Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling.

BACKGROUND: Multimorbidity is typically associated with deficient health-related quality of life in mid-life, and the likelihood of developing multimorbidity in women is elevated. We address the issue of data sparsity in non-prevalent features by clustering the binary data of various rare medical conditions in a cohort of middle-aged women. This study aims to enhance understanding of how multimorbidity affects COVID-19 severity by clustering rare medical conditions and combining them with prevalent features for predictive modeling. The insights gained can guide the development of targeted interventions and improved management strategies for individuals with multiple health conditions. METHODS: The study focuses on a cohort of 4477 female patients, (aged 45-60) in Piedmont, Italy, and utilizes their multimorbidity data prior to the COVID-19 pandemic from their medical history from 2015 to 2019. The COVID-19 severity is determined by the hospitalization status of the patients from February to May 2020. Each patient profile in the dataset is depicted as a binary vector, where each feature denotes the presence or absence of a specific multimorbidity condition. By clustering the sparse medical data, newly engineered features are generated as a bin of features, and they are combined with the prevalent features for COVID-19 severity predictive modeling. RESULTS: From sparse data consisting of 174 input features, we have created a low-dimensional feature matrix of 17 features. Machine Learning algorithms are applied to the reduced sparsity-free data to predict the Covid-19 hospital admission outcome. The performance obtained for the corresponding models are as follows: Logistic Regression (accuracy 0.72, AUC 0.77, F1-score 0.69), Linear Discriminant Analysis (accuracy 0.7, AUC 0.77, F1-score 0.67), and Ada Boost (accuracy 0.7, AUC 0.77, F1-score 0.68). CONCLUSION: Mapping higher-dimensional data to a low-dimensional space can result in information loss, but reducing sparsity can be beneficial for Machine Learning modeling due to improved predictive ability. In this study, we addressed the issue of data sparsity in electronic health records and created a model that incorporates both prevalent and rare medical conditions, leading to more accurate and effective predictive modeling. The identification of complex associations between multimorbidity and the severity of COVID-19 highlights potential areas of focus for future research, including long COVID and intervention efforts.

A Literature Review of Modeling Approaches Applied to Data Collected in Automatic Milking Systems.

Automatic milking systems (AMS) have played a pioneering role in the advancement of Precision Livestock Farming, revolutionizing the dairy farming industry on a global scale. This review specifically targets papers that focus on the use of modeling approaches within the context of AMS. We conducted a thorough review of 60 articles that specifically address the topics of cows' health, production, and behavior/management Machine Learning (ML) emerged as the most widely used method, being present in 63% of the studies, followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic models (7%), and detection algorithms (7%). A significant majority of the reviewed studies (82%) primarily focused on the detection of cows' health, with a specific emphasis on mastitis, while only 11% evaluated milk production. Accurate forecasting of dairy cow milk yield and understanding the deviation between expected and observed milk yields of individual cows can offer significant benefits in dairy cow management. Likewise, the study of cows' behavior and herd management in AMSs is under-explored (7%). Despite the growing utilization of machine learning (ML) techniques in the field of dairy cow management, there remains a lack of a robust methodology for their application. Specifically, we found a substantial disparity in adequately balancing the positive and negative classes within health prediction models.

Potential Impact of Environmental Pollution by Human Antivirals on Avian Influenza Virus Evolution.

Antiviral (AV) drugs are the main line of defense against pandemic influenza. However, different administration policies are applied in countries with different stocks of AV drugs. These policies lead to different occurrences of drug metabolites in the aquatic environment, altering animal behavior with evolutionary consequences on viruses. The aim of this study was to investigate the potential impact of environmental pollution by human antivirals, such as oseltamivir carboxylate (OC), on the evolutionary rate of avian influenza. We used NA, HA, NP, and MP viral segments from two groups of neighboring countries sharing migratory routes of wild birds and characterized by different AV stockpiles. BEAST analyses were performed using the uncorrelated lognormal clock evolutionary model and the Bayesian skyline tree prior model. The ratios between the rate of evolution of the NA gene and the HA, NP, and MP segments were considered. The two groups of countries were compared by analyzing the differences in the ratio distributions. Our analyses highlighted a possible different behavior in the evolution of H5N1 2.3 clade viral strains when OC environmental pollution is present. In conclusion, the widespread consumption of antivirals and their presence in wastewater could influence the selective pressure on viruses.

