A new study co-authored by Dr. Yesim Tozan, assistant professor of Global Health with New York University College of Global Public Health, was published in PLOS Neglected Tropical Diseases titled “A combination of incidence data and mobility proxies from social media predicts the intra-urban spread of dengue in Yogyakarta, Indonesia.”
Recent studies have shown that Twitter can be utilized as a tool for health research, and aggregated large-scale social media data can indicate the risk of infectious disease in real-time with high accuracy and at low cost. However, most of these studies relied primarily on content analysis or text mining, while only a few analyzed the networks of Twitter users. None has incorporated user geolocation data to explain health outcomes at an intra-urban level. Currently dengue early warning systems rely on syndromic surveillance, which lacks completeness and timeliness.
In this study researchers developed an algorithm to estimate a dynamic mobility-weighted incidence index (MI), which quantifies the level of exposure to virus importation in a given neighborhood. The proposed index is based on publicly available social media and routine disease surveillance data, and provides a low-cost source of information for assessing the risk of spread of communicable diseases, such as dengue. This study suggests that the MI index is of utility and significance for dengue surveillance and early warnings systems and can enhance timely decision-making within the public health system.Friday Letter Submission