Determinants of mortality rates from COVID-19: a macro level analysis by extended-beta regression model / Determinantes de las tasas de mortalidad por COVID-19: un análisis de nivel macro mediante el modelo de regresión beta extendida

ABSTRACT Objective 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.

RESUMEN 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.

Time Series Investigation of Milk Production in Major States of India Using ARIMA Modeling

In India, white revolution was started during 1970’s with Operation flood programme. After this revolution, production of milk in India had tremendously increased. Contribution of diary sector has continuously increased in Indian Gross Domestic Product (GDP). Livestock sector has emerged as an essential growth driver of the Indian wealth. This study is associated with time series data of five major milk producing states in 2017-18 in India. The milk production projection has been made using Auto Regressive Integrated Moving average model (ARIMA) for year 2024-25. From the forecasted figures, Uttar Pradesh would be leading states of India in milk production with 37.68 MMT in year 2024-25. Whole India milk production would reach 252.948 MMT in year 2024-25. This projection helps in formulating national agricultural policy as well as proper planning for products into dairy sector