Contents © 2013 Flávio Codeço Coelho - Powered by Nikola
Carl Egon Koppeschaar
The wide availability of Internet has propelled innovations on infectious disease monitoring, culminating in the development of innovative syndromic surveillance methods. Influenzanet monitors the incidence of influenza-like illness based on cohorts of self-reporting volunteers via the internet and has been active in The Netherlands and Belgium (2003), Portugal (2005), Italy (2008), United Kingdom (2009), Sweden (2011), France (2011), Spain (2012) and Ireland (2013). Similar systems have been active in Australia (Flu Tracking, 2007), Mexico (Reporta, 2009) and the United States (Flu Near You, 2011).
Building upon our earlier work related to detecting, modeling and forecasting the spreading of influenza-like illness (ILI), we are now including other infectious diseases in the self-reported participatory surveillance system. The symptoms questionnaires can be differentiated for different regions. In tropical regions the system can monitor dengue and leptospirosis and other tropical disease outbreaks in general, whereas in the temperate climate zones it can cover norovirus, lyme disease, Q-fever, EHEC, sexually transmitted diseases, legionella, measles, mumps and pertussis. At the same time this surveillance system could well include other types of (slower) epidemics (e.g. obesity), age-related and work-related health problems (such as asthma in the construction sector and skin diseases in the agro sector).
To achieve this goal we need to both produce and consume several types of geo-data. We will produce data on the spread of a disease by relying on volunteers and medical doctors who actively (e.g. through uploading data on websites and social media, or by using dedicated apps on smart phones) or passively (automatically uploading of data) produce a dense and high resolution spatio-temporal data set (position, time, health status). Such data can then be combined with census data to produce time resolved maps of developing epidemics.
Two of the most challenging aspects of complexity in the epidemiological context are the structure of interacting populations on many magnitude and spatial scales beyond and within international, interregional, intercultural and linguistic boundaries and their effective interaction by means of multi-length scale transportation and mobility networks.
Our data may be used for prospective computing, forecasting the spread of epidemics, and building different scenarios that would forecast the effect of containment/mitigation measures on the spread of the disease, thus creating a decision support environment for public health decision makers.