Cholera in the Lake Kivu region (DRC): Integrating remote sensing and spatially explicit epidemiological modeling

Abstract

Mathematical models of cholera dynamics can not only help in identifying environmental drivers and processes that influence disease transmission, but may also represent valuable tools for the prediction of the epidemiological patterns in time and space as well as for the allocation of health care resources. Cholera outbreaks have been reported in the Democratic Republic of the Congo since the 1970s. They have been ravaging the shore of Lake Kivu in the east of the country repeatedly during the last decades. Here we employ a spatially explicit, inhomogeneous Markov chain model to describe cholera incidence in eight health zones on the shore of the lake. Remotely sensed data sets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers in addition to baseline seasonality. The effect of human mobility is also modelled mechanistically. We test several models on a multiyear data set of reported cholera cases. The best fourteen models, accounting for different environmental drivers, and selected using the Akaike information criterion, are formally compared via proper cross validation. Among these, the one accounting for seasonality, El Niño Southern Oscillation, precipitation and human mobility outperforms the others in cross validation. Some drivers (such as human mobility and rainfall) are retained only by a few models, possibly indicating that the mechanisms through which they influence cholera dynamics in the area will have to be investigated further.

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

“…[56]). We also note that in endemic settings, provided that space-time data are available, other mathematical tools could be more appropriate than those used in this work, including time-series analysis [19,21,57,58], spatially implicit [20] and Markov chain models [22,59].…”

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

“…A large palette of modeling techniques have been applied to study cholera (for an overview see [32]). Here we choose the MDIMC framework proposed by Reiner et al [13] in which the observed monthly cholera attack rates are categorized into discrete states of cholera intensity, and the space-time dynamics of transmission modeled by a inhomogeneous Markov Chain [13, 32].…”

“…Here we choose the MDIMC framework proposed by Reiner et al [13] in which the observed monthly cholera attack rates are categorized into discrete states of cholera intensity, and the space-time dynamics of transmission modeled by a inhomogeneous Markov Chain [13, 32]. The transition from cholera in state i at time t to state j at time t + 1 ( X k,t = i ) → ( X k,t +1 = j ) occurs with baseline probability p i,j .…”

“…Here, we build on the existing framework of a multi-dimensional inhomogeneous Markov Chain model (MDIMC) for cholera transmission [13, 32]. We specifically incorporate inferred human mobility fluxes based on population distribution to investigate the spatio-temporal dynamics of cholera within the city.…”

Optimizing a remotely sensed proxy for plankton biomass in Lake Kivu

“…To further our understanding of plankton dynamics in Lake Kivu (Figure 1), we assess and attempt to optimize a remotely-sensed proxy for plankton biomass. This could possibly have implications for the mathematical modeling of cholera dynamics around Lake Kivu, described in Finger et al (2014).…”