Insights for brucellosis eradication in Italy through a model-based spread evaluation in grazing livestock - Sicily case study.

Brucellosis is one of the world's major zoonotic pathogens and is responsible for enormous economic losses as well as considerable human morbidity in endemic areas. Definitive control of human brucellosis requires control of brucellosis in livestock through practical solutions that can be easily applied to the field. In Italy, brucellosis remains endemic in several southern provinces, particularly in Sicily Region. The purpose of this paper is to describe the developed brucellosis model and its applications, trying to reproduce as faithfully as possible the complex transmission process of brucellosis accounting for the mixing of grazing animals. The model focuses on the contaminated environment rather than on the infected animal, uses real data from the main grazing areas of the Sicily Region, and aims to identify the best control options for minimizing the spread (and the prevalence) and to reach the eradication within the concerned areas. Simulation results confirmed the efficacy of an earlier application of the controls, showed the control should take place 30 days after going to pasture, and the culling time being negligible. Moreover, results highlighted the importance of the timing of both births and grazing pastures (and their interaction) more than other factors. As these factors are region­specific, the study encourages the adoption of different and new eradication tools, tuned on the grazing and commercial behavior of each region. This study will be further extended to improve the model's adaptability to the real world, with the purpose of making the model an operational tool able to help decision makers in accelerating brucellosis eradication in Italy.

Epidemiological and genomic findings of the first documented Italian outbreak of SARS-CoV-2 Alpha variant of concern.

From 24 December 2020 to 8 February 2021, 163 cases of SARS-CoV-2 Alpha variant of concern (VOC) were identified in Chieti province, Abruzzo region. Epidemiological data allowed the identification of 14 epi-clusters. With one exception, all the epi-clusters were linked to the town of Guardiagrele: 149 contacts formed the network, two-thirds of which were referred to the family/friends context. Real data were then used to estimate transmission parameters. According to our method, the calculated Re(t) was higher than 2 before the 12 December 2020. Similar values were obtained from other studies considering Alpha VOC. Italian sequence data were combined with a random subset of sequences obtained from the GISAID database. Genomic analysis showed close identity between the sequences from Guardiagrele, forming one distinct clade. This would suggest one or limited unspecified viral introductions from outside to Abruzzo region in early December 2020, which led to the diffusion of Alpha VOC in Guardiagrele and in neighbouring municipalities, with very limited inter-regional mixing.

A New Information System for the Management of Non-Epidemic Veterinary Emergencies.

The Italian National Veterinary Services, public health professionals, and policy makers are asked to participate at different levels in the decision-making process for the management of non-epidemic emergencies. A decision support system offering the different administrative and operational emergency management levels with a spatial and decisional tool to be used in the case of natural disasters is still missing at the national level. Within this context, the Italian General Directorate for Animal Health of the Ministry of Health funded a research project for the implementation of a new Veterinary Information System for Non-Epidemic Emergencies (SIVENE), an innovative real-time decision support tool for emergency response in a disaster management scenario. SIVENE was developed according to a multi-layer architecture with four integrated components: the database layer, which was implemented by an RDBMS Oracle 11 g; the ReST service layer, which was created using J2EE, Spring, and MyBatis technologies; the web application (business framework and user interface), which was developed in Angular4 framework using TypeScript language; and the web Geographic Information Systems (GIS), which was realized through the implementation of a geodatabase in Oracle RDBMS 11 g. This system allows us to build up and dynamically create a set of dedicated checklists to be used in the field when gathering the information needed for the management of non-epidemic emergencies; employ the application on mobile devices, such as tablets and smartphones; and use the web GIS to manage and visualize data of veterinary interest and territorial maps of risk and damage.

A Municipality-Based Approach Using Commuting Census Data to Characterize the Vulnerability to Influenza-Like Epidemic: The COVID-19 Application in Italy.

