Objetivo: Probar mediante un modelo econométrico que la cantidad de los fallecidos en el Perú se relacionan de manera significativa con el número de casos infectados de COVID- 19. Método: Investigación de tipo básica, no experimental. Para todo el Perú se ha tomado 52 series (días) y en el caso específico del departamento de Moquegua, 37 series en el periodo del 16 de marzo al 10 de mayo del 2020. Se ha utilizado la base de datos del Ministerio de Salud, de la Sala situacional COVID-19 y de la Gerencia Regional de Salud de Moquegua; se emplea el R y R2 de Pearson. Se generaron modelos de regresión el 10 de mayo (luego de 52 días del primer fallecido), que deben ser contrastados el 31 de mayo. Resultados: Los modelos cumplen con la predicción, con un R2 y un Rho altos y significativos. Conclusiones: Los modelos de predicción de fallecidos son corroborados al 31 de mayo, luego de 73 días del primer fallecido en el Perú. Los niveles de correlación y determinación de Pearson en países que están saliendo de la emergencia del COVID-19, las regiones de todo el Perú tienen una alta y significativa relación entre infectados y fallecidos. A mayor número de infectados mayor número de fallecidos. En número y proporción son adultos y adultos mayores. En un 72,5% son varones. En Moquegua se demuestra que hay relación entre los niveles de exámenes diagnósticos realizados e infectados.
The study looks at how the Covid-19 wave was in Peru, where and when it begins, where and when it culminates. As it faced, the shortcomings that were detected and especially that very little could be done to confront the disease as an emerging country. The wave began in May and ended in August with the greatest number of deaths and then fell. Methodology: Basic, explanatory level, with SINADEF data by region, of the situation room, to get the number of deaths, between January and September 2020/2019. Results. The relationship between infected and deceased was found a Pearson Rho of 0.94. The total death toll model depends on Lima, Huánuco, and Piura. The differences between the deaths of 2019 and 2020 were corroborated with the ANOVA, where a bilateral sig of 0.042 was got. The COVID cycle is found in the cluster algorithm model, of the nine months in 44.4% of them, it generated the highest lethality, between May and August. Conclusion. It is proven that COVID devastated regions of Peru.The model generated by the K-Means algorithm tells us that the COVID-19 cycle began in March and reached its highest peak of deceased and then descended.
The objective of the research was to demonstrate the hypothesis that greater budgets do not generate greater procedural speed in the Constitutional Court of Peru (TC), restricting access to a reasonable time period between 1999-2020. At the methodological level, data was collected from 22 years of Constitutional Court (TC) sentences. From the Ministry of Economy and Finance (MEF), the accruals of the transferred budget. In addition, Pearson's Rho and discriminant analysis for clusters were used. The results highlight that the procedural burden depends on the actions of amparo, habeas corpus and non-compliance with an R2 of 99.93%. There is a delay in resolving lawsuits, while the budget grows. The Rho is 0.245. If the budget grows by 10%, publications would increase by 2.45%. Remunerations grow 7 times in the period and resolutions by 2.36 times. The Rho ratio is 0.33. The discriminant analysis proves that, of the three stages, 1999-2002; 2003-2012; and 2013-2020, in the third stage productivity and celerity decrease, while budgets increase. The processed information allows concluding that the public management of the (TC) does not show procedural celerity in the resolution of the files, in terms of access to justice in a reasonable time.
El Covid-19 ha ocasionado una pandemia mundial, que ha afectado gravemente a la economía de las familias. El objetivo del trabajo fue analizar los efectos del Covid-19 en el empleo y los ingresos familiares del sur del Perú, en periodo de cuarentena por emergencia sanitaria autorizada por el gobierno para mitigar y prevenir la pandemia. Es un estudio básico de nivel descriptivo, con diseño no experimental de nivel explicativo, apoyado en datos cuantitativos. En la aplicación del Chi cuadrado los resultados mostraron que las familias de altos ingresos fueron afectadas en un 30%, las familias de bajos ingresos han sido afectadas hasta un 100%, el aislamiento afectó el 49% de empleos y el ingreso familiar disminuyó en 92% de hogares. La perspectiva del 50% considera que habrá mayor desempleo. Se concluye que la cuarentena por Covid-19 afectó el empleo y los ingresos familiares, las familias de bajos ingresos fueron las más afectadas, el acceso al trabajo independiente se ha visto gravemente afectado por la pandemia.
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