A study of caesar cipher and transposition cipher in jawi messages

Cryptography known as art and science which is used to hide the messages that contain a few policy terminologies. These terminologies in cryptography are plaintext/ messages, ciphertext, encryption, decryption and key. Encryption is a proses to transform the plaintext together with key into ciphertext. Decryption is the reverse process of encryption. Caesar cipher and transposition cipher are two historical ciphers in cryptography. Caesar cipher is a monoalphabetic cipher. It is a substitution cipher which replace each letter in plaintext with another letter to form the ciphertext. Transposition cipher uses a technique which rearrangement letters in plaintext with a keyword and produce the ciphertext. Caesar cipher and Tansposition cipher both are commonly used to encrypt the English letters. The output of encrypted of English letters are known as ciphertext. The attacker can easily cryptanalysed the Caesar cipher by observing the frequency distribution English letters and ciphertext. For Transposition cipher, the cipher can be cracked by knowing the keyword. To date, there is no any research encrypt Jawi letters using Caesar cipher and Transposition cipher. Hence, in this paper encryption and decryption by using Caesar cipher and Transposition cipher in Jawi messages are proposed. Next, the security level of Caesar cipher and Transposition cipher in Jawi messages are compared. The result has shown that both ciphers are still not secure to protect the confidentiality of the Jawi messages

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Background: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. Methods: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). Results: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. Conclusions: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use

The value of open-source clinical science in pandemic response: lessons from ISARIC

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