Evidence map›Paper›PMID 35906580›Full record

ArticleBMC public health2022

ARIMA and ARIMA-ERNN models for prediction of pertussis incidence in mainland China from 2004 to 2021.

Meng Wang, Jinhua Pan, Xinghui Li, Mengying Li, Zhixi Liu, Qi Zhao, Linyun Luo, Haiping Chen, Sirui Chen, Feng Jiang and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
7.2field-weighted citation impact, top 2% 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

20 citing papers in PubMed, 47 citations in OpenAlex.

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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

13 authors at 3 institutions in 1 country.

Meng Wang *School of Public Health, Fudan University, Shanghai, 200032, China.
Jinhua Pan *Department of Ultrasound Medicine, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, China.
Xinghui LiSchool of Public Health, Fudan University, Shanghai, 200032, China.
Mengying LiSchool of Public Health, Fudan University, Shanghai, 200032, China.
Zhixi LiuSchool of Public Health, Fudan University, Shanghai, 200032, China.
Qi ZhaoSchool of Public Health, Fudan University, Shanghai, 200032, China.
Linyun LuoChina National Biotec Group Company Limited, Beijing, 100024, China.
Haiping ChenChina National Biotec Group Company Limited, Beijing, 100024, China.
Sirui ChenHunan Normal University, Hunan, 410081, China.
Feng JiangInstitute of Expanded Programme On Immunization, Guizhou Provincial Center for Disease Control and Prevention, Guizhou Province, Guiyang, 550004, People's Republic of China.
Liping ZhangMinhang Center for Disease Control and Prevention, Shanghai, 201100, China.
Weibing WangSchool of Public Health, Fudan University, Shanghai, 200032, China. wwb@fudan.edu.cn.
Ying WangSchool of Public Health, Fudan University, Shanghai, 200032, China. wangying1013@fudan.edu.cn.
Fudan University · CNHunan Normal University · CNZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo compare an autoregressive integrated moving average (ARIMA) model with a model that combines ARIMA with the Elman recurrent neural network (ARIMA-ERNN) in predicting the incidence of pertussis in mainland China.

backgroundThe incidence of pertussis has increased rapidly in mainland China since 2016, making the disease an increasing public health threat. There is a pressing need for models capable of accurately predicting the incidence of pertussis in order to guide prevention and control measures. We developed and compared two models for predicting pertussis incidence in mainland China.

methodsData on the incidence of pertussis in mainland China from 2004 to 2019 were obtained from the official website of the Chinese Center for Disease Control and Prevention. An ARIMA model was established using SAS (ver. 9.4) software and an ARIMA-ERNN model was established using MATLAB (ver. R2019a) software. The performances of these models were compared.

resultsFrom 2004 to 2019, there were 104,837 reported cases of pertussis in mainland China, with an increasing incidence over time. The incidence of pertussis showed obvious seasonal characteristics, with the peak lasting from March to September every year. Compared with the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the ARIMA model, those of the ARIMA-ERNN model were 81.43%, 95.97% and 80.86% lower, respectively, in fitting performance. In terms of prediction performance, the MAE, MSE and MAPE were 37.75%, 56.88% and 43.75% lower, respectively.

conclusionThe fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA model. This provides theoretical support for the prediction of infectious diseases and should be beneficial to public health decision making.

Indexed as

Whooping CoughChinaForecastingHumansIncidenceModels, StatisticalNeural Networks, ComputerSoftwareARIMA-ERNN modelARIMA modelPertussisPredictive effect

Identifiers

PMID35906580
PMCPMC9338508
OpenAlexW4288704567

What OpenQuestion holds

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LicenceCC BY
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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.