Evidence map›Paper›PMID 38191421›Full record

ArticleMalaria journal2024

Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach.

Oraya Srimokla, Wirichada Pan-Ngum, Amnat Khamsiriwatchara, Chantana Padungtod, Rungrawee Tipmontree, Noppon Choosri, Sompob Saralamba

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

Article in Malaria journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.4field-weighted citation impact, top 10% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Observational
  2. 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

7 authors at 6 institutions in 2 countries.

Oraya SrimoklaNuffield Department of Medicine, University of Oxford, Broad St, Oxford, OX13AZ, UK.
Wirichada Pan-NgumMahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Ratchawithi Rd, Bangkok, 10400, Thailand.
Amnat KhamsiriwatcharaCenter of Excellence for Biomedical and Public Health Informatics, Faculty of Tropical Medicine, Mahidol University, Ratchawithi Rd, Bangkok, 10400, Thailand.
Chantana PadungtodDivision of Vector Borne Diseases, Department of Disease Control, Ministry of Public Health, Talat Kwan, Bangkok, Nonthaburi, 11000, Thailand.
Rungrawee TipmontreeDivision of Vector Borne Diseases, Department of Disease Control, Ministry of Public Health, Talat Kwan, Bangkok, Nonthaburi, 11000, Thailand.
Noppon ChoosriCollege of Arts, Media and Technology, Chiang Mai University, Sukhothai 5 Alley, Mueang Chiang Mai, Chiang Mai, 50200, Thailand.
Sompob SaralambaMahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Ratchawithi Rd, Bangkok, 10400, Thailand. sompob@tropmedres.ac.
Mahidol University · THChiang Mai University · THDepartment of Disease Control · THMahidol Oxford Tropical Medicine Research Unit · THMinistry of Public Health · THUniversity of Oxford · GB

Funding

Wellcome TrustWellcome Trust 220211
6 · The paper itself

Abstract

backgroundMalaria continues to pose a significant health threat. Rapid identification of malaria infections and the deployment of active surveillance tools are crucial for achieving malaria elimination in regions where malaria is endemic, such as certain areas of Thailand. In this study, an anomaly detection system is introduced as an early warning mechanism for potential malaria outbreaks in countries like Thailand.

methodsUnsupervised clustering-based, and time series-based anomaly detection algorithms are developed and compared to identify abnormal malaria activity in Thailand. Additionally, a user interface tailored for anomaly detection is designed, enabling the Thai malaria surveillance team to utilize these algorithms and visualize regions exhibiting unusual malaria patterns.

resultsNine distinct anomaly detection algorithms we developed. Their efficacy in pinpointing verified outbreaks was assessed using malaria case data from Thailand spanning 2012 to 2022. The historical average threshold-based anomaly detection method triggered three times fewer alerts, while correctly identifying the same number of verified outbreaks when compared to the current method used in Thailand. A limitation of this analysis is the small number of verified outbreaks; further consultation with the Division of Vector Borne Disease could help identify more verified outbreaks. The developed dashboard, designed specifically for anomaly detection, allows disease surveillance professionals to easily identify and visualize unusual malaria activity at a provincial level across Thailand.

conclusionAn enhanced early warning system is proposed to bolster malaria elimination efforts for countries with a similar malaria profile to Thailand. The developed anomaly detection algorithms, after thorough comparison, have been optimized for integration with the current malaria surveillance infrastructure. An anomaly detection dashboard for Thailand is built and supports early detection of abnormal malaria activity. In summary, the proposed early warning system enhances the identification process for provinces at risk of outbreaks and offers easy integration with Thailand's established malaria surveillance framework.

Indexed as

MalariaAlgorithmsCluster AnalysisDisease OutbreaksHumansThailandAnomaly detectionEarly detectionMalariaOutbreak

Identifiers

PMID38191421
PMCPMC10775623
OpenAlexW4390673167

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.