Evidence map›Paper›PMID 32678025›Full record

ArticleInfectious diseases of poverty2020

Inference and prediction of malaria transmission dynamics using time series data.

Benyun Shi, Shan Lin, Qi Tan, Jie Cao, Xiaohong Zhou, Shang Xia, Xiao-Nong Zhou, Jiming Liu

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

Article in Infectious diseases of poverty, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.3field-weighted citation impact, top 18% 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 12 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

8 authors at 4 institutions in 2 countries.

Benyun ShiSchool of Computer Science and Technology, Nanjing Tech University, Nanjing, 211800, Jiangsu, China. benyunshi@outlook.com.ORCID http://orcid.org/0000-0003-2734-3794
Shan LinCollege of Information Engineering, Nanjing University of Finance & Economics, NanjingJiangsu, 210003, China.
Qi TanDepartment of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong.
Jie CaoCollege of Information Engineering, Nanjing University of Finance & Economics, NanjingJiangsu, 210003, China.
Xiaohong ZhouDepartment of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, 510515, Guangdong, China.
Shang XiaNational Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention, Shanghai, 200025, China.
Xiao-Nong ZhouNational Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention, Shanghai, 200025, China. zhouxn1@chinacdc.cn.
Jiming LiuDepartment of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong.
National Institute for Parasitic Diseases · CNHong Kong Baptist University · HKNanjing University of Finance and Economics · CNNanjing Tech University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDisease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time series. To evaluate the severity of an epidemic, statistical methods are widely used to forecast the trend, seasonality, and the possible number of infections of a disease. However, most statistical methods are limited in revealing the underlying dynamics of disease transmission, which may be affected by various impact factors, such as environmental, meteorological, and physiological factors. In this study, we focus on investigating malaria transmission dynamics based on time series data.

methodsA data-driven nonlinear stochastic model is proposed to infer and predict the dynamics of malaria transmission based on the time series of prevalence data. Specifically, the dynamics of malaria transmission is modeled based on the notion of vectorial capacity (VCAP) and entomological inoculation rate (EIR). A particle Markov chain Monte Carlo (PMCMC) method is employed to estimate the model parameters. Accordingly, a one-step-ahead prediction method is proposed to project the number of future malaria infections. Finally, two case studies are carried out on the inference and prediction of Plasmodium vivax transmission in Tengchong and Longling, Yunnan province, China.

resultsThe results show that the trained data-driven stochastic model can well fit the historical time series of P. vivax prevalence data in both counties from 2007 to 2010. Moreover, with well-trained model parameters, the proposed one-step-ahead prediction method can achieve better performances than that of the seasonal autoregressive integrated moving average model with respect to predicting the number of future malaria infections.

conclusionsBy involving dynamically changing impact factors, the proposed data-driven model together with the PMCMC method can successfully (i) depict the dynamics of malaria transmission, and (ii) achieve accurate one-step-ahead prediction about malaria infections. Such a data-driven method has the potential to investigate malaria transmission dynamics in other malaria-endemic countries/regions.

Indexed as

ChinaHumansMalaria, VivaxModels, TheoreticalPlasmodium vivaxSeasons

Identifiers

PMID32678025
PMCPMC7367373
OpenAlexW3043252299

What OpenQuestion holds

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LicenceCC BY
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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.