8,183 research outputs found
Spatial and temporal hot spots of Aedes albopictus abundance inside and outside a South European metropolitan area
Aedes albopictus is a tropical invasive species which in the last decades spread worldwide,
also colonizing temperate regions of Europe and US, where it has become a public health
concern due to its ability to transmit exotic arboviruses, as well as severe nuisance problems
due to its aggressive daytime outdoor biting behaviour. While several studies have
been carried out in order to predict the potential limits of the species expansions based on
eco-climatic parameters, few studies have so far focused on the specific effects of these
variables in shaping its micro-geographic abundance and dynamics. The present study
investigated eco-climatic factors affecting Ae. albopictus abundance and dynamics in metropolitan
and sub-urban/rural sites in Rome (Italy), which was colonized in 1997 and is nowadays
one of the most infested metropolitan areas in Southern Europe. To this aim,
longitudinal adult monitoring was carried out along a 70 km-transect across and beyond the
most urbanized and densely populated metropolitan area. Two fine scale spatiotemporal
datasets (one with reference to a 20m circular buffer around sticky traps used to collect
mosquitoes and the second to a 300m circular buffer within each sampling site) were
exploited to analyze the effect of climatic and socio-environmental variables on Ae. albopictus
abundance and dynamics along the transect. Results showed an association between
highly anthropized habitats and high adult abundance both in metropolitan and sub-urban/
rural areas, with “small green islands” corresponding to hot spots of abundance in the metropolitan
areas only, and a bimodal seasonal dynamics with a second peak of abundance in
autumn, due to heavy rains occurring in the preceding weeks in association with permissive
temperatures. The results provide useful indications to prioritize public mosquito control
measures in temperate urban areas where nuisance, human-mosquito contact and risk of
local arbovirus transmission are likely higher, and highlight potential public health risks also
after the summer months typically associated with high mosquito densities
Techniques to Understand Computer Simulations: Markov Chain Analysis
The aim of this paper is to assist researchers in understanding the dynamics of simulation models that have been implemented and can be run in a computer, i.e. computer models. To do that, we start by explaining (a) that computer models are just input-output functions, (b) that every computer model can be re-implemented in many different formalisms (in particular in most programming languages), leading to alternative representations of the same input-output relation, and (c) that many computer models in the social simulation literature can be usefully represented as time-homogeneous Markov chains. Then we argue that analysing a computer model as a Markov chain can make apparent many features of the model that were not so evident before conducting such analysis. To prove this point, we present the main concepts needed to conduct a formal analysis of any time-homogeneous Markov chain, and we illustrate the usefulness of these concepts by analysing 10 well-known models in the social simulation literature as Markov chains. These models are: • Schelling\'s (1971) model of spatial segregation • Epstein and Axtell\'s (1996) Sugarscape • Miller and Page\'s (2004) standing ovation model • Arthur\'s (1989) model of competing technologies • Axelrod\'s (1986) metanorms models • Takahashi\'s (2000) model of generalized exchange • Axelrod\'s (1997) model of dissemination of culture • Kinnaird\'s (1946) truels • Axelrod and Bennett\'s (1993) model of competing bimodal coalitions • Joyce et al.\'s (2006) model of conditional association In particular, we explain how to characterise the transient and the asymptotic dynamics of these computer models and, where appropriate, how to assess the stochastic stability of their absorbing states. In all cases, the analysis conducted using the theory of Markov chains has yielded useful insights about the dynamics of the computer model under study.Computer Modelling, Simulation, Markov, Stochastic Processes, Analysis, Re-Implementation
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