Evidence map›Paper›PMID 31693708›Full record

ArticlePloS one2019

The effect of climate change on cholera disease: The road ahead using artificial neural network.

Zahra Asadgol, Hamed Mohammadi, Majid Kermani, Alireza Badirzadeh, Mitra Gholami

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 4 pooled it
4.4field-weighted citation impact, top 5% of its field
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

34 citing papers in PubMed, 4 syntheses or guidelines pooled it, 80 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Public health consequences of global climate change. A narrative review.Frontiers in cellular and infection microbiology · 2026
    Review
  7. Article
  8. Article
  9. Cholera in Africa: A Climate Change Crisis.Journal of epidemiology and global health · 2025
    Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. Dissecting Water, Sanitation, and Hygiene (WaSH) to Assess Risk Factors for Cholera in Shashemene, Oromia Region, Ethiopia.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024
    Article
  16. Review
  17. Review
  18. Article
  19. Article
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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

5 authors at 2 institutions in 1 country.

Zahra AsadgolDepartment of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
Hamed MohammadiDepartment of Environmental Health Engineering, School of Public Health, Zanjan University of Medical Sciences, Zanjan, Iran.
Majid KermaniDepartment of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
Alireza BadirzadehDepartment of Parasitology and Mycology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Mitra GholamiDepartment of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
Iran University of Medical Sciences · IRZanjan University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2050. Daily data for cholera infection cases in Qom city, which is located in the center of Iran, were analyzed from 1998 to 2016. To determine the best lag time and combination of inputs, Gamma Test (GT) was applied. General circulation model outputs were utilized to project future climate pattern under two scenarios of Representative Concentration Pathway (RCP2.6 and RCP8.5). Statistical downscaling was done to produce high-resolution synthetic time series weather dataset. ANNs were applied for simulating the impact of climate change on cholera. The observed climate variables including maximum and minimum temperatures and precipitation were tagged as predictors in ANNs. Cholera cases were considered as the target outcome variable. Projected future (2020-2050) climate in previous step was carried out to assess future cholera incidence. A seasonal trend in cholera infection was seen. Our results elucidated that the best lag time was 21 days. According to the results of downscaling tool, future climate in the study area by 2050 will be warmer and wetter. Simulation of cholera cases indicated that there is a clear trend of increasing cholera cases under the worst scenario (RCP8.5) by the year 2050 and the highest cholera cases observe in warmer months. The precipitation was recognized as the most effective input variable by sensitivity analysis. We observed a significant correlation between low precipitation and cholera infection. There is a strong evidence to show that cholera disease is correlated with environment variables, as low precipitation and high temperatures in warmer months could provide the swifter bacterial replication. These conditions in Iran, especially in the central parts, may raise the cholera infection rates. Furthermore, ANNs is an executive tool to simulate the impact of climate change on cholera to estimate the future trend of cholera incidence for adopting protective measures in endemic areas.

Indexed as

Climate ChangeNeural Networks, ComputerCholeraComputer SimulationGeographyHumansIranSeasonsTime Factors

Identifiers

PMID31693708
PMCPMC6834266
OpenAlexW2985626220

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.