Although most cases of coronavirus disease 2019 (COVID-19) have occurred in low-resource countries, little is known about the epidemiology of the disease in such contexts. Data from the Indian states of Tamil Nadu and Andhra Pradesh provide a detailed view into severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission pathways and mortality in a high-incidence setting. Reported cases and deaths have been concentrated in younger cohorts than would be expected from observations in higher-income countries, even after accounting for demographic differences across settings. Among 575,071 individuals exposed to 84,965 confirmed cases, infection probabilities ranged from 4.7 to 10.7% for low-risk and high-risk contact types, respectively. Same-age contacts were associated with the greatest infection risk. Case fatality ratios spanned 0.05% at ages of 5 to 17 years to 16.6% at ages of 85 years or more. Primary data from low-resource countries are urgently needed to guide control measures.

Although most COVID-19 cases have occurred in low-resource countries, there is scarce information on the epidemiology of the disease in such settings. Comprehensive SARS-CoV-2 testing and contact-tracing data from the Indian states of Tamil Nadu and Andhra Pradesh reveal stark contrasts from epidemics affecting high-income countries, with 92.1% of cases and 59.7% of deaths occurring among individuals <65 years old. The per-contact risk of infection is 9.0% (95% confidence interval: 7.5-10.5%) in the household and 2.6% (1.6-3.9%) in the community. Superspreading plays a prominent role in transmission, with 5.4% of cases accounting for 80% of infected contacts. The case-fatality ratio is 1.3% (1.0-1.6%), and median time-to-death is 5 days from testing. Primary data are urgently needed from low- and middle-income countries to guide locally-appropriate control measures.

Since the first coronavirus disease 2019 (COVID-19) outbreak appeared in Wuhan, mainland China on December 31, 2019, the geographical spread of the epidemic was swift. Malaysia is one of the countries that were hit substantially by the outbreak, particularly in the second wave. This study aims to simulate the infectious trend and trajectory of COVID-19 to understand the severity of the disease and determine the approximate number of days required for the trend to decline. The number of confirmed positive infectious cases [as reported by Ministry of Health, Malaysia (MOH)] were used from January 25, 2020 to March 31, 2020. This study simulated the infectious count for the same duration to assess the predictive capability of the Susceptible-Infectious-Recovered (SIR) model. The same model was used to project the simulation trajectory of confirmed positive infectious cases for 80 days from the beginning of the outbreak and extended the trajectory for another 30 days to obtain an overall picture of the severity of the disease in Malaysia. The transmission rate, β also been utilized to predict the cumulative number of infectious individuals. Using the SIR model, the simulated infectious cases count obtained was not far from the actual count. The simulated trend was able to mimic the actual count and capture the actual spikes approximately. The infectious trajectory simulation for 80 days and the extended trajectory for 110 days depicts that the inclining trend has peaked and ended and will decline towards late April 2020. Furthermore, the predicted cumulative number of infectious individuals tallies with the preparations undertaken by the MOH. The simulation indicates the severity of COVID-19 disease in Malaysia, suggesting a peak of infectiousness in mid-March 2020 and a probable decline in late April 2020. Overall, the study findings indicate that outbreak control measures such as the Movement Control Order (MCO), social distancing and increased hygienic awareness is needed to control the transmission of the outbreak in Malaysia.

Credit scoring is an important tool used by financial institutions to correctly identifydefaulters and non-defaulters. Support Vector Machines (SVM) and Random Forest (RF) are theArtificial Intelligence techniques that have been attracting interest due to their flexibility to accountfor various data patterns. Both are black-box models which are sensitive to hyperparameter settings.Feature selection can be performed on SVM to enable explanation with the reduced features,whereas feature importance computed by RF can be used for model explanation. The benefitsof accuracy and interpretation allow for significant improvement in the area of credit risk andcredit scoring. This paper proposes the use of Harmony Search (HS), to form a hybrid HS-SVM toperform feature selection and hyperparameter tuning simultaneously, and a hybrid HS-RF to tunethe hyperparameters. A Modified HS (MHS) is also proposed with the main objective to achievecomparable results as the standard HS with a shorter computational time. MHS consists of fourmain modifications in the standard HS: (i) Elitism selection during memory consideration insteadof random selection, (ii) dynamic exploration and exploitation operators in place of the originalstatic operators, (iii) a self-adjusted bandwidth operator, and (iv) inclusion of additional terminationcriteria to reach faster convergence. Along with parallel computing, MHS effectively reduces thecomputational time of the proposed hybrid models. The proposed hybrid models are comparedwith standard statistical models across three different datasets commonly used in credit scoringstudies. The computational results show that MHS-RF is most robust in terms of model performance,model explainability and computational time.

In Thane, female migrants faced higher vulnerabilities and risks of HIV infection than male migrants. Consequently, innovative strategies are required to address these particular needs of female migrants.