Descrizione del progetto
Una nuova teoria dei processi epidemici non markoviani
La crisi da coronavirus ha rivelato una serie di lacune all’interno dell’epidemiologia tradizionale. Ad esempio, ignora il grafico del contatto umano e implicitamente presuppone una popolazione omogenea senza una struttura di grafico specifica. Dal punto di vista della fisica, i due processi più importanti sono come il virus salta da A a B e come A e B entrano in contatto. Anche se la maggior parte degli studi sulle epidemie nelle reti presumono implicitamente dinamiche di diffusione di tipo markoviano, per le quali le teorie matematiche sono ben sviluppate, questo non è il caso della pandemia di COVID-19. In questo contesto, il progetto ViSioN, finanziato dal CER, svilupperà la teoria di processi epidemici non markoviani per cercare di stabilire quanto durerà una pandemia e quando si verificherà un picco.
Obiettivo
ViSioN presents my Network Science view on virus spread in networks, in which the duality between the virus transmission process and the contact graph is key.
The devastating Corona crisis reveals two major shortcomings in traditional epidemiology. First, it ignores the human contact graph and implicitly assumes a homogeneous population without specific graph structure. Second, most models for the virus spreading process relate to a Markovian setting, with exponential infection and curing times, leading to an exponential decay of the epidemic. Measurements, however, point to significantly different infection and curing time distributions. In addition, digital technology can help in constructing the contact graph and combined with medical testing, all infected can be detected, thus avoiding a second wave.
Building on my pioneering work on Markovian epidemics in networks, I will complement the recipe book of epidemic model ingredients with corresponding algorithms/software for next pandemic outbreaks. I will develop the theory of non-Markovian epidemic process on networks, a surprisingly missing element today, because non-Markovian theory is needed to tell, based on the characteristic infection and curing times of the virus, how long a pandemic will last and when the peak occurs. Next, I will combine all available measurement technologies to construct the best possible contact graph via temporal networking or adaptive networking. Finally, I will explore how accurately infections can be predicted under partial information of process and contact graph.
ViSioN’s outcomes will allow to predict, manage and control any epidemic in the best possible way. Moreover, as epidemics are part of the larger class of “local rule–global emergence” systems, my outcomes will be directly beneficial for the other members of this broad and abundant class, and find applications ranging from computer malware spread to human brain surgery.
Campo scientifico
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-ADG - Advanced GrantIstituzione ospitante
2628 CN Delft
Paesi Bassi