BACKGROUND: Haiti is in the midst of a cholera epidemic. Surveillance data for formulating models of the epidemic are limited, but such models can aid understanding of epidemic processes and help define control strategies. OBJECTIVE: To predict, by using a mathematical model, the sequence and timing of regional cholera epidemics in Haiti and explore the potential effects of disease-control strategies. DESIGN: Compartmental mathematical model allowing person-to-person and waterborne transmission of cholera. Within- and between-region epidemic spread was modeled, with the latter dependent on population sizes and distance between regional centroids (a "gravity" model). SETTING: Haiti, 2010 to 2011. DATA SOURCES: Haitian hospitalization data, 2009 census data, literature-derived parameter values, and model calibration. MEASUREMENTS: Dates of epidemic onset and hospitalizations. RESULTS: The plausible range for cholera's basic reproductive number (R(0), defined as the number of secondary cases per primary case in a susceptible population without intervention) was 2.06 to 2.78. The order and timing of regional cholera outbreaks predicted by the gravity model were closely correlated with empirical observations. Analysis of changes in disease dynamics over time suggests that public health interventions have substantially affected this epidemic. A limited vaccine supply provided late in the epidemic was projected to have a modest effect. LIMITATIONS: Assumptions were simplified, which was necessary for modeling. Projections are based on the initial dynamics of the epidemic, which may change. CONCLUSION: Despite limited surveillance data from the cholera epidemic in Haiti, a model simulating between-region disease transmission according to population and distance closely reproduces reported disease patterns. This model is a tool that planners, policymakers, and medical personnel seeking to manage the epidemic could use immediately.
BACKGROUND: Haiti is in the midst of a cholera epidemic. Surveillance data for formulating models of the epidemic are limited, but such models can aid understanding of epidemic processes and help define control strategies. OBJECTIVE: To predict, by using a mathematical model, the sequence and timing of regional cholera epidemics in Haiti and explore the potential effects of disease-control strategies. DESIGN: Compartmental mathematical model allowing person-to-person and waterborne transmission of cholera. Within- and between-region epidemic spread was modeled, with the latter dependent on population sizes and distance between regional centroids (a "gravity" model). SETTING: Haiti, 2010 to 2011. DATA SOURCES: Haitian hospitalization data, 2009 census data, literature-derived parameter values, and model calibration. MEASUREMENTS: Dates of epidemic onset and hospitalizations. RESULTS: The plausible range for cholera's basic reproductive number (R(0), defined as the number of secondary cases per primary case in a susceptible population without intervention) was 2.06 to 2.78. The order and timing of regional cholera outbreaks predicted by the gravity model were closely correlated with empirical observations. Analysis of changes in disease dynamics over time suggests that public health interventions have substantially affected this epidemic. A limited vaccine supply provided late in the epidemic was projected to have a modest effect. LIMITATIONS: Assumptions were simplified, which was necessary for modeling. Projections are based on the initial dynamics of the epidemic, which may change. CONCLUSION: Despite limited surveillance data from the cholera epidemic in Haiti, a model simulating between-region disease transmission according to population and distance closely reproduces reported disease patterns. This model is a tool that planners, policymakers, and medical personnel seeking to manage the epidemic could use immediately.
Authors: Robert C Reiner; Aaron A King; Michael Emch; Mohammad Yunus; A S G Faruque; Mercedes Pascual Journal: Proc Natl Acad Sci U S A Date: 2012-01-23 Impact factor: 11.205
Authors: L Mari; E Bertuzzo; L Righetto; R Casagrandi; M Gatto; I Rodriguez-Iturbe; A Rinaldo Journal: J R Soc Interface Date: 2011-07-13 Impact factor: 4.118
Authors: Alicia N M Kraay; James Trostle; Andrew F Brouwer; William Cevallos; Joseph N S Eisenberg Journal: Epidemiology Date: 2018-01 Impact factor: 4.822