Author: Lionel Roques; Etienne Klein; Julien Papaix; Samuel Soubeyrand
Title: Mechanistic-statistical SIR modelling for early estimation of the actual number of cases and mortality rate from COVID-19 Document date: 2020_3_24
ID: dqg8fkca_13
Snippet: Statistical inference. The unknown parameters are α, t 0 and κ. The parameter γ(t) is computed indirectly, using the estimated value of I(t), the data on D(t) (assumed to be exact) and the relationship (2). The likelihood L is defined as the probability of the observations (here, the increments {δ t }) conditionally on the parameters. Using the observation model (3), and assuming that the incrementsδ t are independent conditionally on the un.....
Document: Statistical inference. The unknown parameters are α, t 0 and κ. The parameter γ(t) is computed indirectly, using the estimated value of I(t), the data on D(t) (assumed to be exact) and the relationship (2). The likelihood L is defined as the probability of the observations (here, the increments {δ t }) conditionally on the parameters. Using the observation model (3), and assuming that the incrementsδ t are independent conditionally on the underlying SIR process and that the number of tests n t is known, we get:
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