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

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.02.20051557 doi: medRxiv preprint data set; this is indicated as the black dots on the curves in Fig. 3 . More specifically, in our intervention strategies we take r = 0.44 and α = 0.21 for t < t 0 , as obtained from the fit showed in Fig. 1(b) , whereas for t > t 0 we suppose two possible scenarios: mitigation and suppression. In the first type of scenario, shown in Fig. 3(a) , we set α = α and consider two mitigation strategies: i) action 1 with r = 0.5 (blue) and ii) action 2 with r = 0.6 (red). In the second scenario shown in Fig. 3 (a), we consider instead r = r and implement two suppression strategies: i) action 1 with α = 0.3 (red) and ii) action 2 with α = 0.9 (blue). Of course, only intervention strategies with t 0 > T , where T denotes the 'present time' (i.e., the time of the last data point) would be of practical value in an ongoing epidemics. (For the Italian data, T = 40.) Nevertheless, for t 0 < T the efficiency curve is still of some interest, as it indicates the relative reduction in fatalities that would have resulted had the corresponding strategy been adopted at some earlier time t 0 in the past. In all cases shown in Fig. 3 , we see that there is a quick decrease in efficiency as the adoption time increases. This confirms the commonly held view that there is only a narrow window of opportunity, after the outbreak of an epidemic, during which effective countermeasures can be taken that could have a significant impact in the final outcome.