A novel machine learning-based web application for field identification of infectious and inflammatory disorders of the central nervous system in cattle.

BACKGROUND: Central nervous system (CNS) infections in cattle are a major cause of economic loss and mortality. Machine learning (ML) techniques are gaining widespread application in solving predictive tasks in both human and veterinary medicine. OBJECTIVES: Our primary aim was to develop and compare ML models that could predict the likelihood of a CNS disorder of infectious or inflammatory origin in neurologically-impaired cattle. Our secondary aim was to create a user-friendly web application based on the ML model for the diagnosis of infection and inflammation of the CNS. ANIMALS: Ninety-eight cattle with CNS infection and 86 with CNS disorders of other origin. METHODS: Retrospective observational study. Six different ML methods (logistic regression [LR]; support vector machine [SVM]; random forest [RF]; multilayer perceptron [MLP]; K-nearest neighbors [KNN]; gradient boosting [GB]) were compared for their ability to predict whether an infectious or inflammatory disease was present based on demographics, neurological examination findings, and cerebrospinal fluid (CSF) analysis. RESULTS: All 6 methods had high prediction accuracy (≥80%). The accuracy of the LR model was significantly higher (0.843 ± 0.005; receiver operating characteristic [ROC] curve 0.907 ± 0.005 ) than the other models and was selected for implementation in a web application. CONCLUSION AND CLINICAL IMPORTANCE: Our findings support the use of ML algorithms as promising tools for veterinarians to improve diagnosis. The open-access web application may aid clinicians in achieving correct diagnosis of infectious and inflammatory neurological disorders in livestock, with the added benefit of promoting appropriate use of antimicrobials.

Fetlock Joint Angle Pattern and Range of Motion Quantification Using Two Synchronized Wearable Inertial Sensors per Limb in Sound Horses and Horses with Single Limb Naturally Occurring Lameness.

Fetlock joint angle (FJA) pattern is a sensitive indicator of lameness. The first aim of this study is to describe a network of inertial measurement units system (IMUs) for quantifying FJA simultaneously in all limbs. The second aim is to evaluate the accuracy of IMUs for quantifying the sagittal plane FJA overground in comparison to bi-dimensional (2-D) optical motion capture (OMC). 14 horses (7 free from lameness and 7 lame) were enrolled and analyzed with both systems at walk and trot on a firm surface. All enrolled horses were instrumented with 8 IMUs (a pair for each limb) positioned at the dorsal aspect of the metacarpal/metatarsal bone and pastern and acquiring data at 200 Hz. Passive markers were glued on the center of rotation of carpus/tarsus, fetlock, and distal interphalangeal joint, and video footages were captured at 60 Hz and digitalized for OMC acquisition. The IMU system accuracy was reported as Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC). The Granger Causality Test (GCT) and the Bland−Altman analysis were computed between the IMUs and OMC patterns to determine the agreement between the two systems. The proposed IMU system was able to provide FJAs in all limbs using a patented method for sensor calibration and related algorithms. Fetlock joint range of motion (FJROM) variability of three consecutive strides was analyzed in the population through 3-way ANOVA. FJA patterns quantified by IMUs demonstrated high accuracy at the walk (RMSE 8.23° ± 3.74°; PCC 0.95 ± 0.03) and trot (RMSE 9.44° ± 3.96°; PCC 0.96 ± 0.02) on both sound (RMSE 7.91° ± 3.19°; PCC 0.97 ± 0.03) and lame horses (RMSE 9.78° ± 4.33°; PCC 0.95 ± 0.03). The two systems' measurements agreed (mean bias around 0) and produced patterns that were in temporal agreement in 97.33% of the cases (p < 0.01). The main source of variability between left and right FJROM in the population was the presence of lameness (p < 0.0001) and accounted for 28.46% of this total variation. IMUs system accurately quantified sagittal plane FJA at walk and trot in both sound and lame horses.

Fine-Grained Agent-Based Modeling to Predict Covid-19 Spreading and Effect of Policies in Large-Scale Scenarios.

