Large-Scale Modelling of the Environmentally-Driven Population Dynamics of Temperate Aedes albopictus (Skuse)

The Asian tiger mosquito, Aedes albopictus, is a highly invasive vector species. It is a proven vector of dengue and chikungunya viruses, with the potential to host a further 24 arboviruses. It has recently expanded its geographical range, threatening many countries in the Middle East, Mediterranean, Europe and North America. Here, we investigate the theoretical limitations of its range expansion by developing an environmentally-driven mathematical model of its population dynamics. We focus on the temperate strain of Ae. albopictus and compile a comprehensive literature-based database of physiological parameters. As a novel approach, we link its population dynamics to globally-available environmental datasets by performing inference on all parameters. We adopt a Bayesian approach using experimental data as prior knowledge and the surveillance dataset of Emilia-Romagna, Italy, as evidence. The model accounts for temperature, precipitation, human population density and photoperiod as the main environmental drivers, and, in addition, incorporates the mechanism of diapause and a simple breeding site model. The model demonstrates high predictive skill over the reference region and beyond, confirming most of the current reports of vector presence in Europe. One of the main hypotheses derived from the model is the survival of Ae. albopictus populations through harsh winter conditions. The model, constrained by the environmental datasets, requires that either diapausing eggs or adult vectors have increased cold resistance. The model also suggests that temperature and photoperiod control diapause initiation and termination differentially. We demonstrate that it is possible to account for unobserved properties and constraints, such as differences between laboratory and field conditions, to derive reliable inferences on the environmental dependence of Ae. albopictus populations

Legionella and legionellosis in touristic-recreational facilities. Influence of climate factors and geostatistical analysis in Southern Italy (2001-2017)

Legionella is the causative agent of Legionnaires' disease, a flu-like illness normally acquired following inhalation or aspiration of contaminated water aerosols. Our recent studies revealed that climatic parameters can increase the number of reported cases of community-acquired Legionnaires' disease. Here, we evaluated the presence of Legionella in water networks and the distribution of Legionnaires' disease cases associated with touristic-recreational facilities in the Apulia region (southern Italy) during the period 2001-2017 using geostatistical and climatic analyses. Geostatistical analysis data revealed that the area with the highest concentration of Legionella in water systems also had the greatest number of cases of Legionnaires' disease associated with touristic-recreational facilities. Climatic analysis showed that higher daily temperature excursion (difference between maximum and minimum temperature) on the day of sampling was more often associated with Legionella-positive samples than Legionella-negative samples. In addition, our data highlighted an increased risk of Legionnaires' disease with increases in precipitation and average temperature and with decreases in daily temperature excursion (difference between maximum and minimum temperature over the course of 24 h in the days of incubation period of disease) and minimum temperature. Healthcare professionals should be aware of this phenomenon and be particularly vigilant for cases of community-acquired pneumonia during such climatic conditions and among the tourist population. The innovative geo-statistical approach used in this study could be applied in other contexts when evaluating the effects of climatic conditions on the incidence of Legionella infections

Relationship Between Child Survival and Malaria Transmission: An Analysis of the Malaria Transmission Intensity and Mortality Burden Across Africa (MTIMBA) Project Data in Rufiji Demographic Surveillance System, Tanzania.

The precise nature of the relationship between malaria mortality and levels of transmission is unclear. Due to methodological limitations, earlier efforts to assess the linkage have lead to inconclusive results. The malaria transmission intensity and mortality burden across Africa (MTIMBA) project initiated by the INDEPTH Network collected longitudinally entomological data within a number of sites in sub-Saharan Africa to study this relationship. This work linked the MTIMBA entomology database with the routinely collected vital events within the Rufiji Demographic Surveillance System to analyse the transmission-mortality relation in the region. Bayesian Bernoulli spatio-temporal Cox proportional hazards models with village clustering, adjusted for age and insecticide-treated nets (ITNs), were fitted to assess the relation between mortality and malaria transmission measured by entomology inoculation rate (EIR). EIR was predicted at household locations using transmission models and it was incorporated in the model as a covariate with measure of uncertainty. Effects of covariates estimated by the model are reported as hazard ratios (HR) with 95% Bayesian confidence interval (BCI) and spatial and temporal parameters are presented. Separate analysis was carried out for neonates, infants and children 1-4 years of age. No significant relation between all-cause mortality and intensity of malaria transmission was indicated at any age in childhood. However, a strong age effect was shown. Comparing effects of ITN and EIR on mortality at different age categories, a decrease in protective efficacy of ITN was observed (i.e. neonates: HR = 0.65; 95% BCI: 0.39-1.05; infants: HR = 0.72; 95% BCI:0.48-1.07; children 1-4 years: HR = 0.88; 95% BCI: 0.62-1.23) and reduction on the effect of malaria transmission exposure was detected (i.e. neonates: HR = 1.15; 95% BCI:0.95-1.36; infants: HR = 1.13; 95% BCI:0.98-1.25; children 1-4 years: HR = 1.04; 95% BCI:0.89-1.18). A very strong spatial correlation was also observed. These results imply that assessing the malaria transmission-mortality relation involves more than the knowledge on the performance of interventions and control measures. This relation depends on the levels of malaria endemicity and transmission intensity, which varies significantly between different settings. Thus, sub-regions analyses are necessary to validate and assess reproducibility of findings