Internet-based surveillance systems for monitoring emerging infectious diseases

Abstract

Emerging infectious diseases present a complex challenge to public health officials and governments; these challenges have been compounded by rapidly shifting patterns of human behaviour and globalisation. The increase in emerging infectious diseases has led to calls for new technologies and approaches for detection, tracking, reporting, and response. Internet-based surveillance systems offer a novel and developing means of monitoring conditions of public health concern, including emerging infectious diseases. We review studies that have exploited internet use and search trends to monitor two such diseases: influenza and dengue. Internet-based surveillance systems have good congruence with traditional surveillance approaches. Additionally, internet-based approaches are logistically and economically appealing. However, they do not have the capacity to replace traditional surveillance systems; they should not be viewed as an alternative, but rather an extension. Future research should focus on using data generated through internet-based surveillance and response systems to bolster the capacity of traditional surveillance systems for emerging infectious diseases.

Assessing the impact of a health intervention via user-generated Internet content

“…Numerous works have provided statistical proof for the predictive capabilities of these resources with applications spreading across the domains of finance (Bollen et al 2011), politics (O'Connor et al 2010;Lampos et al 2013) and healthcare (Ginsberg et al 2009;Culotta 2010). Focusing on the domain of health, the development of models for nowcasting infectious diseases, such as influenza-like illness (ILI), 1 has been a central theme (Milinovich et al 2014). Initial indications that content from Yahoo's (Polgreen et al 2008) or Google's (Ginsberg et al 2009) search engine are good ILI indicators, were followed by a series of approaches using the microblogging platform of Twitter as an alternative, publicly available source Signorini et al 2011;Lamb et al 2013).…”

La vigilancia de los eventos sanitarios a través de los medios de comunicación en el Perú

“…La vigilancia de eventos sanitarios es una estrategia importante de la vigilancia basada en la notificación de casos, porque permite reconocer eventos que no son identificados por los sistemas de vigilancia tradicionales, principalmente eventos de poca magnitud como brotes de ETA o el inicio de epidemias, entre otros (5,6,13,14) .…”

“…Además, el estudio realizado por Gluskin et al (16) en 17 estados de México, demostró que la búsqueda de eventos de salud en el internet, como casos de dengue, es una herramienta Por otro lado, la vigilancia de eventos sanitarios en medios de comunicación que conduce el CDC del MINSA ha contribuido a mejorar la respuesta a los brotes, epizootias y EVISAP, porque ha promovido que el nivel local y regional investigue un importante número de eventos no notificados por los sistemas regulares. La mejora en la respuesta frente a brotes y eventos de importancia en salud pública, también ha sido señalado en otros estudios y por OMS (1,17) , por ejemplo, han servido para desencadenar alertas sobre brotes de influenza, o potenciales emergencias de salud pública (5) , que han activado la respuesta rápida de los servicios de salud pública. En este sentido, estos sistemas han permitido mejorar la respuesta y el control de estos eventos y, por ende, contribuyen al mejoramiento de la salud pública, como se ha mencionado en algunas revisiones sistemáticas de otros sistemas basados en medios de comunicación y redes sociales (15,17) .…”

Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study (Preprint)

“…[8] Alternative strategies have been proposed, including using different data sources, such as meteorological or demographic data, combined with ILI surveillance network data, [9][10][11] or big data, particularly web data. [12] With over 3.2 billion web users, data flows from the internet are huge and of all types. They can be from social networks (e.g, Facebook,Twitter), viewing sites, (e.g, YouTube, Netflix), shopping sites, (e.g, Amazon, Cdiscount), but also from sales or rentals website between particulars (e.g, Craigslist, Airbnb).…”