team_name: Robert Walraven
model_name: EmpericalSkewedGaussian
model_abbr: RobertWalraven-ESG
model_contributors:
  - name: Robert Walraven
    email: walraven@multiwareinc.com
website_url: http://rwalraven.com/COVID19
license: cc-by-4.0
team_model_designation: primary
methods: Multiple skewed gaussian distribution peaks fit to raw data
team_funding: None (I'm a retired Experimental Physicist working at home)
data_inputs: Existing case and death raw JHU data plus hospitalization data from HealthData.gov.
methods_long: 'The incremental cases and deaths of a single outbreak predicted by
  a simple SEIR model can be approximated closely with a particular skewed gaussian
  distribution that we developed that has four empirical parameters: height, position,
  left growth rate, and right decay rate. The ESG model assumes the JHU data consists
  of independent outbreaks at different times, and fits multiple peaks to the data,
  keeping the number of peaks to a minimum in order to fit with high accuracy the
  data after Loess smoothing.  The most recent peaks are then used to generate a daily
  forecast forward three months.  Hospitalization data and the pattern of older peaks
  is used to visually confirm the forecasts look correct and if not, manual adjustments
  are made to the forecast. The ESG model is a emperical mathematical model that makes
  no epidemiological assumptions and has no epidemiological parameters, so can serve
  as a baseline forecast against which to compare SEIR-based models.'