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Oct 2022 DOI 10.14302/issn.2643-2811.jmbr-22-4258
This work proposes a modification of a compartmental-type model based on the Susceptible-Exposed-Infected-Recovered (SEIR) scheme to describe the dynamics of contagion by Covid-19 in any part of the world, including also the numerical and analytical calculation of the mantissa and the resolution of a partial differential equation system. As an example, the different incidents that occurred in Singapore, Spain and Venezuela are analyzed to demonstrate the usefulness of the methodology developed in this work that can be seamlessly extended to other regions.
Dec 2020 DOI 10.14302/issn.2692-1537.ijcv-20-3635
This work characterizes the transmission dynamics of the cases registered by Covid-19 in Venezuela. The needed input data were obtained from the official gazettes issued by the Government of Venezuela, from March 15 to September 9, 2020. Later, the value of the mantissa was determined, revealing the impact of the different outbreaks with special attention to the events at the baseball stadium in Nueva Esparta State, and the Las Pulgas Market located in Maracaibo. Finally, a mathematical model based on four epidemic waves revealed that the cases are increasing significantly over time after the episode that occurred in the Las Pulgas Market.
Mar 2022 DOI 10.14302/issn.2692-1537.ijcv-22-4108
In this work we present how it is possible to visualize the different coronavirus outbreaks from the calculation of the mantissa from the daily record of cases by Covid-19.This curve can also indicate the speed of propagation of the disease from the calculated infection rate. These conclusions are derived from the respective calculations in four different countries: Australia, Brazil, Italy, and Venezuela.
Nov 2021 DOI 10.14302/issn.2691-8862.jvat-21-3991
The objective of this paper is to apply datadriven discovery of dynamics modeling to obtain a system of differential equations that allows us to describe the transmission dynamics of Covid-19, based on the number of confirmed cases and deaths reported daily. This methodology was applied in four different countries: Brazil, Colombia, Venezuela, and the United States. The main advantage is that only one differential equation is needed to characterize the dynamic of Covid-19 without any mathematical assumption.
Aug 2021 DOI 10.14302/issn.2692-1537.ijcv-21-3918
Mathematical and computational studies of Covid-19 have underestimated the influence that other countries have on their daily records. To visualize this, a Granger causality analysis was implemented in Python to determine if the cases registered in Brazil, Chile, Colombia, Ecuador, Panama, Paraguay, Peru and the USA have any effect on Venezuela, and between all of them. Finally, this paper highlights the need to incorporate causality analysis employing only the cases of Covid-19 to improve mid and long term forecasts.
Aug 2020 DOI 10.14302/issn.2692-1537.ijcv-20-3498
The paper proposes a new visualization scheme for the registry of Covid-19 cases by calculating the mantissa of the registered ones, so there is no need of performing complicated mathematical calculations. As an example, six countries are randomly selected: Australia, Brazil, China, Colombia, Portugal and Venezuela. The results show that China is the only country that keeps the epidemic under control, while Australia begins a new outbreak after having previously controlled the epidemic. Colombia and Portugal show a very similar behavior of registered cases and, finally, we can see that Venezuela, Brazil, Portugal, and Colombia present a growth of cases that may trigger new outbreaks in the future. Results are obtained from data registered at Johns Hopkins University until July 18th, 2020.
Jul 2020 DOI 10.14302/issn.2692-1537.ijcv-20-3453
The present work analyzes the registered cases of Covid-19 throughout the world according the data registered at Johns Hopkins University. We selected 15 countries to analyze their data. In alphabetical order the countries are: Argentina, Australia, Brazil, Chile, China, Colombia, Germany, India, Italy, Mexico, Peru, Portugal, Spain, United States and Venezuela. With this information, three different studies were carried out. First, the data was validated using Benford's Law which is based on forensic techniques that allow us to guarantee the integrity of the information. Later, we calculated the value of the basic reproduction number (R0), ie., the number of secondary host infections caused by one primary host infection that helps us to determine if a country has an outbreak of Covid-19. Finally, we show that the best representation for the change in the number of cases in the time is to calculate the mantissa value, ie., the floating number obtained from the logarithm of the data.
May 2020 DOI 10.14302/issn.2692-1537.ijcv-20-3376
The goal of this paper is to analyze the registered cases of people who have been infected with Covid-19 registered from throughout the world, using a digital forensic analysis technique that is based on Benford's Law. Twenty-three countries were randomly chosen for this analysis: China, India, Germany, Brazil, Venezuela, Netherlands, Italy, Colombia, Russia, Norway, South Africa, Portugal, Singapore, United Kingdom, Chile, Ecuador, Egypt, Denmark, Ireland, France, Belgium, Australia and Croatia.. We calculate on the p-values based on Pearson χ2 and Mantissa Arc Test according to the results obtained with the first digit. If any country fails these two tests, a third proof will be carried out based on the Freedman-Watson test. The results indicated that results from Italy, Portugal, Netherlands, United Kingdom, Denmark, Belgium and Chile are suspicions of data manipulation because the numbers fail the Benford’s Law according to the results obtained until April 30, 2020. However, it is necessary to carry out further studies in these countries in order to ensure that they countries manipulate or altered the information.