Analysis of the early Covid-19 epidemic curve in Germany by regression models with change points

Abstract

We analyze the Covid-19 epidemic curve from March to end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analyzed by a Poisson trend regression model with change points. The change points are estimated directly from the data without further assumptions. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between March 9th and 13th for the time series of infections: from a strong increase to a stagnation or a slight decrease. Another change was found between March 24th and March 31st, where the decline intensified. These two major changes can be related to different governmental measures. On March, 11th, Chancellor Merkel appealed for social distancing in a press conference with the Robert Koch Institute (RKI) and a ban on major events with more than 1000 visitors (March 10th) was issued. The other change point at the end of March could be related to the shutdown in Germany. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases

COVID-19: Navigieren im Unbekannten

“…Rechnet man dann noch die Zeiten bis zur Durchführung eines Abstriches für eine laboranalytische Untersuchung, die Laborlaufzeiten und die Zeiten der Meldewege hinzu, sind bereits 10 Tage vergangen, wenn die Meldung in den Landes-und Bundesstatistiken erscheint, dort bewertet und zur Grundlage von Maßnahmen genommen wird. Und analog werden sich die Effekte von rasch eingeleiteten Gegenmaßnahmen wiederum erst 10 Tage später erstmals an den Meldefällen erkennen lassen [1].…”