Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies

Giovani L. Vasconcelos; Antônio M. S. Macêdo; Raydonal Ospina; Francisco A. G. Almeida; Gerson C. Duarte-Filho; Inês C. L. Souza

As already mentioned, an intervention strategy in our model is defined by the two parameters r and α of the new Richards model after the adoption time t 0 ; see Eq. (6) . Recall that the parameter r in the RGM corresponds to the growth rate at the early stage of the epidemics; hence an early intervention should, in our language, seek to reduce this parameter, i.e., r < r. We shall therefore refer to this type of intervention as 'mitigation'. Similarly, at later stages of the epidemics one should try to increase the parameter α, i.e., we want to have α > α, so as to force the levelling off of the curve of cumulative cases as soon as possible after the intervention; this type of strategy will thus be said to be of the 'suppression' type. More specifically, in our model we can define two general sets of intervention strategies: i) mitigation, when we take α = α and r < r; and ii) suppression, meaning r = r and α > α. (More generally, a mixed strategy would modify both r and α, but for our purposes here it is preferable to study separately the effects of these two parameters.)