Neural networks to model COVID-19 dynamics and allocate healthcare resources.

Abdullah Ali H Ahmadini, Yashpal Singh Raghav, Ali M Mahnashi, Khalid Ul Islam Rather, Irfan Ali

Abstract read

In one paragraph

Article in Scientific reports, 2025. 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

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

5 authors.

Abdullah Ali H AhmadiniDepartment of Mathematics, College of Science, Jazan University, P.O. Box. 114, Jazan, 45142, Kingdom of Saudi Arabia.ORCID https://orcid.org/0000-0002-9740-7207

Yashpal Singh RaghavDepartment of Mathematics, College of Science, Jazan University, P.O. Box. 114, Jazan, 45142, Kingdom of Saudi Arabia.ORCID https://orcid.org/0000-0002-9410-9079

Ali M MahnashiDepartment of Mathematics, College of Science, Jazan University, P.O. Box. 114, Jazan, 45142, Kingdom of Saudi Arabia.ORCID https://orcid.org/0009-0004-8068-068X

Khalid Ul Islam RatherDivision of Statistics & Computer Science, SKUAST-Jammu, Jammu, 180009, India.ORCID https://orcid.org/0000-0002-4631-5652

Irfan AliDepartment of Statistics and Operations Research, Aligarh Muslim University, Aligarh, 202002, India. irfii.st@amu.ac.in.ORCID https://orcid.org/0000-0002-1790-5450

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents a neural network-based framework for COVID-19 transmission prediction and healthcare resource optimization. The model achieves high prediction accuracy by integrating epidemiological, mobility, vaccination, and environmental data and enables dynamic resource allocation. The results demonstrate significant improvements in forecasting performance and healthcare preparedness compared to traditional models. This work enhances decision-making in pandemic management by leveraging machine learning for real-time operational efficiency.

Indexed as

COVID-19Health ResourcesNeural Networks, ComputerResource AllocationHumansMachine LearningPandemicsSARS-CoV-2COVID-19 forecastingEpidemiological modelingHealthcare resource optimizationMachine learningNeural networks

Identifiers

PMID40312406

DOI10.1038/s41598-025-00153-9

PMCPMC12045988

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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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.