The temporal patterns of disease severity and prevalence in schistosomiasis

Schistosomiasis is one of the most widespread public health problems in the world. In this work, we introduce an eco-epidemiological model for its transmission and dynamics with the purpose of explaining both intra-and inter-annual fluctuations of disease severity and prevalence. The model takes the form of a system of nonlinear differential equations that incorporate biological complexity associated with schistosome's life cycle, including a prepatent period in snails (i.e., the time between initial infection and onset of infectiousness). Nonlinear analysis is used to explore the parametric conditions that produce different temporal patterns (stationary, endemic, periodic, and chaotic). For the time-invariant model, we identify a transcritical and a Hopf bifurcation in the space of the human and snail infection parameters. The first corresponds to the occurrence of an endemic equilibrium, while the latter marks the transition to interannual periodic oscillations. We then investigate a more realistic time-varying model in which fertility of the intermediate host population is assumed to seasonally vary. We show that seasonality can give rise to a cascade of period-doubling bifurcations leading to chaos for larger, though realistic, values of the amplitude of the seasonal variation of fertility. (C) 2015 AIP Publishing LLC

Movement Strategies of Seed Predators as Determinants of Plant Recruitment Patterns

Plant recruitment in nature exhibits several distinctive patterns ranging from hump shaped to monotonically decreasing with distance from the seed source. We investigate the role of post-dispersal seed predation in shaping these patterns, introducing a new mechanistic model that explicitly accounts for the movement strategy used by seed eaters. The model consists of two partial differential equations describing the spatiotemporal dynamics of both seed and predator densities. The movement strategy is defined by how predators move in response to the different cues they can use to search for seeds. These cues may be seed density, seed intake, distance from the plant, density of conspecific foragers, or a mixture of these four.The model is able to reproduce all the basic plant recruitment pat-terns found in the field. We compare the results to those of the ideal free distribution (IFD) theory and show that hump-shaped plant recruitment patterns cannot be generated by IFD predators and, in general, by foragers that respond exclusively to seed density. These foragers can produce only non increasing patterns, the shapes of which are determined by the foragers’ navigation capacities. In contrast, hump-shaped patterns can be produced by distance-responsive predators or by foragers that use conspecifics as a cue for seed abundanc

Spatially explicit effective reproduction numbers from incidence and mobility data

Current methods for near real-time estimation of effective reproduction numbers from surveillance data overlook mobility fluxes of infectors and susceptible individuals within a spatially connected network (the metapopulation). Exchanges of infections among different communities may thus be misrepresented unless explicitly measured and accounted for in the renewal equations. Here, we first derive the equations that include spatially explicit effective reproduction numbers, ℛk(t), in an arbitrary community k. These equations embed a suitable connection matrix blending mobility among connected communities and mobility-related containment measures. Then, we propose a tool to estimate, in a Bayesian framework involving particle filtering, the values of ℛk(t) maximizing a suitable likelihood function reproducing observed patterns of infections in space and time. We validate our tools against synthetic data and apply them to real COVID-19 epidemiological records in a severely affected and carefully monitored Italian region. Differences arising between connected and disconnected reproduction numbers (the latter being calculated with existing methods, to which our formulation reduces by setting mobility to zero) suggest that current standards may be improved in their estimation of disease transmission over time

Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis

Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic and environmental conditions, and human mobility. The model is parameterized by tapping several available geodatabases and a large dataset of mobile phone traces. It reliably reproduces the observed spatial patterns of regional schistosomiasis prevalence throughout the country, provided that spatial heterogeneity and human mobility are suitably accounted for. Specifically, a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of disease prevalence, while the inclusion of human mobility significantly improves the explanatory power of the model. Concerning human movement, we find that moderate mobility may reduce disease prevalence, whereas either high or low mobility may result in increased prevalence of infection. The effects of control strategies based on exposure and contamination reduction via improved access to safe water or educational campaigns are also analyzed. To our knowledge, this represents the first application of an integrative schistosomiasis transmission model at a whole-country scale

The spatial spread of schistosomiasis: A multidimensional network model applied to Saint-Louis region, Senegal

AbstractSchistosomiasis is a parasitic, water-related disease that is prevalent in tropical and subtropical areas of the world, causing severe and chronic consequences especially among children. Here we study the spatial spread of this disease within a network of connected villages in the endemic region of the Lower Basin of the Senegal River, in Senegal. The analysis is performed by means of a spatially explicit metapopulation model that couples local-scale eco-epidemiological dynamics with spatial mechanisms related to human mobility (estimated from anonymized mobile phone records), snail dispersal and hydrological transport of schistosome larvae along the main water bodies of the region. Results show that the model produces epidemiological patterns consistent with field observations, and point out the key role of spatial connectivity on the spread of the disease. These findings underline the importance of considering different transport pathways in order to elaborate disease control strategies that can be effective within a network of connected populations

