A comparison of transmissibility of SARS-CoV-2 variants of concern.

BACKGROUND: The World Health Organization (WHO) has currently detected five Variants of Concern of SARS-CoV-2 having the WHO labels of 'Alpha', 'Beta', 'Gamma', 'Delta' and 'Omicron'. We aimed to assess and compare the transmissibility of the five VOCs in terms of basic reproduction number, time-varying reproduction number and growth rate. METHODS: Publicly available data on the number of analyzed sequences over two-week windows for each country were extracted from covariants.org and GISAID initiative database. The ten countries which reported the highest number of analyzed sequences for each of the five variants were included in the final dataset and was analyzed using R language. The epidemic curves for each variant were estimated utilizing the two-weekly discretized incidence data using local regression (LOESS) models. The basic reproduction number was estimated with the exponential growth rate method. The time-varying reproduction number was calculated for the estimated epidemic curves by the ratio of the number of new infections generated at time step t to the total infectiousness of infected individuals at time t, using the EpiEstim package. RESULTS: The highest R0 for the variants Alpha (1.22), Beta (1.19), Gamma (1.21), Delta (1.38) and Omicron (1.90) were reported from Japan, Belgium, the United States, France and South Africa, respectively. Nine out of ten epidemic curves with the highest estimated growth rates and reproduction numbers were due to the Omicron variant indicating the highest transmissibility. CONCLUSIONS: The transmissibility was highest in the Omicron variant followed by Delta, Alpha, Gamma and Beta respectively.

Artificial intelligence framework for threat assessment and containment for covid-19 and future epidemics while mitigating the socioeconomic impact to women, children, and underprivileged groups

With the emergency situation that arises with COVID-19, the intense containment strategies adopted by many countries had little or no consideration towards socio-economic ramifications or the impact on women, children, socio­economically underprivileged groups. The existence of many adverse impacts raises questions on the approaches taken and demands proper analysis, scrutiny and review of the policies. Therefore, a framework was developed using the artificial intelligence (Al) techniques to detect, model, and predict the behaviour of the COVID-19 pandemic containment strategies, understanding the socio-economic impact of these strategies on identified diverse vulnerable groups, and the development of AI-based solutions, to predict and manage a future spread of COVID or similar infectious disease outbreaks while mitigating the social and economic toil. Based on generated behaviour and movements, Al tools were developed to conduct contact tracing and socio-economic impact mitigation actions in a more informed, socially conscious and responsible manner in the case of the next wave of COVID-19 infections or a different future infectious disease. © 2022, National Science Foundation. All rights reserved.

Spatial analysis of COVD-19 and socio-economic factors in Sri Lanka

The spread of the global COVID-19 pandemic affected Sri Lanka similar to how it affected other countries across the globe. The Sri Lankan government took many preventive measures to suppress the pandemic spread. To aid policy makers in taking these preventive measures, we propose a novel district-wise clustering based approach. Using freely available data from the Epidemiological Department of Sri Lanka. a cluster analysis was carried out based on the COVID-19 data and the demographic data of districts. K-Means clustering and spectral clustering models were the selected clustering techniques in this study. From the many district-wise socio-economic factors. population, population density, monthly expenditure and the education level were identified as the demographic variables that exhibit a high similarity with COVID-19 clusters. This approach will positively impact the preventive measures suggested by the relevant policy making parties of the Sri Lankan government.

Severe acute respiratory syndrome coronavirus 2 vaccines - a review of adverse effects and safety profiles

Since the emergence of SARS CoV-2 in 2019, the global population has had to implement major lifestyle changes in order to circumvent death and severe morbidity caused in the wake of the virus, as well as endure the consequences on the economy and social wellbeing. However, restrictions posed by the pandemic have not deterred scientists from engineering a solution to this in the form of vaccines against SARS CoV-2. Although their efficacy has been proven, recent reports have claimed that these vaccines may not be as safe as they are promoted to be, and that they may cause adverse effects that outweigh their benefits. This article offers an objective perspective of the unfavourable side effects of five widely used vaccine candidates for SARS CoV-2, BNT162b2, mRNA 1273, ChAdOx1 nCoV-19, Sputnik V and BBIBP CorV, to highlight that the panic associated with vaccination is unjustified in light of recent developments.

Source code and secondary data of the stochastic process based COVID-19 simulation model.

The novel coronavirus disease (COVID-19) culminated in a pandemic with many countries affected in varying stages. We aimed to develop a simulation environment for COVID-19 spread, taking environmental and social factors into account. This program consists of three main components; a stochastic process-based model for simulating epidemics, a basic reproduction number estimation unit and a graphics generator. The model can take a variety of environmental factors as input and simulate expected behaviours of the infection spread, enabling policymakers and the scientific community to test the effects of different mitigation strategies in a sandbox.

A stochastic process based modular tool-box for simulating COVID-19 infection spread.

Background: The novel coronavirus disease (COVID-19) culminated in a pandemic with many countries affected in varying stages. We aimed to develop a simulation environment for COVID-19 spread, taking environmental and social factors into account. Methods: The program was written in R language. A stochastic point process simulation model for simulating epidemics, a maximum-likelihood estimation model, an exponential growth rate model for calculating the basic reproduction number (R0), and functions for generating graphical representations of the simulations were utilized.Geographical area definition, population size, the number of initial infected individuals, period of simulation, parameters accounting for the radius of spread like masks usage, mobility level, intrinsic viral virulence, average infectious period, fraction of population vaccinated, time of vaccination, the efficacy of the vaccine, presence or absence of quarantine centers, time of establishment of quarantine centers, the efficacy of case detection and average time to quarantine from the detection of the infection were considered. Results: When the defined parameters were input, the model performed successfully producing the epidemic curve, R0 and an animation of infection spread. It was found that when parameters of known epidemics such as COVID-19 in California, Texas and, Florida were input, the epidemic curve generated was comparable to the epidemic curve in reality. Conclusion: This model can be utilized by many countries to visualize the effects of various mitigation strategies applied in their stage of disease and for policy makers to make informed decisions. It is applicable to many infectious diseases and hence can be used for research and educational purposes.