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Background Climate change and globalization contribute to the expansion of mosquito vectors and their associated pathogens. Long spared, temperate regions have had to deal with the emergence of arboviruses traditionally confined to tropical regions. Chikungunya virus (CHIKV) was reported for the first time in Europe in 2007, causing a localized outbreak in Italy, which then recurred repeatedly over the years in other European localities. This raises the question of climate effects, particularly temperature, on the dynamics of vector-borne viruses. The objective of this study is to improve the understanding of the molecular mechanisms set up in the vector in response to temperature. Methods We combine three complementary approaches by examining Aedes albopictus mosquito gene expression (transcriptomics), bacterial flora (metagenomics) and CHIKV evolutionary dynamics (genomics) induced by viral infection and temperature changes. Results We show that temperature alters profoundly mosquito gene expression, bacterial microbiome and viral population diversity. We observe that (i) CHIKV infection upregulated most genes (mainly in immune and stress-related pathways) at 20°C but not at 28°C, (ii) CHIKV infection significantly increased the abundance of Enterobacteriaceae Serratia marcescens at 28°C and (iii) CHIKV evolutionary dynamics were different according to temperature. Conclusion The substantial changes detected in the vectorial system (the vector and its bacterial microbiota, and the arbovirus) lead to temperature-specific adjustments to reach the ultimate goal of arbovirus transmission; at 20°C and 28°C, the Asian tiger mosquito Ae. albopictus was able to transmit CHIKV at the same efficiency. Therefore, CHIKV is likely to continue its expansion in the northern regions and could become a public health problem in more countries than those already affected in Europe. [ABSTRACT FROM AUTHOR]

The risk from natural catastrophes is typically estimated using complex simulation models involving multiple stochastic components in a nested structure. This risk is principally assessed via the mean annual loss, and selected quantiles of the annual loss. Determining an appropriate simulation strategy is important in order to achieve satisfactory convergence of these statistics, without excessive computation time and data storage requirements. This necessitates an understanding of the relative contribution of each of the stochastic components to the total variance of the statistics. A simple framework using random effects models and analysis of variance is used to partition the variance of the annual loss, which permits calculation of the variance of the mean annual loss with varying numbers of samples of each of the components. An extension to quantiles is developed using the empirical distribution function in combination with bootstrapping. The methods are applied to a European flood model, where the primary stochastic component relates to the frequency and severity of flood events, and three secondary components relate to defence levels, exposure locations and building vulnerability. As expected, it is found that the uncertainty due to the secondary components increases as the size of the portfolio of exposures decreases, and is higher for industrial and commercial business, compared with residential for all statistics of interest. In addition, interesting insights are gained as to the impact of flood defences on convergence. [ABSTRACT FROM AUTHOR]