Abstract
The appearance and fast spreading of Covid-19 took the international community by surprise. Collaboration between researchers, public health workers, and politicians has been established to deal with the epidemic. One important contribution from researchers in epidemiology is the analysis of trends so that both the current state and short-term future trends can be carefully evaluated. Gompertz model has been shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate showing the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity and it allows short-term predictions and longer-term estimations. This model has been employed to fit the cumulative cases of Covid-19 from several European countries. Results show that there are systematic differences in spreading velocity among countries. The model predictions provide a reliable picture of the short-term evolution in countries that are in the initial stages of the Covid-19 outbreak, and may permit researchers to uncover some characteristics of the long-term evolution. These predictions can also be generalized to calculate short-term hospital and intensive care units (ICU) requirements.
Highlights
A disease outbreak is always a challenge for public health control systems
While the logistic equation produces a symmetric bell-shaped function for new cases, the Gompertz model gives rise to an asymmetric function with fast growth of new cases combined with a slow decrease, which is closer to the distribution of new cases observed in different countries during some epidemics. In this manuscript we demonstrate that the asymmetric nature of the Gompertz model is the proper framework to study epidemics in which control measures are at the heart of the evolution, since it captures the dynamic nature of the variation due to social distance measures
We perform a systematic analysis of the dynamics of the cumulative cases of Covid-19 in different regions in China where the spreading of the epidemic finished; see for example the three regions shown in Fig 2 where the Gompertz function has been fitted
Summary
A disease outbreak is always a challenge for public health control systems. When the outbreak is caused by a new agent able to cause a pandemic, the challenge is even greater and should involve the whole research community as well. Globalization plays a double role in this context; on the one hand, it increases the risk of the outbreak evolving towards a pandemic, while on the other, the sharing of data and strategies increases the likelihood of controling it. The new SARS-CoV-2 virus (severe acute respiratory syndrome coronavirus 2) has put the international community at the brink of a global disaster. National and local governments are working with public health agencies hand in hand to slow down, and eventually control, the spread of Covid-19 [1]
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