Author: Glenn F Webb; Pierre Magal; Zhihua Liu; Ousmane Seydi
Title: A model to predict COVID-19 epidemics with applications to South Korea, Italy, and Spain Document date: 2020_4_10
ID: c95lntyp_22
Snippet: These formulas for I 0 , E 0 , U 0 , t 0 , τ 0 , and R 0 were derived in [3] . Their values connect the Phase II reported cases data to the parameterisation and initialisation of our differential equations model. During Phase II of the epidemic, τ (t) ≡ τ 0 is constant. When strong government measures such as isolation, quarantine, and public closings are implemented, Phase III begins. The timing of the implementation of these measures, and .....
Document: These formulas for I 0 , E 0 , U 0 , t 0 , τ 0 , and R 0 were derived in [3] . Their values connect the Phase II reported cases data to the parameterisation and initialisation of our differential equations model. During Phase II of the epidemic, τ (t) ≡ τ 0 is constant. When strong government measures such as isolation, quarantine, and public closings are implemented, Phase III begins. The timing of the implementation of these measures, and their impact on disease transmission, is complex. We use an exponentially decreasing time-dependent transmission rate τ (t) in Phase III to incorporate these effects. The formula for τ (t), which includes Phase III beginning on day N , is
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