Evidence map›Paper›PMID 41102300›Full record

ArticleScientific reports2025

Investigating environmental determinants and spatiotemporal dynamics of highly pathogenic avian influenza H5N1 outbreaks in India through machine learning.

Suresh Kuralayanapalya Puttahonnappa, Jayashree Anandakumar, Nagendra Nath Barman, Raaga Rajkumar, Krishnamoorthy Paramanandham, Sharanagouda S Patil, Sanjay Lamba, Archana Veeranagouda Patil, Baldev Raj Gulati

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

9 authors.

Suresh Kuralayanapalya PuttahonnappaICAR-National Institute of Veterinary Epidemiology & Disease Informatics, 4HG6+5QW, Ramagondanahalli, Unnamed Road, Yelahanka, Bengaluru, Karnataka, 560064, India. suresh.kp@icar.org.in.
Jayashree AnandakumarICAR-National Institute of Veterinary Epidemiology & Disease Informatics, 4HG6+5QW, Ramagondanahalli, Unnamed Road, Yelahanka, Bengaluru, Karnataka, 560064, India.
Nagendra Nath BarmanVeterinary Microbiology, College of Veterinary Science, Assam Agricultural University, Khanapara, Guwahati, Assam, India.
Raaga RajkumarICAR-National Institute of Veterinary Epidemiology & Disease Informatics, 4HG6+5QW, Ramagondanahalli, Unnamed Road, Yelahanka, Bengaluru, Karnataka, 560064, India.
Krishnamoorthy ParamanandhamICAR-National Institute of Veterinary Epidemiology & Disease Informatics, 4HG6+5QW, Ramagondanahalli, Unnamed Road, Yelahanka, Bengaluru, Karnataka, 560064, India.
Sharanagouda S PatilICAR-National Institute of Veterinary Epidemiology & Disease Informatics, 4HG6+5QW, Ramagondanahalli, Unnamed Road, Yelahanka, Bengaluru, Karnataka, 560064, India.
Sanjay LambaTata Institute for Genetics and Society, Bengaluru, Karnataka, India.
Archana Veeranagouda PatilICAR-National Institute of Veterinary Epidemiology & Disease Informatics, 4HG6+5QW, Ramagondanahalli, Unnamed Road, Yelahanka, Bengaluru, Karnataka, 560064, India.
Baldev Raj GulatiICAR-National Institute of Veterinary Epidemiology & Disease Informatics, 4HG6+5QW, Ramagondanahalli, Unnamed Road, Yelahanka, Bengaluru, Karnataka, 560064, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Avian Influenza (AI), caused by highly pathogenic strains of influenza viruses, poses a significant threat to poultry populations and public health worldwide. This study offers a comprehensive evaluation of the spatial and temporal dynamics of HPAI outbreaks in India, employing a multidisciplinary approach that integrates geospatial analysis, machine learning modelling, remote sensing, and environmental risk factor assessment. The resulting incidence map provides a powerful visual representation, offering valuable insights into the distribution and concentration of HPAI (Highly Pathogenic Avian Influenza) outbreaks across the country. The study identifies a peak in HPAI outbreaks during the winter and spring seasons. Critical environmental variables such as air temperature, enhanced vegetation index (EVI), leaf area index (LAI), potential evapotranspiration (PET), rain precipitation rate, specific humidity, and wind speed are identified as significant predictors of HPAI risk. Ensemble technology was adopted by integrating high-performing random forest (RF) and classification tree (CT) models for HPAI risk assessment. The risk map generated and estimated basic reproduction numbers (R

Indexed as

Disease OutbreaksInfluenza A Virus, H5N1 SubtypeInfluenza in BirdsMachine LearningAnimalsHumansIndiaPoultryRisk AssessmentSeasonsSpatio-Temporal AnalysisBasic reproduction numberEnvironmental and remote sensing parametersHighly pathogenic avian influenzaOutbreakRisk mapSpatiotemporal analysis

Identifiers

PMID41102300
PMCPMC12533099

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.