas with a lower daily growth rate, which is good for the health case system, are pushing out the dates when they are likely to peak in cases and also when it may be safe to go to back to work.  This illustrates the flattening of the curve, but does have the likely effect of prolonging the economic and social impact. 

We hope that these projections are a helpful guide as you plan for when your business may be able to re-open. 

We update these forecasts everyday, as each new day of data becomes available.  Please feel free to share the link to these forecasts with anyone you feel might find it helpful. 

Also, feel free to contact us with any questions or suggestions. 

Stay safe, 

Dr. Mark Chrystal
Jeff Rix


Additional Research Citations:

Plans-Rubio P. The vaccination coverage required to establish herd immunity against influenza viruses. Prev Med. 2012; 55: 72-77. doi: 10.1016/j.ypmed.2012.02.015

Pandemic Influenza Outbreak Research Modelling Team (Pan-InfORM), Fisman D. Modelling an influenza pandemic: A guide for the perplexed. CMAJ. 2009;181(3-4):171–173. doi:10.1503/cmaj.090885

Colizza V, Barrat A, Barthelemy M, Valleron A-J, Vespignani A (2007) Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions. PLoS Med 4(1): e13. https://doi.org/10.1371/journal.pmed.0040013

Chowell G, Sattenspiel L, Bansal S, Viboud C. Mathematical models to characterize early epidemic growth: A review. Phys Life Rev. 2016;18:66–97. doi:10.1016/j.plrev.2016.07.005

Ridenhour B, Kowalik JM, Shay DK. Unraveling R0: considerations for public health applications. Am J Public Health. 2014;104(2):e32–e41. doi:10.2105/AJPH.2013.301704

Have more questions?

contact us here

Stop relying on outdated customer analysis, use cutting-edge technology to quickly identify the actions that will take your business to the next level...