Author: Alessandro Rovetta; Akshaya Srikanth Bhagavathula
Title: Modelling the epidemiological trend and behavior of COVID-19 in Italy Document date: 2020_3_23
ID: 05m50voc_10
Snippet: is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.19.20038968 doi: medRxiv preprint By entering the initial values for the incubation time 1/σ, recovery time 1/γ, basic reproduction number R0, and number of infected I 0 on March 1, 2020, the software prints the S.E.I.R. values day by day. The best epidemic parameters were estimated through continuous iteration until the "closest values.....
Document: is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.19.20038968 doi: medRxiv preprint By entering the initial values for the incubation time 1/σ, recovery time 1/γ, basic reproduction number R0, and number of infected I 0 on March 1, 2020, the software prints the S.E.I.R. values day by day. The best epidemic parameters were estimated through continuous iteration until the "closest values to the real ones" were reached until March 14, 2020. The number of initial incubates was calculated with the formula E 0 =R0·I 0 .
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