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

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 trend regression model with change points. The change points are estimated directly from the data. 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 decrease. Another change was found between March 25th and March 29th, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the epidemic for the age group 80+ resulting in a turning point at the end of March. 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.

Analysis of the COVID-19 pandemic in Bavaria: adjusting for misclassification

BackgroundReported COVID-19 case numbers are key to monitoring pandemic spread and decision-making on policy measures but require careful interpretation as they depend substantially on testing strategy. A high and targeted testing activity is essential for a successful Test-Trace-Isolate strategy. However, it also leads to increased numbers of false-positives and can foster a debate on the actual pandemic state, which can slow down action and acceptance of containment measures. AimWe evaluate the impact of misclassification in COVID-19 diagnostics on reported case numbers and estimated numbers of disease onsets (epidemic curve). MethodsWe developed a statistical adjustment of reported case numbers for erroneous diagnostic results that facilitates a misclassification-adjusted real-time estimation of the epidemic curve based on nowcasting. Under realistic misclassification scenarios, we provide adjusted case numbers for Germany and illustrate misclassification-adjusted nowcasting for Bavarian data. ResultsWe quantify the impact of diagnostic misclassification on time-series of reported case numbers, highlighting the relevance of a specificity smaller than one when test activity changes over time. Adjusting for misclassification, we find that the increase of cases starting in July might have been smaller than indicated by raw case counts, but cannot be fully explained by increasing numbers of false-positives due to increased testing. The effect of misclassification becomes negligible when true incidence is high. ConclusionsAdjusting case numbers for misclassification can improve this important measure on short-term dynamics of the pandemic and should be considered in data-based surveillance. Further limitations of case reporting data exist and have to be considered.

Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection

As several countries gradually release social distancing measures, rapid detection of new localised COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (Automatic Selection of Models and Outlier Detection for Epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterise the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggest ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. We illustrate our method using publicly available data of NHS Pathways reporting potential COVID-19 cases in England at a fine spatial scale, for which we provide a template automated analysis pipeline. ASMODEE is implemented in the free R package trendbreaker.