In February 2020, Italy became the epicenter for COVID-19 in Europe, and at the beginning of March, the Italian Government put in place emergency measures to restrict population movement. Aim of our analysis is to provide a better understanding of the epidemiological context of COVID-19 in Italy, using commuting data at a high spatial resolution, characterizing the territory in terms of vulnerability. We used a Susceptible-Infectious stochastic model and we estimated a municipality-specific infection contact rate () to capture the susceptibility to the disease. We identified in Lombardy, Veneto and Emilia Romagna regions (52% of all Italian cases) significant clusters of high , due to the simultaneous presence of connections between municipalities and high population density. Local simulated spreading in regions, with different levels of infection observed, showed different disease geographical patterns due to different values and commuting systems. In addition, we produced a vulnerability map (in the Abruzzi region as an example) by simulating the epidemic considering each municipality as a seed. The result shows the highest vulnerability values in areas with commercial hubs, close to the highest populated cities and the most industrial area. Our results highlight how human mobility can affect the epidemic, identifying particular situations in which the health authorities can promptly intervene to control the disease spread.

EpiExploreR: A Shiny Web Application for the Analysis of Animal Disease Data.

Emerging and re-emerging infectious diseases are a significant public and animal health threat. In some zoonosis, the early detection of virus spread in animals is a crucial early warning for humans. The analyses of animal surveillance data are therefore of paramount importance for public health authorities to identify the appropriate control measure and intervention strategies in case of epidemics. The interaction among host, vectors, pathogen and environment require the analysis of more complex and diverse data coming from different sources. There is a wide range of spatiotemporal methods that can be applied as a surveillance tool for cluster detection, identification of risk areas and risk factors and disease transmission pattern evaluation. However, despite the growing effort, most of the recent integrated applications still lack of managing simultaneously different datasets and at the same time making available an analytical tool for a complete epidemiological assessment. In this paper, we present EpiExploreR, a user-friendly, flexible, R-Shiny web application. EpiExploreR provides tools integrating common approaches to analyze spatiotemporal data on animal diseases in Italy, including notified outbreaks, surveillance of vectors, animal movements data and remotely sensed data. Data exploration and analysis results are displayed through an interactive map, tables and graphs. EpiExploreR is addressed to scientists and researchers, including public and animal health professionals wishing to test hypotheses and explore data on surveillance activities.

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.

Disease persistence on temporal contact networks accounting for heterogeneous infectious periods.

The infectious period of a transmissible disease is a key factor for disease spread and persistence. Epidemic models on networks typically assume an identical average infectious period for all individuals, thus allowing an analytical treatment. This simplifying assumption is, however, often unrealistic, as hosts may have different infectious periods, due, for instance, to individual host-pathogen interactions or inhomogeneous access to treatment. While previous work accounted for this heterogeneity in static networks, a full theoretical understanding of the interplay of varying infectious periods and time-evolving contacts is still missing. Here, we consider a susceptible-infectious-susceptible epidemic on a temporal network with host-specific average infectious periods, and develop an analytical framework to estimate the epidemic threshold, i.e. the critical transmissibility for disease spread in the host population. Integrating contact data for transmission with outbreak data and epidemiological estimates, we apply our framework to three real-world case studies exploring different epidemic contexts-the persistence of bovine tuberculosis in southern Italy, the spread of nosocomial infections in a hospital, and the diffusion of pandemic influenza in a school. We find that the homogeneous parametrization may cause important biases in the assessment of the epidemic risk of the host population. Our approach is also able to identify groups of hosts mostly responsible for disease diffusion who may be targeted for prevention and control, aiding public health interventions.

Network-based assessment of the vulnerability of Italian regions to bovine brucellosis.

