The reproduction number of COVID-19 and its correlation with public health interventions

Abstract

Throughout the past four months, no number has dominated the public media more persistently than the reproduction number of COVID-19. This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning and uncertainty quantification. During the early outbreak, the reproduction number was 4.5±21.4, with maximum values of 6.5 and 5.9 in Spain and France. As of today, it has dropped to 0.7±20.2, with minimum values of 0.4 and 0.3 in Austria and France. We found a strong correlation between passenger air travel and the reproduction number with a time delay of 12.6±22.7 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health.

Data-driven Public Health and Social Intervention Modeling to Promote Effective Policy Responses to Curtailing the COVID-19 Pandemic

“…7). Studies confirm that mitigation measures have been the greatest "weapons" to fight COVID-19 effectively [1,3,14]. More importantly, they provide the differences, time, and rates of dropping the reproduction number R0 that had been made due to different levels of mitigation measures that have been enforced over their study periods.…”

“…Although there is still too much unknown, a lot of lessons have been learned since its outbreaks in China, South Korea, Japan, and Europe [7][8]. Given the results from Dehning et al [1] and Lika et al [3], we believe that more research on improving SEIR modeling will play a critical role in facilitating the decision-making on public health and social interventions, which can surely help state policymakers enact and implement state policy responses with scientific rigor in combating the COVID-19 pandemic in each of the states across the United States.…”

Forecasting the COVID-19 Outbreak: An Application of Arima and Fuzzy Time Series Models

“…The full spectrum of COVID-19 ranges from the common cold, fever, mild respiratory complications to severe progressive pneumonia, multi-organ failure, and death. This virus is devilishly transmissible with the reproduction number (R0) ranging from 1.5 to 6.5 globally, i.e., each infected individual can infect at least 1.5 other individuals [2,3]. Thus, each infection causes furthermore and the outbreak will continue to grow.…”

COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

“…However, reliable data is only available for the cumulative number of the infected population. This difficulty is further pronounced for more complicated models accounting for the individuals who have been infected but are not yet infectious themselves (SEIR model), for asymptomatic cases [23], [24] or for the effect of government policies [25]. From a mathematical viewpoint, this requires us to calibrate a large number of parameters that characterize a complex nonlinear dynamical system using partial and incomplete observations.…”