[Risk of transmission of SARS-CoV-2: multi-criteria spatial evaluation in a municipality of Colombia, 2020]

OBJECTIVE

To zoning the risk of SARS-CoV-2 transmission in Villavicencio, Colombia, through a multi-criteria spatial evaluation.

MATERIALS AND METHODS

A multi-criteria evaluation model was implemented, through a hierarchical analysis process, integrated into a Geographic Information System. As criteria, descriptive attributes of the threats and vulnerabilities of viral transmission identified by means of an epidemiological model were included, on the same dimensionless numerical scale and proportional to the probability of contagion; the alternatives evaluated correspond to spatial entities represented by pixels. The criteria were weighted according to the expert judgment of the evaluators, with whom the calculation of a normalized matrix of relative priorities was performed, which allowed the estimation of a vector of weights, the degree of inconsistency of which was admissible. The magnitude of the risk was calculated with a weighted summation of the evaluation of the criteria, according to a map algebra geoprocessing.

RESULTS

The spatial heterogeneity of the risk of SARS-CoV-2 transmission was described in Villavicencio, allowing the identification of the areas with the highest probability of transmission, located in neighborhoods characterized by high socioeconomic vulnerability.

CONCLUSIONS

The cartographic representation derived from the implementation of a multicriteria model, integrated to a Geographical Information System, in the SARS-CoV-2 transmission risk analysis, constitutes a relevant methodological contribution for decision-making defining strategies of mitigation at the local level, facilitating the location and optimization of resources by the health authorities.

[Estimation of serial interval and basic reproductive number for imported cases of COVID-19]

OBJECTIVE

To estimate the serial interval and the basic reproduction number of COVID-19 between imported cases during the containment phase in Pereira-Colombia, 2020.

METHOD

A quantitative study was carried out to determine the transmission dynamics for COVID-19. Field epidemiological data were used, which included 12 laboratory-confirmed cases with RT-PCR for imported SARS-CoV-2 and their corresponding confirmed secondary cases, including family and social contacts.

RESULTS

The serial intervals in COVID-19 fit a Gamma distribution, with a mean of the serial interval of 3.8 days (2.7) and an R0 of 1.7 (95% CI 1.06-2.7) lower than that found in other populations with onset of the outbreak.

CONCLUSIONS

A serial interval lower than the incubation period such as that estimated in this study, suggests a presymptomatic transmission period that according to other investigations reaches an average peak at 3.8 days, suggesting that during the field epidemiological investigation the search for contacts Narrowing is performed from at least 2 days before the onset of symptoms of the initial case.

A new fuzzy fractal control approach of non-linear dynamic systems: The case of controlling the COVID-19 pandemics

This article is presenting a first attempt on a proposed fuzzy fractal control method for efficiently controlling nonlinear dynamic systems. The main goal is to combine the main advantages of fractal theoretical concepts and fuzzy logic theory for achieving efficient control of nonlinear dynamic systems. The concept coming from Fractal theory, known as the fractal dimension, can be utilized to measure the complexity of the dynamic behavior of a non-linear plant. On the other hand, fuzzy logic theory can be used to represent and capture the expert knowledge in controlling a plant. In addition, fuzzy logic enables to manage the uncertainty involved in the decision-making process for achieving efficient control of a non-linear plant. We illustrate the proposed fuzzy fractal control method with the current worldwide situation that requires achieving an efficient control of the COVID-19 pandemics.

Estimación de Parámetros para un Modelo del SARS-CoV-2 en Ecuador en Presencia de Incertidumbre

En este artículo presentamos el modelo matemático de tipo compartimental con individuos asintomáticos utilizado por el Centro de Modelización Matemática (MODEMAT) para estudiar la propagación del SARS-CoV-2 en Ecuador, así como el esquema variacional de tipo bayesiano desarrollado para estimar los diferentes parámetros del modelo, en presencia de incertidumbre de los datos observados. Esta estimación permite realizar actualizaciones periódicas del número efectivo de reproducción, así como proyecciones a corto plazo, mediante métodos de ensamble de la incidencia de la epidemia.

Statistical data driven approach of COVID-19 in Ecuador: R 0 and R t estimation via new method