Modeling and forecasting the spread of COVID-19 remains an open problem for several reasons. One of these concerns the difficulty to model a complex system at a high resolution (fine-grained) level at which the spread can be simulated by taking into account individual features. Agent-based modeling usually needs to find an optimal trade-off between the resolution of the simulation and the population size. Indeed, modeling single individuals usually leads to simulations of smaller populations or the use of meta-populations. In this article, we propose a solution to efficiently model the Covid-19 spread in Lombardy, themost populated Italian region with about ten million people. In particular, the model described in this paper is, to the best of our knowledge, the first attempt in literature to model a large population at the single-individual level. To achieve this goal, we propose a framework that implements: i. a scale-free model of the social contacts combining a sociability rate, demographic information, and geographical assumptions; ii. a multi-agent system relying on the actor model and the High-Performance Computing technology to efficiently implement ten million concurrent agents. We simulated the epidemic scenario from January to April 2020 and from August to December 2020, modeling the government's lockdown policies and people's mask-wearing habits. The social modeling approach we propose could be rapidly adapted for modeling future epidemics at their early stage in scenarios where little prior knowledge is available.

Caprine Arthritis Encephalitis Virus Disease Modelling Review.

Mathematical modelling is used in disease studies to assess the economical impacts of diseases, as well as to better understand the epidemiological dynamics of the biological and environmental factors that are associated with disease spreading. For an incurable disease such as Caprine Arthritis Encephalitis (CAE), this knowledge is extremely valuable. However, the application of modelling techniques to CAE disease studies has not been significantly explored in the literature. The purpose of the present work was to review the published studies, highlighting their scope, strengths and limitations, as well to provide ideas for future modelling approaches for studying CAE disease. The reviewed studies were divided into the following two major themes: Mathematical epidemiological modelling and statistical modelling. Regarding the epidemiological modelling studies, two groups of models have been addressed in the literature: With and without the sexual transmission component. Regarding the statistical modelling studies, the reviewed articles varied on modelling assumptions and goals. These studies modelled the dairy production, the CAE risk factors and the hypothesis of CAE being a risk factor for other diseases. Finally, the present work concludes with further suggestions for modelling studies on CAE.

Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches.

BACKGROUND: Frailty is one of the most critical age-related conditions in older adults. It is often recognized as a syndrome of physiological decline in late life, characterized by a marked vulnerability to adverse health outcomes. A clear operational definition of frailty, however, has not been agreed so far. There is a wide range of studies on the detection of frailty and their association with mortality. Several of these studies have focused on the possible risk factors associated with frailty in the elderly population while predicting who will be at increased risk of frailty is still overlooked in clinical settings. OBJECTIVE: The objective of our study was to develop predictive models for frailty conditions in older people using different machine learning methods based on a database of clinical characteristics and socioeconomic factors. METHODS: An administrative health database containing 1,095,612 elderly people aged 65 or older with 58 input variables and 6 output variables was used. We first identify and define six problems/outputs as surrogates of frailty. We then resolve the imbalanced nature of the data through resampling process and a comparative study between the different machine learning (ML) algorithms - Artificial neural network (ANN), Genetic programming (GP), Support vector machines (SVM), Random Forest (RF), Logistic regression (LR) and Decision tree (DT) - was carried out. The performance of each model was evaluated using a separate unseen dataset. RESULTS: Predicting mortality outcome has shown higher performance with ANN (TPR 0.81, TNR 0.76, accuracy 0.78, F1-score 0.79) and SVM (TPR 0.77, TNR 0.80, accuracy 0.79, F1-score 0.78) than predicting the other outcomes. On average, over the six problems, the DT classifier has shown the lowest accuracy, while other models (GP, LR, RF, ANN, and SVM) performed better. All models have shown lower accuracy in predicting an event of an emergency admission with red code than predicting fracture and disability. In predicting urgent hospitalization, only SVM achieved better performance (TPR 0.75, TNR 0.77, accuracy 0.73, F1-score 0.76) with the 10-fold cross validation compared with other models in all evaluation metrics. CONCLUSIONS: We developed machine learning models for predicting frailty conditions (mortality, urgent hospitalization, disability, fracture, and emergency admission). The results show that the prediction performance of machine learning models significantly varies from problem to problem in terms of different evaluation metrics. Through further improvement, the model that performs better can be used as a base for developing decision-support tools to improve early identification and prediction of frail older adults.

Compartmentalized evolution of Bovine Viral Diarrhoea Virus type 2 in an immunotolerant persistently infected cow.

