Evidence map›Paper›PMID 41557813›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

Real-time spatiotemporal tracking of infectious outbreaks in confined environments with a host-pathogen agent-based system.

Suhas Srinivasan, Jeffrey King, Jacob M Collins, Andres Colubri, Dmitry Korkin

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

5 authors.

Suhas SrinivasanData Science Program, Worcester Polytechnic Institute, Worcester, MA 01609.ORCID 0000-0001-8309-9648
Jeffrey KingEarthshot Labs, Mill Valley, CA 94941.
Jacob M CollinsDepartment of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605.ORCID 0000-0003-1662-0987
Andres ColubriDepartment of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605.ORCID 0000-0001-5559-9661
Dmitry KorkinData Science Program, Worcester Polytechnic Institute, Worcester, MA 01609.ORCID 0000-0002-3875-9085

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deadly infection outbreaks in confined spaces, whether it is a COVID-19 outbreak on a cruise ship or measles and stomach flu outbreaks in schools, can be characterized by their rapid spread due to the abundance of common spaces, shared airways, and high population density. Preventing future outbreaks and developing efficient mitigation protocols can benefit from advanced computational modeling approaches. Here, we developed an agent-based modeling approach to study the spatiotemporal dynamics of an infection outbreak in a confined environment caused by a specific pathogen, and to determine effective containment protocols. The approach integrates the 3D geographic information system of a confined environment, behavior of the hosts, key biological parameters about the pathogen obtained from the experimental data, and the general mechanics of host-pathogen and pathogen-fomite interactions. To assess our approach, we applied it to the historical data of infectious outbreaks caused by norovirus, H1N1 influenza A, and SARS-CoV-2 viruses. Our AI-GIS Infection Dynamics (AGID) model accurately predicted daily infection numbers, correctly identified the day when the CDC vessel sanitation protocol would be triggered, singled out key biological parameters affecting the infection spread, and propose pathogen-specific changes to existing containment protocols. Our work advances the understanding of infection spread on cruise ships while offering insights applicable to other similar confined settings, such as nursing homes, schools, and hospitals. By providing a robust framework for real-time outbreak modeling, this study proposes more effective containment protocols and enhances our preparedness for managing infectious diseases and emerging pathogens in confined environments.

Indexed as

COVID-19Disease OutbreaksHost-Pathogen InteractionsGeographic Information SystemsHumansInfluenza A Virus, H1N1 SubtypeNorovirusSARS-CoV-2Spatio-Temporal Analysisagent-based modelingAIepidemiologyinfection dynamicsinfectious diseases

Identifiers

PMID41557813
PMCPMC12849694

What OpenQuestion holds

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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.