Evidence map›Paper›PMID 38969474›Full record

ArticleThe Lancet. Planetary health2024

Mapping the distribution of Nipah virus infections: a geospatial modelling analysis.

Yan-Qun Sun, Yuan-Yuan Zhang, Mei-Chen Liu, Jin-Jin Chen, Ting-Ting Li, Yan-Ning Liu, Ling-Yu Zhang, Tao Wang, Lin-Jie Yu, Tian-Le Che and 9 more

Abstract read
In one paragraph

Article in The Lancet. Planetary health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–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

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  17. Strengthening preparedness and response to emerging henipavirus diversity.Frontiers in cellular and infection microbiology · 2026
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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

19 authors.

Yan-Qun SunState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China; Nanjing Municipal Center for Disease Control and Prevention, Affiliated Nanjing Center for Disease Control and Prevention of Nanjing Medical University, Nanjing, China.
Yuan-Yuan ZhangState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Mei-Chen LiuState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China; The First Affiliated Hospital, Anhui Medical University, Hefei, China.
Jin-Jin ChenState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Ting-Ting LiState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China; School of Public Health, Guizhou Medical University, Guiyang, China.
Yan-Ning LiuState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Ling-Yu ZhangState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Tao WangState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Lin-Jie YuState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Tian-Le CheState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Tian TangState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Qiang XuState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Chen-Long LvState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Bao-Gui JiangState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Nick GoldingTelethon Kids Institute, Nedlands, WA, Australia; School of Population Health, Curtin University, Bentley, WA, Australia; Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Max L MehlmanDepartment of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Simon I HayDepartment of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. Electronic address: sihay@uw.edu.
Li-Qun FangState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China. Electronic address: fang_lq@163.com.
Wei LiuState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China; The First Affiliated Hospital, Anhui Medical University, Hefei, China. Electronic address: liuwei@bmi.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNipah virus is a zoonotic paramyxovirus responsible for disease outbreaks with high fatality rates in south and southeast Asia. However, knowledge of the potential geographical extent and risk patterns of the virus is poor. We aimed to establish an integrated spatiotemporal and phylogenetic database of Nipah virus infections in humans and animals across south and southeast Asia.

methodsIn this geospatial modelling analysis, we developed an integrated database containing information on the distribution of Nipah virus infections in humans and animals from 1998 to 2021. We conducted phylodynamic analysis to examine the evolution and migration pathways of the virus and meta-analyses to estimate the adjusted case-fatality rate. We used two boosted regression tree models to identify the potential ecological drivers of Nipah virus occurrences in spillover events and endemic areas, and mapped potential risk areas for Nipah virus endemicity.

findings749 people and eight bat species across nine countries were documented as being infected with Nipah virus. On the basis of 66 complete genomes of the virus, we identified two clades-the Bangladesh clade and the Malaysia clade-with the time of the most recent common ancestor estimated to be 1863. Adjusted case-fatality rates varied widely between countries and were higher for the Bangladesh clade than for the Malaysia clade. Multivariable meta-regression analysis revealed significant relationships between case-fatality rate estimates and viral clade (p=0·0021), source country (p=0·016), proportion of male patients (p=0·036), and travel time to health-care facilities (p=0·036). Temperature-related bioclimate variables and the probability of occurrence of Pteropus medius were important contributors to both the spillover and the endemic infection models.

interpretationThe suitable niches for Nipah virus are more extensive than previously reported. Future surveillance efforts should focus on high-risk areas informed by updated projections. Specifically, intensifying zoonotic surveillance efforts, enhancing laboratory testing capacity, and implementing public health education in projected high-risk areas where no human cases have been reported to date will be crucial. Additionally, strengthening wildlife surveillance and investigating potential modes of transmission in regions with documented human cases is needed.

fundingThe Key Research and Development Program of China.

Indexed as

Henipavirus InfectionsNipah VirusAnimalsAsia, SoutheasternChiropteraHumansPhylogenyZoonoses

Identifiers

PMID38969474
PMCPMC12513742

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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.