The endemic circulation of bovine brucellosis in cattle herds has a markedly negative impact on economy, due to decreased fertility, increased abortion rates, reduced milk and meat production. It also poses a direct threat to human health. In Italy, despite the long lasting efforts and the considerable economic investment, complete eradication of this disease still eludes the southern regions, as opposed to the northern regions that are disease-free. Here we introduced a novel quantitative network-based approach able to fully exploit the highly resolved databases of cattle trade movements and outbreak reports to yield estimates of the vulnerability of a cattle market to brucellosis. Tested on the affected regions, the introduced vulnerability indicator was shown to be accurate in predicting the number of bovine brucellosis outbreaks (Spearman r= 0.82, p= 0.04), thus confirming the suitability of our tool for epidemic risk assessment. We evaluated the dependence of regional vulnerability to brucellosis on a set of factors including premises spatial distribution, trading patterns, farming practices, herd market value, compliance to outbreak regulations, and exploring different epidemiological conditions. Animal trade movements were identified as a major route for brucellosis spread between farms (r=0.85,p<10-5 between vulnerability and number of inbound movements), with an additional potential risk attributed to the use of shared pastures (r=0.4,p=0.04). By comparing the vulnerability of disease-free regions in the north to affected regions in the south, we found that more intense trade and higher market value of the cattle sector in the north (r=0.56,p=0.01) likely inducing more efficient biosafety measures, together with poor compliance to trade restrictions following outbreaks in the south were key factors explaining the diverse success in eradicating brucellosis. Our modeling scheme is both synthetic and effective in gauging regional vulnerability to brucellosis persistence. Its general formulation makes it adaptable to other diseases and host species, providing a useful tool for veterinary epidemiology and policy assessment.

A Web Geographic Information System to share data and explorative analysis tools: The application to West Nile disease in the Mediterranean basin.

BACKGROUND: In the last decades an increasing number of West Nile Disease cases was observed in equines and humans in the Mediterranean basin and surveillance systems are set up in numerous countries to manage and control the disease. The collection, storage and distribution of information on the spread of the disease becomes important for a shared intervention and control strategy. To this end, a Web Geographic Information System has been developed and disease data, climatic and environmental remote sensed data, full genome sequences of selected isolated strains are made available. This paper describes the Disease Monitoring Dashboard (DMD) web system application, the tools available for the preliminary analysis on climatic and environmental factors and the other interactive tools for epidemiological analysis. METHODS: WNV occurrence data are collected from multiple official and unofficial sources. Whole genome sequences and metadata of WNV strains are retrieved from public databases or generated in the framework of the Italian surveillance activities. Climatic and environmental data are provided by NASA website. The Geographical Information System is composed by Oracle 10g Database and ESRI ArcGIS Server 10.03; the web mapping client application is developed with the ArcGIS API for Javascript and Phylocanvas library to facilitate and optimize the mash-up approach. ESRI ArcSDE 10.1 has been used to store spatial data. RESULTS: The DMD application is accessible through a generic web browser at https://netmed.izs.it/networkMediterraneo/. The system collects data through on-line forms and automated procedures and visualizes data as interactive graphs, maps and tables. The spatial and temporal dynamic visualization of disease events is managed by a time slider that returns results on both map and epidemiological curve. Climatic and environmental data can be associated to cases through python procedures and downloaded as Excel files. CONCLUSIONS: The system compiles multiple datasets through user-friendly web tools; it integrates entomological, veterinary and human surveillance, molecular information on pathogens and environmental and climatic data. The principal result of the DMD development is the transfer and dissemination of knowledge and technologies to develop strategies for integrated prevention and control measures of animal and human diseases.

A New Weighted Degree Centrality Measure: The Application in an Animal Disease Epidemic.

In recent years researchers have investigated a growing number of weighted heterogeneous networks, where connections are not merely binary entities, but are proportional to the intensity or capacity of the connections among the various elements. Different degree centrality measures have been proposed for this kind of networks. In this work we propose weighted degree and strength centrality measures (WDC and WSC). Using a reducing factor we correct classical centrality measures (CD) to account for tie weights distribution. The bigger the departure from equal weights distribution, the greater the reduction. These measures are applied to a real network of Italian livestock movements as an example. A simulation model has been developed to predict disease spread into Italian regions according to animal movements and animal population density. Model's results, expressed as infected regions and number of times a region gets infected, were related to weighted and classical degree centrality measures. WDC and WSC were shown to be more efficient in predicting node's risk and vulnerability. The proposed measures and their application in an animal network could be used to support surveillance and infection control strategy plans.

