Abstract
BackgroundThis has been the first time in recent history when extreme measures that have deep and wide impact on our economic and social systems, such as lock downs and border closings, have been adopted at a global scale. These measures have been taken in response to the severe acute respiratory syndrome coronavirus SARS-CoV-2 pandemic, declared a Public Health Emergency of International Concern on 30 January 2020. Epidemic models are being used by governments across the world to inform social distancing and other public health strategies to reduce the spread of the virus. These models, which vary widely in their complexity, simulate interventions by manipulating model parameters that control social mixing, healthcare provision and other behavioral and environmental processes of disease transmission and recovery. The validity of these parameters is challenged by the uncertainty of the impact on disease transmission from socio-economic factors and public health interventions. Although sensitivity of the models to small variations in parameters are often carried out, the forecasting accuracy of these models is rarely investigated during an outbreak.MethodsWe fitted a stochastic transmission model on reported cases, recoveries and deaths associated with the infection of SARS-CoV-2 across 101 countries that had adopted at least one social-distancing policy by 15 May 2020. The dynamics of disease transmission was represented in terms of the daily effective reproduction number (Rt). Countries were grouped according to their initial temporal Rt patterns using a hierarchical clustering algorithm. We then computed the time lagged cross correlation among the daily number of policies implemented (policy volume), the daily effective reproduction number, and the daily incidence counts for each country. Finally, we provided forecasts of incidence counts up to 30-days from the time of prediction for each country repeated over 230 daily rolling windows from 15 May to 31 Dec 2020. The forecasting accuracy of the model when Rt is updated every time a new prediction is made was compared with the accuracy using a static Rt.FindingsWe identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between any two interventions were associated with a reduction on the duration of outbreaks (with correlation coefficients of -0.26 and 0.24 respectively). Sustained social distancing appeared to play a role in the prevention of the second transmission peak. By 15 May 2020, the average of the median Rt across examined countries had reduced from its peak of 20.5 (17.79, 23.20) to 1.3 (0.94, 1.74).The time lagged cross correlation analysis revealed that increased policy volume was associated with lower future Rt (the negative correlation was minimized when Rt lagged the policy volume by 75 days), while a lower Rt was associated with lower future policy volume (the positive correlation was maximized when Rt led by 102 days). Rt led the daily incidence counts by 78 days, with lower incidence counts being associated with lower future policy volume (the positive correlation was maximized when counts led the volume by 135 days). On the other hand, higher policy volume was not associated with lower incidence counts within a lag of up to 180 days.The outbreak prediction accuracy of the stochastic transmission model using dynamically updated Rt produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when Rt was kept constant. Prediction accuracy declined with forecasting time.InterpretationUnderstanding the evolution of the daily effective reproduction number during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths. This is because Rt provides an early signal of the efficacy of containment measures. Using updated Rt values produces significantly better predictions of future outbreaks. Our results found a substantial variation in the effect of early public health interventions on the evolution of Rt over time and across countries, which could not be explained solely by the timing and number of the adopted interventions. This suggests that further knowledge about the idiosyncrasy of the implementation and effectiveness thereof is required. Although sustained containment measures have successfully lowered growth rate of disease transmission, more than half of the studied countries failed to maintain an effective reproduction number close to or below 1. This resulted in continued growth in reported cases.
Evolution of disease transmission rate during the course of SARS-COV-2: Patterns and determinants
