Evidence map›Paper›PMID 40162254›Full record

ArticlemedRxiv : the preprint server for health sciences2025

A Flexible Framework for Local-Level Estimation of the Effective Reproductive Number in Geographic Regions with Sparse Data.

Md Sakhawat Hossain, Ravi Goyal, Natasha K Martin, Victor DeGruttola, Mohammad Mihrab Chowdhury, Christopher McMahan, Lior Rennert

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Md Sakhawat HossainDepartment of Public Health Sciences, Clemson University, Clemson, SC, USA.
Ravi GoyalDivision of Infectious Diseases & Global Public Health, University of California San Diego, La Jolla, CA, USA.
Natasha K MartinDivision of Infectious Diseases & Global Public Health, University of California San Diego, La Jolla, CA, USA.
Victor DeGruttolaDivision of Biostatistics, Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, San Diego, California, USA.
Mohammad Mihrab ChowdhuryDepartment of Public Health Sciences, Clemson University, Clemson, SC, USA.
Christopher McMahanCenter for Public Health Modeling and Response, Clemson University, Clemson, SC, USA.
Lior RennertDepartment of Public Health Sciences, Clemson University, Clemson, SC, USA.ORCID 0000-0001-5275-7273

Funding

Developing a dynamic modeling framework for surveillance, prediction, and real-time resource allocation to reduce health disparities during Covid-19 and future pandemicsR01LM014193 · NLM · CLEMSON UNIVERSITY · PI Lior Rennert · 2023 to 2026
$2.5M
NLM NIH HHS R01 LM014193
6 · The paper itself

Abstract

Background: Our research focuses on local-level estimation of the effective reproductive number, which describes the transmissibility of an infectious disease and represents the average number of individuals one infectious person infects at a given time. The ability to accurately estimate the infectious disease reproductive number in geographically granular regions is critical for disaster planning and resource allocation. However, not all regions have sufficient infectious disease outcome data; this lack of data presents a significant challenge for accurate estimation. Methods: To overcome this challenge, we propose a two-step approach that incorporates existing Results: We applied our method to estimate Conclusion: These findings demonstrate that the proposed methodology is a useful tool for small-area estimation of

Indexed as

Bayesian statisticsEffective reproductive numberInfectious disease modelingPredictionSmall area estimation

Identifiers

PMID40162254
PMCPMC11952488

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.