# COVID-19 superposition principle death model for the US event

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 In the previous reports, Japan case is modeled. In early of this year, Japan national institute confirmed that Japan had infected serially by two major viruses. Following the information, it was found that the case of Japan can be illustrated with the conjunction of two Gompertz curves derived from the differential equation. US event has been well modeled with a Gompertz curve, however, the difference between the model and reported data becomes non-negligible recently. We can obtain good explanation with the method applied to Japan case.Principal is straightforward one. First step is to divide the death number trend into the first and second part. Applying Gompertz equation to the first trend and obtain a curve F, then subtract F from the raw trend to obtain second infection trend. Applying Gompertz equation to the second trend which is free from the first infection effect, and obtain a curve L. A curve F+L is the intended answer. obtain trend of US data country = "US"; urlCovid19 = "https://pomber.github.io/covid19/timeseries.json"; data \ = Map[Association, Association[Import[urlCovid19]][ country], {1}]; ddata = {DateObject[#["date"]], #["deaths"]} & /@ data; firstday = ddata[[1, 1]]; Last[ddata]  get US basic data entity = "US"; population = Normal[Entity["Country", entity]["Population"]]  get data for the estimation peeled = Map[{QuantityMagnitude[#[[1]] - firstday], #[[2]]} &, ddata]; tidyd = Map[First, Gather[peeled, #1[[2]] == #2[[2]] &]]; ListPlot[tidyd]  get death-number per unit observation period i5 = BlockMap[#[[2]] - #[[1]] &, tidyd, 2, 1]; i6 = Map[(#[[2]]/#[[1]]) &, i5]; i7 = First[Transpose[Drop[i4, 1]]];  get first part, where dividing report day is set to 155 i5 = Select[tidyd, #[[1]] <= 155 &];  set initial guess for the first part start = 70; model0 = n b^Exp[-c ( t - start)]; initialguess = {n -> 120000, c -> 0.03, b -> 0.02}; Show[ Plot[model0 /. initialguess, {t, 0, 1000}, PlotRange -> {{0, 200}, {0, 150000}}], ListPlot[i5] ]  estimate a Gompertz curve fitted ans = FindFit[i5, model0, initialguess /. Rule -> List, t, MaxIterations -> 200]  obtain second part subtracted the effect of first part pdays = First[Transpose[tidyd]]; death = Last[Transpose[tidyd]]; ListPlot[diff = Transpose@{pdays, death - (model0 /. ans /. t -> pdays)}]  set initial guess of subtracted second part start = 145; model1 = n b^Exp[-c ( t - start)]; initialguess = {n -> 40000, c -> 0.05 , b -> 0.03}; Show[ Plot[model1 /. initialguess, {t, 0, 1000}, PlotRange -> {{0, 200}, {0, 40000}}], ListPlot[diff] ]  get Gompertz curve fitted ans3 = FindFit[diff, model1, initialguess /. Rule -> List, t, MaxIterations -> 200] upto = 220; Show[ ListPlot[diff], Plot[model1 /. ans3, {t, 0, upto}, PlotLegends -> Placed[Text[Style["Second invasion", Bold]], {0.32, 0.75}]], PlotRange -> {{0, upto}, {0, 40000}}]  calculate percentage of death number percent = 100 Divide[(model0 /. ans) + (model1 /. ans3) /. ans /. t -> Infinity, population]  Finally obtained curve and reported death number of US fdate = First[ddata][[1]]; ldate = Last[ddata][[1]]; upto = 250; Show[ Plot[(model0 /. ans) + (model1 /. ans3), {t, 0, upto}, PlotRange -> {{0, upto}, {0, 200000}}, PlotStyle -> {Red, Thickness[0.005]}], ListPlot[tidyd, PlotLegends -> Placed[Text[ Style[country <> "\n" <> "Estimated death ratio: " <> ToString[DecimalForm[percent, 3]] <> " %", Bold]], {0.3, 0.75}]], ImageMargins -> 20, Frame -> True, PlotLabel -> DateString[fdate] <> "-" <> DateString[ldate], FrameLabel -> {Style["Days passed from the first report day", 12], Style["Accumulated death", 12]}, LabelStyle -> {Black}]  Output of the code Keep in mind that this model is supposed of the virus generic variation in US.