1,720,968 research outputs found
Determinantes de las tasas de mortalidad por COVID-19: un análisis de nivel macro mediante el modelo de regresión beta extendida
No full textObjective The specific mortality rate (MR) due to COVID-19 is a useful indicator for monitoring and evaluating the health strategies of health systems in the pandemic era. The main objective of this study is to estimate the effects of social, health, and economic factors on MRs in 176 countries.
Material and Methods Beta regression models were used, and MRs were estimated as the total number of deaths divided by the total number of confirmed cases (infection fatality rate) until December 2, 2021.
Results The primary findings revealed heterogeneity in mortality rates between regions and countries. The estimated coefficients showed different patterns of association between the explanatory variables and mortality rates. In the American region, the results showed a strange pattern and nearly insignificant effect for almost all variables. In Asian countries, we found a significant effect of GDP per capita and the share of the population aged 65 years and older on mortality rates, whereas on the African continent, the significant variables affecting mortality rates were GDP per capita, human development index, and share of population aged 65 years and older. Finally, in the European region, we did not find clear evidence of an association between the explanatory variables and mortality rates.
Conclusion These results show, in a heterogeneous way among regions, the impact of aging, development level and population density (especially with forms of distancing) on increasing the risk of death from the coronavirus. In conclusion, the pandemic has succeeded in demonstrating chaotic patterns of associations with social, health, and economic factors.Objetivo La tasa de mortalidad específica (TM) por COVID-19 es un indicador útil para monitorear y evaluar las estrategias de salud de los sistemas de salud en la era de la pandemia. El objetivo principal de este estudio es estimar los efectos de los factores sociales, de salud y económicos sobre las RM en 176 países.
Materiales y Métodos Se utilizaron modelos de regresión Beta y las RM se estimaron como el número total de muertes dividido por el número total de casos confirmados (tasa de letalidad por infección) hasta el 2 de diciembre de 2021.
Resultados Los principales hallazgos revelaron heterogeneidad en las tasas de mortalidad entre regiones y países. Los coeficientes estimados mostraron diferentes patrones de asociación entre las variables explicativas y las tasas de mortalidad. En la región americana, los resultados mostraron un patrón extraño y un efecto casi insignificante para casi todas las variables. En los países asiáticos, encontramos un efecto significativo del PIB per cápita y la proporción de la población de 65 años o más sobre las tasas de mortalidad, mientras que en el continente africano, las variables significativas que afectaron las tasas de mortalidad fueron el PIB per cápita, el índice de desarrollo humano y porcentaje de la población de 65 años y más. Finalmente, en la región europea, no encontramos evidencia clara de una asociación entre las variables explicativas y las tasas de mortalidad.
Conclusión Estos resultados muestran, de manera heterogénea entre regiones, el impacto del envejecimiento, el nivel de desarrollo y la densidad de población (especialmente con formas de distanciamiento) en el aumento del riesgo de muerte por coronavirus. En conclusión, la pandemia ha logrado demostrar patrones caóticos de asociaciones con factores sociales, de salud y económicos
Forecasting using Fuzzy Time Series
Get PDFThis chapter is a very short introduction to Fuzzy Time Series (FTS) models. The aim is to present an overview of the concepts of fuzzy logic, fuzzy set theory, and fuzzy time series framework. Accordingly, the chapter has a full application dimension of the FTS models as a main vocation. The R program was used to fit and forecast the principal FTS models, where real datasets of road traffic accidents in Algeria have been used. This chapter is organized as follows; the first section presents the concept of fuzzy logic, the second section is devoted to the Fuzzy Time Series, where we define a fuzzy set and universe of discourse. The third section summarizes the main models of fuzzy time series, precisely; we presented the (Song & Chissom, 1993) model, the (Chen, 1996) model, the Heuristic (Huarng, 2001) model, the (Abbasov & Mamedova, 2003) model, the (Chen & Hsu, 2004) model, and the (Singh, 2008) model. The fourth section is a case application of these models on the number of accidents in Algeria; the “AnalyzeTS” package of the R program was used to demonstrate the steps of estimation and forecasting
The Trajectory of Corona-virus and Peoples’ Concern in Africa: A Markov Switching Regression Model Based on Google Trends® Analysis
No full textCoronavirus pandemic put more pressures on people concerns and behavior in all countries. The aim is to estimate the dynamic correlations and potential effect of the virus spreading on the dynamic of people\u27s concern in four African countries (South Africa, Egypt, Nigeria and Algeria). Based on Google Trends analysis and dynamic of the virus outbreak from March 13, 2020 to August 28, 2020, two-state Markov Switching (MS) regression model was fitted. The findings revealed a weak-positive correlation between Google Trends and new confirmed cases for all countries. For causality inference, MS estimation showed a weak effect of the new confirmed cases on the waves of people interest over the study period. We think that virus spreading effect is vanished by other factors (such Media coverage). This work revealed the importance of web search tools (like Google Trends) in providing policy-makers utile information during periods of pandemic and health
Reserves, Prices, and Policy: An Empirical Analysis of Strategic Crop Reserves in Arab Countries
Get PDFSubject and purpose of work: In recent years, global food systems have faced challenges like disasters, extreme weather events, and market fluctuations, such as the Ukraine-Russia conflict. This study analyzes strategic crop reserves, specifically for wheat and rice, in Arab countries. It examines the objectives and obstacles associated with these reserves.
