Evidence map›Paper›PMID 41493813›Full record

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

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

Erratum issuedAbstract 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. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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 116024.ORCID 0000-0002-4927-1827
Rui Deng *School of Mathematical Sciences, Dalian University of Technology, Dalian, China 116024.
Sitong Liu *School of Mathematical Sciences, Dalian University of Technology, Dalian, China 116024.ORCID 0009-0005-1236-720X
Qing YaoDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032.ORCID 0000-0002-5222-8977
Jeffrey ShamanDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032.ORCID 0000-0002-7216-7809
Bryan T GrenfellDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544.
Cécile ViboudFogarty International Center, NIH, Bethesda, MD 20892.
Sen PeiDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032.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
HHS | Centers for Disease Control and Prevention (CDC) 75D30122C14289HHS | Centers for Disease Control and Prevention (CDC) U01CK000592HHS | NIH (NIH) AI163023MOST | National Natural Science Foundation of China (NSFC) 12371516NCEZID CDC HHS U01 CK000592NIAID NIH HHS R01 AI163023NSF (NSF) DMS-2229605Princeton Catalysis Initiative NAPrinceton Precision Health NAPU | High Meadows Environmental Institute, Princeton University (HMEI) NA
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 severe acute respiratory syndrome coronavirus 2 in the United States 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 United States? 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.

Indexed as

COVID-19Influenza, HumanPandemicsHumansInfluenza A Virus, H1N1 SubtypeSARS-CoV-2Spatio-Temporal AnalysisStochastic ProcessesUnited Stateshuman mobilitypandemic respiratory virusesspatial spreadstatistical inferencesuperspreading

Identifiers

PMID41493813
PMCPMC12799117

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.