Evidence map›Paper›PMID 38238022›Full record

ArticleBMJ health & care informatics2024

Rapidly scalable and low-cost public health surveillance reporting system for COVID-19.

Vivek Jason Jayaraj, Chiu-Wan Ng, Victor Chee-Wai Hoe, Diane Woei-Quan Chong, Sanjay Rampal

Open access · goldAbstract read
In one paragraph

Article in BMJ health & care informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 99% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 2 institutions in 1 country.

Vivek Jason JayarajSector for Biostatistics & Data Repository, National Institutes of Health, Ministry of Health, Shah Alam, Malaysia.ORCID http://orcid.org/0000-0002-8131-9878
Chiu-Wan NgCentre for Epidemiology and Evidence-based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Victor Chee-Wai HoeCentre for Epidemiology and Evidence-based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Diane Woei-Quan ChongHealth Systems Research, National Institutes of Health, Ministry of Health, Shah Alam, Malaysia.
Sanjay RampalCentre for Epidemiology and Evidence-based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia srampal@ummc.edu.my.
University of Malaya · MYMinistry of Health · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveData-driven innovations are essential in strengthening disease control. We developed a low-cost, open-source system for robust epidemiological intelligence in response to the COVID-19 crisis, prioritising scalability, reproducibility and dynamic reporting.

methodsA five-tiered workflow of data acquisition; processing; databasing, sharing, version control; visualisation; and monitoring was used. COVID-19 data were initially collated from press releases and then transitioned to official sources.

resultsKey COVID-19 indicators were tabulated and visualised, deployed using open-source hosting in October 2022. The system demonstrated high performance, handling extensive data volumes, with a 92.5% user conversion rate, evidencing its value and adaptability.

conclusionThis cost-effective, scalable solution aids health specialists and authorities in tracking disease burden, particularly in low-resource settings. Such innovations are critical in health crises like COVID-19 and adaptable to diverse health scenarios.

Indexed as

COVID-19HumansPublic Health SurveillanceReproducibility of ResultsData Interpretation, StatisticalData VisualizationHealth CommunicationInformation DisseminationPublic health informatics

Identifiers

PMID38238022
PMCPMC11077347
OpenAlexW4391035904

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.