ABSTRACT
Aim: This paper aimed to study the effect of the vaccine on the reproduction rate of coronavirus in Africa from January 2021 to November 2021. Subject and methods: Functional data analysis (FDA), a relatively new area in statistics, can describe, analyze, and predict data collected over time, space, or other continuum measures in many countries every day and is increasingly common across scientific domains. For our data, the first step of functional data is smoothing. We used the B-spline method to smooth our data. Then, we apply the function-on-scalar and Bayes function-on-scalar models to fit our data. Results: Our results indicate a statistically significant relationship between the vaccine and the rate of virus reproduction and spread. When the vaccination rate falls, the reproduction rate also decreases. Furthermore, we found that the effect of latitude and the region on the reproduction rate depends on the region. We discovered that in Middle Africa, from the beginning of the year until the end of the summer, the impact is negative, implying that the virus spread due to a decrease in the vaccination rates. Conclusion: The study found that vaccination rates significantly impact the virus's reproduction rate.
ABSTRACT
Purpose>The authors present the impact of the coronavirus disease 2019 (COVID-19) pandemic on community lifelines. The state machinery has several departments to secure essential lifelines during disasters and epidemics. Many countries have formed national disaster management authorities to deal with manmade and natural disasters. Typical lifelines include food, water, safety and security, continuity of services, medicines and healthcare equipment, gas, oil and electricity supplies, telecommunication services, transportation means and education system. Supply chain systems are often affected by disasters, which should have alternative sources and routes. Doctors, nurses and medics are front-line soldiers against diseases during pandemics.Design/methodology/approach>The COVID-19 pandemic has revealed how much we all are connected yet unprepared for natural disasters. Political leaders prioritize infrastructures, education but overlook the health sector. During the recent pandemic, developed countries faced more mortalities, fatalities and casualties than developing countries. This work surveys the impact of the COVID-19 pandemic on health, energy, environment, industry, education and food supply lines.Findings>The COVID-19 pandemic caused 7% reductions in greenhouse gas (GHG) emissions during global lockdowns. In addition, COVID-19 has affected social fabric, behaviors, cultures and official routines. Around 2.84 bn doses have been administrated, with approximately 806 m people (10.3% of the world population) are fully vaccinated around the world to date. Most developed vaccines are being evaluated for new variants like alpha, beta, gamma, epsilons and delta first detected in the UK, South Africa, Brazil, USA and India. The COVID-19 pandemic has affected all sectors in society, yet this paper critically reviews the impact of COVID-19 on health and energy lifelines.Practical implications>This paper critically reviews the health and energy lifelines during pandemic COVID-19 and explains how these essential services were interrupted.Originality/value>This paper critically reviews the health and energy lifelines during pandemic COVID-19 and explains how these essential services were interrupted.
ABSTRACT
The COVID-19 pandemic has appeared as the predominant disease of the 21st century at the end of 2019 and was a drastic start with thousands of casualties and the COVID-19 victims in 2020. Due to the drastic effect, COVID-19 scientists are trying to work on pandemic diseases and Governments are interested in the development of methodologies that will minimize the losses and speed up the process of cure by providing vaccines and treatment for such pandemics. The development of a new vaccine for any pandemic requires long in vitro and in vivo trials to use. Thus the strategies require understanding how the pandemic is spreading in terms of affected cases and casualties occurring from this disease, here we developed a forecasting model that can predict the no of cases and deaths due to pandemic and that can help the researcher, government, and other stakeholders to devise their strategies so that the damages can be minimized. This model can also be used for the judicial distribution of resources as it provides the estimates of the number of casualties and number of deaths with high accuracy, Government and policymakers on the basis of forecasted value can plan in a better way. The model efficiency is discussed on the basis of the available dataset of John Hopkins University repository in the period when the disease was first reported in the six countries till the mid of May 2020, the model was developed on the basis of this data, and then it is tested by forecasting the no of deaths and cases for next 7 days, where the proposed strategy provided excellent forecasting. The forecast models are developed for six countries including Pakistan, India, Afghanistan, Iran, Italy, and China using polynomial regression of degrees 3-5. But the models are analyzed up to the 6th-degree and the suitable models are selected based on higher adjusted R-square (R2 ) and lower root-mean-square error and the mean absolute percentage error (MAPE). The values of R2 are greater than 99% for all countries other than China whereas for China this R2 was 97%. The high values of R2 and Low value of MAPE statistics increase the validity of proposed models to forecast the total no cases and total no of deaths in all countries. Iran, Italy, and Afghanistan also show a mild decreasing trend but the number of cases is far higher than the decrease percentage. Although India is expected to have a consistent result, more or less it depicts some other biasing factors which should be figured out in separate research.