Evidence map›Paper›PMID 41798855›Full record

ArticleInfectious Disease Modelling2026

Estimation of transmission distance between cases of (re-)emerging respiratory infectious diseases and its potential application in outbreak response.

Wenlong Zhu, Xueyao Liang, Xinyu Wang, Wenyong Zhou, Qianyi Ge, Minyi Yang, Bo Zheng, Zexuan Wen, Zhixi Liu, Ye Yao and 2 more

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Article in Infectious Disease Modelling, 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Wenlong ZhuShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.
Xueyao LiangDepartment of Epidemiology, School of Public Health, Fudan University, Shanghai, China.
Xinyu WangDepartment of Epidemiology, School of Public Health, Fudan University, Shanghai, China.
Wenyong ZhouShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.
Qianyi GeDepartment of Epidemiology, School of Public Health, Fudan University, Shanghai, China.
Minyi YangShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.
Bo ZhengDepartment of Epidemiology, School of Public Health, Fudan University, Shanghai, China.
Zexuan WenShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.
Zhixi LiuDepartment of Epidemiology, School of Public Health, Fudan University, Shanghai, China.
Ye YaoShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.
Yang LiuDepartment of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Weibing WangShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantifying transmission distance helps to understand infectious disease spread patterns, but few studies have assessed this for (re-)emerging respiratory infectious diseases. The COVID-19 pandemic's extensive surveillance and high-quality data in China offer a strong model for studying the spread pattern of respiratory infections. We collected COVID-19 outbreak data from June 2020 to December 2022 in mainland China to estimate transmission distances, examine the association between urban characteristics and transmission distance, and explore its implications in outbreak response. The mean transmission distance of all outbreaks was 18.94 km. This distance varied by SARS-CoV-2 variants, with wild-type outbreaks showing shortest transmission distance (3.08 km) and Omicron outbreaks showing longest transmission distance (20.17 km). Transmission distance was positively associated with city size and total length of roads. Potential transmission areas identified as areas within transmission distance of the first 100 cases contained 63.84-99.61% of cases. Providing testing services to residents in these areas helped to control the outbreak. Our results suggest that transmission distances can help predict where emerging respiratory infectious diseases may spread next spatially and guide the implementation of targeted interventions.

Indexed as

Emerging respiratory infectious diseasesSpatiotemporal spreadTransmission chainTransmission distance

Identifiers

PMID41798855
PMCPMC12966655

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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.