Floquet theory for seasonal environmental forcing of spatially explicit waterborne epidemics

The transmission of waterborne pathogens is a complex process that is heavily linked to the spatial characteristics of the underlying environmental matrix as well as to the temporal variability of the relevant hydroclimatological drivers. In this work, we propose a time-varying, spatially explicit network model for the dynamics of waterborne diseases. Applying Floquet theory, which allows to extend results of local stability analysis to periodic dynamical systems, we find conditions for pathogen invasion and establishment in systems characterized by fluctuating environmental forcing, thus extending to time-varying contexts the generalized reproduction numbers recently obtained for spatially explicit epidemiology of waterborne disease. We show that temporal variability may have multifaceted effects on the invasion threshold, as it can either favor pathogen invasion or make it less likely. Moreover, environmental fluctuations characterized by distinctive geographical signatures can produce diversified, highly nontrivial effects on pathogen invasion. Our study is complemented by numerical simulations, which show that pathogen establishment is neither necessary nor sufficient for large epidemic outbreaks to occur in time-varying environments. Finally, we show that our framework can be used to reliably characterize the early geography of epidemic outbreaks triggered by fluctuating environmental conditions

Effects of rapid maxillary expander and delaire mask treatment on airway sagittal domention in pediatrc patirnts affected by class III malocclusion and obstructive sleep apnea syndeome

Abstract: Obstructive sleep apnea syndrome (OSAS) is a sleep-related breathing disorder that is very common in pediatric patients. In the literature, there are very few studies concerning the association between OSAS and class III malocclusion in children. The use of a rapid maxillary expander (RME) in association with the Delaire mask is a common treatment protocol for class III malocclusion. The aim of this work was to evaluate the cephalometric variations of upper airway dimensions and OSArelated clinical conditions after orthodontic treatment with an RME and the Delaire mask, as recorded in pediatric patients with a class III malocclusion who were affected by OSAS. In this preliminary study, 14 pediatric patients with mixed dentition, aged between 6 and 10 years, were selected. All patients were treated with an RME and the Delaire mask. Pre- and post-treatment cephalometric radiographs were traced, analyzed, and compared. The results demonstrated a significant increase in the upper airway linear measurements and the nasopharyngeal and oropharyngeal dimensions (p ≤ 0.05). This increase creates an improvement in airway patency and in OSAS-related clinical conditions. The use of the RME in association with the Delaire mask can be effective in the treatment of pediatric patients with a class III malocclusion who are affected by OSAS

CoCoNet: towards coast to coast networks of marine protected areas (from the shore to the high and deep sea), coupled with sea-based wind energy potential

This volume contains the main results of the EC FP7 “The Ocean of Tomorrow” Project CoCoNet, divided in two sections: 1) a set of guidelines to design networks of Marine Protected Areas in the Mediterranean and the Black Seas; 2) a smart wind chart that will allow evaluating the possibility of installing Offshore Wind Farms in both seas. The concept of Cells of Ecosystem Functioning, based on connectivity, is introduced to define natural units of management and conservation. The definition of Good Environmental Status, as defined in the Marine Strategy Framework Directive, is fully embraced to set the objectives of the project, by adopting a holistic approach that integrates a full set of disciplines, ranging from physics to bio-ecology, economics, engineering and many sub-disciplines. The CoCoNet Consortium involved scientist sfrom 22 states, based in Africa, Asia, and Europe, contributing to build a coherent scientific community.peer-reviewe

Generalized reproduction numbers and the prediction of patterns in waterborne disease

Understanding, predicting, and controlling outbreaks of waterborne diseases are crucial goals of public health policies, but pose challenging problems because infection patterns are influenced by spatial structure and temporal asynchrony. Although explicit spatial modeling is made possible by widespread data mapping of hydrology, transportation infrastructure, population distribution, and sanitation, the precise condition underwhich awaterborne disease epidemic can start in a spatially explicit setting is still lacking. Here we show that the requirement that all the local reproduction numbers R 0 be larger than unity is neither necessary nor sufficient for outbreaks to occur when local settlements are connected by networks of primary and secondary infection mechanisms. To determine onset conditions, we derive general analytical expressions for a reproduction matrix G0, explicitly accounting for spatial distributions of human settlements and pathogen transmission via hydrological and human mobility networks. At disease onset, a generalized reproduction number Λ0 (the dominant eigenvalue of G0) must be larger than unity.We also show that geographical outbreak patterns in complex environments are linked to the dominant eigenvector and to spectral properties of G0. Tests against data and computations for the 2010 Haiti and 2000 KwaZulu-Natal cholera outbreaks, as well as against computations for metapopulation networks, demonstrate that eigenvectors of G0 provide a synthetic and effective tool for predicting the disease course in space and time. Networked connectivity models, describing the interplay between hydrology, epidemiology, and social behavior sustaining human mobility, thus prove to be key tools for emergency management of waterborne infections