ArticleFrontiers in public health2020
Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic.
Article in Frontiers in public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- Identifying COVID-19 peaks using early warning signals.PLoS computational biology · 2025Article
- Early warning signals do not predict a warming-induced experimental epidemic.PLOS global public health · 2025Article
- The potential of resilience indicators to anticipate infectious disease outbreaks, a systematic review and guide.PLOS global public health · 2023Article
- Factors associated with self-reported avoidance of harm reduction services during the COVID-19 pandemic by people who use drugs in five cities in the United States and Canada.Drug and alcohol dependence · 2022Article
- Identifying critical transitions in complex diseases.Journal of biosciences · 2022Review
- Early warning signal reliability varies with COVID-19 waves.Biology letters · 2021Article
- Early warning signals of infectious disease transitions: a review.Journal of the Royal Society, Interface · 2021Review
- Non-pharmaceutical interventions during the COVID-19 pandemic: A review.Physics reports · 2021Review
- Understanding the spatio-temporal pattern of COVID-19 outbreak in India using GIS and India's response in managing the pandemic.Regional science policy & practice · 2020Article
- Investigating the dynamics of COVID-19 pandemic in India under lockdown.Chaos, solitons, and fractals · 2020Article
- Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic.Frontiers in public health · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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