Evidence map›Paper›PMID 38046063›Full record

ArticleVirusdisease2023

Application of machine learning models for risk estimation and risk prediction of classical swine fever in Assam, India.

Kuralayanapalya Puttahonnappa Suresh, Nagendra Nath Barman, Tarushree Bari, Dikshitha Jagadish, Bylaiah Sushma, H V Darshan, Sharanagouda S Patil, Mousumi Bora, Abhijit Deka

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Article in Virusdisease, 2023. 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
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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

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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. Review
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.

Kuralayanapalya Puttahonnappa SureshSpatial Epidemiology Laboratory, Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics, Yelahanka, Bengaluru, Karnataka India.
Nagendra Nath BarmanDepartment of Veterinary Microbiology, College of Veterinary Science, Assam Agricultural University, Guwahati, India.
Tarushree BariSpatial Epidemiology Laboratory, Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics, Yelahanka, Bengaluru, Karnataka India.
Dikshitha JagadishSpatial Epidemiology Laboratory, Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics, Yelahanka, Bengaluru, Karnataka India.
Bylaiah SushmaDepartment of Computer Science and Engineering, M S Ramaiah Institute of Technology, Matthikere, Bengaluru, Karnataka India.
H V DarshanSpatial Epidemiology Laboratory, Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics, Yelahanka, Bengaluru, Karnataka India.
Sharanagouda S PatilVirology Laboratory, Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics, Yelahanka, Bengaluru, Karnataka India.
Mousumi BoraDepartment of Veterinary Microbiology, College of Veterinary Science, Assam Agricultural University, Guwahati, India.
Abhijit DekaDepartment of Veterinary Pathology, College of Veterinary Science, Assam Agricultural University, Guwahati, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present study is aimed to develop an early warning system of Classical swine fever (CSF) disease by applying machine learning models and to study the climate-disease relationship with respect to the spatial occurrence and outbreaks of the disease in the north-eastern state of Assam, India. The disease incidence data from the year 2005 to 2021 was used. The linear discriminant analysis (LDA) revealed that significant environmental and remote sensing risk factors like air temperature, enhanced vegetation index, land surface temperature, potential evaporation rate and wind speed were significantly contributing to CSF incidences in Assam. Furthermore, the climate-based disease modelling was applied to relevant ecological and environmental risk factors determined using LDA and risk maps were generated. The western and eastern regions of the state were predicted to be at high risk of CSF with presence of significant hotspots. For the districts that are significantly clustered, the Basic reproduction number (R Supplementary Information: The online version contains supplementary material available at 10.1007/s13337-023-00847-6.

Indexed as

AssamClassical swine feverIncidenceMachine learningRisk modellingSwine

Identifiers

PMID38046063
PMCPMC10686966

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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.