Alessandro Vespignani | |
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Born | April 4, 1965 |
Alma mater | Sapienza University of Rome |
Known for | network science Mathematical modelling of infectious disease computational epidemiology |
Awards | List of American Physical Society Fellows Fellows of the Network Science Society Order of the Star of Italy Fellow of the American Association for the Advancement of Science |
Scientific career | |
Institutions | Leiden University International Centre for Theoretical Physics University of Paris-Sud Indiana University Northeastern University |
Alessandro Vespignani (born April 4, 1965) is an Italian-American physicist, best known for his work on complex networks, and particularly for work on the applications of network theory to the mathematical modeling of infectious disease, applications of computational epidemiology, and for studies of the topological properties of the Internet. He is currently the Sternberg Family Distinguished University Professor of Physics, Computer Science and Health Sciences at Northeastern University, [1] where he is the director of the Network Science Institute.
Vespignani and his team have contributed mathematical and computational modeling analysis on several disease outbreaks, including 2009 H1N1 flu, Ebola epidemic in West Africa, Zika epidemic, and the Covid-19 pandemic.
Vespignani is author, together with Romualdo Pastor-Satorras, of the book Evolution and Structure of the Internet. Together with Alain Barrat and Marc Barthelemy he has published in 2008 the monograph Dynamical Processes on Complex Networks.
Vespignani received his undergraduate degree and Ph.D., both in physics and both from the University of Rome “La Sapienza”, in 1990 and 1993, respectively. Following postdoctoral research at Yale University and Leiden University, he worked at the International Centre for Theoretical Physics in Trieste for five years, and briefly at the University of Paris-Sud, before moving to Indiana University in 2004, [2] and then to Northeastern University in 2011. [3]
Vespignani has worked in a number of areas of physics, including characterization of non-equilibrium phenomena and phase transitions, computer science, network science and computational epidemiology. He has collaborated with, among others, Luciano Pietronero, Benoit Mandelbrot, Betz Halloran, Ira Longini, and David Lazer. He describes his current research as being focused on "interdisciplinary application of statistical and numerical simulation methods in the analysis of epidemic and spreading phenomena and the study of biological, social and technological networks." [4]
He is best known, however, for his work on complex networks. Of particular note is his work with Romualdo Pastor-Satorras, in which the two demonstrated that for a disease propagating on a random scale-free network the transmission probability or infectivity necessary to sustain an outbreak tends to zero in the limit of large network size. Vespignani’s works on modeling the spatial spread of epidemics includes the realistic and data-driven modeling of emerging infectious diseases, [5] and contributions to computational epidemiology by developing specific tools for the analysis of the global spread of epidemics. [6] [7]
During the COVID-19 pandemic, Vespignani’s team investigated [8] how travel and quarantine influenced the dynamics of the spread of SARS-CoV-2. [9] The modeling analysis mapped the early dispersal of infections and the temporal windows of the introduction of SARS-CoV-2 and onset of local transmission in Europe and the USA, [10] showing that hidden outbreaks were spreading almost completely undetected in major US cities. [11] Vespignani research contributed also to covid forecasting [12] [13] and scenario analysis. [14]
Vespignani is an elected fellow of the American Physical Society and the Network Science Society. He has been inducted in the Academia Europaea (section Physics and Engineering) in 2011.
A pandemic is an epidemic of an infectious disease that has spread across a large region, for instance multiple continents or worldwide, affecting a substantial number of individuals. Widespread endemic diseases with a stable number of infected individuals such as recurrences of seasonal influenza are generally excluded as they occur simultaneously in large regions of the globe rather than being spread worldwide.
A zoonosis or zoonotic disease is an infectious disease of humans caused by a pathogen that can jump from a non-human to a human and vice versa.
An epidemic is the rapid spread of disease to a large number of hosts in a given population within a short period of time. For example, in meningococcal infections, an attack rate in excess of 15 cases per 100,000 people for two consecutive weeks is considered an epidemic.
In epidemiology, the basic reproduction number, or basic reproductive number, denoted , of an infection is the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection. The definition assumes that no other individuals are infected or immunized. Some definitions, such as that of the Australian Department of Health, add the absence of "any deliberate intervention in disease transmission". The basic reproduction number is not necessarily the same as the effective reproduction number , which is the number of cases generated in the current state of a population, which does not have to be the uninfected state. is a dimensionless number and not a time rate, which would have units of time−1, or units of time like doubling time.
In epidemiology, an infection is said to be endemic in a specific population or populated place when that infection is constantly present, or maintained at a baseline level, without extra infections being brought into the group as a result of travel or similar means. The term describes the distribution (spread) of an infectious disease among a group of people or within a populated area. An endemic disease always has a steady, predictable number of people getting sick, but that number can be high (hyperendemic) or low (hypoendemic), and the disease can be severe or mild. Also, a disease that is usually endemic can become epidemic.
