LAUSR

Abstract Mobile phones and short message service (SMS) have been widely used in disease control and prevention. Personalized SMSs further allows real-time, precisely targeted interventions that achieve better cost-effectiveness. Few SMSs are personalized based on spatiotemporal travel behavior of individuals, which plays an important role in disease spread. We proposed a set of SMS policies tailored to individuals' travel behavior derived from massive mobile phone tracking records. These policies tend to alter spatial, temporal, or spatiotemporal patterns of individuals' daily activities, in order to reduce the risk of disease spread. Taking Shenzhen city, China, as a study area, we simulated and evaluated these policies for Dengue Fever intervention. Our simulation results show that the spatially targeting policy that discourages discretionary trips produces the highest cost-effectiveness to control disease spread in areas with high importation risk. For the entire city, however, the temporally targeting policy that shifts individuals’ travel schedules achieves the best cost-effectiveness. Our study contributes to a new ground of precise public health that calls for individualized, real-time, and accurately targeted interventions. Utilizing big mobile phone data, we present a novel approach to design, simulate, and evaluate space-time precise intervention for disease control.