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Generalized reproduction numbers and the prediction of patterns in waterborne disease
Contributed by Andrea Rinaldo, October 10, 2012 (sent for review July 14, 2012)

Abstract
Understanding, predicting, and controlling outbreaks of waterborne diseases are crucial goals of public health policies, but pose challenging problems because infection patterns are influenced by spatial structure and temporal asynchrony. Although explicit spatial modeling is made possible by widespread data mapping of hydrology, transportation infrastructure, population distribution, and sanitation, the precise condition under which a waterborne disease epidemic can start in a spatially explicit setting is still lacking. Here we show that the requirement that all the local reproduction numbers be larger than unity is neither necessary nor sufficient for outbreaks to occur when local settlements are connected by networks of primary and secondary infection mechanisms. To determine onset conditions, we derive general analytical expressions for a reproduction matrix
, explicitly accounting for spatial distributions of human settlements and pathogen transmission via hydrological and human mobility networks. At disease onset, a generalized reproduction number
(the dominant eigenvalue of
) must be larger than unity. We also show that geographical outbreak patterns in complex environments are linked to the dominant eigenvector and to spectral properties of
. Tests against data and computations for the 2010 Haiti and 2000 KwaZulu-Natal cholera outbreaks, as well as against computations for metapopulation networks, demonstrate that eigenvectors of
provide a synthetic and effective tool for predicting the disease course in space and time. Networked connectivity models, describing the interplay between hydrology, epidemiology, and social behavior sustaining human mobility, thus prove to be key tools for emergency management of waterborne infections.
Footnotes
- ↵1To whom correspondence should be addressed. E-mail: andrea.rinaldo{at}epfl.ch.
Author contributions: M.G., L.M., E.B., R.C., I.R.-I., and A.R. designed research; M.G., L.M., E.B., R.C., L.R., and A.R. performed research; M.G., L.M., E.B., R.C., L.R., and A.R. analyzed data; and M.G., L.M., I.R.-I., and A.R. wrote the paper.
The authors declare no conflict of interest.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1217567109/-/DCSupplemental.
Freely available online through the PNAS open access option.
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