Spatial Modelling of Contribution of Individual Level Risk Factors for Mortality From Middle East Respiratory Syndrome Coronavirus in the Arabian Peninsula

Adegboye, Oyelola A.; Gayawan, Ezra; Hanna, Fahad

doi:10.20944/preprints201703.0033.v1

Cited by 8 publications

(14 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The "novel coronavirus-infected pneumonia (NCIP)" with unknown causes was reported to the World Health Organization (WHO) by Wuhan Municipal Health Commission in late December 2019 [1]. The disease is called as Corona Virus Disease 2019 (COVID-19) and its etiological agent was officially named SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2; after being provisionally called HCoV- 19), which was found to have 75-80% nucleotide identity to SARS-CoV [2]. Although coronaviruses have been acknowledged as minor human pathogens until the turn of the century, they were responsible for important outbreaks such as SARS-CoV and middle east respiratory syndrome coronavirus (MERS-CoV) infectious diseases in the last couple of decades [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…The disease is called as Corona Virus Disease 2019 (COVID-19) and its etiological agent was officially named SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2; after being provisionally called HCoV- 19), which was found to have 75-80% nucleotide identity to SARS-CoV [2]. Although coronaviruses have been acknowledged as minor human pathogens until the turn of the century, they were responsible for important outbreaks such as SARS-CoV and middle east respiratory syndrome coronavirus (MERS-CoV) infectious diseases in the last couple of decades [1,2]. Despite the low severity of COVID-19 as compared with SARS and MERS, SARS-CoV-2 affects a huge number of people in a short space of time due to its extremely high transmission rate [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Although coronaviruses have been acknowledged as minor human pathogens until the turn of the century, they were responsible for important outbreaks such as SARS-CoV and middle east respiratory syndrome coronavirus (MERS-CoV) infectious diseases in the last couple of decades [1,2]. Despite the low severity of COVID-19 as compared with SARS and MERS, SARS-CoV-2 affects a huge number of people in a short space of time due to its extremely high transmission rate [1,2]. The COVID-19 epidemic in mainland China has spread from Hubei province to the rest of China in around a month [1].…”

Section: Introductionmentioning

confidence: 99%

“…Emergency of international concern and its status was thereafter upgraded from epidemic to pandemic on March 11 [3]. Till 24th February, more than 28 countries were affected by COVID-19 and 80,000 confirmed cases announced; while 77,150 of the cases were recorded in china [2]. By the end of April, 158 countries all over the world reported 3,195,816 confirmed COVID-19 cases with 227,648 deaths.…”

Section: Introductionmentioning

confidence: 99%

“…Globally, the number of infected people with COVID-19 is 17 times that of the SARS-CoV outbreak in Guangdong, China in 2003 and 50 times that of the MERS-CoV outbreak in 2012, which also became global public health concern. The high rate of COVID-19 mortality underlines the need for a greater understanding of the causes and spatial spread of disease which in turn could shed lights on the world outbreaks' prediction and consequently improve public health policies [2]. Heretofore, many researchers have applied spatial analysis tools to quantify the spatial patterns of phenomena.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020)

Shariati

Mesgari

Kasraee

et al. 2020

J Environ Health Sci Engineer

View full text Add to dashboard Cite

Understanding the spatial distribution of coronavirus disease 2019 (COVID-19) cases can provide valuable information to anticipate the world outbreaks and in turn improve public health policies. In this study, the cumulative incidence rate (CIR) and cumulative mortality rate (CMR) of all countries affected by the new corona outbreak were calculated at the end of March and April, 2020. Prior to the implementation of hot spot analysis, the spatial autocorrelation results of CIR were obtained. Hot spot analysis and Anselin Local Moran's I indices were then applied to accurately locate high and low-risk clusters of COVID-19 globally. San Marino and Italy revealed the highest CMR by the end of March, though Belgium took the place of Italy as of 30th April. At the end of the research period (by 30th April), the CIR showed obvious spatial clustering. Accordingly, southern, northern and western Europe were detected in the high-high clusters demonstrating an increased risk of COVID-19 in these regions and also the surrounding areas. Countries of northern Africa exhibited a clustering of hot spots, with a confidence level above 95%, even though these areas assigned low CIR values. The hot spots accounted for nearly 70% of CIR. Furthermore, analysis of clusters and outliers demonstrated that these countries are situated in the low-high outlier pattern. Most of the surveyed countries that exhibited clustering of high values (hot spot) with a confidence level of 99% (by 31st March) and 95% (by 30th April) were dedicated higher CIR values. In conclusion, hot spot analysis coupled with Anselin local Moran's I provides a scrupulous and objective approach to determine the locations of statistically significant clusters of COVID-19 cases shedding light on the high-risk districts.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020)

