Supervisor: Dr Nicola Perra
Project description:
Socioeconomic factors can fundamentally shape the dynamics and impact of infectious diseases. For example, during the COVID-19 pandemic, health outcomes have been significantly different across socioeconomic strata, with clear inequalities in the distribution of infections, hospitalizations, and deaths.In the early stages of the pandemic, such inequalities were strongly linked to the affordability of non-pharmaceutical interventions and have been observed in radically different mobility reductions across socioeconomic groups. Furthermore, inequities in health outcomes have been further driven by disparities in vaccine distribution, and accessibility.
Despite a clear understanding of their importance, socioeconomic dimensions are often neglected in traditional disease mathematical models. As result, the development of modelling frameworks able to include socioeconomic factors explicitly is largely missing and needed.
The project aims at tackling this limitation by developing new classes of models that factor different socioeconomic indicators in their core. The project will generalize the main traditional frameworks such as i) compartmental models that describe a single and homogeneously mixed populations ii) spatially aware models that account for human mobility coupling subpopulations and iii) network models that consider physical interactions between individuals to populations characterized also by socioeconomic indicators. In doing so, the proposed research will help characterize the mechanisms coupling socioeconomic strata and disease spreading at different scales.
The project will leverage the wide range of datasets collected and made available during the COVID-19 pandemic by companies (i.e., Facebook, Google, SafeGraph), researchers, and governments describing epidemiological data (e.g., cases, deaths, hospitalizations), relevant human behaviours (e.g., mobility, contacts patterns, vaccination uptake), and governmental intervention policies to guide and validate the modelling efforts.
The project will involve an interdisciplinary range of methods from mathematical epidemiology, statistics, data science, network science, and computational social science.
Further information:
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