Author: Norden E Huang; Fangli Qiao; Ka-Kit Tung
Title: A data-driven tool for tracking and predicting the course of COVID-19 epidemic as it evolves Document date: 2020_3_30
ID: mxen3n0k_77
Snippet: Our time-dependent net infection rate generalizes this concept to be independent of 448 the SIR or other models and be applicable at later times as well: If in the course of an 449 epidemic, α(t ) is positive, the number of infected will grow exponentially, reaching 450 a peak number of infected when α(t ) = 0 at t = t p . Then the total number of active 451 infected will decrease exponentially. One could in analogy to R 0 , define a time-452 d.....
Document: Our time-dependent net infection rate generalizes this concept to be independent of 448 the SIR or other models and be applicable at later times as well: If in the course of an 449 epidemic, α(t ) is positive, the number of infected will grow exponentially, reaching 450 a peak number of infected when α(t ) = 0 at t = t p . Then the total number of active 451 infected will decrease exponentially. One could in analogy to R 0 , define a time-452 dependent Reproductive Number R t = α(t )T −1 , so that if this number is greater 453 (less) than 1 the number of infected will grow (decrease) at time t . We will here 454 use α(t ) directly. 455 456
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