World country dataset from: John Hopkins University Center for System Science and Engineering John Hopkins University dataset, which is updated daily in DATA1 The name of the latest time sereies (since 22/3):
Spanish region dataset. Confirmed cases, hospitalised cases, Health intensive care units cases (UCI), deaths cases and recovered cases by Autonomous Community of Spain available at Situation of COVID-19 in Spain from Instituto de Salud Carlos III. Data updated daily in DATA2.
Spanish region dataset. Confirmed cases, hospitalised cases, Health intensive care units cases (UCI), deaths cases and recovered cases by regions of Italy available at COVID-19 Italia - Monitoraggio situazioneDipartimento della Protezione Civile from Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile. Data updated daily in DATA3.
Related with the idea of “flattening the curve”, we consider the curve (\(r_{1}^{(j)}(t)\)) that captures how growth rate changes over time. Besides, we smooth this signal to avoid the effect of sudden changes in notification (such as the weekend effect).
Objective: Predict the growth rate at horizon \(k\) using the past during the last 15 days of growth rate H\(_1\):
\[R_{1}(0)=\{r_1^{(j)}(-14),\ldots,r_1^{(j)}(0)\}\]
Filtering:
Fit the model. Three functional models of the general regression are constructed: \(r_{k}^{(j)}(0) = f(R_{1}(0)) + \epsilon\), where the difference lies in the form of the \(f\):
Predictions:
This work has been supported by Project MTM2016-76969-P from Ministerio de Economía y Competitividad - Agencia Estatal de Investigación and European Regional Development Fund (ERDF) and IAP network StUDyS from Belgian Science Policy.
Thanks to Diego Campanario for creating the Shiny server.