Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh.

Human mobility plays a major role in the spatial dissemination of infectious diseases. We develop a spatio-temporal stochastic model for influenza-like disease spread based on estimates of human mobility. The model is informed by mobile phone mobility data collected in Bangladesh. We compare predictions of models informed by daily mobility data (reference) with that of models informed by time-averaged mobility data, and mobility model approximations. We find that the gravity model overestimates the spatial synchrony, while the radiation model underestimates the spatial synchrony. Using time-averaged mobility resulted in spatial spreading patterns comparable to the daily mobility model. We fit the model to 2014-2017 influenza data from sentinel hospitals in Bangladesh, using a sequential version of approximate Bayesian computation. We find a good agreement between our estimated model and the case data. We estimate transmissibility and regional spread of influenza in Bangladesh, which are useful for policy planning. Time-averaged mobility appears to be a good proxy for human mobility when modelling infectious diseases. This motivates a more general use of the time-averaged mobility, with important implications for future studies and outbreak control. Moreover, time-averaged mobility is subject to less privacy concerns than daily mobility, containing less temporal information on individual movements.

The Cholera Phone: Diarrheal Disease Surveillance by Mobile Phone in Bangladesh.

Existing methodologies to record diarrheal disease incidence in households have limitations due to a high-episode recall error outside a 48-hour window. Our objective was to use mobile phones for reporting diarrheal episodes in households to provide real-time incidence data with minimum resource consumption and low recall error. From June 2014 to June 2015, we enrolled 417 low-income households in Dhaka, Bangladesh, and asked them to report diarrheal episodes to a call center. A team of data collectors then visited persons reporting the episode to collect data. In addition, each month, the team conducted in-home surveys on diarrhea incidence for a preceding 48-hour period. The mobile phone surveillance reported an incidence of 0.16 cases per person-year (95% CI: 0.13-0.19), with 117 reported diarrhea cases, and the routine in-home survey detected an incidence of 0.33 cases per person-year (95% CI: 0.18-0.60), the incidence rate ratio was 2.11 (95% CI: 1.08-3.78). During focus group discussions, participants reported a lack in motivation to report diarrhea by phone because of the absence of provision of intervening treatment following reporting. Mobile phone technology can provide a unique tool for real-time disease reporting. The phone surveillance in this study reported a lower incidence of diarrhea than an in-home survey, possibly because of the absence of intervention and, therefore, a perceived lack of incentive to report. However, this study reports the untapped potential of mobile phones in monitoring infectious disease incidence in a low-income setting.