Evidence map›Paper›PMID 33849655›Full record

ArticleInfectious diseases of poverty2021

A remote management system for control and surveillance of echinococcosis: design and implementation based on internet of things.

Shi-Jie Yang, Ning Xiao, Jing-Zhong Li, Yu Feng, Jun-Ying Ma, Gong-Sang Quzhen, Qing Yu, Ting Zhang, Shi-Cheng Yi, Xiao-Nong Zhou

Open access · goldAbstract read
In one paragraph

Article in Infectious diseases of poverty, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.4field-weighted citation impact, top 21% 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

6 citing papers in PubMed, 13 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

10 authors at 3 institutions in 1 country.

Shi-Jie YangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Ning XiaoNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Jing-Zhong LiTibet Center for Disease Control and Prevention, NHC Key Laboratory of Echinococcosis Prevention and Control, Lhasa, China.
Yu FengDepartment of Parasitic Diseases, Gansu Center for Disease Control and Prevention, Lanzhou, China.
Jun-Ying MaQinghai Institute for Endemic Disease Prevention and Control, Xining, China.
Gong-Sang QuzhenTibet Center for Disease Control and Prevention, NHC Key Laboratory of Echinococcosis Prevention and Control, Lhasa, China.
Qing YuNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Ting ZhangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Shi-Cheng YiShanghai Yier Information Technology Co., Ltd, Shanghai, China.
Xiao-Nong ZhouNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China. zhouxn1@chinacdc.cn.
National Institute for Parasitic Diseases · CNGansu Provincial Center for Disease Control and Prevention · CNQinghai Institute for Endemic Diease Prevention and Control · CN

Funding

The Ganzi Prefecture Workgroup Projects for Echinococcosis Prevention and Control, China CDC 2016, 2017, 2018,2019the Key R & D Transformation Projects, Qinghai 2020-SF-133the NHC Key Laboratory of Echinococcosis Prevention and Control Project,Supported by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences, 2019PT320004
6 · The paper itself

Abstract

backgroundAs a neglected cross-species parasitic disease transmitted between canines and livestock, echinococcosis remains a global public health concern with a heavy disease burden. In China, especially in the epidemic pastoral communities on the Qinghai-Tibet Plateau, the harsh climate, low socio-economic status, poor overall hygiene, and remote and insufficient access to all owned dogs exacerbate the difficulty in implementing the ambitious control programme for echinococcosis. We aimed to design and implement a remote management system (RMS) based on internet of things (IoT) for control and surveillance of echinococcosis by combining deworming devices to realise long-distance smart deworming control, smooth statistical analysis and result display. New methods and tools are urgently needed to increase the deworming coverage and frequency, promote real-time scientific surveillance, and prevent transmission of echinococcosis in remoted transmission areas.

methodsFrom 2016 to 2019, we had cooperated and developed the smart collar and smart feeder with the Central Research Institute of Shanghai Electric Group Co., Ltd. (Shanghai, China) and Shenzhen Jizhi Future Technology Co., Ltd. (Shenzhen, China). From September 2019 to March 2020, We had proposed the RMS based on IoT as a novel tool to control smart deworming devices to deliver efficient praziquantel (PZQ) baits to dogs regularly and automatically and also as a smart digital management platform to monitor, analyse, and display the epidemic trends of echinococcosis dynamically, in real time in Hezuo City, Gannan Tibetan Autonomous Prefecture, Gansu Province, China. Starting from January 2018, The RMS has been maintained and upgraded by Shanghai Yier Information Technology Co., Ltd (Shanghai, China). The database was based on MySQL tools and the Chi-square test was used to probe the difference and changes of variables in different groups.

resultsThe smart collars are fully capable of anti-collision, waterproof, and cold-proof performance, and the battery's energy is sufficient, the anti-collision rate, water-proof rate, cold-proof rate and voltage normal rate is 99.6% (521/523), 100.0% (523/523), 100.0% (523/523) and 100.0% (523/523), respectively. The RMS can accurately analyse the monitoring data and parameters including positive rates of canine faeces, and the prevalence of echinococcosis in the general population livestock, and children. The data of dogs deworming and surveillance for echinococcosis is able to be controlled using RMS and has expanded gradually in townships to the whole Hezuo region. The automatic delivering PZQ rate, collar positioning rate, deliver PZQ reminding rate, and fault report rate is 91.1% (1914/2102), 92.1% (13 580/14 745), 92.1% (1936/2102) and 84.7% (1287/1519), respectively. After using the RMS from 2019, the missing rate of monitoring data decreased from 32.1% (9/28) to 0 (0/16). A total of 48 administrators (3, 3, 8, 11, 23 at the provincial, municipal, county, township, village levels, respectively) participated in the questionnaire survey, with 93.8% of its overall satisfaction rate.

conclusionsThe existing difficulties and challenges in the way of prevention and control for echinococcosis can partially be resolved using the innovative, IoT-based technologies and tools. The proposed RMS advance the upgrade of existing manual prevention and control models for echinococcosis, especially in the current ongoing COVID-19 pandemic, as social distance and community blockade continue.

Indexed as

RoboticsAnimalsCOVID-19Dog DiseasesDogsEchinococcosisEpidemiological MonitoringInternet of ThingsPandemicsQuarantineTibetControlDesignEchinococcosisImplementationInternet of ThingsRemote Management SystemSurveillance

Identifiers

PMID33849655
PMCPMC8042360
OpenAlexW3102548376

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.