This project aims to create the knowledge, technologies and frameworks to increase the effectiveness of detecting, controlling and eradicating emerging and exotic (vector-borne) diseases in European livestock. European animal-health is threatened by a range of infectious diseases, many of them vector-borne. This necessitates the development of innovative statistical, mathematical and computational frameworks, that integrate knowledge on animals, vectors and the environment, to understand and predict the dynamics of disease spread and propose options for an effective targeted management. Our approach is based on four complementary and synergistic elements that play to the strengths of the five partner institutions: Formulation of a generic modeling framework for the spread of vector-borne diseases to assess how spatial spread of infection by vectors can be integrated into spread by local transmission and movement of infected livestock. Investigation of a common tool for monitoring and surveillance of vectors, identifying optimal ways in which this should be employed and assessing delays to detection. Assessment of practical control measures to understand under what conditions and routes of transmission different control measures (applied individually or in combination) are most effective. Focus on specific case studies that pose a substantial risk to the European livestock industry and for which data is available; including Rift Valley fever, Bluetongue, African Swine fever and Swine Vesicular disease. The results generated will allow optimized disease surveillance and control, leading to rapid effective response to emerging epidemics. The five European countries represented have a variety of environmental conditions and trading patterns; this will allow targeting of country-specific problems, as well as placing the actions in a pan-European perspective in which we consider control of transboundary diseases to ensure the safety of the European market.
ANIHWA collaborative project
role: Vittoria Colizza is the PI of the French partner
HARMSFLU - Harmonising multiple scales for data-driven computational approaches to the modeling of influenza spread.
New advances in science and medicine help us gain ground against certain infectious diseases, yet new infections continue to emerge that spread rapidly into the population and frequently reach pandemic proportions causing significant human and economic costs. Computational epidemiology, as an interdisciplinary field integrating complex systems with statistical physics approaches, computational sciences, mathematical epidemiology, Information Communication Technologies (ICT) and Geographic Information Systems, can help confronting this reality by offering new tools as important as medical, clinical, genetic or molecular diagnosis tools - namely, computational models. With less than 10 years since the first publications, models have offered an additional insight in response planning. The progress has been dramatic. As a by-product, however, such progress has also created an increased demand for quantitative, realistic, detailed and reliable data-driven computational models for the simulation of epidemic spread to guide decision-making processes. Used for the first time during an influenza pandemic event in the 2009 H1N1 case, models have indeed also uncovered their current limits. While intrinsically multi-scale and unfolding at several different spatial and temporal levels - from human-to-human transmission, to population level, space and mobility, up to the environment - infectious diseases transmission has been modeled so far by targeting specific geotemporal scales, typically treating each of them separately. Can we harmonize the multiple scales, interlinked one to each other, and intrinsically relevant for the description of the spread of infectious diseases in human population? The HarMS-flu project proposes an interdisciplinary research effort aimed at answering this question, with the potential to transform our understanding of the population-disease-environment system and our ability to plan/react/control a newly emerging pandemic.
ANR collaborative project
role: Vittoria Colizza is the coordinator
PREDEMICS - Preparedness, prediction and prevention of emerging zoonotic viruses with pandemic potential using multi-disciplinary approaches.
The capacity of zoonotic RNA viruses to emerge as major agents of human disease can appear limitless. Current intervention strategies have demonstrated limited success. Rapid, innovative and effective solutions are needed to reduce the apparently accelerating process of zoonotic disease emergence. We will study the following zoonotic viruses with epidemic potential in Europe: influenza virus, hepatitis E virus, viruses of the Japanese encephalitis serocomplex and lyssaviruses. These diverse viruses arise from the main reservoirs and vectors of potentially emerging viral diseases and use the three major routes of transmission: respiratory, faecal-oral and vector borne. Inter-disciplinary studies will generate valuable data on patterns of crossing the species barrier, transmission and disease emergence, including ecological and anthropological factors which determine virus availability and opportunities for exposure and infection. We will unravel the complex biological interactions between the virus and the recipient hosts that drive the viral adaptation and elucidate the factors determining the ability of the viruses to spread to and between humans (including pandemic spread). Furthermore, immune mechanisms of protection and novel prevention strategies will be investigated. Data will be compiled in a unique and freely accessible data-sharing platform to build a framework for analysing the drivers of pathogen emergence. Modelling, building on the analysis of key data, will focus on the extent to which pathogen trajectories are predictable and will identify high-risk situations and environments. This will allow improvement of disease surveillance, control, preparedness and intervention.
EU FP7 Health Project
role: Vittoria Colizza is the team leader.
Swine mobility and infectious diseases.
Experience has shown that the implementation of control measures and rapid containment are crucial for the eradication and control of outbreaks of highly infectious diseases in farmed animals. However, in real life conditions, such implementation is often complicated by the specific features of the disease and by logistics/management issues. The swine vesicular disease (SVD) is a disease that is present in some regions of Italy, with periodic epidemics that have affected large areas of central and southern Italy in the recent past. Classical swine fever (CSF), a disease eradicated in Italy, is one of the diseases at greater risk of re-introduction, because of the presence of infection in large parts of the Balkans and Eastern Europe. The existence of farms with a high turn-over of animals and the problems related to the timeliness of intervention in cases of suspected infection (such as delayed epidemiological investigations, poor enforcement of biosafety, etc.), are just some of the most critical factors for the spread of these diseases. The African swine fever (ASF) is also present in some areas of the Region of Sardinia, as well as in large parts of Eastern Europe, particularly in the former Soviet Caucasian states and Russia. This project aims at identifying and characterizing the epidemiological risk connected to the period between the occurrence of the infection and its detection, in order to implement rapid and efficient containment strategies.
Italian Ministry of Health, Research Project
role: Vittoria Colizza is the team leader
Previously funded projects.
EpiFor - Complexity and predictability of epidemics: toward a computational infrastructure for epidemic forecasts.
EpiFor is Colizza's research project selected by the ERC's first funding grant competition for Starting Grants in 2007. The project integrates methods of complex systems with statistical physics approaches, computational sciences and mathematical epidemiology in order to model and analyze epidemic spreading processes. Its main objectives are the basic theoretical understanding of multi-scale and agent based modeling approaches and their predictive power; and the development of computational approaches and data integration tools that will provide a realistic modeling framework for the analysis of observed epidemic outbreaks and the forecast of patterns of emerging diseases.
ERC Starting Grant
role : Vittoria Colizza is the PI of the project.
Livestock movements represent the main mean of propagation of animal infectious diseases. The analysis of the dynamical pattern of the cattle trade movements, along with the numerical simulations of diseases spreading is the crucial ingredient to devise efficient preventive and control strategies against epidemic outbreaks. By integrating detailed data of bovines’ displacements and leveraging on the network science approach, this project aims at developing a computational framework to study real epidemic outbreaks.
Italian Ministry of Health, Research Project
role: team leader
DynaNets - Computing real-world phenomena with dynamically changing complex networks.
Recent advances in experimental techniques such as detectors, sensors, and scanners have opened up new windows into physical and biological processes on many levels of detail. The challenge is to study not only the fundamental processes on separate spatio-temporal scales, but also their mutual coupling and the resulting emergent properties. Understanding, quantifying and handling this information complexity is one of the biggest scientific challenges of our time. DynaNets will study and develop a new paradigm of computing through Dynamically Changing Complex Networks reproducing the way nature processes information. It will develop theory and methods of dynamical networks providing us with new insights into the underlying processes of nature, economy, and society.
EU FP7 FET Open STREP Project
role: Vittoria Colizza is the WP leader and Andrea Apolloni is the post-doc working on the project.