Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic

Abstract

The COVID-19 outbreak was first declared an international public health, and it was later deemed a pandemic. In most countries, the COVID-19 incidence curve rises sharply over a short period of time, suggesting a transition from a disease-free (or low-burden disease) equilibrium state to a sustained infected (or high-burden disease) state. Such a transition is often known to exhibit characteristics of "critical slowing down." Critical slowing down can be, in general, successfully detected using many statistical measures, such as variance, lag-1 autocorrelation, density ratio, and skewness. Here, we report an empirical test of this phenomena on the COVID-19 datasets of nine countries, including India, China, and the United States. For most of the datasets, increases in variance and autocorrelation predict the onset of a critical transition. Our analysis suggests two key features in predicting the COVID-19 incidence curve for a specific country: (a) the timing of strict social distancing and/or lockdown interventions implemented and (b) the fraction of a nation's population being affected by COVID-19 at that time. Furthermore, using satellite data of nitrogen dioxide as an indicator of lockdown efficacy, we found that countries where lockdown was implemented early and firmly have been successful in reducing COVID-19 spread. These results are essential for designing effective strategies to control the spread/resurgence of infectious pandemics.

Early warning signals of infectious disease transitions: a review

“…In particular, O'Regan & Drake [21] discussed the limitations of Gaussian detrending for diseases that decline rapidly, finding that, even for slowly changing diseases, smaller window sizes did not capture the fluctuations and larger window sizes did not successfully remove the slowly varying trend. Even with its recognized limitations, Gaussian detrending is a popular method in the EWS literature for disease transitions [21,22,33,40].…”

“…Many methods have been proposed to measure the performance of EWSs, such as thresholding EWSs with a constant value [27,28], thresholding with the long-run standard deviation [13] or using Kendall's τ statistic. Kendall's τ statistic is the most popular approach in the literature [21,22,31,[33][34][35]40], as it gives a quantitative measure of the increasing or decreasing trend of the EWS. However, the use of Kendall's τ score can be problematic as it describes the overall trend over the time period considered, with the challenge being selecting an appropriate time period.…”

“…There exist various ways to determine the best time period for empirical studies on historical data, such as using an expert's opinion to infer the time of transition [38], using an appropriate mathematical model to retrospectively identify when the effective reproduction number is 1 [28] or approximating when the system underwent a critical transition using the rate of change of incidence [40]. In these examples, Kendall's τ score can be calculated on a reasonable time period before the estimated transition.…”

“…Emerging diseases can surge rapidly on a fast time scale, for instance the emergence of the SARS-COV-2 pathogen which causes the disease COVID-19. Although COVID-19 has been the focus of recent studies on EWSs [40][41][42][43], further work is required to understand the performance of EWSs subject to factors affecting the speed of a disease, sampling frequency and time scale of data. Indeed, the speed of disease has been explored in simulation studies, finding that the rising autocorrelation trend typically reaches 1 at the transition unless the system has a fast speed of emergence [28].…”

Understanding the spatio‐temporal pattern of COVID‐19 outbreak in India using GIS and India's response in managing the pandemic

“…Inter household contact plays an agency role in malaria spreading. In India, it has been reported that cities are mainly playing the COVID-19 epicentre, which indicates a systematic spatial pattern (Kaur et al, 2020).…”