Evidence map›Paper›PMID 39974019›Full record

ArticlemedRxiv : the preprint server for health sciences2025

A Binary Prototype for Time-Series Surveillance and Intervention.

Jason Olejarz, Till Hoffmann, Alex Zapf, Douaa Mugahid, Ross Molinaro, Chadwick Brown, Artem Boltyenkov, Taras Dudykevych, Ankit Gupta, Marc Lipsitch and 5 more

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

15 authors.

Jason OlejarzDepartment of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Till HoffmannDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Alex ZapfDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Douaa MugahidDepartment of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Ross MolinaroSiemens Healthcare Diagnostics, Inc., Tarrytown, NY 10591, USA.
Chadwick BrownSiemens Healthcare Diagnostics, Inc., Tarrytown, NY 10591, USA.
Artem BoltyenkovSiemens Healthcare Diagnostics, Inc., Tarrytown, NY 10591, USA.
Taras DudykevychSiemens Healthcare Diagnostics, Inc., Tarrytown, NY 10591, USA.
Ankit GuptaSiemens Healthcare Diagnostics, Inc., Tarrytown, NY 10591, USA.
Marc LipsitchDepartment of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Rifat AtunDepartment of Global Health Systems, Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Jukka-Pekka OnnelaDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Sarah FortuneDepartment of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Rangarajan SampathSiemens Healthcare Diagnostics, Inc., Tarrytown, NY 10591, USA.
Yonatan H GradDepartment of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.

Funding

Casual, Statistical and Mathematical Modeling with Serologic DataU01CA261277 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANAGE, WILLIAM, LIPSITCH, MARC · 2020 to 2023
$2.8M
NCI NIH HHS U01 CA261277
6 · The paper itself

Abstract

Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting on these signals is lacking. We addressed this gap by formulating a hidden Markov-style model for time-series surveillance, where the system state, the observed data, and the decision rule are all binary. We incur a delayed cost,

Indexed as

Anomaly detectionCost-benefit analysisHealthcareInfectious diseasesOptimizationQuality control

Identifiers

PMID39974019
PMCPMC11838624

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.