RESUMEN
Experiments and models suggest that climate affects mosquito-borne disease transmission. However, disease transmission involves complex nonlinear interactions between climate and population dynamics, which makes detecting climate drivers at the population level challenging. By analysing incidence data, estimated susceptible population size, and climate data with methods based on nonlinear time series analysis (collectively referred to as empirical dynamic modelling), we identified drivers and their interactive effects on dengue dynamics in San Juan, Puerto Rico. Climatic forcing arose only when susceptible availability was high: temperature and rainfall had net positive and negative effects respectively. By capturing mechanistic, nonlinear and context-dependent effects of population susceptibility, temperature and rainfall on dengue transmission empirically, our model improves forecast skill over recent, state-of-the-art models for dengue incidence. Together, these results provide empirical evidence that the interdependence of host population susceptibility and climate drives dengue dynamics in a nonlinear and complex, yet predictable way.
Asunto(s)
Dengue , Animales , Dengue/epidemiología , Susceptibilidad a Enfermedades , Dinámica Poblacional , Puerto Rico/epidemiología , TemperaturaRESUMEN
For dengue fever and other seasonal epidemics we show how the stability of the preceding inter-outbreak period can predict subsequent total outbreak magnitude, and that a feasible stability metric can be computed from incidence data alone. As an observable of a dynamical system, incidence data contains information about the underlying mechanisms: climatic drivers, changing serotype pools, the ecology of the vector populations, and evolving viral strains. We present mathematical arguments to suggest a connection between stability measured in incidence data during the inter-outbreak period and the size of the effective susceptible population. The method is illustrated with an analysis of dengue incidence in San Juan, Puerto Rico, where forecasts can be made as early as three to four months ahead of an outbreak. These results have immediate significance for public health planning, and can be used in combination with existing forecasting methods and more comprehensive dengue models.