Using online social networks to track a pandemic: A systematic review

Abstract

OSN data contain significant information that can be used to track a pandemic. Different from traditional surveys and clinical reports, in which the data collection process is time consuming at costly rates, OSN data can be collected almost in real time at a cheaper cost. Additionally, the geographical and temporal information can provide exploratory analysis of spatiotemporal dynamics of infectious disease spread. However, on one hand, an OSN-based surveillance system requires comprehensive adoption, enhanced geographical identification system, and advanced algorithms and computational linguistics to eliminate its limitations and challenges. On the other hand, OSN is probably to never replace traditional surveillance, but it can offer complementary data that can work best when integrated with traditional data.

Estimando el número de casos de COVID-19 a tiempo real utilizando un formulario web a través de las redes sociales: Proyecto COVID-19-TRENDS

“…Desgraciadamente no detectaría los portadores asintomáticos. Sin embargo, creemos que nuestro enfoque es nuevo, Page 3 of 6 J o u r n a l P r e -p r o o f con bajo coste y rápidamente aplicable para detectar el porcentaje de población afectada cuando un brote de un agente infeccioso desborda los sistemas existentes o no se detecta mediante los sistemas convencionales de alerta temprana como sucedió con la COVID-19 [10].…”

A Review on Event-Based Epidemic Surveillance Systems that Support the Arabic Language

“…Related works of systems proposed for epidemics of seasonal influenza can be seen in the Google Flu Trends system [19], MappyHealth application [67], Tracking Flu Infections on Twitter [68], detecting influenza epidemics by analyzing Twitter messages [69], HealthTweets.org: a Platform for Public Health Surveillance Using Twitter [45], and the ARGO system for monitoring dengue fever epidemics [49]. For further reading on these types of systems, a systematic review of the literature conducted on proposed systems published between 2004 and 2015 that detect and track a pandemic using online social networks was published in 2016 by [70]. In addition, the most recent review was presented by Pollett et al [71] in 2017 for evaluating the internet-based biosurveillance performance of diseases caused by bacteria, parasites and viruses.…”

Identifying the influential spreaders in multilayer interactions of online social networks

“…, is associated an adjacency matrix = { }. Such a network can be represented by the set layer = [A [1] , A [2] , A [3] … … … . .…”

“…OSNs' lens provide researchers and scientists with exceptional prospects to understand individuals at scale and to analyze human behavioral patterns, otherwise impossible [1]. The data generated by OSNs users have been utilized in various applications [2][3][4]. The huge rise of OSNs driven by communication technology revolution has intensely renovated the platform of human interactions.…”