Data Driven epidemiological Models:
Current Trends and new Threats

Overview

Epidemics have invaded populations throughout history threatening the existence of humankind. Today’s world continues to be confronted by endemic, emerging, and reemerging infectious disease outbreaks. These threats differ widely in terms of severity and extent, with varying consequences on morbidity and mortality, as well as for a complex set of social and economic effects. Recent outbreaks of an array of infectious diseases such as Ebola, Severe Acute Respiratory Syndrome (as SARS and COVID-19), and pandemic Influenza H1N1, have raised the global concern in public health. Moreover, they showed that a key factor for disease control is human behavior.
Together with rising population growth rates in areas where health infrastructure is weak, the issue concern is also magnified by climate change that is driving epidemics, civil conflict in poor communities, and pathogen adaptation to control measures. These concerns call for concerted efforts to mitigate the impact of the epidemic on the masses.

Aim

Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as mathematics, data science and artificial intelligence, epidemiology, and climate change science experts, to assess infectious disease spread and associated social/economic risks.
The school aims at analysing the role of data-driven methodologies for pandemic modelling and control.
The impact for the early carrier researchers that will participate to this summer school will be:
• Exposure to the state-of-the-art interplaying scientific topics:
• Exposure to world first class scholars that will deliver the lectures;
• Potential increase of employability beyond academia: public health institutes, health ministry
and non-governmental organisations.rea, and to put the students in contact with some of the international experts that are currently trying to solve them.

Courses will include:

• Course on Visualisation, exploration, and statistical analysis of epidemiological data will be given.
• A review of the available methodologies in Machine Learning, Dynamical Systems and their interaction.
• Discussion of challenges faced in the development of data-driven strategies to mitigate combat the spreading of infectious diseases.
• Development and analysis of data-driven models to:
(i) monitor the epidemic evolution;
(ii) assess the effectiveness of applied control measures;
(iii) model and predict the spread of the epidemic;
(iv) make timely decisions to manage, prevent and control the spread of infectious diseases;
(v) discuss their application to past or present epidemics, such as COVID-19, as well as their
potential application to future epidemics.