Dr. Samuel Scarpino
University of Vermont, USA
On the predictability of infectious disease outbreaks.
Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and their shared environment. As a result, predicting when, where, and how far diseases will spread requires a complex systems approach to modeling. Recent studies have demonstrated that predicting different components of outbreaks is feasible. Therefore, advancing both the science and practice of disease forecasting now requires testing for the presence of fundamental limits to outbreak prediction. To investigate the question of outbreak prediction, we study the information theoretic limits to forecasting across a broad set of infectious diseases using permutation entropy as a model independent measure of predictability. Studying the predictability of a diverse collection of historical outbreaks we identify a fundamental entropy barrier for infectious disease time series forecasting. However, we find that for most diseases this barrier to prediction is often well beyond the time scale of single outbreaks, implying prediction is likely to succeed. We also find that the forecast horizon varies by disease and demonstrate that both shifting model structures and social network heterogeneity are the most likely mechanisms for the observed differences in predictability across contagions.
Samuel V. Scarpino is a complex systems scientist investigating questions at the intersection of network science and human behavior. His work spans a broad range of topics, including: infectious disease modeling, forecasting in complex systems, genetic topology of disease, and decision making under uncertainty.
Sam's publications on Ebola, whooping cough, and influenza have been covered by the New York Times, NPR, the Economist, Smithsonian Magazine, and numerous other venues. Sam is currently an Assistant Professor of Mathematics & Statistics and is a core faculty member in the Complex Systems Center at the University of Vermont.
He earned a Ph.D. in integrative biology from The University of Texas at Austin in 2013 was a Santa Fe Institute Omidyar Postdoctoral Fellow from 2013 - 2016. For a current CV and complete list of publications, please see scarpino.github.io
Dr. Marco Ajelli
Northeastern University, USA & Bruno Kessler Foundation, Italy
What modeling can tell on Ebola.
Ebola stroke hard the West Africa in 2013–2016 causing more than 28,000 recorded cases, including 11,000+ deaths - a figure likely largely
underestimating the actual death toll. The lost among health care works and the need to isolate Ebola patients from the other ones, posed an unprecedented challenge to the health infrastructures of the most affected countries. Modeling was instrumental to increase situational awareness and in driving a strong response from governmental and non-governmental organizations. In this talk I will review four works where we used computational modeling to provide forecasts on the epidemic spread, analyze the effectiveness of a set of intervention strategies, and fill major gaps in the knowledge of Ebola epidemiology and transmission.
Dr. Marco Ajelli was born on Nov. 28, 1982 in Trento, Italy. He received both B.S. and M.Sc. degrees in Mathematics, and the Ph.D. in Information and Communication Technology from the University of Trento, in 2004, 2006, and 2009, respectively. He currently holds an Associate Research Scientist at the Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA, while on leave from a tenured Senior Research Scientist position at the Bruno Kessler Foundation, Trento, Italy.
Since 2008 Dr. Ajelli have authored 45 peer-reviewed research contributions, including top journals (such as The Lancet Infectious Diseases, BMC Medicine, Proceedings of the Royal Society B: Biological Sciences, American Journal of Epidemiology, PLOS Computational Biology) in several research areas (e.g., medicine, epidemiology, public health, computer science, applied mathematics, biology). According to Google Scholar, his works have attracted 1000+ citations in the last five years, for an h-index of 20.
In his research Dr. Ajelli uses computational modeling, in conjunction with statistical data analysis, in order to provide a quantitative framework for understanding epidemiological factors and population processes shaping infectious diseases spread. The goal of his research is to advance the state of the art of infectious diseases modeling for public health decision-making and, by means of that, to positively impact on the health of the population.