Abstract
Understanding geographic population dynamics of mosquitoes is an essential requirement for estimating the risk of mosquito-borne disease transmission and geographically targeted interventions. However, the use of population dynamics measures, such as the intrinsic growth rate, as predictors in spatio-temporal point processes has not been investigated before. In this work we compared the predictive accuracy of four spatio-temporal log-Gaussian Cox models: (i) With no predictors; (ii) mosquito abundance as predictor; (iii) intrinsic growth rate as predictor; (iv) intrinsic growth rate and mosquito abundance as predictors. This analysis is based on Aedes aegypti mosquito surveillance and human dengue data obtained from the urban area of Caratinga, Brazil. We used a statistical Moran Curve approach to estimate the intrinsic growth rate and a zero inflated Poisson kriging model for estimating mosquito abundance at locations of dengue cases. The incidence of dengue cases was positively associated with mosquito intrinsic growth rate and this model outperformed, in terms of predictive accuracy, the abundance and the null models. The latter includes only the spatio-temporal random effect but no predictors. In the light of these results we suggest that the intrinsic growth rate should be investigated further as a potential tool for predicting the risk of dengue transmission and targeting health interventions for vector-borne diseases.
Highlights
Understanding mosquito population dynamics, the regulation of insect populations in different environments, is fundamental to develop models for estimating the entomological risk of mosquito-borne disease transmission that can be used for effective mosquito surveillance and control, e.g. precision public health
Comparing predictors for dengue cases model 348 We evaluated if any spatio-temporal association exists between the incidence of dengue cases and the mosquito intrinsic growth rate and abundance
Achieving the global dengue control strategy, which calls for at least 50% reduction in the disease mortality burden and a minimum of 25% reduction in incidence by 2020 (World Health Organization 2012), requires innovative approaches and interventions that go beyond simple disease surveillance or ecological analyses
Summary
Understanding mosquito population dynamics, the regulation of insect populations in different environments, is fundamental to develop models for estimating the entomological risk of mosquito-borne disease transmission that can be used for effective mosquito surveillance and control, e.g. precision public health. Despite the variety of methods and their availability (Tonnang, Hervé et al 2017), current risk assessment models for vector-borne diseases are still often based on static species occurrence models, e.g. spatialization of presence/absence or count abundance, which lack information about insect population dynamics, i.e. mortality, fertility and density dependence effects (see for example the recommendations from (Ehrlen and Morris 2015)). When longitudinal data is available, dynamic models can provide essential information for vector borne disease risk management (Cromwell, Stoddard et al 2017, Tonnang, Hervé et al 58 2017). DD is modulated by social and trophic interactions, such as cannibalism, competition, crowding, cooperation, diseases, herbivory, mutualism, parasitism, parasitoidism, predation, and reproductive behaviour (Gerber, McCallum et al 2005, Herrando-Pérez, Delean et al 2012)
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