Article
Modeling the impact of global warming on vectorborne infections
School of Medicine, University of São Paulo and LIM01HCFMUSP, SP, Brazil.
Physics of Life Reviews (Impact Factor: 7.48). 01/2011; 8(2):16999. DOI: 10.1016/j.plrev.2011.01.001 Source: PubMed
ABSTRACT
Global warming will certainly affect the abundance and distribution of disease vectors. The effect of global warming, however, depends on the complex interaction between the human host population and the causative infectious agent. In this work we review some mathematical models that were proposed to study the impact of the increase in ambient temperature on the spread and gravity of some insecttransmitted diseases.

 "There is a growing need to understand the critical parameters in the transmission and persistence of these diseases and to develop effective strategies for prevention and control. There are many models for dengue in the literature investigating different aspects of its spread and behavior (Focks et al. (1995), Ferguson et al. (1999), Favier et al. (2006)) from standard mosquitoborne disease models (Esteva and Vargas (1998)) to models incorporating space (Chowell et al., 2007), seasonality and temperature dependence (Hartley et al., 2002; Massad et al., 2011), crossimmunity with multiple strains (Wearing and Rohani, 2006; Feng and VelascoHernandez, 1997; Adams et al., 2006), and effectiveness of control measures (Chao et al., 2012). For the purposes of this study, we will restrict our model to one representative dengue serotype and to models without explicit seasonality. "
Article: Comparing dengue and chikungunya emergence and endemic transmission in A. aegypti and A. albopictus
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ABSTRACT: Chikungunya and dengue are reemerging mosquitoborne infectious diseases that are of increasing concern as human travel and expanding mosquito ranges increase the risk of spread. We seek to understand the differences in transient and endemic behavior of chikungunya and dengue; risk of emergence for different virusvector assemblages; and the role that virus evolution plays in disease dynamics and risk. To address these questions, we adapt a mathematical mosquitoborne disease model to chikungunya and dengue in Aedes aegypti and Aedes albopictus mosquitoes. We derive analytical threshold conditions and important dimensionless parameters for virus transmission; perform sensitivity analysis on quantities of interest such as the basic reproduction number, endemic equilibrium, and first epidemic peak; and compute distributions for the quantities of interest across parameter ranges. We found that chikungunya and dengue exhibit different transient dynamics and longterm endemic levels. While the order of most sensitive parameters is preserved across vectorvirus combinations, the magnitude of sensitivity is different across scenarios, indicating that risk of invasion or an outbreak can change with vectorvirus assemblages. We found that the dengue – A. aegypti and new Rèunion strain of chikungunya – A. albopictus systems represent the highest risk across the range of parameters considered. These results inform future experimental and field research efforts and point toward effective mitigation strategies adapted to each disease. 
 "There is a growing need to understand the critical parameters in the transmission and persistence of these diseases and to develop effective strategies for prevention and control. There are many models for dengue in the literature investigating different aspects of its spread and behavior (Focks et al. (1995), Ferguson et al. (1999), Favier et al. (2006)) from standard mosquitoborne disease models (Esteva and Vargas (1998)) to models incorporating space (Chowell et al., 2007), seasonality and temperature dependence (Hartley et al., 2002; Massad et al., 2011), crossimmunity with multiple strains (Wearing and Rohani, 2006; Feng and VelascoHernandez, 1997; Adams et al., 2006), and effectiveness of control measures (Chao et al., 2012). For the purposes of this study, we will restrict our model to one representative dengue serotype and to models without explicit seasonality. "

 "and í µí± í µí± (í µí±¡) = (í µí± 1 − í µí± 2 sin(2í µí¼í µí±í µí±¡ + í µí¼)) is a factor mimicking seasonal influences in the mosquito population [5] [24]. The seasonal influence was considered in another paper [21]. "
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ABSTRACT: To determine the maximum equilibrium prevalence of mosquitoborne microparasitic infections, this paper proposes a general model for vectorborne infections which is flexible enough to comprise the dynamics of a great number of the known diseases transmitted by arthropods. From equilibrium analysis, we determined the number of infected vectors as an explicit function of the model's parameters and the prevalence of infection in the hosts. From the analysis, it is also possible to derive the basic reproduction number and the equilibrium force of infection as a function of those parameters and variables. From the force of infection, we were able to conclude that, depending on the disease's structure and the model's parameters, there is a maximum value of equilibrium prevalence for each of the mosquitoborne microparasitic infections. The analysis is exemplified by the cases of malaria and dengue fever. With the values of the parameters chosen to illustrate those calculations, the maximum equilibrium prevalence found was 31% and 0.02% for malaria and dengue, respectively. The equilibrium analysis demonstrated that there is a maximum prevalence for the mosquitoborne microparasitic infections.
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