Climate-driven endemic cholera is modulated by human mobility in a megacity

Abstract

Although a differential sensitivity of cholera dynamics to climate variability has been reported in the spatially heterogeneous megacity of Dhaka, Bangladesh, the specific patterns of spread of the resulting risk within the city remain unclear. We build on an established probabilistic spatial model to investigate the importance and role of human mobility in modulating spatial cholera transmission. Mobility fluxes were inferred using a straightforward and generalizable methodology that relies on mapping population density based on a high resolution urban footprint product, and a parameter-free human mobility model. In accordance with previous findings, we highlight the higher sensitivity to the El Niño Southern Oscillation (ENSO) in the highly populated urban center than in the more rural periphery. More significantly, our results show that cholera risk is largely transmitted from the climate-sensitive core to the periphery of the city, with implications for the planning of control efforts. In addition, including human mobility improves the outbreak prediction performance of the model with an 11 month lead. The interplay between climatic and human mobility factors in cholera transmission is discussed from the perspective of the rapid growth of megacities across the developing world.

Cholera forecast for Dhaka, Bangladesh, with the 2015-2016 El Niño: Lessons learned

“…The Niño3.4 value observed in the region for January 2016 (2.60) is comparable in magnitude to the one observed for the large El Niño event of 1998 (2.56), when Dhaka suffered the largest cholera outbreak in the last 20 years and one of the worst floods in its history affecting more than 50% of the city's area [ 22 ]. We focus on cholera reported cases from the core of the city for the process-based model, based on the heightened sensitivity to ENSO in this region, the low number of cases in the periphery and its low contribution to the force of infection in the core [ 15 , 23 ]. The monthly surveillance data are divided into a training set from January 1995 to December 2010, used to fit the mechanistic model, and an “out-of-fit” set from January 2011 to October 2016, used to evaluate the predictability of the model.…”

Methods for Model Calibration under High Uncertainty: Modeling Cholera in Bangladesh

“…Ours is not the first model of cholera in Bangladesh. In a review of the recent literature, we found several studies 28,48–56 that used models to simulate cholera cases and deaths in Bangladesh. Of those that reported comparable results or parameters, 4 of the models 28,49,50,53 were calibrated to data from rural Bangladesh and may be less applicable to urban areas because of differences in treatment and access to clean water and sanitation and also because of variation in population density, social networks, and migration patterns.…”

Climate forcing and infectious disease transmission in urban landscapes: integrating demographic and socioeconomic heterogeneity

“…These results suggest that hosts can also move vector‐borne diseases among neighborhoods . In the case of waterborne diseases, an extension of a transmission model of cholera that downscaled population size to map population density at high spatial resolution, and then inferred movement fluxes using a radiation model, fits the spatiotemporal burden of the disease significantly better than its predecessor with only near‐neighbor effects . Last, variation in urban typologies can exhibit association with disease transmission as a surrogate of socioeconomic and demographic factors.…”

“…For instance, socioeconomic inequality can influence disease incidence, and, in turn, spatial variation in infection levels can promote differences in wealth. Moreover, despite a growing literature on the association between urbanization and infectious diseases, transmission models typically consider urban systems at coarse scales, with no explicit consideration of different levels of spatial resolution and patterns of connectivity (but see Refs. for exceptions).…”