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

Abstract

The main objective of the present paper is twofold: first, to model the fatality curves of the COVID-19 disease, as represented by the cumulative number of deaths as a function of time; and second, to use the corresponding mathematical model to study the effectiveness of possible intervention strategies. We applied the Richards growth model (RGM) to the COVID-19 fatality curves from several countries, where we used the data from the Johns Hopkins University database up to April 1, 2020. Countries selected for analysis were China, Italy, Spain, Iran, and Brazil. The RGM was shown to describe very well the fatality curves of China, which is in a late stage of the COVID-19 outbreak, as well as of Italy, Spain, and Iran, which supposedly are in the middle of the outbreak at the time of this writing. As for Brazil, which is still in the so-called exponential growth regime, we used the generalized growth model which is more appropriate for such cases. An analytic formula for the efficiency of intervention strategies within the context of the RGM is derived. Our findings show that there is only a narrow window of opportunity, after the onset of the epidemic, during which effective countermeasures can be taken. We applied our intervention model to the COVID-19 fatality curve of Italy to illustrate the effect of several possible interventions.All rights reserved. No reuse allowed without permission.(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level

“…The latter type of model is based on linear or nonlinear regression and only empirical data of infections and/or confirmed cases of disease (or death) is required for model estimation (Batista 2020a,b, Chowell et al 2014, 2015, Li 2018, Ma 2020, Pell et al 2018. Recently, there have already been several attempts to model the SARS-CoV-2 pandemic on the country (or even world) level, by using either the original or extended SIR model (Batista 2020b), the logistic growth model (Batista 2020a, Vasconcelos et al 2020, or both (Zhou et al 2020).…”

Power law behaviour in the saturation regime of fatality curves of the COVID-19 pandemic

“…Polynomial early growth has been observed across a range of epidemics before 14 . It has also been recently identified in the COVID-19 data for several countries 7,24,26 .…”

“…Failing to do that may significantly increase the total number of victims at the end of the pandemic. It is also worth noting that the subexponential growth recently documented in COVID-19 epidemic data has been usually attributed to the early adoption of mitigation measures 7,24,25 . On the flip side of the coin, the subexponentially slow approach to the plateau that we have reported here seems to be, in part, a consequence of relaxing these measures.…”

“…Power-law behaviour is a distinct signature of complexity, and in such a guise they are expected to be present in epidemic dynamics as well. Evidences of complexity in the COVID-19 epidemic dynamics have indeed been observed in a number of recent works, most noticeably in the form of a power-law behaviour in the early growth regime of case and death curves [2][3][4][5][6][7] , but also in the long-time asymptotic of the probability to become infected [8][9][10] . It has also been pointed out that such complex behaviour lies outside the range of applicability of standard SIR-type models, and a number of alternative dynamics have been suggested, such as the recent proposal of a random walk on a hierarchic landscape of social clusters [11][12][13] .…”

“…insight into the early to intermediate stages of the disease 7 . Once the epidemic reaches a later phase, the BLM should then be used for a more complete description of the full evolution.…”