Author: Antonio Scala; Andrea Flori; Alessandro Spelta; Emanuele Brugnoli; Matteo Cinelli; Walter Quattrociocchi; Fabio Pammolli
Title: Between Geography and Demography: Key Interdependencies and Exit Mechanisms for Covid-19 Document date: 2020_4_14
ID: bf098qcr_87
Snippet: From mobility data, we know that k = l =k T kl /T kk 1 and T kk ∼ 1; in particular, from Facebook mobility data we can estimate k ∼ 10 −3 . If all the neighbors of a given region k are fully infected (i.e. i l = 1 ∀ l = k) and i k (t 0 ) = 0, then the variation of i k can be approximated as ∂ t i k ∼ k +(β−γ) i k . Namely, as soon as i k > k , i k will grow exponentially according to ∂ t i k ∼ (β − γ) i k and k will become.....
Document: From mobility data, we know that k = l =k T kl /T kk 1 and T kk ∼ 1; in particular, from Facebook mobility data we can estimate k ∼ 10 −3 . If all the neighbors of a given region k are fully infected (i.e. i l = 1 ∀ l = k) and i k (t 0 ) = 0, then the variation of i k can be approximated as ∂ t i k ∼ k +(β−γ) i k . Namely, as soon as i k > k , i k will grow exponentially according to ∂ t i k ∼ (β − γ) i k and k will become irrelevant; that is to say, the dynamics of the regions will decouple. On the other hand, if epidemic is decaying everywhere, then i l 1 ∀ l = k; thus l =k T kl i l k and equation again decouple, having each region followed Eq. 4 separately. An alternative source of network information comes from 24 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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