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ABSTRACT / The COVID-19 pandemic highlighted how mathematical modeling is an integral part of the response to infectious disease epidemics. But it also exposed fundamental methodological challenges. Data tracking epidemics, human mobility, contacts and behavior have consistently increased in size, quality and resolution: Can models turn them into accurate epidemic estimates and reliable public health recommendations? In my talk, I will show that this is possible only if progress in the theory matches the progress made in data generation and collection. I will do this through two case studies. First, I will show that the interplay between the efficacy of prevention and the structure of contacts in the community determines which immunization strategy is most effective, which is often not what previous established theory suggested. I will also show that this finding improves the distribution of pre-exposure prophylaxis (PrEP) of HIV. Then, I will introduce epidemic graph diagrams (EGD), novel representations to compute the epidemic threshold directly from arbitrarily complex data on contacts, disease and control efforts, and show how to use them to compute the epidemic threshold. The epidemic threshold, which estimates the potential for an infection to spread in a host population, quantifying epidemic risk throughout epidemic emergence, mitigation, and control. While models increasingly integrated realistic host contacts, no parallel development occurred with matching detail in disease progression and interventions. This narrowed the use of the epidemic threshold to oversimplified disease and control descriptions. I will use EGD to overcome thsese limitations, and test them on two public health challenges: influenza and sexually transmitted infections.
BIO / Eugenio Valdano is a researcher at the Pierre Louis Institute of Epidemiology and Public Health (IPLESP) of the French National Institute of Health and Medical Research (INSERM), and Sorbonne Université, in Paris, France. He is an infectious disease epidemiologist with a background in theoretical physics. Eugenio got his MSc in physics from the University of Torino, Italy, and then his PhD in epidemiology and public health from Sorbonne University in Paris, France. Eugenio focuses on developing data-rich mathematical models to study how infectious diseases spread, in human and animal populations. Patterns of human mobility and mixing influence the likelihood of epidemic outbreaks, drive their evolution, and determine the condition for disease containment and elimination. He designs theoretical models to combine data on human behavior and epidemiological data to understand epidemics, make scenarios, help guide public health interventions. He does this using techniques from statistical physics, complex networks science and statistics. Recently, he has been working on COVID-19, and HIV/AIDS.