Shariati

Mesgari

Kasraee

et al. 2020

J Environ Health Sci Engineer

View full text Add to dashboard Cite

show abstract

The transmission dynamics of Middle East Respiratory Syndrome coronavirus

Rui

Wang

et al. 2022

Travel Medicine and Infectious Disease

View full text Add to dashboard Cite

Background In this study, we aimed to quantify the contribution of different transmission routes of the Middle East respiratory syndrome (MERS) and determine its transmissibility. Methods Based on the natural history and transmission features of MERS in different countries, a susceptible-exposed-symptomatic-asymptomatic-recovered/death (SEIARD) model and a multi-route dynamic model (MMDM). The SEIARD model and MMDM were adopted to simulate MERS in South Korea and Saudi Arabia, respectively. Data on reported MERS cases in the two countries were obtained from the World Health Organization. Thereafter, the next generation matrix method was employed to derive the equation for the basic reproduction number ( R 0 ), and the model fitting procedure was adopted to calculate the R 0 values corresponding to these different countries. Results In South Korea, ‘Person-to-Person’ transmission was identified as the main mode of MERS transmission in healthcare settings, while in Saudi Arabia, in addition to ‘Person-to-Person’ transmission, ‘Host-to-Host’ and ‘Host-to-Person’ transmission also occurred under certain scenarios, with camels being the main host. Further, the fitting results showed that the SEIARD model and MMDM fitted the data well. The mean R 0 value was 8.59 (95% confidence interval [CI]: 0–28.02) for MERS in South Korea, and for MERS in Saudi Arabia, it was 1.15 and 1.02 (95% CI: 0.86–1.44) for the ‘Person-to-Person’ and ‘Camel-to-Camel’ transmission routes, respectively. Conclusions The SEIARD and MMDM model can be used to simulate the transmission of MERS in different countries. Additionally, in Saudi Arabia, the transmissibility of MERS was almost the same among hosts (camels) and humans.

show abstract

Factors associated with recovery delay in a sample of patients diagnosed by MERS‐CoV rRT‐PCR: A Saudi Arabian multicenter retrospective study

Ahmed

Al‐Jahdali

Alaqeel

et al. 2018

Influenza Resp Viruses

View full text Add to dashboard Cite

BackgroundResearch evidence exists that poor prognosis is common in Middle East respiratory syndrome coronavirus (MERS‐CoV) patients.ObjectivesThis study estimates recovery delay intervals and identifies associated factors in a sample of Saudi Arabian patients admitted for suspected MERS‐CoV and diagnosed by rRT‐PCR assay.MethodsA multicenter retrospective study was conducted on 829 patients admitted between September 2012 and June 2016 and diagnosed by rRT‐PCR procedures to have MERS‐CoV and non‐MERS‐CoV infection in which 396 achieved recovery. Detailed medical charts were reviewed for each patient who achieved recovery. Time intervals in days were calculated from presentation to the initial rRT‐PCR diagnosis (diagnosis delay) and from the initial rRT‐PCR diagnosis to recovery (recovery delay).ResultsThe median recovery delay in our sample was 5 days. According to the multivariate negative binomial model, elderly (age ≥ 65), MERS‐CoV infection, ICU admission, and abnormal radiology findings were associated with longer recovery delay (adjusted relative risk (aRR): 1.741, 2.138, 2.048, and 1.473, respectively). Camel contact and the presence of respiratory symptoms at presentation were associated with a shorter recovery delay (expedited recovery) (aRR: 0.267 and 0.537, respectively). Diagnosis delay is a positive predictor for recovery delay (r = .421; P = .001).ConclusionsThe study evidence supports that longer recovery delay was seen in patients of older age, MERS‐CoV infection, ICU admission, and abnormal radiology findings. Shorter recovery delay was found in patients who had camel contact and respiratory symptoms at presentation. These findings may help us understand clinical decision making on directing hospital resources toward prompt screening, monitoring, and implementing clinical recovery and treatment strategies.

show abstract

Spatial Modelling of Contribution of Individual Level Risk Factors for Mortality From Middle East Respiratory Syndrome Coronavirus in the Arabian Peninsula

Abstract: Background

Cited by 8 publications

References 18 publications

Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020)

Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020)

The transmission dynamics of Middle East Respiratory Syndrome coronavirus

Factors associated with recovery delay in a sample of patients diagnosed by MERS‐CoV rRT‐PCR: A Saudi Arabian multicenter retrospective study

Contact Info

Product

Resources

About