Evidence map›Paper›PMID 41421987›Full record

ArticleBMC public health2025

Predicting the incidence of common intestinal infectious diseases in Changzhou, China based on environmental factors and deep learning.

Xianzhi Zheng, Lei Qiao, Hao Hong, Yixin Zhang, Qinhui Fan, Jinglan Dai, Jingyi Zhao, Fang Yao, Sipeng Shen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Xianzhi Zheng *Changzhou Institute for Advanced Study of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, Nanjing Medical University, Changzhou, 213022, China.
Lei Qiao *Changzhou Institute for Advanced Study of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, Nanjing Medical University, Changzhou, 213022, China.
Hao HongDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Building Room 406, 101 Longmian Avenue, Nanjing, Jiangsu, 211166, China.
Yixin ZhangDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Building Room 406, 101 Longmian Avenue, Nanjing, Jiangsu, 211166, China.
Qinhui FanDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Building Room 406, 101 Longmian Avenue, Nanjing, Jiangsu, 211166, China.
Jinglan DaiDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Building Room 406, 101 Longmian Avenue, Nanjing, Jiangsu, 211166, China.
Jingyi ZhaoDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Building Room 406, 101 Longmian Avenue, Nanjing, Jiangsu, 211166, China.
Fang YaoChangzhou Institute for Advanced Study of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, Nanjing Medical University, Changzhou, 213022, China. yaofang0519@163.com.
Sipeng ShenChangzhou Institute for Advanced Study of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, Nanjing Medical University, Changzhou, 213022, China. sshen@njmu.edu.cn.

Funding

National Natural Science Foundation of China 82373685Open Research Fund Program of Changzhou Institute for Advanced Study of Public Health, Nanjing Medical University No. CPHM202301
6 · The paper itself

Abstract

backgroundIntestinal infectious disease is a common infectious disease that is closely related to meteorological conditions and air pollution factors. We aim to construct a short-term prediction model for the daily incidence of common intestinal infectious diseases in Changzhou city.

methodsThe daily incidence data of hand, foot, and mouth disease and other infectious diarrhea in Changzhou and the daily meteorological data and air pollutant data in the same period were collected from May 13, 2014 to December 31, 2024. The meteorological data consisted of temperature, humidity, wind speed, air pressure, etc. Air pollutant data included PM

resultsAmong all the evaluated models, the STL-T-L hybrid model showed the best prediction performance on the test set, with RMSE of 6.337, MAE of 4.524, MAPE of 58.482%, MASE of 0.638. The prediction model built in this study considered the historical incidence of the disease and incorporated various meteorological and air pollution factors. The results showed that the STL-T-L model incorporating these features achieved the best prediction results.

conclusionThe STL-T-L model can effectively predict the common intestinal infectious diseases and can be used as a tool for monitoring and early warning of intestinal infectious diseases in Changzhou.

Indexed as

Deep LearningHand, Foot and Mouth DiseaseAir PollutantsAir PollutionChinaForecastingHumansIncidenceAir PollutantsDeep learningEnvironmental factorsIntestinal infectious diseasesPrediction model

Identifiers

PMID41421987
PMCPMC12837124

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.