Evidence map›Paper›PMID 40502584›Full record

ArticlemedRxiv : the preprint server for health sciences2025

serocalculator, an R package for estimating seroincidence from cross-sectional serological data.

Kristina W Lai, Chris Orwa, Jessica C Seidman, Denise O Garrett, Samir K Saha, Dipesh Tamrakar, Farah Naz Qamar, Richelle C Charles, Jason R Andrews, Peter Teunis and 2 more

Abstract readPreprint
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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Kristina W LaiDepartment of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA.ORCID 0000-0002-5761-7437
Chris OrwaSkyeHi Technologies, Nairobi, Kenya.
Jessica C SeidmanSabin Vaccine Institute, Washington, DC, USA.ORCID 0009-0005-1645-3789
Denise O GarrettSabin Vaccine Institute, Washington, DC, USA.ORCID 0000-0001-6411-3459
Samir K SahaChild Health Research Foundation, Dhaka, Bangladesh.ORCID 0000-0003-3820-0748
Dipesh TamrakarDhulikhel Hospital, Kathmandu University Hospital, Dhulikhel, Nepal.ORCID 0000-0002-0772-3653
Farah Naz QamarDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Richelle C CharlesHarvard Medical School, Harvard University, Boston, MA, USA.ORCID 0000-0002-8881-1849
Jason R AndrewsDivision of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-5967-251X
Peter TeunisHubert Department of Global Health, Center for Global Safe WASH, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
Kristen AiemjoyDepartment of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA.ORCID 0000-0003-1886-2699
Douglas Ezra MorrisonDepartment of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA.ORCID 0000-0002-7195-830X

Funding

Estimating the Seroincidence of Melioidosis, Typhoid Fever and Scrub Typhus from Cross-sectional SerosurveysK01TW012177 · FIC · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Kristen Aiemjoy · 2022 to 2026
$901k
Serocalculator: Estimating Incidence Rates from Serological DataR21AI176416 · NIAID · UNIVERSITY OF CALIFORNIA AT DAVIS · PI AIEMJOY, KRISTEN, MORRISON, DOUGLAS EZRA · 2023 to 2024
$422k
Seroepidemiology of Enteric Fever: Advancing Methods to Characterize the Population-Level Force of Infection in a Changing Global LandscapeF31AI194664 · NIAID · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Kristina W Lai · 2025 to 2026
$93k
FIC NIH HHS K01 TW012177NIAID NIH HHS F31 AI194664NIAID NIH HHS R21 AI176416
6 · The paper itself

Abstract

Motivation: Seroincidence-the rate of new infections in a population-is a key measure for understanding pathogen transmission dynamics and informing public health action. Estimating seroincidence from cross-sectional data is complicated by antibody waning, cross-reactivity, and individual heterogeneity in antibody responses. Implementation: General features: The package supports overall and stratified seroincidence estimation using single or multiple biomarkers. It requires three inputs: (1) a pre-estimated seroresponse model characterizing post-infection antibody waning; (2) noise parameters capturing biological and assay-related variability; and (3) quantitative antibody responses from a cross-sectional survey. It is computationally efficient, well-documented, and includes a point-and-click R Shiny interface. These features promote usability across research and public health. Availability: The package

Identifiers

PMID40502584
PMCPMC12155044

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.