The impact of current and future control measures on the spread of COVID-19 in Germany

Abstract

The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe with about 2.2 million confirmed cases and more than 150,000 deaths as of April 17, 2020 [37]. In view of most recent information on testing activity [32], we present here an update of our initial work [4]. In this work, mathematical models have been developed to study the spread of COVID-19 among the population in Germany and to asses the impact of non-pharmaceutical interventions. Systems of differential equations of SEIR type are extended here to account for undetected infections, as well as for stages of infections and age groups. The models are calibrated on data until April 5, data from April 6 to 14 are used for model validation. We simulate different possible strategies for the mitigation of the current outbreak, slowing down the spread of the virus and thus reducing the peak in daily diagnosed cases, the demand for hospitalization or intensive care units admissions, and eventually the number of fatalities. Our results suggest that a partial (and gradual) lifting of introduced control measures could soon be possible if accompanied by further increased testing activity, strict isolation of detected cases and reduced contact to risk groups.

Face Masks Considerably Reduce Covid-19 Cases in Germany

“…Our approach goes in line with various studies that have already tried to better understand the effect of public health measures on the spread of Covid-19 (Barbarossa et al, 2020, Hartl et al, 2020, Donsimoni et al, 2020, Dehning et al, 2020, Gros et al, 2020, Adamik et al, 2020. However, these earlier studies all take an aggregate approach in the sense that they look at implementation dates for a certain measure and search for subsequent changes in the national incidence.…”

The Signicance of the Detection Ratio for Predictions on the Outcome of an Epidemic - A Message from Mathematical Modelers

“…In order to illustrate how different assumptions on the detection ratio (DR) affect predictions of the epidemic's course, we show here simulation results for a few scenarios under the assumptions of high detection ratio (DR ≈ 40%), medium detection ratio (DR ≈ 10%, and low detection ratio (DR ≈ 2.5%) each. The model used for simulation is an extended version of the classical susceptible -exposedinfectious -recovered (SEIR) system, with three age groups and different compartments of infectiuous individuals (based on our previous work [3]). Case and death counts reported in Germany by the Robert Koch Institute (RKI) [22] as of April 24, 2020 were used for model calibration.…”

“…In the attempt to predict the course of the outbreak and to possibly achieve its mitigation, mathematical models have been devised to predict the course and possible outcomes for different countries have been presented [10,19,3,24,13]. It was noted by several authors [28,5,15] that an important parameter of the epidemic is the detection ratio, meaning the percentage of infections that are actually discovered.…”

“…We have previously proposed mathematical models for the dynamics of COVID-19 infections in Germany, in particular taking into account the effect of current and possible non-pharmaceutical control measures [2,3]. For the simulated scenarios in our most recent work [3], we assumed detection ratios closer to the upper end of the range detailed above and remarked that the predicted fatality numbers should be expected to look very different when a lower detection ratio is assumed.…”

Delay-robust Estimation of the Reproduction Number and Comparative Evaluation on Generated Synthetic Data

“…Finally, the last input needed by the data generation is the number of cases in the initial six days. For this parameter, we chose [1,2,2,3,3,4]. With these parameters we generate the synthetic dataset; it is shown in Figure 4.…”