Traditionally, surveillance systems for dengue and other infectious diseases locate each individual case by home address, aggregate these locations to small areas, and monitor the number of cases in each area over time. However, human mobility plays a key role in dengue transmission, especially due to the mosquito day-biting habit, and relying solely on individuals' residential address as a proxy for dengue infection ignores a multitude of exposures that individuals are subjected to during their daily routines. Residence locations may be a poor indicator of the actual regions where humans and infected vectors tend to interact more, and hence, provide little information for dengue prevention. The increasing availability of geolocated data in online platforms such as Twitter offers a unique opportunity: in addition to identifying diseased individuals based on the textual content, we can also follow them in time and space as they move on the map and model their movement patterns. Comparing the observed mobility patterns for case and control individuals can provide relevant information to detect localized regions with higher risk of dengue infection. Incorporating the mobility of individuals into risk modeling requires the development of new spatial models that can cope with this type of data in a principled way and efficient algorithms to deal with the ever-growing amount of data. We propose new spatial scan models and exploit geo-located data from Twitter to detect geographic clusters of dengue infection risk.
Objective: We develop new spatial scan models that use individuals' movement data, rather than a single location per individual, in order to identify areas with a high relative risk of infection by dengue disease.