Bovine viral diarrhea virus (BVDV) is one of the most important pathogens of cattle worldwide. BVDV-1 is widely distributed in Italy, while BVDV-2 has been detected occasionally. BVDV can be classified in two biotypes, cytopathic (CP) or noncytopathic (NCP). The characteristic of the virus is linked with the infection of a pregnant dam with a NCP strain: due to viral establishment before maturation of the fetal immune system the calf remains persistently infected (PI) and immunotolerant to the infecting BVDV strain. Thanks to their immunotolerance, PI animals represent a unique model to study the viral distribution and compartmentalization in absence of immunoresponse in vivo. In the present study, NGS sequencing was used to characterize the BVDV2 viral strain infecting a PI calf and to describe the viral quasispecies in tissues. Even if the consensus sequences obtained by all the samples were highly similar, quasispecies was described evaluating the presence and the frequency of variants among all the sequencing reads in each tissue. The results suggest a high heterogeneity of the infecting viral strain suggesting viral compartmentalization. The quasispecies analysis highlights the complex dynamics of viral population structure and can increase the knowledge about viral evolution in BVDV-2 persistently infected animals.

Farm productive contexts and the dynamics of bovine viral diarrhea (BVD) transmission.

Bovine viral diarrhea (BVD) is a viral disease that affects cattle and that is endemic to many European countries. It has a markedly negative impact on the economy, through reduced milk production, abortions, and a shorter lifespan of the infected animals. Cows becoming infected during gestation may give birth to Persistently Infected (PI) calves, which remain highly infective throughout their life, due to the lack of immune response to the virus. As a result, they are the key driver of the persistence of the disease both at herd scale, and at the national level. In the latter case, the trade-driven movements of PIs, or gestating cows carrying PIs, are responsible for the spatial dispersion of BVD. Past modeling approaches to BVD transmission have either focused on within-herd or between-herd transmission. A comprehensive portrayal, however, targeting both the generation of PIs within a herd, and their displacement throughout the country due to trade transactions, is still missing. We overcome this by designing a multiscale metapopulation model of the spatial transmission of BVD, accounting for both within-herd infection dynamics, and its spatial dispersion. We focus on Italy, a country where BVD is endemic and seroprevalence is very high. By integrating simple within-herd dynamics of PI generation, and the highly-resolved cattle movement dataset available, our model requires minimal arbitrary assumptions on its parameterization. We use our model to study the role of the different productive contexts of the Italian market, and test possible intervention strategies aimed at prevalence reduction. We find that dairy farms are the main drivers of BVD persistence in Italy, and any control strategy targeting these farms would lead to significantly higher prevalence reduction, with respect to targeting other production compartments. Our multiscale metapopulation model is a simple yet effective tool for studying BVD dispersion and persistence at country level, and is a good instrument for testing targeted strategies aimed at the containment or elimination of this disease. Furthermore, it can readily be applied to any national market for which cattle movement data is available.

An effective management strategy for the control of two lentiviruses in goat breedings.

Caprine Arthritis Encephalitis is an endemic disease in goat breedings, caused by viral strains belonging to the Small Ruminant Lentivirus group and characterized by a progressive chronic course. Its clinical signs are not immediately recognizable and can only be detected via costly serological tests. No vaccine is available. Two main strategies for fighting it are in common use. The "test-and-slaughter" approach, that selects infected goats and directly slaughters them, is expensive, time consuming and often leads to endemic low level persistence of the infection. Alternatively, newborns are removed from their mothers to be raised by healthy goats. After weaning they would rejoin their breeds, but then they could still be subject to horizontal contagion. In this study a mathematical model that considers the cocirculation of two different SRLV viral genotypes (B and E) is devised and analyzed, based on the key assumption of perfect cross-protection between the two genotypes' infections. Two strategic measures arise from its analysis, that are strongly recommended and whose implementation is encouraged: in the presence of both genotypes, the farmer should not isolate the newborns from their mothers but rather raise them with all the other animals. In the case of genotype-B-only affected farm, serological testing and mother-offspring separation should still be considered the best strategy for CAEV control. These strategies completely reverse the current removal policy and, in due conditions, would lead to disease eradication. These represent very reasonable and cheap measures for the eventual control of the epidemics.

Field application of an indirect gE ELISA on pooled milk samples for the control of IBR in free and marker vaccinated dairy herds.

