Clustering Pseudo Time Series: Exploring Trajectories in the Ageing Process.

Investigating the natural ageing process typically involves the use of extensive longitudinal datasets that can capture changes associated with the progression of ageing. However, they are often resource-intensive and time-consuming to conduct. Cross-sectional data, on the other hand, provides a snapshot of a population at many different ages and can capture many disease processes but do not incorporate the time dimension. Pseudo time series can be reconstructed from cross sectional data, with the aim to explore dynamic processes (such as the ageing process). In this paper we focus on employing pseudo time series analysis on cross-sectional population data that we constrain using age information to create realistic trajectories of people with different degrees of cardiovascular disease. We then use clustering methods to construct and label trajectory-based phenotypes, aiming to enhance our understanding of ageing and disease progression.

On the assessment of more reliable COVID-19 infected number: the italian case.

COVID-19 (SARS-CoV-2) is the most recent pandemic disease the world is currently managing. It started in China at the end of 2019, and it is diffusing throughout Italy, one of the most affected countries, and it is currently spreading through European countries and USA. Patients affected by COVID-19 are identified employing medical swabs applied mainly to (i) citizens with COVID-19 symptoms such as flu or high temperature, or (ii) citizens that had contacts with COVID-19 patients. A percentage of COVID-19 affected patients needs hospitalisation, whereas a portion needs to be treated in Intensive Care Units (ICUs). Nevertheless, it is a matter of current intuition that COVID-19 infected citizens are more than those detected, and sometime the infection is detected too late. Thus there are many efforts in both tracking people activities as well as diffusing low cost reliable COVID-19 tests for early detection. Starting from mortality rates of diseases caused by viruses in the same family (e.g. MERS, SARS, H1N1), we study the relations between the number of COVID-19 infections and the number of deaths, through Italian regions. We thus assess several infections being higher than the ones currently measured. We thus focus on the characterisation of the pandemic diffusion by estimating the infected number of patients versus the number of death. We use such an estimated number of infections, to foresee the effects of restriction actions adopted by governments to constrain virus diffusion. We finally think that our model can support the healthcare system to react when COVID-19 is increasing.

Intensive Care Unit Resource Planning During COVID-19 Emergency at the Regional Level: the Italian case.

Severe acute respiratory syndrome COVID-19 (SARS-CoV-2) has been declared a worldwide emergency and a pandemic disease by the World Health Organisation (WHO). It started in China in December 2019, and it is currently rapidly spreading throughout Italy, which is now the most affected country after China. There is great attention for the diffusion and evolution of the COVID-19 infection which started from the north (particularly in the Lombardia region) and it is now rapidly affecting other Italian regions. We investigate on the impact of patients hospitalisation in Intensive Care Units (ICUs) at a regional and subregional granularity. We propose a model derived from well-known models in epidemic to estimate the needed number of places in intensive care units. The model will help decision-makers to plan resources in the short and medium-term in order to guarantee appropriate treatments to all patients needing it. We analyse Italian data at regional level up to March 15th aiming to: (i) support health and government decision-makers in gathering rapid and efficient decisions on increasing health structures capacities (in terms of ICU slots) and (ii) define a scalable geographic model to plan emergency and future COVID-19 patients management using reallocating them among health structures. Finally, the here proposed model can be useful in countries where COVID-19 is not yet strongly diffused.