Scielo RSS <![CDATA[Revista mexicana de ingeniería biomédica]]> http://www.scielo.org.mx/rss.php?pid=0188-953220210001&lang=es vol. 42 num. 1 lang. es <![CDATA[SciELO Logo]]> http://www.scielo.org.mx/img/en/fbpelogp.gif http://www.scielo.org.mx <![CDATA[Saliva analysis using FTIR spectroscopy to detect possible SARS-CoV-2 (COVID-19) virus carriers]]> http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0188-95322021000100001&lng=es&nrm=iso&tlng=es <![CDATA[The Impact of Staying at Home on Controlling the Spread of COVID -19: Strategy of Control]]> http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0188-95322021000100101&lng=es&nrm=iso&tlng=es Abstract In this paper, we present a new mathematical model to describe the evolution of the COVID-19 in countries under the state of emergency. Where the COVID-19 pandemic is sweeping country after country. The Italian and Moroccan authorities have declared a state of emergency in response to the growing threat of this novel coronavirus (COVID-19) outbreak by March 09 and 20, respectively. In-state of emergency, citizens cannot go out to public spaces without special authorization from local authorities. But after all these efforts exerted by these authorities, the number of new cases of the COVID-19 continues to rise significantly, which confirms the lack of commitment of some citizens. First, we aim to investigate the cause of new infections despite all strategies of control followed in these countries including media reports, awareness, and treatment, self-distancing and quarantine, by estimating the number of these people who underestimate the lives and safety of citizens and put them at risk. To do this, we use real data of the COVID-19 in Italy and Morocco to estimate the parameters of the model, and then we predict the number of these populations. Second, we propose an optimal control strategy that could be the optimal and the efficient way for the Moroccan and Italian authorities and other countries to make the state of emergency more efficient and to control the spread of the COVID-19. The model is analyzed for both countries and then to compare the implications of the obtained results. Numerical examples are provided to illustrate the efficiency of the strategy of control that we propose and to show what would have been happened in Morocco and Italy if this strategy of control was applied early. <![CDATA[Un Método para la Evaluación del Riesgo de Exposición al Virus de COVID-19 Usando los Datos de Locación]]> http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0188-95322021000100102&lng=es&nrm=iso&tlng=es Abstract One of the main reasons for the widespread dissemination of COVID-19 is that many infected people are asymptomatic. Consequently, they likely spread the virus to other people as they continue their everyday life. This emphasizes the importance for targeting high-risk groups for the diagnosis of COVID-19 (with real-time PCR techniques). However, the availability of the necessary technology and resources may be limited in certain towns, cities or countries. Thus, the challenge is to determine a criterion in order to prioritize the suspected cases most in need of testing. The aim of the present study was to develop a method for evaluating the risk of exposure to COVID-19 infection based on geolocation data. The risk is expressed as a score that will be instrumental in optimally applying the COVID-19 test to suspected cases representing the highest probability of exposure. It can be easily and quickly implemented with easily accessible open source tools. A simulation was herein conducted with data from four people, assigning infection to one of them. The results show the feasibility of assessing the risk of exposure with the new methodology. Additionally, the data obtained might provide insights into the sometimes complicated patterns of virus propagation.<hr/>Resumen Una de las principales razones del esparcimiento del COVID-19 es que muchas de las personas infectadas son asintomáticas. Así entonces, al continuar con su vida diaria estas personas contagiadas son susceptibles a contagiar el virus a otras personas sin siquiera imaginarlo. Actualmente el diagnostico de COVID-19 se lleva a cabo usando técnicas de PCR en tiempo real. Sin embargo, la disponibilidad de dichas pruebas puede ser limitada en algunos países o ciudades. En este sentido determinar un criterio que permita definir a cuáles casos sospechosos deben de ser aplicada la prueba resulta un reto importante. En este artículo se presenta un método que permite evaluar el riesgo de exposición de una persona al COVID-19 que está basado en el uso de los datos locación. El método propuesto puede ser rápida y fácilmente implementada utilizando herramientas de código abierto existentes actualmente. El método propuesto fue probado utilizando datos de cuatro personas simulando a uno de ellos como portador del virus. Los resultados muestran la factibilidad del método propuesto para evaluar el riesgo de exposición. Además, los datos que se obtienen pueden ser potencialmente utilizados para un mejor entendimiento de los patrones de dispersión del virus. <![CDATA[ANOVA en la comparación de tres métodos para rastrear COVID-19 en nueve países]]> http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0188-95322021000100103&lng=es&nrm=iso&tlng=es ABSTRACT A new coronavirus denominated first 2019-nCoV and later SARS-CoV-2 was found in Wuhan, China in December of 2019. This paper compares three mathematical methods: nonlinear regression, SIR, and SEIR epidemic models, to track the covid-19 disease in nine countries affected by the SARS-CoV-2 virus, to help epidemiologists to know the disease trajectory, considering initial data in the pandemic, mainly 100 days from the beginning. To evaluate the results obtained with the three methods one-way ANOVA is applied. The average of predicted infected cases with SARS-CoV-2, obtained with the mentioned methods was: for United States of America 1,098,508, followed by Spain with 226,721, Italy with 202,953, France with 183,897 United Kingdom with 182,190, Germany with 159,407, Canada with 58,696, Mexico with 50,366 and Argentina with 4,860 in average. The one-way ANOVA does not show a significant difference among the results of the projected infected cases by SARS-CoV-2, using nonlinear regression, SIR, and SEIR epidemic methods. The above could mean that initially any method can be used to model the pandemic course.<hr/>RESUMEN Un nuevo coronavirus denominado primero 2019-nCoV y más tarde SARS-CoV-2 fue encontrado en Wuhan, China en diciembre de 2019. El objetivo de este trabajo es comparar tres métodos matemáticos: regresión no lineal, modelos epidemiológicos SIR y SEIR, para rastrear la enfermedad del COVID-19 en nueve países infectados por el virus SARS-CoV-2, con el propósito de ayudar al epidemiólogo a conocer el curso de la pandemia, considerando principalmente sus primeros 100 días. Para evaluar los resultados obtenidos de la aplicación de los tres métodos, se aplicó ANOVA de una vía. El número promedio de casos infectados con SARS-CoV-2, obtenidos con los tres métodos descritos son: para Estados Unidos 1,098,508, seguido de España con 226,721, Italia con 202,953, Francia con 183,897 Reino Unido con 182,190, Alemania con 159,407, Canadá con 58,696, México con 50,366 y Argentina con 4,860 en promedio. El ANOVA de una vía no muestra diferencias significativas entre los resultados de los casos infectados proyectados por SARS-CoV-2, utilizando la regresión no lineal y los métodos SIR and SEIR. Lo anterior podría señalar que cualquiera de los tres métodos estudiados puede modelar el curso de la pandemia en las condiciones descritas para cada uno.