XXXX in Haiti, 2010: using a transmission model to explain spatial spread of disease and identify optimal control interventions.

@entity3081 @entity1 @entity3082
BACKGROUND: Haiti is in the midst of a @entity3081 . Surveillance data for formulating models of the @entity3082 are limited, but such models can aid understanding of @entity3082 processes and help define control strategies. OBJECTIVE: To predict, by using a mathematical model, the sequence and timing of regional @entity3081 epidemics in Haiti and explore the potential effects of disease-control strategies. DESIGN: Compartmental mathematical model allowing @entity1 -to- @entity1 and waterborne transmission of @entity3081 . Within- and between-region @entity3082 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 @entity3082 onset and hospitalizations. RESULTS: The plausible range for @entity3081 '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 @entity3081 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 @entity3082 . A limited vaccine supply provided late in the @entity3082 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 @entity3082 , which may change. CONCLUSION: Despite limited surveillance data from the @entity3081 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 @entity3082 could use immediately.