Material and methods: different statistical methods have been used, including regression analysis and neural network prediction models.
Results: Findings reveal significant agricultural production deficits in Arab economies. However, some countries maintain substantial crop reserves. We found an inverse relationship between wheat reserves and wheat prices . Additionally, energy prices correlate positively with agricultural commodity prices. Forecasting models anticipate short-term global grain stock stability but predict short-term increases in agricultural price indices (until 2024) followed by long-term decreases (by 2030).
Conclusions: Policymakers should support agricultural strategies, particularly for strategic crops. To address current challenges, we suggest securing long-term contracts for strategic crops, diversifying suppliers, and avoiding reliance on a few sources
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Proposal for a Unified Forecast Accuracy Index (UFAI): Toward Multidimensional and Context-Aware Forecast Evaluation
No full textForecast accuracy evaluation is a cornerstone in fields as diverse as finance, public health, energy, and meteorology. However, traditional reliance on single-error metrics—such as MAE, RMSE, or MAPE—offers only a fragmented view of a model’s performance, often obscuring critical dimensions like systematic bias, volatility, directional behavior, or shape fidelity. To overcome these limitations, this study proposes the Unified Forecast Accuracy Index (UFAI), a multidimensional and composite metric that consolidates several facets of forecasting quality into a single, interpretable score. UFAI integrates four normalized sub-indices—bias, variance, directional accuracy, and shape preservation—each capturing a distinct performance characteristic. The framework accommodates multiple weighting schemes: equal weighting for simplicity, expert-informed weighting to reflect domain-specific priorities, and data-driven weighting based on statistical principles such as Principal Component Analysis and entropy measures. This flexibility enables users to adapt the index to diverse forecasting objectives and application contexts. The article details the mathematical formulation of each sub-index, discusses the theoretical soundness and practical implications of different weighting strategies, and demonstrates the utility of UFAI through comparative model evaluations. Emphasis is placed on the index’s normalization, interpretability, robustness to outliers, and extensibility to future use cases such as multi-horizon and probabilistic forecasts. By offering a more integrated and context-aware assessment tool, the UFAI marks a significant advancement in forecast evaluation methodology, supporting more reliable model selection and ultimately enhancing decision-making in data-driven environments
Forecasting Models Based on Fuzzy Logic: An Application on International Coffee Prices
No full textIn recent decades, Fuzzy Time Series (FTS) has become a competitive, sometimes complementary, approach to classical time series methods such as that of Box-Jenkins. This study has two different purposes: a theoretical purpose, presenting an overview of the fuzzy logic and fuzzy time series models, and a practical purpose, which is to estimate and forecast monthly international coffee prices during the period 2000-2022. Analysing and forecasting the dynamics of coffee prices is of great interest to producers, consumers, and other market actors in managing and making rational decisions. The findings showed that international coffee prices exhibited significant fluctuations, with large increases and decreases influenced mainly by the level of top-ranked producers. The forecasted results revealed that a decrease in prices during the next six months (Jan 2023 to June 2023) is expected. Based on the results, it is also clear that the FTS models are more flexible and can be applied in forecasting time-series variables. At the same time, volatility and, sometimes, the unexpected swingsin coffee prices continue to draw more criticism and raise different issues regarding the roles of the markets and countries in ensuring food security.(original abstract
Volatility in Agricultural Commodities Prices, Case of Sugar Prices: Evidence of ARIMA-GARCH Family Models
No full textThis article focused on analyzing the volatility in agricultural commodities prices, where the class of ARMA models with ARCH errors were used. Maximum Likelihood and Least Squares estimates of the parameters of the model and their covariance matrices are noted and incorporated into techniques for the model building based upon the application of the usual Box-Jenkins methodology of identification, estimation, and diagnostic checking to the ARMA equation, the ARCH equation, and the full model. The techniques are applied to sugar prices daily time series over the period (1962-2020). It is seen that fits well the data among other competitive models
- …
