Charting the Next Pandemic: Modeling Infectious Disease Spreading in the Data Science Age, Paperback

This book provides an introduction to the computational and complex systems modeling of the global spreading of infectious diseases. The latest developments in the area of contagion processes modeling are discussed, and readers are exposed to real world examples of data-model integration impacting the decision-making process. Recent advances in computational science and the increasing availability of real-world data are making it possible to develop realistic scenarios and real-time forecasts of the global spreading of emerging health threats.
The first part of the book guides the reader through sophisticated complex systems modeling techniques with a non-technical and visual approach, explaining and illustrating the construction of the modern framework used to project the spread of pandemics and epidemics. Models can be used to transform data to knowledge that is intuitively communicated by powerful infographics and for this reason, the second part of the book focuses on a set of charts that illustrate possible scenarios of future pandemics. The visual atlas contained allows the reader to identify commonalities and patterns in emerging health threats, as well as explore the wide range of models and data that can be used by policy makers to anticipate trends, evaluate risks and eventually manage future events.
Charting the Next Pandemic puts the reader in the position to explore different pandemic scenarios and to understand the potential impact of available containment and prevention strategies. This book emphasizes the importance of a global perspective in the assessment of emerging health threats and captures the possible evolution of the next pandemic, while at the same time providing the intelligence needed to fight it. The text will appeal to a wide range of audiences with diverse technical backgrounds.

Ana Pastore y Piontti is an Associate Research Scientist in the Laboratory for the Modeling of Biological and Socio-technical Systems (MOBS Lab) at Northeastern University in Boston, USA. Her research focuses on the characterization and modeling of the spread of infectious diseases, by integrating methods of complex systems with statistical physics approaches.

Nicola Perra is a Senior Lecturer in network science at the University of Greenwich, UK. His research interests revolve around the study of human dynamics, digital epidemiology, and network science.

Luca Rossi is a Senior Researcher at the Institute for Scientific Interchange in Torino, Italy. Luca's research focuses on the mathematical and computational modeling of contagion processes in structured populations, in particular human transmittable infectious diseases.

Nicole Samay is a Senior Graphic Designer in the Network Science Institute at Northeastern University in Boston. She works with researchers to develop and adapt data visualizations, with a particular focus on information design and spreading processes.

Alessandro Vespignani is the Sternberg Distinguished Professor at Northeastern University in Boston. He is Fellow of the American Physical Society, member of the Academy of Europe, and Fellow of the Institute for Quantitative Social Sciences at Harvard University. Vespignani is focusing his research activity in modeling diffusion phenomena in complex systems, including data-driven computational approaches to infectious diseases spread.

With
Corrado Gioannini worked for several years in the private sector, in IT companies, developing his skills in software development and management. He is now a research leader at the Complex Systems and Networks group at ISI Foundation, where he coordinates the development of the GLEAMviz Simulator software framework.

Marcelo F. C. Gomes is a researcher on infectious disease modeling and surveillance at Fundaçăo Oswaldo Cruz's Scientific Computing Program (Fiocruz, PROCC). His main research focus is on combining reported cases from public health agencies and human mobility for the development of risk analysis and nowcasting tools.

Bruno Gonçalves is a Moore-Sloan Fellow at NYU's Center for Data Science. With a background in Physics and Computer Science, his career has revolved around the use of datasets from sources as diverse as Apache web logs, Wikipedia edits, Twitter posts, Epidemiological reports, and census data to analyze and model Human Behavior and Mobility. More recently he has focused on the application of machine learning and neural network techniques to analyze large geolocated datasets.