Advanced learning of COVID-19: insights from comparative modeling, machine learning and deep learning of aggregate data for informed forecasting at population and individual level
| Componente | Qualifica | Struttura | Categoria |
|---|---|---|---|
| Andrea Saglietto | specializzando | Università di Torino | Altro personale aggregato Sapienza o esterni, titolari di borse di studio di ricerca |
| Riccardo Taiello | studente | Dipartimento di Informatica, Sapienza Universita' di Roma | Altro personale aggregato Sapienza o esterni, titolari di borse di studio di ricerca |
Coronavirus-associated disease 2019 (COVID-2019) has caused a pandemic with unprecedented mortality, morbidity and economic implications. Policymakers and epidemiologists are trying their best to predict aggregate-level trends and inform decision-makers, while physicians and patients look for novel approaches for individual risk prognostication and aversion. Yet, limited evidence is available to support accurate estimation of COVID-19 burden and forecast future trends or predict individual risk.
One of the key challenges in analyzing aggregate COVID-19 data is the multidimensional interplay between aggregate variables,
individual variables, time series features, repeated data, moderators, and dependent variables. This is a key challenge yet
also an opportunity to improve forecasting, if tackled with modern self-learning big data science approaches. It is also evident that
standard analytical approaches, limited to few independent variables and one or two dependent variables cannot inform accurately on
future trends at aggregate level, nor on individual risk prediction. Indeed, clinical risk prediction models developed to data for COVID-19
are limited in scope and accuracy. Finally, to date no model has been able to inferentially test the effectiveness of proposed interventions (eg hard lockdown) or define the risk benefit profile of them (eg survival vs unemployment).