Abstract
The effective reproductive number is an important statistic that was widely used during the pandemic to make public policy decisions. Huisman and collaborators proposed a robust method to estimate the effective reproductive number of SARS-CoV-2 using incidence data (Huisman et al., 2022). The method assumes by default that the data follows a gamma distribution. Deviations from this assumption are known to affect the accuracy of the method.
The overarching goal was to gauge how applicable the method in question is. Concretely I set out and explore incidence data from different countries to build distributions, analyze them, and compare them.
I found that there were noticeable differences in the distribution of the data from different countries (compared to the assumed gamma distribution), which in turn caused noticeable differences in the effective reproductive number estimates when accounting for the empirical distributions. This in turn allowed us to accurately characterize the applicability of the estimation method.
Distribution of delays for every country. All delays are with respect to onset date. Dashed lines indicate the mean of the distribution.
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References
2022
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Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2
Jana S. Huisman, Jérémie Scire, Daniel C. Angst, and 5 more authors
medRxiv, Mar 2022
The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis work was supported by the Swiss National Science Foundation (SNSF) through grant number 31CA30_196267 (to TS), 200021_172603 (to MHM), 310030B_176401 (to SB), and NRP72 grant 407240-167121 (to SB and TS).Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:No ethics commitee was involved as no clinical trial data was used.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe source code of the pipeline is available at https://github.com/covid-19-Re/shiny-dailyRe. The resulting estimates (data) are available at: https://github.com/covid-19-Re/dailyRe-Data. The code necessary to reproduce the figures in the paper is at: https://github.com/covid-19-Re/paper-code. The underlying linelist data for Switzerland was provided to us by the Federal Office of Public Health, and is not publicly available due to privacy concerns. For all other countries, the incidence data can be downloaded from public sources using the pipeline in the repository mentioned above. https://github.com/covid-19-Re/shiny-dailyRe https://github.com/covid-19-Re/dailyRe-Data https://github.com/covid-19-Re/paper-code
