Browsing by Author "Pawar, Samraat"

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    The Role of Vector Trait Variation in Vector-Borne Disease Dynamics
    Cator, Lauren J.; Johnson, Leah R.; Mordecai, Erin A.; El Moustaid, Fadoua; Smallwood, Thomas R. C.; LaDeau, Shannon L.; Johansson, Michael A.; Hudson, Peter J.; Boots, Michael; Thomas, Matthew B.; Power, Alison G.; Pawar, Samraat (2020-07-10)
    Many important endemic and emerging diseases are transmitted by vectors that are biting arthropods. The functional traits of vectors can affect pathogen transmission rates directly and also through their effect on vector population dynamics. Increasing empirical evidence shows that vector traits vary significantly across individuals, populations, and environmental conditions, and at time scales relevant to disease transmission dynamics. Here, we review empirical evidence for variation in vector traits and how this trait variation is currently incorporated into mathematical models of vector-borne disease transmission. We argue that mechanistically incorporating trait variation into these models, by explicitly capturing its effects on vector fitness and abundance, can improve the reliability of their predictions in a changing world. We provide a conceptual framework for incorporating trait variation into vector-borne disease transmission models, and highlight key empirical and theoretical challenges. This framework provides a means to conceptualize how traits can be incorporated in vector borne disease systems, and identifies key areas in which trait variation can be explored. Determining when and to what extent it is important to incorporate trait variation into vector borne disease models remains an important, outstanding question.
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    Understanding uncertainty in temperature effects on vector-borne disease: a Bayesian approach
    Johnson, Leah R.; Ben-Horin, Tal; Lafferty, Kevin D.; McNally, Amy; Mordecai, Erin A.; Paaijmans, Krijn P.; Pawar, Samraat; Ryan, Sadie J. (Ecological Society of America, 2015)
    Extrinsic environmental factors influence the distribution and population dynamics of many organisms, including insects that are of concern for human health and agriculture. This is particularly true for vector-borne infectious diseases like malaria, which is a major source of morbidity and mortality in humans. Understanding the mechanistic links between environment and population processes for these diseases is key to predicting the consequences of climate change on transmission and for developing effective interventions. An important measure of the intensity of disease transmission is the reproductive number R₀. However, understanding the mechanisms linking R₀ and temperature, an environmental factor driving disease risk, can be challenging because the data available for parameterization are often poor. To address this, we show how a Bayesian approach can help identify critical uncertainties in components of R₀ and how this uncertainty is propagated into the estimate of R₀. Most notably, we find that different parameters dominate the uncertainty at different temperature regimes: bite rate from 15°C to 25°C; fecundity across all temperatures, but especially ~25–32°C; mortality from 20°C to 30°C; parasite development rate at ~15–16°C and again at ~33–35°C. Focusing empirical studies on these parameters and corresponding temperature ranges would be the most efficient way to improve estimates of R₀. While we focus on malaria, our methods apply to improving process-based models more generally, including epidemiological, physiological niche, and species distribution models.