Reassessment of the 2010–2011 Haiti cholera outbreak and rainfall-driven multiseason projections

Abstract

Mathematical models can provide key insights into the course of an ongoing epidemic, potentially aiding real-time emergency management in allocating health care resources and by anticipating the impact of alternative interventions. We study the ex post reliability of predictions of the 2010-2011 Haiti cholera outbreak from four independent modeling studies that appeared almost simultaneously during the unfolding epidemic. We consider the impact of different approaches to the modeling of spatial spread of Vibrio cholerae and mechanisms of cholera transmission, accounting for the dynamics of susceptible and infected individuals within different local human communities. To explain resurgences of the epidemic, we go on to include waning immunity and a mechanism explicitly accounting for rainfall as a driver of enhanced disease transmission. The formal comparative analysis is carried out via the Akaike information criterion (AIC) to measure the added information provided by each process modeled, discounting for the added parameters. A generalized model for Haitian epidemic cholera and the related uncertainty is thus proposed and applied to the year-long dataset of reported cases now available. The model allows us to draw predictions on longer-term epidemic cholera in Haiti from multiseason Monte Carlo runs, carried out up to January 2014 by using suitable rainfall fields forecasts. Lessons learned and open issues are discussed and placed in perspective. We conclude that, despite differences in methods that can be tested through model-guided field validation, mathematical modeling of large-scale outbreaks emerges as an essential component of future cholera epidemic control.waterborne diseases | epidemiology | ecohydrology | human mobility |

Spatially Explicit Conditions for Waterborne Pathogen Invasion

“…Here we assume that the entries of Q are given by gravity model (5), in which , where D is the g(d) p exp (Ϫd/D) distance parameter of the exponential kernel Mari et al 2012b). Although this mobility model is not expected to fully capture the complexity of real human movement patterns, gravity-like models have been widely applied in the epidemiological literature to describe the impact of human mobility on the course of a suite of human diseases, including influenza (Viboud et al 2006;Eggo et al 2010), HIV infection (Thomas 1999), measles (Xia et al 2004;Bharti et al 2008), and, very recently, cholera Chao et al 2011;Tuite et al 2011;Mari et al 2012aMari et al , 2012bRinaldo et al 2012). Figure 2B shows that human mobility can significantly favor waterborne disease epidemics.…”

“…The transport process is assumed to be conservative, that is, . Possible topological n P p 1 ij jp1 structures for the hydrological network range from simple one-dimensional lattices to more realistic mathematical characterizations, such as Peano basins (as in Gatto et al 2012), optimal channel networks Rodriguez-Iturbe et al 1992; see below for details), or real river systems (e.g., Bertuzzo et al 2008;Mari et al 2012a;Rinaldo et al 2012). As for the human-mobility network, we assume that the nodes of this second layer correspond to those of the hydrological layer, whereas edges are defined by connections among communities.…”

“…While metapopulation dynamics was incorporated into traditional SI or SIR models many years ago (Bolker and Grenfell 1995;Swinton et al 1998;Gog et al 2002;Arino et al 2005), the addition of human mobility to spatial models of waterborne diseases is more recent. This has been accomplished via either diffusion-based or gravity-like models Chao et al 2011;Tuite et al 2011;Gatto et al 2012;Mari et al 2012aMari et al , 2012bRinaldo et al 2012). The wide availability, via geographic information systems, of spatial data on hydrology, road networks, population distribution, and sanitation makes these models applicable to specific situations in each country and for each disease to be studied.…”

“…To understand, control, and predict epidemics, development of appropriate mechanistic models is fundamental, as they can play an important role in devising appropriate intervention measures Tuite et al 2011;Mari et al 2012a;Rinaldo et al 2012). After the seminal model proposed by Capasso and Paveri-Fontana (1979), Codeço (2001) developed a system of three ordinary differential equations where, in addition to the compartments of susceptible (S) and infected (I) that characterize traditional microparasitological models, one equation accounts for the population dynamics of bacteria (B) in the water reservoir.…”

Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation

“…In just a few years, the DREAM algorithm has found widespread application and use in numerous different fields, including (among others) atmospheric chemistry (Partridge et al, 2011(Partridge et al, , 2012, biogeosciences (Scharnagl et al, 2010;Braakhekke et al, 2013;Ahrens and Reichstein, 2014;Dumont et al, 2014;Starrfelt and Kaste, 2014), biology (Coelho et al, 2011;Zaoli et al, 2014), chemistry (Owejan et al, 2012;Tarasevich et al, 2013;DeCaluwe et al, 2014;Gentsch et al, 2014), ecohydrology (Dekker et al, 2010), ecology (Barthel et al, 2011;Gentsch et al, 2014;Iizumi et al, 2014;Zilliox and Goselin, 2014), economics and quantitative finance (Bauwens et al, 2011;Lise et al, 2012;Lise, 2013), epidemiology (Mari et al, 2011;Rinaldo et al, 2012;Leventhal et al, 2013), geophysics (Bikowski et al, 2012;Linde and Vrugt, 2013;Carbajal et al, 2014;Lochbühler et al, 2014Lochbühler et al, , 2015, geostatistics (Minasny et al, 2011;Sun et al, 2013), hydrogeophysics (Hinnell et al, 2011), hydrologeology (Keating et al, 2010;Laloy et al, 2013;Malama et al, 2013), hydrology (Vrugt et al, 2008a(Vrugt et al, , 2009a…”