On the probability of extinction of the Haiti cholera epidemic

Abstract

More than three years after its appearance in Haiti, cholera has already caused more than 8,500 deaths and 695,000 infections and it is feared to become endemic. However, no clear evidence of a stable environmental reservoir of pathogenic Vibrio cholerae, the infective agent of the disease, has emerged so far, suggesting the possibility that the transmission cycle of the disease is being maintained by bacteria freshly shed by infected individuals. Should this be the case, cholera could in principle be eradicated from Haiti. Here, we develop a framework for the estimation of the probability of extinction of the epidemic based on current information on epidemiological dynamics and health-care practice. Cholera spreading is modeled by an individual-based spatially-explicit stochastic model that accounts for the dynamics of susceptible, infected and recovered individuals hosted in different local communities connected through hydrologic and human mobility networks. Our results indicate that the probability that the epidemic goes extinct before the end of 2016 is of the order of 1 %. This low probability of extinction highlights the need for more targeted and effective interventions to possibly stop cholera in Haiti.

On the predictive ability of mechanistic models for the Haitian cholera epidemic

“…Several mathematical models of cholera transmission have been developed to describe the course of the Haitian epidemic [9][10][11][12][13][14][15][16][17][18]. Their application to the ongoing epidemic was made possible by the immediate release of epidemiological data, initially recorded by the disease-surveillance systems set up by the Haitian government in the aftermath of the earthquake and later by the National Cholera Surveillance System [1], as well as by the widespread availability of georeferenced environmental datasets.…”

“…Those models were different in assumptions, spatial resolution and degrees of spatial coupling, but they all addressed the dynamics of susceptibles, infected individuals and bacterial concentrations in a discrete and geographically referenced set of local human communities. Mathematical modelling of the ongoing Haitian cholera epidemic has certainly provided important insights, especially concerning spatial transmission mechanisms [10][11][12]14], rainfall patterns [14,15,17], intervention strategies [9][10][11][12]16,17], local basic reproduction numbers [16], conditions for large-scale pathogen invasion [13] and the probability of epidemic extinction [18]. Most models provided predictions about the unfolding of the epidemic [9,10,14,17,18].…”

“…To this end, we adopt the model proposed in [14] and later refined in [18]. This model has been shown to reproduce cholera transmission dynamics in Haiti in a fairly accurate and robust way.…”

“…Mathematical modelling of the ongoing Haitian cholera epidemic has certainly provided important insights, especially concerning spatial transmission mechanisms [10][11][12]14], rainfall patterns [14,15,17], intervention strategies [9][10][11][12]16,17], local basic reproduction numbers [16], conditions for large-scale pathogen invasion [13] and the probability of epidemic extinction [18]. Most models provided predictions about the unfolding of the epidemic [9,10,14,17,18]. However, while predictive models have been successfully used in endemic settings [19][20][21][22], the predictive power of mathematical models of cholera transmission has never been evaluated formally and systematically in an epidemic setting (but see [14] for a reassessment of early predictions of the Haitian epidemic).…”

“…The model builds on a spatially explicit epidemiological framework [23,24] that has already been applied to both past [25,26] and ongoing cholera epidemics [10,17]. To study the role of spatial settings on the predictive potential of cholera modelling, we consider two different spatial scales, namely a fine-grained subdivision of the Haitian territory into hydrological units [14,18], and a coarse subdivision into administrative departments (e.g. [9,12,16]).…”