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Aug 2023 DOI 10.14302/issn.2641-9181.ijnr-23-4658
Oakley ReginaCorresponding author
Brucella species cause a high burden of disease globally, infecting both humans and animals; however, One Health has been under-appreciated in Colombia. This study aimed to determine the seroprevalence of Brucella spp. in two remote indigenous communities from the Sierra Nevada de Santa Marta, Colombia. These communities live in close contact with their livestock, indicating a potential susceptibility to zoonotic pathogens. The livestock routinely kept by these communities include cattle, small ruminants and pigs, the known hosts of human pathogenic Brucella spp.. A low level of exposure to Brucella spp. was documented, with only one positive participant among 539 participants (0.2%; 95% CI 0.0 – 1.0). Nevertheless, due to the high risk that zoonoses pose, we recommend discussions with the community for the potential establishment of One Health surveillance studies for the early detection and prevention of future zoonotic disease threats.
Nov 2021 DOI 10.14302/issn.2691-8862.jvat-21-3991
Isea RaúlCorresponding author
Fundación Instituto de Estudios Avanzados, Hoyo de la Puerta, Baruta, Venezuela.
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
Isea RaúlCorresponding author
Fundación Instituto de Estudios Avanzados, Hoyo de la Puerta, Baruta, Venezuela.
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
Isea RaúlCorresponding author
Fundación Instituto de Estudios Avanzados, Hoyo de la Puerta, Baruta. Venezuela.
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
Isea RaulCorresponding author
Fundación Instituto de Estudios Avanzados IDEA, Hoyo de la Puerta, Baruta, Venezuela
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
Isea RaulCorresponding author
Fundación Instituto de Estudios Avanzados IDEA, Hoyo de la Puerta, Baruta, Venezuela
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.