Cross-Validation Comparison of COVID-19 Forecast Models.

Many papers have proposed forecasting models and some are accurate and others are not. Due to the debatable quality of collected data about COVID-19, this study aims to compare univariate time series models with cross-validation and different forecast periods to propose the best one. We used the data titled "Coronavirus Pandemic (COVID-19)" from "'Our World in Data" about cases for the period of 31 December 2019 to 21 November 2020. The Mean Absolute Percentage Error (MAPE) is computed per model to make the choice of the best fit. Among the univariate models, Error Trend Season (ETS), Exponential smoothing with multiplicative error-trend, and ARIMA; we got that the best one is ETS with additive error-trend and no season. The findings revealed that with the ETS model, we need at least 100 days to have good forecasts with a MAPE threshold of 5%

Spatial econometric modeling of malaria distribution in Burkina Faso

This study focuses on the spatial distribution of malaria rate in Burkina Faso. For this purpose, we used 2020 geographical and confirmed malaria cases data from the National Institute of Statistics and Demography of Burkina Faso. We also accessed climate data on the French weather history website. We deployed relevant spatial statistical tools to address the notions of neighborhood matrix, spatial autocorrelation, and spatial heterogeneity between geographical observations. Ordinary Least Squares (OLS) regression model, Spatial Error Model (SEM), Spatial Autoregressive model (SAR), and the spatial Durbin Model (SDM) were computed using cross-validation to ensure the reliability of our findings. The Akaike information criterion (AIC) was used to select the most appropriate model for our study. The specification tests conclude that there is a spatial dependence between the observations. The SDM was chosen as the best-fitting data for modeling geolocated malaria rates. This outcome reveals that environmental indicators, population literacy rate, and rural population size by region significantly affect the country’s geolocated malaria rate. Policymakers can use these findings to make informed decisions related to malaria spread

A novel family of distributions: Properties, inequality measures and applications to socio economic development indicators

In this paper, we focused on two families of distributions: the Topp–Leone Kumaraswamy family and a novel proposed family of distributions. Subsequently, we explore their composition, leading to a novel family of distributions exhibiting compelling features for data modeling. Specifically, we examine a special member of this novel family, employing the inverse exponential distribution as the cumulative density function. We establish the mathematical properties, investigate the moments and the stochastic properties, and propose a parameter estimation method based on the maximum likelihood of the new model. To assess the applicability of our model, we gather data related to development indicators in Benin Republic. Additionally, employing competing models, we analyze some real-life data and compare the results to the novel distribution. Model performance is evaluated in terms of fitting observed data, and we conduct an in-depth interpretation of the outcomes. This study makes a significant contribution by introducing a novel family of distributions tailored for modeling development indicators. The findings of this research may have substantial implications for statistical analysis and decision-making in the context of Benin’s economic and social development

A new index to assess economic diplomacy in emerging countries

This paper aims to examine the role of economic diplomacy in attracting foreign capital to emerging countries, by developing a composite index measuring diplomatic activity. We focus on what extent does economic diplomacy influence the inflow of foreign capital to emerging countries. Then, we used data from fifty-five (55) developing economies in 2018. The composite index for diplomatic activity is constructed using principal component analysis. Further, we investigated the effect of this index on foreign capital inflows using linear regression based on the ordinary least squares method. The results indicate that the increase in the number of embassies alone does not significantly influence the evolution of diplomatic action. However, diplomacy plays a non-negligible role in attracting foreign capital. Our results demonstrate a positive and significant link between diplomacy and foreign funding, highlighting the importance of this tool for attracting investment and supporting growth in these countries. The findings of this work are going to serve both scientific and practitioners’ communities as it sheds light on the larger debate around the growing role of economic diplomacy in emerging countries in the context of globalization. Moreover, it provides a useful tool for measuring the effectiveness of foreign policies and their impact on economic expansion

Power Topp–Leone exponential negative family of distributions with numerical illustrations to engineering and biological data

This article puts forth a novel category of probability distributions obtained from the Topp–Leone distribution, the inverse-KK exponential distribution, and the power functions. To obtain this new family, we used the original cumulative distribution functions. After introducing this new family, we gave the motivations that led us to this end and the basis of the new family obtained, followed by the mathematical properties related to the family. Then, we presented the statistic order, the quantile function, the series expansion, the moments, and the entropy (Shannon, Reiny, and Tsallis), and we estimated the parameters by the maximum likelihood method. Finally, using real data, we presented numerical results through data analysis with a comparison of rival models

Power Lambert uniform distribution: Statistical properties, actuarial measures, regression analysis, and applications

Here, we present a new bounded distribution known as the power Lambert uniform distribution, and we deduce some of its statistical properties such as quantile function, moments, incomplete moments, mean residual life and mean inactivity time, Lorenz, Bonferroni, and Zenga curves, and order statistics. We presented different shapes of the probability density function and the hazard function of the proposed model. Eleven traditional methods are used to estimate its parameters. The behavior of these estimators is investigated using simulation results. Some actuarial measures are derived mathematically for our proposed model. Some numerical computations for these actuarial measures are given for some choices of parameters and significance levels. A new quantile regression model is constructed based on the new unit distribution. The maximum likelihood estimation method is used to estimate the unknown parameters of the regression model. Furthermore, the usability of the new distribution and regression models is demonstrated with the COVID-19 and educational datasets, respectively

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

Utilizing various statistical methods to model the impact of the COVID-19 pandemic on Gross domestic product

Gross Domestic Product (GDP) is one of the key macroeconomic aggregates that measures the added value produced in a country during a period. In the contemporary world, macroeconomic uncertainty, among others due to the COVID-19 pandemic and the conflict in Ukraine, and GDP prediction remain important goals in public policy making. This study aims to predict Benin's GDP through a unidimensional statistical approach and machine learning techniques. For this purpose, GDP data were collected from the Central Bank of the West African States (BCEAO) website from 1960 to 2021. The predictions are based on comparing classical statistical and machine learning methods. For the classical statistical methods, we investigated the Autoregressive Integrated Moving Average (ARIMA) and Error Trend Seasonality (ETS) forecasting models. As for the machine learning methods, the K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM) forecasting models proved to be sound. The findings revealed that the statistical models (ARIMA and ETS) better predict Benin's GDP. However, machine learning models (KNN and LSTM) also provide a wide range of results that can be used to analyze Benin's economic growth

A New Power Topp-Leone distribution with applications to engineering and industry data.

We introduced a brand-new member of the family that is going to be referred to as the New Power Topp-Leone Generated (NPTL-G). This new member is one of a kind. Given the major functions that created this new member, important mathematical aspects are discussed in as much detail as possible. We derived some functions for the new one, included the Rényi entropy, the qf, series development, and moment weighted probabilities. Moreover, to estimate the values of the parameters of our model that were not known, we employed the maximum likelihood technique. In addition, two actual datasets from the real world were investigated in order to bring attention to the possible applications of this novel distribution. This new model performs better than three key rivals based on the measurements that were collected

<i>Q</i>₁, <i>M</i>, <i>Q</i>₃, <i>S</i> and <i>K</i> for MPTLILx.

Q1, M, Q3, S and K for MPTLILx.</p