Selected article for: "euclidean distance and weighted sum"
Author: Jean Roch Donsimoni; Rene Glawion; Bodo Plachter; Klaus Waelde
Title: Projecting the Spread of COVID19 for Germany
  • Document date: 2020_3_30
    • ID: neba2o7n_62
    Snippet: The most interesting parameters are those that allow us to match data reported by RKI. To do so, we minimize the Euclidean distance between the reported data and the predicted values of the model. We target a weighted sum of the squared di¤erence between N ever 2 (t) from (9) and observation and the newly-sick N new 2 (t) from (10) and observation. More precisely, parameters a; ; and are obtained from min a; ; ; T t=1 N ever.....
    Document: The most interesting parameters are those that allow us to match data reported by RKI. To do so, we minimize the Euclidean distance between the reported data and the predicted values of the model. We target a weighted sum of the squared di¤erence between N ever 2 (t) from (9) and observation and the newly-sick N new 2 (t) from (10) and observation. More precisely, parameters a; ; and are obtained from min a; ; ; T t=1 N ever

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