Effects of social distancing and isolation on epidemic spreading modeled via dynamical density functional theory

Vrugt, Michael te; Bickmann, Jens; Wittkowski, Raphael

doi:10.1038/s41467-020-19024-0

Cited by 90 publications

(69 citation statements)

References 91 publications

(89 reference statements)

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“…In Verachi et al (2020), the SIR model is used to evaluate the cost of management strategy. In Vrugt et al (2020), an SIR mathematical model with a dynamical density function is used for the spread of disease. In , a stochastic SIR mathematical model for a COVID-19 is developed to find the spread of the disease controlling value.…”

Section: Literature Survey On Mathematical Modelsmentioning

confidence: 99%

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

Selvakumar

Lokesh

2021

Soft Comput

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The World Health Organization (WHO) on December 31, 2019, was informed of several cases of respiratory diseases of unknown origin in the city of Wuhan in the Chinese Province of Hubei, the clinical manifestations of which were similar to those of viral pneumonia and manifested as fever, cough, and shortness of breath. And, the disease caused by the virus is named the new coronavirus disease 2019 and it will be abbreviated as 2019-nCoV and COVID-19. As of January 30, 2020, the WHO classified this epidemic as a global health emergency (Chung et al. in Radiology 295(1):202–207, 2020). It is an international real-life problem. Due to deaths, globally everyone is under fear. Now, it is the responsibility of researchers to give hope to the people. In this article, we aim to better protect people and general pandemic preparedness by predicting the lifetime of the disease-causing virus using three mathematical models. This article deals with a complex real-life problem people face all over the world, an international real-life problem. The main focus is on the USA due to large infection and death due to coronavirus and thereby the life of every individual is uncertain. The death counts of the USA from February 29 to April 22, 2020, are used in this article as a data set. The death counts of the USA are fitted by the solutions of three mathematical models and a solution to an international problem is achieved. Based on the death rate, the lifetime of the coronavirus COVID-19 is predicted as 1464.76 days from February 29, 2020. That is, after March 2024 there will be no death in the USA due to COVID-19 if everyone follows the guidelines of WHO and the advice of healthcare workers. People and government can get prepared for this situation and many lives can be saved. It is the contribution of soft computing. Finally, this article suggests several steps to control the spread and severity of the disease. The research work, the lifetime prediction presented in this article is entirely new and differs from all other articles in the literature.

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Section: Literature Survey On Mathematical Modelsmentioning

confidence: 99%

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

Selvakumar

Lokesh

2021

Soft Comput

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“…A similar connection with the basic SIR model has been recently introduced in Ref. (18) by using a generalised diffusion equation, also referred to as dynamical-density functional theory DDFT. This work could be nicely extended by coupling the basic DDFT equation with our controlled SIR model.…”

mentioning

confidence: 93%

More than a year after the onset of the CoVid-19 pandemic in the UK: lessons learned from a minimalistic model capturing essential features including social awareness and policy making

Durán-Olivencia

Kalliadasis

2021

Preprint

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The number of new daily SARS-CoV-2 infections experienced an abrupt increase during the last quarter of 2020 in almost every European country. The phenomenological explanation offered was a new mutation of the virus, first identified in the UK. We use publicly available data in combination with a time-delayed controlled SIR model, which captures the effects of preventive measures and concomitant social response on the spreading of the virus. The model, which has a unique transmission rate, enables us to reproduce the waves of infection occurred in the UK. This suggests that the new SARS-CoV-2 UK variant is as transmissible as previous strains. Our findings reveal that the sudden surge in cases was in fact related to the relaxation of preventive measures and social awareness. We also simulate the combined effects of restrictions and vaccination campaigns in 2021, demonstrating that lockdown policies are not fully effective to flatten the curve; fully effective mitigation can only be achieved via a vigorous vaccination campaign. As a matter of fact, incorporating recent data about vaccine efficacy, our simulations advocate that the UK might have overcome the worse of the CoVid-19 pandemic, provided that the vaccination campaign maintains a rate of approximately 140k jabs per day.

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“…In parallel, studies aiming to determine the virus dynamics, its geographical distribution and the peaks of the pandemic in given regions are necessary and useful for correct planning of immediate actions by states management. In this direction, several attempts to model the spread of the virus have been conducted recently [7, 6, 5, 9, 10, 11], including machine learning approaches [12]. Nonetheless, based on reports of these works that were more closely analyzed [5, 7, 6, 12], we found that many of these methods rely on parameters that are dependent on the advance of the spreading and on the regional context.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Covid-19 dynamics in Brazil: a data driven approach

Pereira¹,

Guérin²,

Júnior

et al. 2020

Preprint

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This paper has a twofold contribution. The first is a data driven approach for predicting the Covid-19 pandemic dynamics, based on data from more advanced countries. The second is to report and discuss the results obtained with this approach for Brazilian states, as of May 4th, 2020. We start by presenting preliminary results obtained by training an LSTM-SAE network, which are somewhat disappointing. Then, our main approach consists in an initial clustering of the world regions for which data is available and where the pandemic is at an advanced stage, based on a set of manually engineered features representing a country's response to the early spread of the pandemic. A Modified Auto-Encoder network is then trained from these clusters and learns to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks. Finally, curve fitting is carried out on the predictions in order to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated between the 25th of April and the 19th of May 2020. Predicted numbers reach a total of 240 thousand infected Brazilians, distributed

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Effects of social distancing and isolation on epidemic spreading modeled via dynamical density functional theory

Cited by 90 publications

References 91 publications

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

More than a year after the onset of the CoVid-19 pandemic in the UK: lessons learned from a minimalistic model capturing essential features including social awareness and policy making

Forecasting Covid-19 dynamics in Brazil: a data driven approach

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