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Epidemiological Characteristics of COVID-19 Confirmed Cases in Muscat Governorate, Sultanate of Oman

Open Journal of Epidemiology 2021年第1期11卷 56-69页

作者： Lamya Al Balushi Fatma Al Fahdi Thamra Al Ghafri Mohammed Amin Jeffrey Singh Balqees Al Siyabi Mariam Al Kalbani Maya Al Kindi Fatma Al Balushi Haleema Al Ghazaili Malak Al Alwai salwa Al Mashari Hanan Al Kindi Ahlam Al Rumhi Ibtisam Al Shidhani Zainb Al Balushi Padmamohan J. Kurup Fatma Al Ajmi Disease Surveillance and Control Directorate General for Health Services Muscat Governorate Ministry of Health Muscat Oman Directorate General for Health Services Muscat Governorate Ministry of Health Muscat Oman Primary Health Care Directorate General for Health Services Muscat Governorate Ministry of Health Muscat Oman Directorate General of Khoula Hospital Muscat Oman Directorate General for Disease Surveillance and Control Ministry of Health Muscat Oman

Introduction: The coronavirus disease 2019 (COVID-19) was declared as pandemic by WHO by March 11th. First case reported in Oman was on 24th February 2020 and later the country went through stages of epidemic progression. This study describes the sociodemographic and epidemiological characteristics of confirmed COVID-19 cases in Muscat governorate and related outcomes. Materials and Methods: This is a descriptive, exploratory analysis of all lab confirmed COVID 19 cases that were reported from 1st February to 31st May 2020. Data for the study was primarily extracted from notifications system established for surveillance (Tarassud). Secondary data sources were, contact listings and hospital medical records. Results: 11,648 initial cases of confirmed COVID-19 infections were included. The mean age was 35 years, 84.7% (N = 9862) were males, 25.9% (N = 3017) were Omanis, and 74.1% (N = 8631) were expatriates of which Indian origin were the majority (37%). Fever and cough were the most common presentations (46.3% and 29.5% respectively). Diabetes and hypertension were the most common comorbidities (4.9% and 4.6% respectively). Hospital admission was required for 7% (N = 811) of the total reported cases, out of them 171 cases (21%) were admitted to ICU, where 107 (13.2%) were ventilated. The case fatality rate (CFR) was 0.9%. 158 clusters containing 2949 contacts were identified from case records and categorised into 3 groups based on their exposure settings. The incubation period measured was 8 days (IQR 4.0 - 15.0) for workplace, 8 days (IQR 4 - 17) for dormitory and 4 days (IQR 2.0 - 7.0) for family groups. The secondary attack rate (SAR) estimated was 41.6% (95% CI: 0.34 - 0.48), 52% (95% CI: 0.40 - 0.63) and 33% (95% CI: 0.27 - 0.38) for workplace, dormitory and family groups, respectively. Conclusion: Results of this study, determine the transmission trend of COVID-19 in a country with high immigran

关键词： COVID-19 Epidemiological Characteristics Infectious Disease Cluster

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Data Envelopment Analysis Evaluation Framework of Hotel Services

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通讯和计算机（中英文版） 2013年第2期10卷 193-201页

作者： Noor Afni Deraman Junaida Md Said Ana salwa Shafie Nor Rashidah Paujah @ Ismail Fadhlina Izzah Saman Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA (Perak) 32610 Perak Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA (Melaka) Kampus Bandaraya 75300 Malaysia

Hotels are one of the serviced-based industries that contribute to the positive growth of modern economic of Malaysia through tourism activities. Due to this fact, the Malaysia government has included this sector into the Ninth Malaysia Plan and provided a lot of incentive to boost its performance. Thus, there is a need of mechanism to measure the performance of this industry to ensure it is on a good track. This research aims at designing a framework of DEA (data envelopment analysis) in order to evaluate the performance of hotels. The main objectives of this research are to develop DEA framework for analyzing hotel performance and to evaluate the DEA framework using a set of data. For this purpose, a CCR models has been adopted and the implementation is done using the LINGO software. Based on previous study, seven inputs and three outputs as indicator of the hotel efficiency and productivity have been identified. Using these variables, a set of questionnaire has been constructed and distributed to 20 hotels in Ipoh. Only 16 hotels gave cooperation but the sample is quite diversified. All of the data collected are used to test the model that measures the efficiency, rank and benchmarks of the hotels. The findings of this hotel, in year 2009, three hotels are efficient which rated as four star and three star hotels. The most inefficient hotel scored only 0.1125 which fall under one star hotel. Finally, the researchers suggest that a similar study is done using AI(Artificial Intelligence) overcome limitations of DEA approach.

关键词：三星级酒店数据包络分析服务框架 DEA方法 LINGO软件评价马来西亚 CCR模型

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