Mers

Memish, Z.

doi:10.1016/j.ijid.2016.11.061

Cited by 3 publications

(2 citation statements)

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“…After applying exclusion criteria, there were 105 papers met our criteria from title and abstract analysis. On further review, 76 of these studies did not compare proportions of patients with symptoms in laboratory confirmed EVD cases with non-EVD, primarily investigating predictors for mortality among EVD survivors and those who died [ 18 – 22 ]. A further 21 papers were excluded as they exclusively investigated either a paediatric or pregnant population.…”

Section: Resultsmentioning

confidence: 99%

Meta-analysis of predictive symptoms for Ebola virus disease

2020

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Introduction One of the leading challenges in the 2013-2016 West African Ebola virus disease (EVD) outbreak was how best to quickly identify patients with EVD, separating them from those without the disease, in order to maximise limited isolation bed capacity and keep health systems functioning. Methodology We performed a systematic literature review to identify all published data on EVD clinical symptoms in adult patients. Data was dual extracted, and random effects meta-analysis performed for each symptom to identify symptoms with the greatest risk for EVD infection. Results Symptoms usually presenting late in illness that were more than twice as likely to predict a diagnosis of Ebola, were confusion (pOR 3.04,

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Section: Resultsmentioning

confidence: 99%

Meta-analysis of predictive symptoms for Ebola virus disease

2020

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“…The disease has been brought under control in all Middle Eastern countries except Saudi Arabia where each month, new cases are still reported. Studies have confirmed that this disease is zoonotic and camels are its significant reservoir [3,4]. People in close contact with infected camels and health care professionals who care for infected patients have a high risk of acquiring the infection.…”

Section: Introductionmentioning

confidence: 94%

Shiny Framework Based Visualization and Analytics Tool for Middle East Respiratory Syndrome

John

Shaiba

2019

Communications in Computer and Information Science

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People in the Middle East have been affected by the Middle East Respiratory Syndrome CoronaVirus (MERS Co-V) since 2012. New cases are continuously reported especially in the Kingdom of Saudi Arabia, and the risk of exposure remains an issue. Data visualization plays a vital role in effective analysis of the data. In this paper, we introduce an interactive visualization application for MERS data collected from the Control and Command Centre, Ministry of Health website of Saudi Arabia. The data corresponding to the period from January 1, 2019 to February 28, 2019 was used in the present work. The attributes considered include gender, age, date of reporting, city, region, camel contact, description and status of the patient. The visualization tool has been developed using Shiny framework of R programming language. The application presents information in the form of interactive plots, maps and tables. The salient feature of the tool is that users can view and download data corresponding to the period of their choice. This tool can help decision makers in the detailed analysis of data and hence devise measures to prevent the spread of the disease.

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