Rift Valley fever trasmission dynamics described by compartmental models.

Rift Valley fever (RVF) is one of the most important zoonotic Transboundary Animal Diseases able to cross international borders and cause devastating effect on animal health and food security. Climate changes and the presence of competent vectors in the most of the current RVF-free temperate countries strongly support the inclusion of RVF virus (RVFV) among the most significant emerging viral threats for public and animal health. The transmission of RVFV is driven by complex eco-climatic factors making the epidemiology of RVF infection difficult to study and to understand. Mathematical, statistical and spatial models are often used to explain the mechanisms underlying these biological processes, providing new and effective tools to plan measures for public health protection. In this paper we performed a systematic literature review on RVF published papers with the aim of identifying and describing the most recent papers developing compartmental models for the study of RVFV transmission dynamics.

OIEBTLABNET: the web-based network of the OIE Bluetongue Reference Laboratories.

Bluetongue (BT) is a mild to severe disease of domestic and wild ruminants caused by the Bluetongue virus (BTV) and generally transmitted by Culicoides biting midges. Its occurrence also determines a livestock trade ban in affected countries with severe economic consequences on national and international trade. For this reason, in May 2011, the OIE encouraged the OIE Reference Laboratories to establish and maintain a BT network to provide expertise and training to the OIE and OIE Member Countries for BT diagnosis, surveillance and control. The network is constantly sustained by world leading scientists in the field of virology, epidemiology, serology, entomology and vaccine development. The website, available at http://oiebtnet.izs.it/btlabnet/, hosts an Information System containing data on BTV outbreaks and strains and a WebGIS that distributes maps on BTV occurrence. In this paper we describe the applications and present the benefits derived from the use of the WebGIS in the context of BT international surveillance network.

Analysis of climatic factors involved in the BTV-1 incursion in Central Italy in 2014.

In 2012, six years after the previous epidemic, Bluetongue virus serotype 1 (BTV-1) re-emerged in Sardinia causing a limited number of outbreaks. Due to impossibility of implementing a vaccination campaign, the BTV-1 then spread all over the island in 2013 with about 7,000 outbreaks and, in September 2013, the virus reached Central Italy, with a limited number of outbreaks located along the Tyrrhenian coast. The surveillance system in place in Italy detected viral circulation during the following winter, when a few seroconversions were notified. Starting from mid July 2014, a huge number of outbreaks were reported and the disease spread toward inland territories, affecting Umbria, Abruzzo and Marche. In 2014, BTV-1 affected areas where Culicoides species belonging to the Obsoletus and Pulicaris complexes were identified as main vectors. The analysis of temperature and rainfall in Central Italy revealed a significant warmer winter (2013-2014) and a cooler and rainy summer season (2014). These climatic aspects might have certainly favored the overwintering of the virus in local vector or host populations in the Tyrrhenian coast, and, then, the spread of the virus to the rest of Central Italy. However, the heavy circulation of BTV-1 and the severity of clinical outbreaks recorded leave a number of 'open questions' that are currently under investigations.

The Arbo­zoonet Information System.

The Arbo­zoonet Information System has been developed as part of the 'International Network for Capacity Building for the Control of Emerging Viral Vector Borne Zoonotic Diseases (Arbo­zoonet)' project. The project aims to create common knowledge, sharing data, expertise, experiences, and scientific information on West Nile Disease (WND), Crimean­Congo haemorrhagic fever (CCHF), and Rift Valley fever (RVF). These arthropod­borne diseases of domestic and wild animals can affect humans, posing great threat to public health. Since November 2011, when the Schmallenberg virus (SBV) has been discovered for the first time in Northern Europe, the Arbo­zoonet Information System has been used in order to collect information on newly discovered disease and to manage the epidemic emergency. The system monitors the geographical distribution and epidemiological evolution of CCHF, RVF, and WND since 1946. More recently, it has also been deployed to monitor the SBV data. The Arbo­zoonet Information System includes a web application for the management of the database in which data are stored and a WebGIS application to explore spatial disease distributions, facilitating the epidemiological analysis. The WebGIS application is an effective tool to show and share the information and to facilitate the exchange and dissemination of relevant data among project's participants.

