Software Estimation Lessons Learned from Covid-19 Forecasting
Code Complete author, Steve McConnell, has been an active contributor to the CDC’s Ensemble model, which is the coronavirus forecast of record for the CDC. In addition to Steve’s forecasts, the Ensemble model receives contributions from teams at Johns Hopkins University, MIT, Harvard, USC, IHME, Google, Microsoft, Los Alamos National Labs, and other well-known organizations. A total of more than 40 high profile, prestigious institutions contribute to the model.
The forecast methods used by the teams are, for the most part, public information. Teams have submitted more than 4000 forecast sets, which includes more than 200,000 individual forecasts. This data set of forecast methods, forecasts, and how each forecast actually performed provides an unmatched opportunity to learn lessons about effective and ineffective forecasting techniques.
Steve is not an epidemiologist, but his forecast model, CovidComplete, has consistently been among the top 5 most accurate forecast models, and it is frequently the single best performing model.
Coronavirus forecasting has benefited from lessons learned in software estimation. Join Steve to see just how much the world of software estimation can benefit from lessons learned forecasting the pandemic
Thu 3 JunDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
17:00 - 17:45
Keynote SessionSEH 2021 at SEH Room
Chair(s): Frances Paulisch Siemens Healthineers
|Software Estimation Lessons Learned from Covid-19 Forecasting|
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