Evidence map›Paper›PMID 41358293›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.

Renquan Zhang, Rui Deng, Sitong Liu, Qing Yao, Jeffrey Shaman, Bryan T Grenfell, Cécile Viboud, Sen Pei

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

5 · Who and what money

Authors and funding

8 authors.

Renquan ZhangSchool of Mathematical Sciences, Dalian University of Technology, Dalian, China.ORCID 0000-0002-4927-1827
Rui DengSchool of Mathematical Sciences, Dalian University of Technology, Dalian, China.
Sitong LiuSchool of Mathematical Sciences, Dalian University of Technology, Dalian, China.
Qing YaoDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
Jeffrey ShamanDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
Bryan T GrenfellDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
Cécile ViboudFogarty International Center, National Institutes of Health, Bethesda, MD, USA.
Sen PeiDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.ORCID 0000-0002-7072-2995

Funding

Quantifying Error Growth to Improve Infectious Disease Forecast AccuracyR01AI163023 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI JEFFREY L SHAMAN · 2021 to 2026
$3.3M
Early detection and inference for emerging infectious agents in data-sparse settingsR35GM156799 · NIGMS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI PEI, SEN · 2025 to 2025
$2.1M
NCEZID CDC HHS U01 CK000592NIAID NIH HHS R01 AI163023NIGMS NIH HHS R35 GM156799
6 · The paper itself

Abstract

Understanding the geographic spread of emerging respiratory viruses is critical for pandemic preparedness, yet the early spatiotemporal dynamics of the 2009 H1N1 pandemic influenza and SARS-CoV-2 in the United States (US) remain unclear. While mobility and genomic data have revealed important aspects of pandemic spatial spread, several key questions remain: Did the two pandemics follow similar spatial transmission routes? How rapidly did they spread across the US? What role did stochastic processes play in early spatial transmission? To address these questions, we integrated high-resolution disease data with a robust, data-efficient inference framework combining air travel, commuting flows, and pathogen superspreading potentials to reconstruct their spatial spread across US metropolitan areas. The two pandemics exhibited distinct transmission pathways across locations; however, both pandemics established local circulation in most metropolitan areas within weeks, driven by several shared transmission hubs. Early spatial spread was more strongly associated with air travel than with commuting, though stochastic dynamics introduced substantial uncertainty in transmission routes, creating challenges for timely detection and control. Simulations indicate that broad wastewater surveillance coverage beyond top transmission hubs coupled with effective infection control may slow initial spatial expansion. Our findings highlight the rapid, stochastic spread of pandemic respiratory pathogens and the difficulties of early outbreak containment.

Identifiers

PMID41358293
PMCPMC12676404

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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.