The growth of COVID-19 pandemic throughout more than 213 countries around the world have put a lot of pressures on governments and health services to try to stop the rapid expansion of the pandemic. During 2009, H1N1 Influenza pandemic, statistical and mathematical methods were used to track how the virus spreads around countries. Most of these models that were developed at the beginning of the XXI century are based on the classical susceptible-infected-recovered (SIR) model developed almost a hundred years ago. The evolution of this model allows us to forecast and compute basic and effective reproduction numbers (R t and R 0 ), measures that quantify the epidemic potential of a pathogen and estimates different scenarios. In this study, we present a traditional estimation technique forR 0 with statistical distributions by best fitting and a Bayesian approach based on continuous feed of prior distributions to obtain posterior distributions and computing real timeR t . We use data from COVID-19 officially reported cases in Ecuador since the first confirmed case on February 29th. Because of the lack of data, in the case ofR 0 we compare two methods for the estimation of these parameters below exponential growth and maximum likelihood estimation. We do not make any assumption about the evolution of cases due to limited information and we use previous methods to compare scenarios aboutR 0 and in the case ofR t we used Bayesian inference to model uncertainty in contagious proposing a new modification to the well-known model of Bettencourt and Ribeiro based on a time window of m days to improve estimations. EcuadorianR 0 with exponential growth criteria was 3.45 and with the maximum likelihood estimation method was 2.93. The results show that Guayas, Pichincha and Manabí were the provinces with the highest number of cases due to COVID-19. Some reasons explain the increased transmissibility in these localities: massive events, population density, cities dispersion patterns, and the delayed time of public health actions to contain pandemic. In conclusion, this is a novel approach that allow us to measure infection dynamics and outbreak distribution when not enough detailed data is available. The use of this model can be used to predict pandemic distribution and to implement data-based effective measures.

Factores epidemiológicos Ro y Re durante la COVID-19: ¿qué son y en qué difieren?

Las enfermedades infecciosas son aquellas causadas por microbios patógenos como algunas bacterias, virus, parásitos, entre otros. Estas enfermedades pueden transmitirse, directa o indirectamente, de un individuo a otro 1. La epidemiología estudia los procesos de salud y enfermedad que afectan a las poblaciones. Se interesa por conocer las características de los grupos que se ven afectados; cómo se distribuyen geográficamente y en el tiempo los eventos de salud y enfermedad; con qué frecuencia se manifiestan y cuáles son las causas o factores asociados a su surgimiento 2.

Deriving risk maps from epidemiological models of vector borne diseases: State-of-the-art and suggestions for best practice

Epidemiological models (EMs) are widely used to predict the temporal outbreak risk of vector-borne diseases (VBDs). EMs typically use the basic reproduction number (R₀), a threshold quantity, to indicate risk. To provide an overall view of the risk, these model outputs can be transformed into spatial risk maps, using various aggregation methods (e.g. average R₀ over time, cumulative number of days with R₀ > 1). However, there is no standardized methodology available for this. Depending on the specific aggregation methods used, the yielded spatial risk maps may have considerably different interpretations. Additionally, the method used to visualize the aggregated data also affects the perceived spatial patterns. In this review, we compare commonly used aggregation and visualization methods and discuss the respective interpretation of risk maps. Research publications using epidemiological modelling methods were drawn from Web of Science. Only publications containing maps of R₀ transformed from EMs were considered for the analysis. An example EM was applied to illustrate how aggregation and visualization methods affect the final presentations of risk maps. Risk maps can be generated to show duration, intensity and spatio-temporal dynamics of potential outbreak risk of VBDs. We show that 1) different temporal aggregation methods lead to different interpretations; 2) similar spatial patterns do not necessarily bear the same meaning; 3) visualization methods considerably affect how results are perceived, and thus should be applied with caution. We recommend mapping both intensity and duration of the VBD outbreak risk, using small time-steps to show spatio-temporal dynamics when possible.

Uso de modelo predictivo para la dinámica de transmisión del COVID-19 en Colombia

Introducción: los coronavirus son una amplia familia de virus que logran causar enfermedades tanto en animales como en humanos. En humanos, se sabe que varios coronavirus ocasionan infecciones respiratorias que consiguen ir desde el resfriado común hasta enfermedades más complicadas como el síndrome respiratorio de Oriente Medio (MERS) y el síndrome respiratorio agudo severo (SRAS). El coronavirus que se ha manifestado más recientemente causa la enfermedad por coronavirus COVID-19. Objetivo: presentar un pronóstico con el uso del modelo lineal de Brown de la dinámica de transmisión del COVID- 19 en Colombia. Materiales y métodos: para desarrollar la investigación se utilizó la base de datos de las personas infectadas con el COVID-19, la información de los datos corresponde al período 6 de marzo 2020 al 10 de mayo de 2020.  Para su análisis de predicción se manejó el método de predicción modelo de BROWN, utilizando el paquete estadístico SPSS v.25. Resultados: se determinó por análisis de predicción que el número total de infectados por el COVID-19 en Colombia a 31 de agosto de 2020 estarán alrededor de 69848, de esta población 41110 corresponde a hombres y 28738 a mujeres Conclusiones: se evidenció una alta población de casos confirmados por  coronavirus en Colombia a 31 de agosto de 2020; esto pone en alerta la red pública hospitalaria del país, además que obliga a las personas y comunidades a mantenerse en cuarentena por la emergencia sanitaria.