The current Covid-19 pandemic has made it clear that understanding, predicting and controlling an epidemic process is extremely difficult given the level of interconnectedness of the society in which we live.
The most recent modeling approaches for studying the spread and control of infectious diseases are based on so-called computational epidemiology: large-scale, high-resolution simulations based on sophisticated compartmental models.
In the course of the pandemic, however, two critical vulnerabilities of computational epidemiological models have emerged.
The first is that in order to work, such models must rely on a substrate of very detailed data, and, unfortunately, many countries, including Italy, do not possess such data. The absence of reliable mobility databases has limited computational models and forced deficient states to pay very bitter social consequences.
The second critical point concerns instead the peculiarity of Covid-19 to give rise not only to symptomatic patients but also asymptomatic and paucisymptomatic ones, making monitoring even more difficult.
In addition to these two critical points there are a number of aspects that have emerged in this pandemic that highlight further gaps in our understanding of the complex epidemiological dynamics, such as the role of super-spreaders and the linear growth of infection curves.
The “lightning” production of a vaccine for Covid-19 also opened another question, namely how to plan the vaccination campaign at the same time as the dynamics of contagions (and not during an endemic phase, as is usually the case).
So it was decided to launch a research project that aims to shed light on these specific critical points, leveraging the multidisciplinary expertise present within the CREF.
It will aim to create a high-resolution multilayer mobility database for Italy, to develop new strategies for contact tracing, to study network effects and to improve vaccination strategies in real time.
#covid19 #epidemiology #cref