Propagation analysis and prediction of the COVID-19

Li, Lixiang; Yang, Zihang; Dang, Zhongkai; Meng, Chunlei; Huang, Jingze; Meng, Hao Tian; Wang, Deyu; Chen, Guanhua; Zhang, Jiaxuan; Peng, Haipeng

doi:10.1016/j.idm.2020.03.002

Cited by 212 publications

(156 citation statements)

References 9 publications

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“…Recently [1], we demonstrated that the proposed [2][3][4] Gaussian time evolution for the daily number of cases (deaths or alternatively infections) at time t c(t) = c max e − t−tmax w 2 (1) provides a quantitatively correct description for the monitored rates in 25 different countries. Here, c max is the maximum number of daily cases at peak time t max and w a characteristic duration.…”

Section: Introductionmentioning

confidence: 99%

“…Inverting c(t 0 ) = 1 readily yields ln(c max ) = (t 0 − t max ) 2 /w 2 , or t 0 = t max − w ln c max (3) To simplify notation, besides absolute time t, we introduce two more times. First, there is the time relative to the peak time, denoted by = t − t max (4) so that negative (positive) correspond to times before (after) the peak time. Second, there is the dimensionless time x = − /w.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian Doubling Times and Reproduction Factors of the COVID-19 Pandemic Disease

Kröger

Schlickeiser

2020

Front. Phys.

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The Gauss model for the time evolution of the first corona pandemic wave is rendered useful in the estimation of peak times, amount of required equipment, and the forecasting of fade out times. At the same time, it is probably the simplest analytically tractable model that allows us to quantitatively forecast the time evolution of infections and fatalities during a pandemic wave. In light of the various descriptors, such as doubling times and reproduction factors, currently in use to judge the lockdowns and other measures that aim to prevent spreading of the virus, we hereby provide both exact and simple approximate relationships between the two relevant parameters of the Gauss model (peak time and width), the transient behavior of two versions of doubling times, and three variants of reproduction factors, including basic reproduction numbers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gaussian Doubling Times and Reproduction Factors of the COVID-19 Pandemic Disease

Kröger

Schlickeiser

2020

Front. Phys.

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“…Forecast predictions configure a pivotal strategy for decision-making in the evaluation of measures against COVID-19 [10][11][12][13] and should be closely considered by governments and health authorities. In this work, we have used the Weibull distribution on the number of daily new cases and Responsible Editor: Fernando R. Spilki deaths data of COVID-19 to predict the evolution of the pandemic [12].…”

Section: Introductionmentioning

confidence: 99%

Forecast predictions for the COVID-19 pandemic in Brazil by statistical modeling using the Weibull distribution for daily new cases and deaths

Moreau

2020

Braz J Microbiol

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COVID-19 has killed more than 500,000 people worldwide and more than 60,000 in Brazil. Since there are no specific drugs or vaccines, the available tools against COVID-19 are preventive, such as the use of personal protective equipment, social distancing, lockdowns, and mass testing. Such measures are hindered in Brazil due to a restrict budget, low educational level of the population, and misleading attitudes from the federal authorities. Predictions for COVID-19 are of pivotal importance to subsidize and mobilize health authorities' efforts in applying the necessary preventive strategies. The Weibull distribution was used to model the forecast prediction of COVID-19, in four scenarios, based on the curve of daily new deaths as a function of time. The date in which the number of daily new deaths will fall below the rate of 3 deaths per millionthe average level in which some countries start to relax the stay-at-home measureswas estimated. If the daily new deaths curve was bending today (i.e., about 1250 deaths per day), the predicted date would be on July 5. Forecast predictions allowed the estimation of overall death toll at the end of the outbreak. Our results suggest that each additional day that lasts to bend the daily new deaths curve may correspond to additional 1685 deaths at the end of COVID-19 outbreak in Brazil (R 2 = 0.9890). Predictions of the outbreak can be used to guide Brazilian health authorities in the decision-making to properly fight COVID-19 pandemic.

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“…Forecast predictions configure a pivotal strategy for decision making in the evaluation of measures against COVID-19 [10,13] and should be closely considered by governments and health authorities. In this work, we have used the Weibull distribution on the number of daily new cases and deaths of COVID-19 data to predict the evolution of pandemic [14].…”

Section: Introductionmentioning

confidence: 99%

Forecast predictions for the COVID-19 pandemic in Brazil by statistical modeling using the Weibull distribution for daily new cases and deaths

Moreau

2020

Preprint

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COVID-19 has killed more than 330.000 people worldwide and more than 21,000 Brazilians. Since there are no specific drugs or vaccines, the available tools against COVID-19 are preventive, such as the use of personal protective equipment, social distancing, lockdowns and mass testing. Such measures are hindered in Brazil due to a restrict budget, low educational level of the population and misleading attitudes from the federal authorities. Predictions for COVID-19 are of pivotal importance to subsidize and mobilize health authorities’ efforts in applying the necessary preventive strategies. The Weibull distribution was used to model the forecast prediction of COVID-19, in four scenarios, based on the curve of daily new deaths as a function of time. The date in which the number of daily new deaths will fall below the rate of 3 deaths per million, the mean level considered by some countries to relax stay-at-home measures, was estimated. If the daily new deaths curve was bended today (i. e. about 1,250 deaths per day), the predicted date would be on June 18th. Analysis of the lethality rate allowed the estimation of daily new cases and total death toll at the end of the outbreak. Our results suggest that each additional day that lasts to bend the daily new deaths curve can correspond to additional 3,718 deaths at the end of COVID-19 outbreak in Brazil (R2 = 0.9938). Predictions of the outbreak can be used to guide Brazilian health authorities in the decision making to properly fight COVID-19 pandemic.

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Propagation analysis and prediction of the COVID-19

Cited by 212 publications

References 9 publications

Gaussian Doubling Times and Reproduction Factors of the COVID-19 Pandemic Disease

Gaussian Doubling Times and Reproduction Factors of the COVID-19 Pandemic Disease

Forecast predictions for the COVID-19 pandemic in Brazil by statistical modeling using the Weibull distribution for daily new cases and deaths

Forecast predictions for the COVID-19 pandemic in Brazil by statistical modeling using the Weibull distribution for daily new cases and deaths

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