Evidence map›Paper›PMID 42178993›Full record

ArticleEpidemiology and infection2026

Unjustified Poisson assumptions lead to overconfident estimates of the effective reproductive number.

Barbora Němcová, Isaac H Goldstein, Jessalyn Sebastian, Volodymyr M Minin, Johannes Bracher

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epidemiology and infection, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Inferring pathogen superspreading potential using early spatial spread patterns.medRxiv : the preprint server for health sciences · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Barbora NěmcováInstitute of Statistics, https://ror.org/04t3en479Karlsruhe Institute of Technology, Germany.ORCID https://orcid.org/0009-0004-7565-4145
Isaac H GoldsteinDepartment of Statistics, https://ror.org/00f54p054Stanford University, USA.
Jessalyn SebastianDepartment of Statistics, https://ror.org/04gyf1771University of California Irvine, USA.
Volodymyr M MininDepartment of Statistics, https://ror.org/04gyf1771University of California Irvine, USA.
Johannes BracherInstitute of Statistics, https://ror.org/04t3en479Karlsruhe Institute of Technology, Germany.ORCID https://orcid.org/0000-0002-3777-1410

Funding

Deutsche Forschungsgemeinschaft 512483310Division of Microbiology and Infectious Diseases R01AI170204
6 · The paper itself

Abstract

Time-varying effective reproductive numbers of infectious diseases are commonly estimated using renewal equation models. In the widely applied R package EpiEstim and various related tools, this approach is combined with a Poisson distributional assumption. This has been criticized on various occasions, mostly on grounds of general model realism or a desire to estimate dispersion parameters. Here, we argue that an important issue arising from the Poisson assumption is that inference about the effective reproductive number becomes overconfident in the presence of overdispersion. The extent to which standard errors are underestimated follows from theory on generalized linear models in a straightforward manner. We therefore recommend to replace the Poisson assumption by quasi-Poisson or negative binomial extensions, and contrast their respective properties. We illustrate our arguments in detailed simulation studies and three case studies on Ebola, pandemic influenza, and COVID-19.

Indexed as

Basic Reproduction NumberInfluenza, HumanComputer SimulationCOVID-19Hemorrhagic Fever, EbolaHumansModels, StatisticalPandemicsPoisson DistributionSARS-CoV-2Effective reproductive numberInfectious disease epidemiologyPoisson distributionRenewal equationUncertainty quantification

Identifiers

PMID42178993
PMCPMC13279973

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.