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:10.1101/2020.03.14.20036202

Cited by 74 publications

(87 citation statements)

References 6 publications

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“…Recently, Peng et al [27] constructed a generalised SEIR model for the spread of SARS-Cov-2 virus in China. López and Rodo [20] modified Peng et al [27]'s model to analyze the data of Spain and Italy up to the end of March.…”

Section: Seir Model For Covid-19 Epidemicmentioning

confidence: 99%

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Evolution of COVID-19 pandemic: Power-law growth and saturation

Chatterjee

Shayak

Ali

et al. 2020

Preprint

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In this paper, we analyze the real-time infection data of COVID-19 epidemic for 21 nations up to May 18, 2020. For most of these nations, the total number of infected individuals exhibits a succession of exponential growth and power-law growth before the flattening of the curve. In particular, we find a universal √ t growth before they reach saturation. India, Singapore, and Sri Lanka have reached up to linear growth (I(t) ∼ t), and they are yet to flatten their curves. Russia and Brazil are still in the power-law (t 2 ) growth regime. Thus, the polynomials of the I(t) curves provide valuable information on the stage of the epidemic evolution. Besides these detailed analyses, we compare the predictions of an extended SEIR model and a delay differential equation-based model with the reported infection data and observed good agreement among them, including the √ t behaviour.

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Section: Seir Model For Covid-19 Epidemicmentioning

confidence: 99%

“…2) is the sum of Q(t), R(t), and D(t). For further details, refer to Peng et al [27], and Lopez and Rodo [20]. We compare the model predictions [Eqs.…”

Section: Seir Model For Covid-19 Epidemicmentioning

confidence: 99%

Evolution of COVID-19 pandemic: Power-law growth and saturation

Chatterjee

Shayak

Ali

et al. 2020

Preprint

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“…For COVID-19 epidemic, some of the new models have managed to provide good forecasts that appears to match with the data. Peng et al [7] constructed a seven-variable model (including quarantined and death variables) for epidemic spread in China and predicted that the daily count of exposed and infectious individuals will be negligible by 30 March 2020. Their predictions are in good agreement with the present data.…”

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confidence: 99%

COVID-19 pandemic: Power law spread and flattening of the curve

Verma

Ali

Chatterjee

2020

Preprint

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In this paper, we analyze the real-time infection data of COVID-19 epidemic for nine nations. Our analysis is up to 7 April 2020. For China and South Korea, who have already flattened their infection curves, the number of infected individuals (I(t)) exhibits power-law behavior before flattening of the curve. Italy has transitioned to the power-law regime for some time. For the other six nations-USA, Spain, Germany, France, Japan, and India-a power-law regime is beginning to appear after exponential growth. We argue that the transition from an exponential regime to a powerlaw regime may act as an indicator for flattening of the epidemic curve. We also argue that long-term community transmission and/or the transmission by asymptomatic carriers traveling long distances may be inducing the power-law growth of the epidemic.

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“…This provides a mechanism for modelling interventions that target 57 contacts between individuals and does not assume the population exists in homogeneous 58 compartments as compartmental models generally do, but also requires a number of 59 assumptions to be made on the behavior and interactions within a population as well as 60 the infectivity of COVID-19. 61 Serial growth models for COVID-19 simulate an epidemic by expressing the number 62 of new infections at a given time as a weighted sum of new infections on previous days 63 usually scaled by the reproductive number, which may be time-varying [69][70][71][72][73]. The 64 weights are sampled from a probability distribution defining the amount of time between 65 an individual being infected and infecting another person.…”

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confidence: 99%

“…68 Statistical models often eschew deterministic population dynamics and fit the 69 observed data as a function of time and possibly other covariates in a regression (or 70 equivalent) framework. Log-linear [74], generalized Richards [75], ARIMA [76,77], 71 exponential [78], Gaussian CDF [79], and logistic [80][81][82] models, which all 72 accommodate the generally sigmoidal shape of the cumulative infection count that is 73 often observed in epidemics, as well as various other models [83][84][85][86] including machine 74 learning algorithms [87][88][89] have been proposed for COVID-19. Murray et al and 75 Woody et al take similar approaches for modeling COVID-19 deaths using the error 76 function (ERF) [90,91].…”

mentioning

confidence: 99%

Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.

Watson

Xiong

Zhang

et al. 2020

Preprint

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Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian nonlinear mixed model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectory. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for infections and deaths in U.S. states. We evaluate forecasting accuracy on a two-week holdout set, finding that the model predicts COVID-19 cases and deaths well, with a mean absolute scaled error of 0.40 for cases and 0.32 for deaths throughout the two-week evaluation period. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Ohio, and Mississippi. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course. Author summaryCOVID-19 models can be roughly classified as mathematical models that simulate disease within a population, including epidemiological compartmental models, or statistical curve-fitting models that fit a function to observed data and extrapolate forward into the future. Bridging this divide, we combine the strengths of curve-fitting statistical models and the structure of epidemiological models, by embedding a Bayesian nonlinear mixed model for case velocity and a machine learning algorithm (random : medRxiv preprint forest) into the framework of a compartmental model. Fusing these models together exploits the particular strengths of each to glean as much information as possible from the currently available data. We also identify the velocity of log cumulative cases as an excellent target for modeling and extrapolating COVID-19 case trajectories. We empirically evaluate the predictive performance of the model and provide predicted trajectories with credible intervals for cumulative confirmed case count, active confirmed infections and COVID-19 deaths for each of the 50 U.S. states. Combining sophisticated data analytic methods with proven epidemiological models offers an empirically grounded strategy for making realistic predictions and quantifying their uncertainty. These predictions indicate substantial variation in the COVID-19 trajectories of U.S. states.

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Propagation analysis and prediction of the COVID-19

Cited by 74 publications

References 6 publications

Evolution of COVID-19 pandemic: Power-law growth and saturation

Evolution of COVID-19 pandemic: Power-law growth and saturation

COVID-19 pandemic: Power law spread and flattening of the curve

Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.

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