BACKGROUND: The aim of the present study was to assess the reliability of a new strategy for monitoring the serological response against Bovine Herpesvirus 1 (BoHV1), the causative agent of infectious bovine rhinotracheitis (IBR). Bulk milk samples have already been identified as cost effective diagnostic matrices for monitoring purposes. Nevertheless, most eradication programs are still based on individual standard assays. In a region of northwestern Italy (Piedmont), the voluntary eradication program for IBR has become economically unsustainable. Being the prevalence of infection still high, glycoprotein E-deleted marker vaccines are commonly used but gE blocking ELISAs are less sensitive on bulk milk samples compared to blood serum. RESULTS: A recently developed indirect gE ELISA showed high versatility when applied to a wide range of matrices. In this study, we applied a faster, cost effective system for the concentration of IgG from pooled milk samples. The IgG enriched fractions were tested using a gE indirect ELISA for monitoring purposes in IBR-positive and IBR-marker-vaccinated herds. Official diagnostic tests were used as gold standard. During a 3 years study, a total 250 herds were involved, including more than 34,500 lactating cows. The proposed method showed a very good agreement with official diagnostic protocols and very good diagnostic performances: only 37 positive animals were not detected across the entire study. CONCLUSIONS: The results highlighted the ability of the proposed method to support the surveillance of IBR in the Piedmont region, reducing the costs without affecting the diagnostic performances.

Soluble Neuregulin1 Down-Regulates Myelination Genes in Schwann Cells.

Peripheral nerves are characterised by the ability to regenerate after injury. Schwann cell activity is fundamental for all steps of peripheral nerve regeneration: immediately after injury they de-differentiate, remove myelin debris, proliferate and repopulate the injured nerve. Soluble Neuregulin1 (NRG1) is a growth factor that is strongly up-regulated and released by Schwann cells immediately after nerve injury. To identify the genes regulated in Schwann cells by soluble NRG1, we performed deep RNA sequencing to generate a transcriptome database and identify all the genes regulated following 6 h stimulation of primary adult rat Schwann cells with soluble recombinant NRG1. Interestingly, the gene ontology analysis of the transcriptome reveals that NRG1 regulates genes belonging to categories that are regulated in the peripheral nerve immediately after an injury. In particular, NRG1 strongly inhibits the expression of genes involved in myelination and in glial cell differentiation, suggesting that NRG1 might be involved in the de-differentiation (or "trans-differentiation") process of Schwann cells from a myelinating to a repair phenotype. Moreover, NRG1 inhibits genes involved in the apoptotic process, and up-regulates genes positively regulating the ribosomal RNA processing, thus suggesting that NRG1 might promote cell survival and stimulate new protein expression. This in vitro transcriptome analysis demonstrates that in Schwann cells NRG1 drives the expression of several genes which partially overlap with genes regulated in vivo after peripheral nerve injury, underlying the pivotal role of NRG1 in the first steps of the nerve regeneration process.

Modeling the effects of variable feeding patterns of larval ticks on the transmission of Borrelia lusitaniae and Borrelia afzelii.

Spirochetes belonging to the Borrelia burgdoferi sensu lato (sl) group cause Lyme Borreliosis (LB), which is the most commonly reported vector-borne zoonosis in Europe. B. burgdorferi sl is maintained in nature in a complex cycle involving Ixodes ricinus ticks and several species of vertebrate hosts. The transmission dynamics of B. burgdorferi sl is complicated by the varying competence of animals for different genospecies of spirochetes that, in turn, vary in their capability of causing disease. In this study, a set of difference equations simplifying the complex interaction between vectors and their hosts (competent and not for Borrelia) is built to gain insights into conditions underlying the dominance of B. lusitaniae (transmitted by lizards to susceptible ticks) and the maintenance of B. afzelii (transmitted by wild rodents) observed in a study area in Tuscany, Italy. Findings, in agreement with field observations, highlight the existence of a threshold for the fraction of larvae feeding on rodents below which the persistence of B. afzelii is not possible. Furthermore, thresholds change as nonlinear functions of the expected number of nymph bites on mice, and the transmission and recovery probabilities. In conclusion, our model provided an insight into mechanisms underlying the relative frequency of different Borrelia genospecies, as observed in field studies.

Infection dynamics on spatial small-world network models.

The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.

Alterations of Cell Proliferation and Apoptosis in the Hypoplastic Reeler Cerebellum.

