The statistics of epidemic transitions

Drake, John M.; Brett, Tobias; Chen, Shiyang; Epureanu, Bogdan I.; Ferrari, Matthew J.; Marty, Éric; Miller, Paige B.; O’Dea, Eamon B.; O’Regan, Suzanne M.; Park, Andrew; Rohani, Pejman

doi:10.1371/journal.pcbi.1006917

Cited by 53 publications

(57 citation statements)

References 53 publications

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“…This sudden transition places a considerable burden on the limited availability of the public health resources required to treat the disease and inhibit its further spread. Most of the studies on sudden transitions concern with catastrophic shifts associated with a saddle-node bifurcation, however epidemic transitions are non-catastrophic and associated with a transcritical bifurcation [17, 19]. In general, an epidemic transition occurs when the basic reproduction number (or ) of the disease becomes greater than one and a population moves from a subcritical to a super-critical state.…”

Section: Introductionmentioning

confidence: 99%

Anticipating the novel coronavirus disease (COVID-19) pandemic

Kaur

Sarkar

Chowdhury

et al. 2020

Preprint

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The infectious novel coronavirus disease COVID-19 outbreak has been declared as a public health emergency of international concern, and later as an epidemic. To date, this outbreak has infected more than one million people and killed over fifty thousand people across the world. In most countries, the COVID-19 incidence curve rises sharply in a short span 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 from one stable state to another state in a relatively short span of time is often termed as a critical transition. Critical transitions can be, in general, successfully forecasted using many statistical measures such as return rate, variance and lag-1 autocorrelation. Here, we report an empirical test of this forecasting on the COVID-19 data sets for nine countries including India, China and the United States. For most of the data sets, an increase in autocorrelation and a decrease in return rate 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 the time of implementation of these interventions. Further, using satellite data of nitrogen dioxide which is emitted predominantly as a result of anthropogenic activities, as an indicator of lockdown policy, we find that in countries where the lockdown was implemented early and strictly have been successful in reducing the extent of transmission of the virus. These results hold important implications for designing effective strategies to control the spread of infectious pandemics. CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

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Section: Introductionmentioning

confidence: 99%

Anticipating the novel coronavirus disease (COVID-19) pandemic

Kaur

Sarkar

Chowdhury

et al. 2020

Preprint

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“…For most host-pathogen models of the infectious disease, the key parameters are difficult to estimate because natural processes are stochastic and transmission events are influenced by other parameters than what can be included in a model of epidemic spread [31]. Thus, large datasets are often needed to reach a good estimate.…”

Section: Resultsmentioning

confidence: 99%

Resetting the Initial Conditions for Calculating Epidemic Spread: COVID-19 Outbreak in Italy

Babac

Mornar

2020

IEEE Access

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Confirmed cases of the disease COVID-19 have spread to more than 200 countries and regions of the world within a few months. Although the authorities report the number of new cases on daily basis, there remains a gap between the number of reported cases and actual number of cases in a population. One way to bridge this gap is to gain more in-depth understanding of the disease. In this paper, we have used the recent findings about the clinical courses of inpatients with COVID-19 to reset the initial conditions of the epidemic process in order to estimate more realistic number of cases in the population. By translating the reported cases certain number of days earlier with regard to an average clinical course of the disease, we have obtained much higher number of cases, which suggests that the actual number of infected cases and death rate might have been higher than reported. Based on the outbreak of COVID-19 in Italy, this paper shows an estimate of the number of infected cases based on infection and removal rates from data during the pandemic.

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“…The COVID-19 incidence curve of total confirmed cases for many countries initially demonstrates a gradual increase near the start of the epidemic and is often followed by a sudden shoot or a transition to a supercritical state (14)(15)(16)(17)(18), as the disease spreads (major outbreak due to human-to-human transmission). This sudden transition places a considerable burden on the limited availability of the public health resources required to treat the disease and inhibit its further spread.…”

Section: Introductionmentioning

confidence: 99%

“…This sudden transition places a considerable burden on the limited availability of the public health resources required to treat the disease and inhibit its further spread. Most of the studies on sudden transitions concern catastrophic shifts associated with a saddle-node bifurcation; however, epidemic transitions are non-catastrophic and associated with a transcritical bifurcation ( 17 , 19 ). In general, an epidemic transition occurs when the basic reproduction number (or

) of the disease becomes >1 and a population moves from a subcritical to a supercritical state.…”

Section: Introductionmentioning

confidence: 99%

Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic

Kaur

Sarkar

Chowdhury

et al. 2020

Front. Public Health

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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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The statistics of epidemic transitions

Cited by 53 publications

References 53 publications

Anticipating the novel coronavirus disease (COVID-19) pandemic

Anticipating the novel coronavirus disease (COVID-19) pandemic

Resetting the Initial Conditions for Calculating Epidemic Spread: COVID-19 Outbreak in Italy

Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic

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