Predicting epidemic risk from past temporal contact data.

Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system's functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system's pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node's loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node's epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies.

Systems for prevention and control of epidemic emergencies.

The development of early warning systems is fundamental for preventing the spread of infectious diseases. Data collection, however, is a costly activity and it is not possible to implement early warning systems everywhere and for all possible events. Hence, tools helping to improve the focus of surveillance efforts are of paramount importance. Risk assessment methods and other provisional modelling techniques may permit to estimate the probability of introduction and spread of infectious diseases in different geographical areas. Similarly, efficient information systems must be in place to assist the veterinary services in case of epidemic emergencies in order to support the prompt application of control measures for the containment of the infection and the reduction of the magnitude of negative consequences. This review describes two recent approaches to the estimation of the probability of introduction and spread of infectious diseases based on the use of risk maps/ spatial modelling and Social Network Analysis (SNA) techniques. The review also describes a web application developed in Italy to help official veterinary services to trace animals in case of outbreaks of infectious diseases.

Optimizing surveillance for livestock disease spreading through animal movements.

The spatial propagation of many livestock infectious diseases critically depends on the animal movements among premises; so the knowledge of movement data may help us to detect, manage and control an outbreak. The identification of robust spreading features of the system is however hampered by the temporal dimension characterizing population interactions through movements. Traditional centrality measures do not provide relevant information as results strongly fluctuate in time and outbreak properties heavily depend on geotemporal initial conditions. By focusing on the case study of cattle displacements in Italy, we aim at characterizing livestock epidemics in terms of robust features useful for planning and control, to deal with temporal fluctuations, sensitivity to initial conditions and missing information during an outbreak. Through spatial disease simulations, we detect spreading paths that are stable across different initial conditions, allowing the clustering of the seeds and reducing the epidemic variability. Paths also allow us to identify premises, called sentinels, having a large probability of being infected and providing critical information on the outbreak origin, as encoded in the clusters. This novel procedure provides a general framework that can be applied to specific diseases, for aiding risk assessment analysis and informing the design of optimal surveillance systems.

Dynamical patterns of cattle trade movements.

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions.

Evaluation of risk and vulnerability using a Disease Flow Centrality measure in dynamic cattle trade networks.

A new method for the calculation of a centrality measure (Disease Flow Centrality, DFC), which takes into account temporal dynamics of livestock movement networks, is proposed. The method is based on a network traversal algorithm which represents an epidemic process more realistically compared with traditional graph traversal algorithms used in the calculation of centrality measures on static networks. The new approach was tested on networks generated from all the registered movements of cattle in Italy in the years 2007, 2008 and 2009 and the results were compared to those obtained by classical centrality measures. The results show that DFC values often differ substantially from those of other centrality measures and that these DFC values tend to be more unstable in time. The DFC offers several advantages for assessing risk and vulnerability of specific holdings and of an entire network, using recent movement data from national livestock databases. Some examples also indicate how the basic approach in the DFC calculation could be expanded into a more complex epidemic model by incorporating weights and how it could be combined with a geo-spatial perspective.

Network analysis of Italian cattle trade patterns and evaluation of risks for potential disease spread.

Livestock movement data represent a valuable source of information to understand the pattern of contacts between premises which may determine the spread of diseases. Social network analysis techniques have been used to analyse the movement patterns of cattle in Italy in 2007. A description of the structure of the Italian cattle industry is presented and the main trade flows and the relations between premises in relation to the potential spread of cattle diseases are investigated. Epidemic simulations have been carried out on the network build out of movement data using a network-based meta-population model. The simulations show the influence of the network structure on the dynamics and size of a hypothetic epidemic and give useful indications on the effects of targeted removal of nodes based on the centrality of premises within the network of animal movements.