Evidence map›Paper›PMID 33867131›Full record

ArticleISA transactions2022

COVID-19 Global Humanitarian Response Plan: An optimal distribution model for high-priority countries.

Ibrahim M Hezam

Abstract read
In one paragraph

Article in ISA transactions, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

1 author.

Ibrahim M HezamStatistics and Operations Research Department, College of Sciences, King Saud University, Riyadh, Saudi Arabia; Department of Mathematics, Ibb University, Ibb, Yemen. Electronic address: ialmishnanah@ksu.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 2019 novel coronavirus disease (COVID-19) has spread rapidly worldwide, and the outbreak of the disease was designated a global pandemic by the World Health Organization. Such outbreaks would certainly be catastrophic for some of the best-ranked health systems and would be more catastrophic in countries with more fragile health systems. Accordingly, the World Health Organization and other organizations have been appealing to donor countries to support a rapid response plan. The primary objectives of this response plan are to appeal for funds from donors and to distribute these funds to the most affected countries based on the requirements.

methodsIn this study, we developed a mathematical model to provide initial insights into the efficient and equitable distribution of urgent funds to high-priority countries. Three phases were proposed for the construction of this mathematical model. In the first phase, the final epidemic sizes in all the target countries were predicted by using three epidemiological models. In the second phase, the urgent requirements for each country were estimated in parallel with the estimates issued by the humanitarian response plan, based on the size of the epidemic and several other factors. In the third and final phase, a multi-objective optimization model was proposed. The first objective was to maximize the funds from donors to cover all the requirements. The second objective was to minimize the unmet demands by ensuring a fair distribution of the urgent funds based on the requirements of the target countries.

resultsPredictions of the basic reproduction numbers and the final epidemic sizes were calculated for all target countries. The urgent requirements were estimated, and the requirements issued by the humanitarian response plan for all target countries were also considered. Moreover, a proposed response plan for the distribution network was demonstrated. Donors must provide urgent funds exceeding US$ 2,608,084,209 to cover at least 40 % of each target country's requirements. Overall, results demonstrate the importance of an urgent and fair distribution of funds to the target countries to overcome the outbreak of COVID-19.

conclusionsRapid responses by donor countries to humanitarian appeals will facilitate the immediate and fair distribution of relief supplies to the poorest countries. This distribution may help to support health systems, restrain the spread of COVID-19, and prevent an unlimited catastrophe.

Indexed as

COVID-19Basic Reproduction NumberHumansPandemicsSARS-CoV-2World Health OrganizationCOVID-19Global Humanitarian Response PlanHigh priority countriesOptimization modeling

Identifiers

PMID33867131
PMCPMC8040533

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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.