Data Driven epidemiological Models: Current Trends and new Threats

Epidemics have invaded populations throughout history threatening the existence of humankind. Todays 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, Zika, Dengue, Middle East Respiratory Syndrome, 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 behaviour.
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 calls for concerted efforts to mitigate the impact of the epidemic on the masses.

To understand the dynamics of infectious disease transmission, mathematical models are used as a tool to study the mechanisms by which diseases spread, to predict the future course of an outbreak and to evaluate strategies to control an epidemic. In particular, data-driven methodologies are crucial for pandemic modelling and control.

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.

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.

Areas of focus
• Mathematical models – deterministic, stochastic, In-host, meta-population, and behavioural models for disease threats and outbreaks;
• Formulating data driven models – AI, ML, study, analysis, surveillance, prediction and intervention, parameter estimation and fitting;
• Climate change, the new threats to epidemics spread and control–rising temperatures, unpredictable rainfall, human/pathogen behaviour, resistance, evolution, adaptation and survival, mutation and variants.

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Scientific program is available on the local website of the school:
https://cimpa2025.uonbi.ac.ke

Official language of the school: english