Prediction of the COVID-19 outbreak based on a realistic stochastic model

Zhang, Y; You, Chunping; Cai, Zhongxiang; Sun, Jun; Hu, Weisheng; Zhou, Xinfa

doi:10.1101/2020.03.10.20033803

Cited by 47 publications

(52 citation statements)

References 29 publications

(33 reference statements)

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“…In the former category, the spread of the disease is modelled by a set of coupled differential equations that account for the most important characteristics of the disease. This approach is largely followed (3)(4)(5). The second category of models is, in some sense, more straightforward and relies on network models to explicitly considers the individuals constituting the population.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the time evolution of the COVID-19 disease in Guadeloupe with a stochastic evolutionary model

Allali

Portecop

Carlès

et al. 2020

Preprint

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medRxiv preprint Predictions on the time-evolution of the number of severe and critical cases of COVID-19 patients in Guadeloupe are presented. A stochastic model is purposely developed to explicitly account for the entire population ( 400000 inhabitants) of Guadeloupe. The available data for Guadeloupe are analysed and combined with general characteristics of the COVID-19 to constrain the parameters of the model. The time-evolution of the number of cases follows the well-known exponential-like model observed at the very beginning of a pandemic outbreak. The exponential growth of the number of infected individuals is controlled by the so-called basic reproductive number, R 0 , defined as the likely number of additional cases generated by a single infectious case during its infectious period T I . Because of the rather long duration of infectious period ( 14 days) a high rate of contamination is sustained during several weeks after the beginning of the containment period. This may constitute a source of discouragement for people restrained to respect strict containment rules. It is then unlikely that, during the containment period, R 0 falls to zero. Fortunately, our models shows that the containment effects are not much sensitive to the exact value of R 0 provided we have R 0 < 0.6. For such conditions, we show that the number of severe and critical cases is highly tempered about 4 to 6 weeks after the beginning of the containment. Also, the maximum number of critical cases (i.e. the cases that may exceed the hospital's intensive care capacity) remains near 30 when R 0 < 0.6. For a larger R 0 = 0.8 a slower decrease of the number of critical cases occurs, leading to a larger number of deceased patients. This last example illustrates the great importance to maintain an as low as possible R 0 during and after the containment period. The rather long delay between the beginning of the containment and the appearance of the slowing-down of the rate of contamination puts a particular strength on the communication and sanitary education of people. To be mostly efficient, this communication must be done by a locally recognised medical staff. We believe that this point is a crucial matter of success. COVID-19 | Time-evolution | Guadeloupe | SAMU | Critical care | Monte Carlo ModelCorrespondence: meriem.allali@chu-guadeloupe.fr A. Description of the model structure. The stochastic model used in the present study falls in the class of exact stochastic Monte Carlo models (2). In such models, the entire population exposed to an infection is represented by a network where each node represents a person. This type of models offers very large possibilities to design epidemic processes that agree with the medical knowledge of the virus dissemination and its possible pathological issues. A set of stochastic rules determines the evolution of each node at each time iteration. The infection simulation process begins by choosing a given number of nodes initialised as "infected" to put the pathogen in the network. Other initial condition...

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Section: Introductionmentioning

confidence: 99%

Prediction of the time evolution of the COVID-19 disease in Guadeloupe with a stochastic evolutionary model

Allali

Portecop

Carlès

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…. 5 The maximum likelihood parameter estimates were ̂= (0.34,306.69,0.09,1.40,5.81, 0.05). We adopt an empirical Bayes approach such that these parameters are treated as known in the following steps.…”

Section: Functional Mixed Effects Modelmentioning

confidence: 99%

“…(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. these forecasting are mainly the susceptible-infected-removed (SIR) models and its variants [5][6][7][8] .…”

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confidence: 99%

Government Responses Matter: Predicting Covid-19 cases in US using an empirical Bayesian time series framework

Liu

Guo

2020

Preprint

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Since the Covid-19 outbreak, researchers have been predicting how the epidemic will evolve, especially the number in each country, through using parametric extrapolations based on the history. In reality, the epidemic progressing in a particular country depends largely on its policy responses and interventions. Since the outbreaks in some countries are earlier than United States, the prediction of US cases can benefit from incorporating the similarity in their trajectories. We propose an empirical Bayesian time series framework to predict US cases using different countries as prior reference.

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“…A understanding of the dynamic of the disease or forecasting of the spread of it would greatly enhance the control and prevention. [1][2][3][4][5][6][7][8][9] On the other hand, the use of models and mathematical methods for theoretical physicists to the study the spread of contagious diseases goes back a least to some works by Daniel Bernoulli in XVIII century on smallpox, [14] where in nowadays, many mathematical models have been proposed and studied for many different diseases. [10][11][12][13] Some diseases as the typhoid fever and also the COVID-19 are spread largely by carriers or individuals who can transmit the disease but who exhibit no overt symptoms.…”

Section: Introductionmentioning

confidence: 99%

Modeling and forecasting of epidemic spreading of the SARS-CoV-2 based in the nonlinear stochastic dynamics

Lima

2020

Preprint

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In this paper, we propose a stochastic model based on Itô diffusion as mathematical model for time evolution of new cases N(t) of the SARS-CoV-2 (COVID-19) in each day t. We propose a correspondent stochastic differential equation (SDE) analogs to classical differential equations for epidemic growing for some diseases as smallpox and typhoid fever. Furthermore, we made an analysis using the Fokker-Planck equation giving an estimating of the new cases in each day t as the mean half-width of the distribution P(N,t) of new cases. Our results display that the model based on Itô diffusion fit well to the results supported by healthy Brazilian agencies due to large uncertain in the official results and to the low number of tests realized generating so a strong randomness in the official data.

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Prediction of the COVID-19 outbreak based on a realistic stochastic model

Cited by 47 publications

References 29 publications

Prediction of the time evolution of the COVID-19 disease in Guadeloupe with a stochastic evolutionary model

Prediction of the time evolution of the COVID-19 disease in Guadeloupe with a stochastic evolutionary model

Government Responses Matter: Predicting Covid-19 cases in US using an empirical Bayesian time series framework

Modeling and forecasting of epidemic spreading of the SARS-CoV-2 based in the nonlinear stochastic dynamics

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