Recent works


Assessing the potential risk of Zika virus epidemics in temperate areas with established Aedes albopictus populations.

REF Guzzetta G, Poletti P, Montarsi F, Baldacchino F, Capelli G, Rizzoli A, Rosà R, Merler (2016) S.Euro Surveill. doi: 10.2807/1560-7917.ES.2016.21.15.30199. 

Based on 2015 abundance of Aedes albopictus in nine northern Italian municipalities with temperate continental/oceanic climate, we estimated the basic reproductive number R0 for Zika virus (ZIKV) to be systematically below the epidemic threshold in most scenarios. Results were sensitive to the value of the probability of mosquito infection after biting a viraemic host. Therefore, further studies are required to improve models and predictions, namely evaluating vector competence and potential non-vector transmissions.
Estimated temporal changes of R0, using baseline parameter values (A) and sensitivity of model predictions with respect to all possible values of the mosquito probability of infection (B-D), Italy, 2015

Estimated temporal changes of R0, using baseline parameter values (A) and sensitivity of model predictions with respect to all possible values of the mosquito probability of infection (B-D), Italy, 2015


The Role of Climatic and Density Dependent
Factors in Shaping Mosquito Population Dynamics: The Case of Culex pipiens in Northwestern Italy

REF: Marini G, Poletti P, Giacobini M, Pugliese A, Merler S, Rosà R (2016) PLoS ONE 11(4): e0154018. doi:10.1371/journal.pone.0154018


Culex pipiens
 mosquito is a species widely spread across Europe and represents a competent vector for many arboviruses such as West Nile virus (WNV), which has been recently circulating in many European countries, causing hundreds of human cases. In order to identify the main determinants of the high heterogeneity in Cxpipiens abundance observed in Piedmont region (Northwestern Italy) among different seasons, we developed a density-dependent stochastic model that takes explicitly into account the role played by temperature, which affects both developmental and mortality rates of different life stages. The model was calibrated with a Markov chain Monte Carlo approach exploring the likelihood of recorded capture data gathered in the study area from 2000 to 2011; in this way, we disentangled the role played by different seasonal eco-climatic factors in shaping the vector abundance. Illustrative simulations have been performed to forecast likely changes if temperature or density–dependent inputs would change. Our analysis suggests that inter-seasonal differences in the mosquito dynamics are largely driven by different temporal patterns of temperature and seasonal-specific larval carrying capacities. Specifically, high temperatures during early spring hasten the onset of the breeding season and increase population abundance in that period, while, high temperatures during the summer can decrease population size by increasing adult mortality. Higher densities of adult mosquitoes are associated with higher larval carrying capacities, which are positively correlated with spring precipitations. Finally, an increase in larval carrying capacity is expected to proportionally increase adult mosquito abundance.

Boxplots (2.5%, 25%, 75% and 97.5% quantile and median) of predicted annual synthetic indexes associated with different temperature inputs (x-axis, from -2.5°C to +2.5°C with respect to actual records). Panel (a) shows the effect on the duration of the breeding season, defined as the difference between the week of the year when the 95% and the 5% of the cumulative captures are reached; panels (b) and (c) show respectively the effect on the timing and the value of the peak capture; panel (d) shows the effect on the total annual captures

Boxplots (2.5%, 25%, 75% and 97.5% quantile and median) of predicted annual synthetic indexes associated with different temperature inputs (x-axis, from -2.5°C to +2.5°C with respect to actual records). Panel (a) shows the effect on the duration of the breeding season, defined as the difference between the week of the year when the 95% and the 5% of the cumulative captures are reached; panels (b) and (c) show respectively the effect on the timing and the value of the peak capture; panel (d) shows the effect on the total annual captures

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