[Interactive, semi-automatized and open source computational model applied to respiratory viruses surveillance]. / Modelo computacional interactivo, semi-automatizado y de código abierto aplicado a la vigilancia de virus respiratorios.

Acute respiratory infections (ARI) are an important cause of morbidity and mortality worldwide, affecting mainly children and the elderly. They are associated with a high economic burden, increased number of medical visits and hospitalizations. The surveillance of the circulation of respiratory viruses can reduce the health care associated costs, and to optimize the health response. A platform based on R and its package Shiny was designed, to create an interactive and friendly web interface for gathering, analysis and publication of the data. The data from the Chilean metropolitan respiratory viruses surveillance network, available since 2006, was uploaded into the platform. Using this platform, the researcher spends less than 1 minute to upload the data, and the analysis and publication is immediate, available to be seen by any user with a device connected to Internet, who can choose the variables to be displayed. With a very low cost, in a short time, and using the R programming language, it was possible to create a simple, and interactive platform, considerably decreasing the upload and analysis time, and increasing the impact and availability of this surveillance.

Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile.

Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM10 and PM2.5) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM10-PM2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filter-based sampler (Partisol 2000i). Variability between sensor units (n = 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM2.5 (normalized root-mean-square error 9-24%) and PM10 (10-37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM2.5 (R2 0.47-0.86) than PM10 (0.24-0.56). The correlations (R2) between the 24-h PM2.5 averages from the sensors and reference instruments were 0.63-0.87 for continuous monitoring and 0.69-0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM2.5 averages after correcting for RH.

Modelo computacional interactivo, semi-automatizado y de código abierto aplicado a la vigilancia de virus respiratorios / Interactive, semi-automatized and open source computational model applied to respiratory viruses surveillance

Resumen Las infecciones respiratorias agudas (IRA) causadas por virus son una importante causa de morbilidad y mortalidad en el mundo, afectando principalmente a niños y adultos mayores. Se asocian a un alto número de consultas y hospitalizaciones, a una significativa sobrecarga del sistema de salud y a un alto costo económico. La vigilancia de virus respiratorios tiene el potencial de ayudar a optimizar la respuesta sanitaria, garantizar la disponibilidad de recursos humanos, racionalizar los recursos y disminuir los costos asociados a la atención en salud. Con el objetivo de optimizar la recolección y visualización de los datos de nuestro actual sistema de vigilancia de virus respiratorios, se diseñó una plataforma basada en R y sus paquetes Shiny, que permite la creación de una interfase web interactiva y amigable para la recolección, análisis y publicación de los datos. Se ingresaron a esta plataforma los datos de la red de vigilancia metropolitana de virus respiratorios disponibles desde 2006. En esta plataforma, el investigador demora menos de un minuto en registrar los datos. El análisis y publicación es inmediato, llegando a cualquier usuario con un dispositivo conectado a Internet, quien puede elegir las variables a consultar. Con un costo muy bajo, en poco tiempo y utilizando el lenguaje de programación R, se logró crear un sistema simple e interactivo, disminuyendo el tiempo de carga y análisis de datos de forma considerable, posiblemente aumentando el impacto y la disponibilidad de esta vigilancia.

Abstract Acute respiratory infections (ARI) are an important cause of morbidity and mortality worldwide, affecting mainly children and the elderly. They are associated with a high economic burden, increased number of medical visits and hospitalizations. The surveillance of the circulation of respiratory viruses can reduce the health care associated costs, and to optimize the health response. A platform based on R and its package Shiny was designed, to create an interactive and friendly web interface for gathering, analysis and publication of the data. The data from the Chilean metropolitan respiratory viruses surveillance network, available since 2006, was uploaded into the platform. Using this platform, the researcher spends less than 1 minute to upload the data, and the analysis and publication is immediate, available to be seen by any user with a device connected to Internet, who can choose the variables to be displayed. With a very low cost, in a short time, and using the R programming language, it was possible to create a simple, and interactive platform, considerably decreasing the upload and analysis time, and increasing the impact and availability of this surveillance.