Artificial Intelligence in Public Health and Epidemiology

Thiébaut, Rodolphe; Thiessard, Frantz

As expected in this section of the Yearbook, the use of digital sources for infectious diseases surveillance leads to many research reports [11] . The originality here is the use of data coming from the EHR [12] , Twitter [8] , or the climate data produced by the US National Aeronautics and Space Administration (NASA) with software [13] dedicated to following the occurrence of infectious disease epidemics of either influenza [8, 12] or malaria [13] . All these studies demonstrating the feasibility of new approaches for the surveillance of infectious diseases still need to be validated for confirming their predictive accuracy and generalizability. Interestingly, we also found studies reporting the results of the surveillance of non-infectious diseases, e.g., road traffic crashes [14] and elevated blood pressure [15] . Road traffic injuries represent a public health issue in low-income countries [16] . Therefore, improvement of surveillance systems is required. Bonnet et al. [14] have experimented a simple affordable approach based on the city's National Police road crash intervention service equipped with geotracers that geolocalized the crash sites and sent their positions by short message service (SMS) to a surveillance platform developed by using the open-source tool, Ushahidi. This system implemented in partnership with the National Police in the city of Ouagadougou required acceptance by officers and authorities. In the other study, the authors showed a new validation of the use of EHR for public health purposes. Here they reproduced the seasonability of blood pressure variations (with a peak in summer) based on the data extracted from EHRs [15] .