Abstract

Containment strategies to combat epidemics such as SARS-CoV-2/COVID-19 require the availability of epidemiological parameters, e.g., the effective reproduction number. Parametric models such as the commonly used susceptible-infected-removed (SIR) compartment models fitted to observed incidence time series have limitations due to the time-dependency of the parameters. Furthermore, fatalities are delayed with respect to the counts of new cases, and the reproduction cycle leads to periodic patterns in incidence time series. Therefore, based on comprehensible nonparametric methods including time-delay correlation analyses, estimates of crucial parameters that characterise the COVID-19 pandemic with a focus on the German epidemic are presented using publicly available time-series data on prevalence and fatalities. The estimates for Germany are compared with the results for seven other countries (France, Italy, the United States of America, the United Kingdom, Spain, Switzerland, and Brazil). The duration from diagnosis to death resulting from delay-time correlations turns out to be 13 days with high accuracy for Germany and Switzerland. For the other countries, the time-to-death durations have wider confidence intervals. With respect to the German data, the two time series of new cases and fatalities exhibit a strong coherence. Based on the time lag between diagnoses and deaths, properly delayed asymptotic as well as instantaneous fatality–case ratios are calculated. The temporal median of the instantaneous fatality–case ratio with time lag of 13 days between cases and deaths for Germany turns out to be . Time courses of asymptotic fatality–case ratios are presented for other countries, which substantially differ during the first half of the pandemic but converge to a narrow range with standard deviation and mean . Similar results are obtained from comparing time courses of instantaneous fatality–case ratios with optimal delay for the 8 exemplarily chosen countries. The basic reproduction number, , for Germany is estimated to be between and depending on the generation time, which is estimated based on a delay autocorrelation analysis. Resonances at about 4 days and 7 days are observed, partially attributable to weekly periodicity of sampling. The instantaneous (time-dependent) reproduction number is estimated from the incident (counts of new) cases, thus allowing us to infer the temporal behaviour of the reproduction number during the epidemic course. The time course of the reproduction number turns out to be consistent with the time-dependent per capita growth.

Highlights

  • The current (2020/2021) hard-to-tackle flood of publications on virological, epidemiological, and sociological aspects of the SARS-CoV-2 coronavirus and its related diseaseCOVID-19 [1,2] along with the concurrent demand by many public health institutions and authorities for intensifying corresponding research in order to quickly gain a deeper understanding of the pandemic entail a dilemma for researchers

  • The instantaneous reproduction number is estimated from the incident cases, allowing us to infer the temporal behaviour of the reproduction number during the epidemic course

  • In a preprint version of this article [24], we used data provided by the European Centre for Disease Prevention and Control (ECDC) [11]; ECDC stopped providing data sampled on a daily bases starting in mid-December 2020 and switched to weekly updates instead

Read more

Summary

Introduction

The current (2020/2021) hard-to-tackle flood of publications on virological, epidemiological, and sociological aspects of the SARS-CoV-2 coronavirus and its related diseaseCOVID-19 [1,2] along with the concurrent demand by many public health institutions and authorities for intensifying corresponding research in order to quickly gain a deeper understanding of the pandemic entail a dilemma for researchers. On the other hand, hesitating to submit may prevent quality research from being published In spite of this dilemma, the present paper is motivated by the hope that the simplicity of the proposed mathematical methodology applied to data on the incidence of COVID-19 cases leads to meaningful insights. Complex models are usually parametric in nature and constructed in order to eventually supply estimates of the involved parameters, such as the basic reproduction number R0 or the instantaneous effective reproduction number R(t). Most of these parameters, such as R(t), are largely time-dependent and are contingent on changing public health policies and social behaviour. In the worst case of a homogeneous time series without epidemic ruptures, the time-delay correlation might be insensitive to detecting the diagnosis-to-death duration.

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call