A vision for estimation of the instantaneous reproductive number

The reproductive number, R<sub>t</sub>, is a popular metric used for monitoring infectious diseases. R<sub>t</sub> describes the expected number of infections that will be generated from a single infection at time t, which maps nicely to the likelihood that disease incidence will increase, decrease, or remain constant in the near future. Although this metric has existed for decades, it became more widely used during the COVID-19 pandemic and there was a subsequent proliferation of new estimation methods and software tools. This rapid development of methods and tools presents many opportunities and challenges for users, researchers, and decision makers. In recognition of this growth, we convened a three-day "collabathon" in September 2024 to bring together researchers and public health practitioners to identify challenges and areas for future development in R<sub>t</sub> estimation and to begin work in these areas. Here we provide a high-level summary of current methods and report on the findings from the collabathon, including a summary of current challenges and recommendations for future development, evaluation and interpretation of R<sub>t</sub>.

Authors

Institutions

Publication Details

Journal
Epidemics
Published
2026-01-29
DOI
https://doi.org/10.1016/j.epidem.2026.100885
Citations
1
Primary Topic
Reproductive tract infections research
Type
article
Field-Weighted Citation Impact
24.35
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A vision for estimation of the instantaneous reproductive number

Laura White, Kaitlyn E. Johnson, Alessandra Urbinati, Brennan Klein et al.
1 citations
Epidemics
Reproductive tract infections research
24.35
article

A vision for estimation of the instantaneous reproductive number

Laura White, Kaitlyn E. Johnson, Alessandra Urbinati, Brennan Klein, Chad Milando, George G. Vega Yon, Guillaume St-Onge, Anne Cori
article en
1 citations

Abstract

The reproductive number, R<sub>t</sub>, is a popular metric used for monitoring infectious diseases. R<sub>t</sub> describes the expected number of infections that will be generated from a single infection at time t, which maps nicely to the likelihood that disease incidence will increase, decrease, or remain constant in the near future. Although this metric has existed for decades, it became more widely used during the COVID-19 pandemic and there was a subsequent proliferation of new estimation methods and software tools. This rapid development of methods and tools presents many opportunities and challenges for users, researchers, and decision makers. In recognition of this growth, we convened a three-day "collabathon" in September 2024 to bring together researchers and public health practitioners to identify challenges and areas for future development in R<sub>t</sub> estimation and to begin work in these areas. Here we provide a high-level summary of current methods and report on the findings from the collabathon, including a summary of current challenges and recommendations for future development, evaluation and interpretation of R<sub>t</sub>.

EpidemicsVol. 54
Boston University (US), Northeastern University (US), University of Utah (US), London School of Hygiene & Tropical Medicine (GB), Imperial College London (GB)
Openalex Percentile: Top 2%
Reproductive tract infections research
24.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A vision for estimation of the instantaneous reproductive number — Laura White, Kaitlyn E. Johnson, et al. · Epidemics (2026) | TGRS Research Map | TGRS