A mutation of the reln gene gives rise to the Reeler mouse (reln (-∕-)) displaying an ataxic phenotype and cerebellar hypoplasia. We have characterized the neurochemistry of postnatal (P0-P60) reln (-∕-) mouse cerebella with specific attention to the intervention of cell proliferation and apoptosis in the P0-P25 interval. Homozygous reln (-∕-) mice and age-matched controls were analyzed by immunofluorescence using primary antibodies against NeuN, calbindin, GFAP, vimentin, SMI32, and GAD67. Proliferation and apoptosis were detected after a single intraperitoneal BrdU injection and by the TUNEL assay with anti-digoxigenin rhodamine-conjugated antibodies. Quantitative analysis with descriptive and predictive statistics was used to calculate cell densities (number/mm(2)) after fluorescent nuclear stain (TCD, total cell density), labeling with BrdU (PrCD, proliferating cell density), or TUNEL (ApoCD, apoptotic cell density). By this approach we first have shown that the temporal pattern of expression of neuronal/glial markers in postnatal cerebellum is not affected by the Reeler mutation. Then, we have demonstrated that the hypoplasia in the Reeler mouse cerebellum is consequent to reduction of cortical size and cellularity (TCD), and that TCD is, in turn, linked to quantitative differences in the extent of cell proliferation and apoptosis, as well as derangements in their temporal trends during postnatal maturation. Finally, we have calculated that PrCD is the most important predictive factor to determine TCD in the cerebellar cortex of the mutants. These results support the notion that, beside the well-known consequences onto the migration of the cerebellar neurons, the lack of Reelin results in a measurable deficit in neural proliferation.

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

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 Cx. pipiens 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.

Sport, how people choose it: A network analysis approach.

In order to investigate the behaviour of athletes in choosing sports, we analyse data from part of the We-Sport database, a vertical social network that links athletes through sports. In particular, we explore connections between people sharing common sports and the role of age and gender by applying "network science" approaches and methods. The results show a disassortative tendency of athletes in choosing sports, a negative correlation between age and number of chosen sports and a positive correlation between age of connected athletes. Some interesting patterns of connection between age classes are depicted. In addition, we propose a method to classify sports, based on the analyses of the behaviour of people practising them. Thanks to this brand new classifications, we highlight the links of class of sports and their unexpected features. We emphasise some gender dependency affinity in choosing sport classes.

Pattern of tick aggregation on mice: larger than expected distribution tail enhances the spread of tick-borne pathogens.

The spread of tick-borne pathogens represents an important threat to human and animal health in many parts of Eurasia. Here, we analysed a 9-year time series of Ixodes ricinus ticks feeding on Apodemus flavicollis mice (main reservoir-competent host for tick-borne encephalitis, TBE) sampled in Trentino (Northern Italy). The tail of the distribution of the number of ticks per host was fitted by three theoretical distributions: Negative Binomial (NB), Poisson-LogNormal (PoiLN), and Power-Law (PL). The fit with theoretical distributions indicated that the tail of the tick infestation pattern on mice is better described by the PL distribution. Moreover, we found that the tail of the distribution significantly changes with seasonal variations in host abundance. In order to investigate the effect of different tails of tick distribution on the invasion of a non-systemically transmitted pathogen, we simulated the transmission of a TBE-like virus between susceptible and infective ticks using a stochastic model. Model simulations indicated different outcomes of disease spreading when considering different distribution laws of ticks among hosts. Specifically, we found that the epidemic threshold and the prevalence equilibria obtained in epidemiological simulations with PL distribution are a good approximation of those observed in simulations feed by the empirical distribution. Moreover, we also found that the epidemic threshold for disease invasion was lower when considering the seasonal variation of tick aggregation.

Interplay of network dynamics and heterogeneity of ties on spreading dynamics.

The structure of a network dramatically affects the spreading phenomena unfolding upon it. The contact distribution of the nodes has long been recognized as the key ingredient in influencing the outbreak events. However, limited knowledge is currently available on the role of the weight of the edges on the persistence of a pathogen. At the same time, recent works showed a strong influence of temporal network dynamics on disease spreading. In this work we provide an analytical understanding, corroborated by numerical simulations, about the conditions for infected stable state in weighted networks. In particular, we reveal the role of heterogeneity of edge weights and of the dynamic assignment of weights on the ties in the network in driving the spread of the epidemic. In this context we show that when weights are dynamically assigned to ties in the network, a heterogeneous distribution is able to hamper the diffusion of the disease, contrary to what happens when weights are fixed in time.