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Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany.

ABSTRACT: The effective reproductive number Rt has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of Rt may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates.

SUBMITTER: Brockhaus EK

PROVIDER: S-EPMC10703420 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany.

Brockhaus Elisabeth K EK Wolffram Daniel D Stadler Tanja T Osthege Michael M Mitra Tanmay T Littek Jonas M JM Krymova Ekaterina E Klesen Anna J AJ Huisman Jana S JS Heyder Stefan S Helleckes Laura M LM An der Heiden Matthias M Funk Sebastian S Abbott Sam S Bracher Johannes J

PLoS computational biology 20231127 11

The effective reproductive number Rt has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreemen ...[more]

PMID: 38011276

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Project description:BackgroundThe effective reproduction number (Rt) quantifies the average number of secondary cases caused by one person with an infectious disease. Near-real-time monitoring of Rt during an outbreak is a major indicator used to monitor changes in disease transmission and assess the effectiveness of interventions. The estimation of Rt usually requires the identification of infected cases in the population, which can prove challenging with the available data, especially when asymptomatic people or with mild symptoms are not usually screened. The purpose of this study was to perform sensitivity analysis of Rt estimates for COVID-19 surveillance in France based on three data sources with different sensitivities and specificities for identifying infected cases.MethodsWe applied a statistical method developed by Cori et al. to estimate Rt using (1) confirmed cases identified from positive virological tests in the population, (2) suspected cases recorded by a national network of emergency departments, and (3) COVID-19 hospital admissions recorded by a national administrative system to manage hospital organization.ResultsRt estimates in France from May 27, 2020, to August 12, 2022, showed similar temporal trends regardless of the dataset. Estimates based on the daily number of confirmed cases provided an earlier signal than the two other sources, with an average lag of 3 and 6 days for estimates based on emergency department visits and hospital admissions, respectively.ConclusionThe COVID-19 experience confirmed that monitoring temporal changes in Rt was a key indicator to help the public health authorities control the outbreak in real time. However, gaining access to data on all infected people in the population in order to estimate Rt is not straightforward in practice. As this analysis has shown, the opportunity to use more readily available data to estimate Rt trends, provided that it is highly correlated with the spread of infection, provides a practical solution for monitoring the COVID-19 pandemic and indeed any other epidemic.

Project description:BackgroundDuring the first wave of the COVID-19 pandemic a high case fatality rate (CFR) was noticed worldwide including also Germany where the first European cases have been observed. The WHO recommended immediate intubation for patients with dyspnoea which has since been revised after reviewing the initial clinical outcome. The objective of this study is to analyze CFR and assess if there is an advantage of a more conservative management of COVID-19 induced hypoxemia.MethodsPCR confirmed COVID-19 infections and death counts were obtained for all German districts from 27 Jan 2020 until 15 Feb 2021 using official reports of Robert Koch Institute Berlin, Germany. A moving average CFRt was constructed by dividing disease related deaths two weeks after a given index day by the number of infections two weeks prior to that date. In addition to a local comparison also mortality outcomes in other German speaking countries were compared.ResultsThe mean CFR is estimated to be 2.92% based on 71.965 fatalities and 2.465.407 cases. There was a large regional scattering of CFRs across the German districts. Differences of the mortality pattern were observed also at state level and preserved across different sex and age groups while being largely independent of case numbers. Although Munich city had higher infection rates, more patients died during the first wave in Hamburg (OR 1.6, 95% CI 1.3-1.9) which was mirrored also by higher death risk at Hamburg intensive care units (OR 2.0, 95% CI 1.3-3.1). While the majority of Munich hospitals favoured a conservative management of hypoxemia including high flow nasal cannula (HFNC), Hamburg hospitals followed a more aggressive scheme of early mechanical ventilation (MV). Austria and Switzerland experienced higher CFRs than Germany during the first wave but after changing their treatment guidelines, both countries experienced lower CFRs during the second wave.ConclusionUsing retrospective public health data, different case fatality rates have been observed across Germany. A more conservative management of COVID-19 induced Adult Respiratory Distress Syndrome (ARDS) is justified also by epidemiological data.

Project description:BACKGROUND:The required efforts, feasibility and predicted success of an intervention strategy against an infectious disease are partially determined by its basic reproduction number, R0. In its simplest form R0 can be understood as the product of the infectious period, the number of infectious contacts and the per-contact transmission probability, which in the case of vector-transmitted diseases necessarily extend to the vector stages. As vectors do not usually recover from infection, they remain infectious for life, which places high significance on the vector's life expectancy. Current methods for estimating the R0 for a vector-borne disease are mostly derived from compartmental modelling frameworks assuming constant vector mortality rates. We hypothesised that some of the assumptions underlying these models can lead to unrealistic high vector life expectancies with important repercussions for R0 estimates. METHODOLOGY AND PRINCIPAL FINDINGS:Here we used a stochastic, individual-based model which allowed us to directly measure the number of secondary infections arising from one index case under different assumptions about vector mortality. Our results confirm that formulas based on age-independent mortality rates can overestimate R0 by nearly 100% compared to our own estimate derived from first principles. We further provide a correction factor that can be used with a standard R0 formula and adjusts for the discrepancies due to erroneous vector age distributions. CONCLUSION:Vector mortality rates play a crucial role for the success and general epidemiology of vector-transmitted diseases. Many modelling efforts intrinsically assume these to be age-independent, which, as clearly demonstrated here, can lead to severe over-estimation of the disease's reproduction number. Our results thus re-emphasise the importance of obtaining field-relevant and species-dependent vector mortality rates, which in turn would facilitate more realistic intervention impact predictions.

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Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany.

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Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany.

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