Comportamiento estocástico de la COVID-19 en la República Bolivariana de Venezuela. ¿Persistencia o antipersistencia en los contagios? / Stochastic behavior of covid-19 in the Bolivarian Republic of Venezuela: persistence or antipersistence in infections?

La novedosa pandemia por coronavirus, etiquetada por la Organización Mundial de la Salud OMS, 2020) como la covid-19, se reportó por primera vez en Wuhan, China, el 31 de diciembre de 2019 y a la fecha, según estimaciones de la misma OMS (2020), en la medida en que se ha extendido a nivel planetario, ha infectado a más de 9,2 millones de personas, de las cuales se reportan más de 500.000 fallecidos y 5.2 millones de pacientes recuperados. En este estudio, aplicaremos el exponente de Hurst (1951) asociado con la estadística fractal para simular la propagación de la covid-19, considerando series temporales de fluctuaciones de nuevos casos diarios de la enfermedad, disponibles a través de un sitio web de referencia de la República Bolivariana de Venezuela, como lo es el Observatorio Nacional de Ciencia, Tecnología e Innovación (ONCTI). Se parte de la hipótesis de que la propagación de la covid-19, puede analizarse en función de las fluctuaciones del crecimiento de nuevos casos diarios de contagio. Para ello, se empleó un método de rango reescalado R/S que permitió calcular el Exponente de Hurst, parámetro estocástico cuyo valor permitió inferir sobre la presencia de correlaciones de largo alcance en la transmisión del virus entre la población. Estudiaremos los efectos de correlación en la propagación de COVID-19 en Venezuela mediante el análisis de las series temporales de nuevos casos después del decreto de Alerta dictado por el Ejecutivo Nacional que convocó a la ciudadanía a "quedarse en casa" mediante una cuarentena social obligatoria. Simularemos el comportamiento a mediano plazo (180 días) considerando las fluctuaciones de los nuevos casos de contagio diarios sobre la base de dos factores: los casos de contagio importados y los contagios comunitarios. En consecuencia, inicialmente examinaremos el origen de correlaciones con grandes fluctuaciones, y posteriormente analizaremos en base a las series de tiempo de nuevos casos diarios de la covid-19 en Venezuela, para luego establecer las correlaciones de largo alcance e inferir sobre la posible la persistencia o antipersistencia de la misma(AU)

The novel coronavirus pandemic, labeled by the World Health Organization (WHO) as Covid-19, was first reported in Wuhan, China, on December 31, 2019. To date, according to the WHO's estimates, it has infected more than 9.2 million people, of whom more than 500,000 are reported dead, and 5.2 million patients have recovered. To deepen in its study, we apply the Hurst exponent, associated with fractal statistics to simulate the spread of Covid-19, considering time series of fluctuations of new daily cases, which are available in a site reference website of the Bolivarian Republic of Venezuela, such as the National Observatory for Science, Technology, and Innovation (ONCTI). This work starts from the hypothesis that the spread of Covid-19 can be analyzed based on the fluctuations in the increase of new daily cases. For this, an R / S rescaled range method was used that allowed calculating the Hurst Exponent, a stochastic parameter whose value allowed inferring the presence of long-range correlations in the virus transmission among the population. We will study the correlation effects in the spread of COVID-19 in Venezuela by analyzing the time series of new cases after the alert decree issued by the Executive branch, which called on citizens to "stay at home" through a mandatory social quarantine. Consequently, initially, we will examine the origin of correlations with large fluctuations, followed by an analysis based on the time series of new daily cases of Covid-19 in Venezuela, in order to establish the long-range correlations and infer about the possible persistence or anti persistence of it(AU)

Evaluation of genome similarities using a wavelet-domain approach

Abstract INTRODUCTION: Tuberculosis is listed among the top 10 causes of deaths worldwide. The resistant strains causing this disease have been considered to be responsible for public health emergencies and health security threats. As stated by the World Health Organization (WHO), around 558,000 different cases coupled with resistance to rifampicin (the most operative first-line drug) have been estimated to date. Therefore, in order to detect the resistant strains using the genomes of Mycobacterium tuberculosis (MTB), we propose a new methodology for the analysis of genomic similarities that associate the different levels of decomposition of the genome (discrete non-decimated wavelet transform) and the Hurst exponent. METHODS: The signals corresponding to the ten analyzed sequences were obtained by assessing GC content, and then these signals were decomposed using the discrete non-decimated wavelet transform along with the Daubechies wavelet with four null moments at five levels of decomposition. The Hurst exponent was calculated at each decomposition level using five different methods. The cluster analysis was performed using the results obtained for the Hurst exponent. RESULTS: The aggregated variance, differenced aggregated variance, and aggregated absolute value methods presented the formation of three groups, whereas the Peng and R/S methods presented the formation of two groups. The aggregated variance method exhibited the best results with respect to the group formation between similar strains. CONCLUSION: The evaluation of Hurst exponent associated with discrete non-decimated wavelet transform can be used as a measure of similarity between genome sequences, thus leading to a refinement in the analysis.