Fluid dynamics of COVID-19 airborne infection suggests urgent data for a scientific design of social distancing

Rosti, Marco E.; Olivieri, Stefano; Cavaiola, Mattia; Seminara, Agnese; Mazzino, Andrea

doi:10.1038/s41598-020-80078-7

Cited by 94 publications

(84 citation statements)

References 47 publications

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“…In Pongkitivanichkul and D. Samart1, T. Tangphati, P. Koomhin, P. Pimton, 6, P. Dam-O, A. Payaka, and P. Channuie, (2020), a renormalization group-inspired logistic function is used to analyze the data of infected cases of the nations in the first phase by taking n=1. Rosti et al (2020), taking the airflow due to cough, predict the reach of infectious droplets to a destination emitted from mouth during a cough. In Scherf et al (2020), the steps are taken in Brazil to manage the pandemic situation and a review is given.…”

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RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models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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“…These flows play a key-role in various applications, from geophysical phenomena [1,2], to food [3,4], and industrial processes [5,6]. In addition, never as in this year, was so clear how important are multiphase flow processes also in host-to-host airborne disease transmission [7][8][9][10][11][12][13]. Compared to single-phase turbulence, the study and mathematical modeling of multiphase turbulence are much more complex, since they require the description of an ever-moving and deforming interface, of its topological modifications, and the underlying turbulent flow.…”

Section: Introductionmentioning

confidence: 99%

Turbulent Flows With Drops and Bubbles: What Numerical Simulations Can Tell Us—Freeman Scholar Lecture

Soligo

Roccon

Soldati

2021

Journal of Fluids Engineering

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Turbulent flows laden with large, deformable drops or bubbles are ubiquitous in nature and in a number of industrial processes. These flows are characterized by a physics acting at many different scales: from the macroscopic length scale of the problem down to the microscopic molecular scale of the interface. Naturally, the numerical resolution of all the scales of the problem, which span about eight to nine orders of magnitude, is not possible, with the consequence that numerical simulations of turbulent multiphase flows impose challenges and require methods able to capture the multi-scale nature of the flow. In this review, we start by describing the numerical methods commonly employed and discussing their advantages and limitations, and then we focus on the issues arising from the limited range of scales that can be possibly solved. Ultimately, the droplet size distribution, a key result of interest for turbulent multiphase flows, is used as a benchmark to compare the capabilities of the different methods and to discuss the main insights that can be drawn from these simulations. Based on this, we define a series of guidelines and best practices that we believe important in the simulation analysis and in the development of new numerical methods.

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“…On the other hand, only the spread of the virus through saliva droplets has been documented, its persistence in air being uncertain [21,22]. This means that the isolation of asymptomatic positive COVID-19 patients in each hospital ward, in compliance with hygiene measures (wearing masks, personal protective equipment -PPE, hand hygiene, etc.…”

Section: Discussionmentioning

confidence: 99%

Management of Cancer Patients During the SARS-CoV-2 Pandemic, in a County in Romania

Leşe¹,

Micu-Chiş²,

Leşe³

2021

EJPM

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Introduction. The SARS-CoV-2 pandemic has profoundly affected health systems by relocating resources, enforcing restrictive measures and burdening health care workers. The study aims to assess the changes in the treatment and the evolution of cancer patients in a county in Romania, with a population of over a half million inhabitants. Patients and method. Patients with breast, gastric, colorectal, and lung cancer were studied over a two-year period: 14 months before the onset of the pandemic and 10 months after its beginning. The number of new cases of cancer, the number of chemotherapy and radiotherapy sessions, the number of surgeries and deaths from these four types of cancer were recorded. Results. During the pandemic, fewer chemotherapy sessions were administered for all cancers (p<0.0001). The number of radiotherapy sessions for breast (p<0.007) and colorectal cancer (p<0.002) increased by banning patients from going to university hospitals and decreased for lung cancer (p<0.03) which has symptoms similar to those of the SARS-CoV-2 virus. Conclusions. Cancer patients had limited access to chemotherapy. Their diagnosis and treatment were included in the same category as non-lifethreatening diseases. Testing, quarantine and travel limitation were not accompanied by additional safe medical services for the oncological patients. The death rate is inconclusive because the time since the onset of the pandemic is shorter than the natural evolution of the types of cancers studied.

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Fluid dynamics of COVID-19 airborne infection suggests urgent data for a scientific design of social distancing

Cited by 94 publications

References 47 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

Turbulent Flows With Drops and Bubbles: What Numerical Simulations Can Tell Us—Freeman Scholar Lecture

Management of Cancer Patients During the SARS-CoV-2 Pandemic, in a County in Romania

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