Disease Spectrum in COVID-19 Cohort with Travel History from Iran

Background: Coronavirus disease 2019 (Covid-19), declared as a pandemic in March 2020, is an acute respiratory tract illness caused by coronavirus 2 (SARS-CoV2) with clinical manifestations ranging from mild upper respiratory tract symptoms to severe pneumonia. Objectives: To determine the disease spectrum of Covid-19 in a cohort with a travel history from Iran. Materials & Methods: This cross-sectional study with a retrospective collection of data was conducted at Agha Khan University, Karachi from 15th March to 19th April 2020. One hundred and fifty-five laboratory-confirmed cases of Covid-19 were recruited from a government quarantine facility. Data were obtained from the Punjab Emergency Services (Rescue 1122) database where a record of SARS-CoV-2 positive cases and quarantined persons is maintained. Study subjects with a travel history to Iran were contacted by telephone to obtain information about demographics, symptoms, and co-morbid conditions.  SPSS version 24 was used to analyze the data. Frequencies and percentages were calculated. Results: Among the returning travelers, 213 had laboratory-confirmed Covid-19, out of which 155 were included in this study. 56.1% were males with a mean age of 40 years. Among the study participants, 91.6% remained asymptomatic throughout the stay, while 8.4 % became symptomatic. 77.5% of the participants had received BCG vaccination in childhood. Among symptomatic cases 15.4% had asthma and 7.7% had hypertension. The most common clinical manifestation was cough which was present in 38.5% of the study participants. None died among the study participants. Conclusion: A mild presentation of COVID-19 was seen in our study participants with 91.6% among them being asymptomatic, while 8.4% were symptomatic. There was a high positivity rate in males as compared to females.  

A Novel Architecture to Integrate Multi-Sourcedatain Distributed Environment

The amount of data has been increasing over the last few years due to the emergence of various end-user applications. These applications utilize cloud computing infrastructure in the data centers. Apart from the increasing volume of data, there are other factors such as variety, velocity, and veracity of the data which result in the problem of big data. Traditional database management systems are not efficient to handle big data. The use of big data platform is necessary to resolve the big data problem. Hadoop is one of the platforms which resolve the problem of big data. Hadoop uses a distributed storage system. Hive and HBase are some of the big data tools for storing big data in Hadoop. They run on top of Hadoop distributed file system (HDFS). Hive is a data warehouse framework for querying and analysis of data that is stored in HDFS.?Hive?is an open-source software that lets programmers analyze large data sets on Hadoop. HBase is a column-oriented, distributed and high fault-tolerant database. It is used to store and manage big data. It can store billions of rows at a time. Both Hive and HBase can be used to store the big data in Hadoop. When the data comes from multiple sources, it is stored into multiple tables in Hive and HBase. As a result, its performance degrades when there is a need to perform join operations. In this thesis, we propose an architecture which stores data from multiple sources into a single HBase table. A new table schema with a unique row key is designed which integrates multi-source data in a table. There is no need to perform join operation in the proposed technique as the data is integrated into a single HBase table. We evaluated the proposed technique using a real testbed by considering a dataset of two publishers. We compare the performance by storing data into Hive and also in the proposed HBase table. Results show improved query performance of the proposed technique as compared to the traditional approach of using join operations in multiple tables in Hive.