Profile Analysis of COVID-19 Patients in Jambi Province

Background: The potential for COVID-19 transmission has increased sharply, so the government must implement various strategies to control the spread, especially in Jambi Province. The number of positive confirmed cases of COVID-19 in Jambi Province until August 26, 2021, was 27,422 people, with a case fatality rate is 2.37%. This condition illustrates that the spread of COVID-19 is increasing every day, so the government has set a lockdown at Level 4. Method: This research aims to analyze the profile of COVID-19 patients in Jambi Province (secondary data analysis) with a cross-sectional study design. Data analysis includes univariate analysis with the mean difference test and Chi-Square test. Result: The results show that the age of COVID-19 patients is significantly different between men and women. Furthermore, based on the Chi-Square test, it shows a significant relationship between age and gender and between region and age with a p-value <0.05. Conclusion: Indeed, the risk of COVID-19 cases increases with age and differs for each gender with a high level of mobility

Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model

In this article, a new hybrid time series model is proposed to predict COVID-19 daily confirmed cases and deaths. Due to the variations and complexity in the data, it is very difficult to predict its future trajectory using linear time series or mathematical models. In this research article, a novel hybrid ensemble empirical mode decomposition and error trend seasonal (EEMD-ETS) model has been developed to forecast the COVID-19 pandemic. The proposed hybrid model decomposes the complex, nonlinear, and nonstationary data into different intrinsic mode functions (IMFs) from low to high frequencies, and a single monotone residue by applying EEMD. The stationarity of each IMF component is checked with the help of the augmented Dicky–Fuller (ADF) test and is then used to build up the EEMD-ETS model, and finally, future predictions have been obtained from the proposed hybrid model. For illustration purposes and to check the performance of the proposed model, four datasets of daily confirmed cases and deaths from COVID-19 in Italy, Germany, the United Kingdom (UK), and France have been used. Similarly, four different statistical metrics, i.e., root mean square error (RMSE), symmetric mean absolute parentage error (sMAPE), mean absolute error (MAE), and mean absolute percentage error (MAPE) have been used for a comparison of different time series models. It is evident from the results that the proposed hybrid EEMD-ETS model outperforms the other time series and machine learning models. Hence, it is worthy to be used as an effective model for the prediction of COVID-19

A new probabilistic model with applications to the wind speed energy data sets

So far in the literature, the two-parameter Weibull distribution and its other extensions are frequently implemented to analyze the wind speed energy data sets. However, based on our study of the literature, there is no published work about analyzing the wind speed energy data sets using new probability distributions that are developed via trigonometric functions. In this paper, I attempt to cover this amazing and interesting research gap. Therefore, I incorporate a trigonometric function, especially, the sine function to introduce a new statistical distributional method. The proposed method is called a new modified sine-G family of distributions. Some distributional properties of the new modified sine-G method are obtained. Using the new modified sine-G method, a new extension of the Weibull distribution called a new modified sine-Weibull distribution is studied. The new modified sine-Weibull distribution is applied to analyze four wind speed energy data sets. All three data sets are taken from the weather station at Sotavento Galicia in the Canary Islands, Spain, located at 43.3544 North and 7.8812 West

Modified generalized Weibull distribution: theory and applications

Abstract This article presents and investigates a modified version of the Weibull distribution that incorporates four parameters and can effectively represent a hazard rate function with a shape resembling a bathtub. Its significance in the fields of lifetime and reliability stems from its ability to model both increasing and decreasing failure rates. The proposed distribution encompasses several well-known models such as the Weibull, extreme value, exponentiated Weibull, generalized Rayleigh, and modified Weibull distributions. The paper derives key mathematical statistics of the proposed distribution, including the quantile function, moments, moment-generating function, and order statistics density. Various mathematical properties of the proposed model are established, and the unknown parameters of the distribution are estimated using different estimation techniques. Furthermore, the effectiveness of these estimators is assessed through numerical simulation studies. Finally, the paper applies the new model and compares it with various existing distributions by analyzing two real-life time data sets

Statistical study for Covid-19 spread during the armed crisis faced by Ukrainians

Russia and Ukraine got into an armed conflict on 24th February 2022. In addition, the World Health Organisation still warns of a fast growth in infections and deaths. Infectious disease remains a serious issue in Ukraine and poorly governed cities, such as those in armed conflicts. During this period of security instability, the coronavirus situation in Ukraine is alarming and needs more attention. In this context, our focus in the current work is to model COVID-19 spread risk from Ukrainian international refugees in neighboring countries. This study aims to estimate the number of daily coronavirus cases among Ukrainian international refugees for informed decisions for the pandemics' spread risk. For that reason, we used “Coronavirus Pandemic (COVID-19)” data from “Our World in Data” (from 2020-03-03 to 2022-02-22) and the data about Ukrainian International Refugees provided by United Nations High Commissioner for Refugees related (from 2022-02-22 to 2022-03-11). We performed ARIMA, TBATS, and ETS and selected the best model. Through a cross-validation process, the findings revealed that around 6 individuals [95% CI: 5%–7%] over 10,000 Ukrainian international refugees are likely COVID-19 cases. ARIMA is the best model to fit the Ukrainian daily number of cases among the refugees fleeing the crisis. On average, they are daily 100 possible COVID-19 cases among Ukrainian international refugees and authorities and humanitarian actors need be informed decisions to control the pandemic and support refugees effectively

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population