Mathematical models can project how infectious diseases progress to show the likely outcome of an epidemic and help inform public health and plant health interventions. Models use basic assumptions or collected statistics along with mathematics to find parameters for various infectious diseases and use those parameters to calculate the effects of different interventions, like mass vaccination programs. The modelling can help decide which intervention(s) to avoid and which to trial, or can predict future growth patterns, etc.
Program for Monitoring Emerging Diseases is among the largest publicly available emerging diseases and outbreak reporting systems in the world. The purpose of ProMED is to promote communication amongst the international infectious disease community, including scientists, physicians, veterinarians, epidemiologists, public health professionals, and others interested in infectious diseases on a global scale. Founded in 1994, ProMED has pioneered the concept of electronic, Internet-based emerging disease and outbreak detection reporting. In 1999, ProMED became a program of the International Society for Infectious Diseases. As of 2016, ProMED has more than 75,000 subscribers in over 185 countries. With an average of 13 posts per day, ProMED provides users with up-to-date information concerning infectious disease outbreaks on a global scale.
In public health, social distancing, also called physical distancing, is a set of non-pharmaceutical interventions or measures intended to prevent the spread of a contagious disease by maintaining a physical distance between people and reducing the number of times people come into close contact with each other. It usually involves keeping a certain distance from others and avoiding gathering together in large groups.
Ira M. Longini is an American biostatistician and infectious disease epidemiologist.
In mathematical modeling of social networks, link-centric preferential attachment is a node's propensity to re-establish links to nodes it has previously been in contact with in time-varying networks. This preferential attachment model relies on nodes keeping memory of previous neighbors up to the current time.
Individual human mobility is the study that describes how individual humans move within a network or system. The concept has been studied in a number of fields originating in the study of demographics. Understanding human mobility has many applications in diverse areas, including spread of diseases, mobile viruses, city planning, traffic engineering, financial market forecasting, and nowcasting of economic well-being.
Targeted immunization strategies are approaches designed to increase the immunization level of populations and decrease the chances of epidemic outbreaks. Though often in regards to use in healthcare practices and the administration of vaccines to prevent biological epidemic outbreaks, these strategies refer in general to immunization schemes in complex networks, biological, social or artificial in nature. Identification of at-risk groups and individuals with higher odds of spreading the disease often plays an important role in these strategies, since targeted immunization in high-risk groups is necessary for effective eradication efforts and has a higher return on investment than immunizing larger but lower-risk groups.
Network medicine is the application of network science towards identifying, preventing, and treating diseases. This field focuses on using network topology and network dynamics towards identifying diseases and developing medical drugs. Biological networks, such as protein-protein interactions and metabolic pathways, are utilized by network medicine. Disease networks, which map relationships between diseases and biological factors, also play an important role in the field. Epidemiology is extensively studied using network science as well; social networks and transportation networks are used to model the spreading of disease across populations. Network medicine is a medically focused area of systems biology.
Air transport network or air transportation network (ATN) is an example of transport networks and spatial networks. The nodes of the network are the airports and the links represent direct flight routes between two airports. Alternatively, cities can be considered as the nodes with links representing direct flight connection between them. Air transport networks can be defined worldwide as well as for one region or for one airline company; the scale of the network can be global or domestic.
Neil Morris Ferguson is a British epidemiologist and professor of mathematical biology, who specialises in the patterns of spread of infectious disease in humans and animals. He is the director of the Jameel Institute, and of the MRC Centre for Global Infectious Disease Analysis, and head of the Department of Infectious Disease Epidemiology in the School of Public Health and Vice-Dean for Academic Development in the Faculty of Medicine, all at Imperial College London.
Caitlin M. Rivers is an American epidemiologist who as Senior Scholar at the Johns Hopkins Center for Health Security and assistant professor at the Johns Hopkins Bloomberg School of Public Health, specializing on improving epidemic preparedness. Rivers is currently working on the American response to the COVID-19 pandemic with a focus on the incorporation of infectious disease modeling and forecasting into public health decision making.
Cécile Viboud is a Staff Scientist based in the Fogarty International Center at the National Institutes of Health, where she is part of the Multinational Influenza Seasonal Mortality Study (MISMS). Viboud specialises in the mortality of infectious disease. Viboud was involved with epidemiological analysis during the COVID-19 pandemic.
Caroline O'Flaherty Buckee is an epidemiologist. She is an associate professor of Epidemiology and is the associate director of the Center for Communicable Disease Dynamics, both at the Harvard T.H. Chan School of Public Health. Buckee is known for her work in digital epidemiology, where mathematical models track mobile and satellite data to understand the transmission of infectious diseases through populations in an effort to understand the spatial dynamics of disease transmission. Her work examines the implications of conducting surveillance and implementing control programs as a way to understand and predict what will happen when dealing with outbreaks of infectious diseases like malaria and COVID-2019.
Natalie E. Dean is an American biostatistician specializing in infectious disease epidemiology. Dean is currently an assistant professor of Biostatistics at the University of Florida. Her research involves epidemiological modeling of outbreaks, including Ebola, Zika and COVID-19.
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