Evidence map›Paper›PMID 42516118›Full record

ArticleBulletin of the World Health Organization2026

Digital epidemiology investments, Saudi Arabia.

Haytham A Sheerah, Ahmed Arafa, Mansour A Alfaya, Ashraf B AlDerbas, Nouf Bin Muammar, Sarah A Alsalhi, Abdulmajeed Y Alahdal, Hessah A Alsalamah, Abdulrahman M Alqahtani, Shada Alsalamah

Abstract read
In one paragraph

Article in Bulletin of the World Health Organization, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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.

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

10 authors.

Haytham A SheerahPopulation Health Deputyship, Ministry of Health, Riyadh, Saudi Arabia.
Ahmed ArafaDepartment of Public Health, Beni-Suef University, Salah Salem Street, 62521 Beni-Suef, Egypt.
Mansour A AlfayaDepartment of Preventive Medicine, Aseer Health Cluster, Aseer, Saudi Arabia.
Ashraf B AlDerbasAdministration of the Primary Care, Aljouf Health Cluster, Aljouf, Saudi Arabia.
Nouf Bin MuammarDepartment of Family and Community Medicine, King Saud University, Riyadh, Saudi Arabia.
Sarah A AlsalhiPreventive Medicine Postgraduate Program, Jeddah First and Second Health Cluster, Jeddah, Saudi Arabia.
Abdulmajeed Y AlahdalCollege of Medicine, Almareefa University, Riyadh, Saudi Arabia.
Hessah A AlsalamahComputer Engineering Department, Al Yamamah University, Riyadh, Saudi Arabia.
Abdulrahman M AlqahtaniDepartment of Preventive Medicine, Aseer Health Cluster, Aseer, Saudi Arabia.
Shada AlsalamahInformation Systems Department, King Saud University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Problem: Traditional epidemiological surveillance methods are often limited by delays in reporting and fragmented data systems. Saudi Arabia faces additional public health challenges from mass gatherings during Hajj and Umrah, an increasing burden of noncommunicable diseases and rapid urbanization, highlighting the need for investing in digital epidemiology. Approach: Saudi Arabia has accelerated digital transformation in health care through Vision 2030 initiatives. The strategies include health information exchange platforms, analytics driven by artificial intelligence, telemedicine services and digital monitoring systems used during Hajj. We review current initiatives to invest in digital epidemiology in Saudi Arabia, implementation challenges and policy priorities. Local setting: Saudi Arabia's health system operates under a predominantly public model. The health ministry is the main provider, regulator and finance provider of most health-care services. Health-care coverage is nearly universal, with citizens receiving services free of charge through the public system. Ongoing reforms aim to gradually decentralize certain functions. Relevant changes: The initiatives under Vision 2030 have supported disease surveillance, data integration and public health response capacities. Existing digital health reforms have created a foundation for integrating digital epidemiology into routine public health practice. However, challenges remain, including fragmented interoperability between institutions, workforce shortages, unequal digital access, and concerns about data governance, privacy and algorithmic bias. Lessons learnt: Saudi Arabia's experience suggests that digital epidemiology is more effective when integrated within broader digital health reforms. Successful implementation requires not only digital infrastructure, but also workforce development, ethical governance, transparency and mechanisms for integrating digital data into public health decision-making.

Indexed as

EpidemiologyArtificial IntelligenceDigital HealthHumansPublic Health InfrastructureSaudi ArabiaTelemedicine

Identifiers

PMID42516118
PMCPMC13404473

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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.