Modeling and Forecasting of COVID-19 Spreading by Delayed Stochastic Differential Equations

The novel coronavirus disease (COVID-19) pneumonia has posed a great threat to the world recent months by causing many deaths and enormous economic damage worldwide. The first case of COVID-19 in Morocco was reported on 2 March 2020, and the number of reported cases has increased day by day. In this work, we extend the well-known SIR compartmental model to deterministic and stochastic time-delayed models in order to predict the epidemiological trend of COVID-19 in Morocco and to assess the potential role of multiple preventive measures and strategies imposed by Moroccan authorities. The main features of the work include the well-posedness of the models and conditions under which the COVID-19 may become extinct or persist in the population. Parameter values have been estimated from real data and numerical simulations are presented for forecasting the COVID-19 spreading as well as verification of theoretical results.

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Epstein-Barr Virus Versus Novel Coronavirus-Induced Hemophagocytic Lymphohistocytosis: The Uncharted Waters

Journal of Investigative Medicine High Impact Case Reports ◽

10.1177/2324709620950107 ◽

2020 ◽

Vol 8 ◽

pp. 232470962095010 ◽

Cited By ~ 1

Author(s):

Rawan Amir ◽

Asim Kichloo ◽

Jagmeet Singh ◽

Ravinder Bhanot ◽

Michael Aljadah ◽

...

Keyword(s):

Bacterial Infections ◽

Epstein Barr Virus ◽

Genetic Mutations ◽

High Grade ◽

The Novel ◽

Barr Virus ◽

First Case ◽

Epstein Barr ◽

Virus Versus ◽

Novel Coronavirus

Hemophagocytic lymphohistocytosis (HLH) is a hyperinflammatory syndrome characterized by fever, hepatosplenomegaly, and pancytopenia. It may be associated with genetic mutations or viral/bacterial infections, most commonly Epstein-Barr virus (EBV) and cytomegalovirus. As for the novel coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), also known as COVID-19 (coronavirus disease-2019), the cytokine storm it triggers can theoretically lead to syndromes similar to HLH. In this article, we report a case of a 28-year-old female who presented with high-grade fevers, found to have both SARS-CoV-2 and EBV infections, and eventually began to show signs of early HLH. To our knowledge, this is the first case reported in literature that raises the possibility of SARS-CoV-2–related HLH development.

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Formulation and Solution of an Inverse Reliability Problem to Simulate the Dynamic Behavior of COVID-19 Pandemic

Trends in Computational and Applied Mathematics ◽

10.5540/tcam.2021.022.01.00091 ◽

2021 ◽

Vol 22 (1) ◽

pp. 91-107

Author(s):

F. S. Lobato ◽

G. M. Platt ◽

G. B. Libotte ◽

A. J. Silva Neto

Keyword(s):

Dynamic Behavior ◽

Differential Evolution Algorithm ◽

Iteration Process ◽

Real Data ◽

The Novel ◽

Design Variables ◽

Different Types ◽

Evolution Algorithm ◽

Novel Coronavirus ◽

Optimization Loop

Different types of mathematical models have been used to predict the dynamic behavior of the novel coronavirus (COVID-19). Many of them involve the formulation and solution of inverse problems. This kind of problem is generally carried out by considering the model, the vector of design variables, and system parameters as deterministic values. In this contribution, a methodology based on a double loop iteration process and devoted to evaluate the influence of uncertainties on inverse problem is evaluated. The inner optimization loop is used to find the solution associated with the highest probability value, and the outer loop is the regular optimization loop used to determine the vector of design variables. For this task, we use an inverse reliability approach and Differential Evolution algorithm. For illustration purposes, the proposed methodology is applied to estimate the parameters of SIRD (Susceptible-Infectious-Recovery-Dead) model associated with dynamic behavior of COVID-19 pandemic considering real data from China's epidemic and uncertainties in the basic reproduction number (R0). The obtained results demonstrate, as expected, that the increase of reliability implies the increase of the objective function value.

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Web Application for Statistical Tracking and Predicting the Evolution of Active Cases with the Novel Coronavirus (SARS-CoV-2)

International Journal of Modeling and Optimization ◽

10.7763/ijmo.2021.v11.780 ◽

2021 ◽

pp. 70-74

Author(s):

Iulia Clitan ◽

◽

Adela Puscasiu ◽

Vlad Muresan ◽

Mihaela Ligia Unguresan ◽

...

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Web Application ◽

Point Of View ◽

The Novel ◽

End User ◽

First Case ◽

Novel Coronavirus ◽

Second Wave ◽

Over Time

Since February 2020, when the first case of infection with SARS COV-2 virus appeared in Romania, the evolution of COVID-19 pandemic continues to have an ascending allure, reaching in September 2020 a second wave of infections as expected. In order to understand the evolution and spread of this disease over time and space, more and more research is focused on obtaining mathematical models that are able to predict the evolution of active cases based on different scenarios and taking into account the numerous inputs that influence the spread of this infection. This paper presents a web responsive application that allows the end user to analyze the evolution of the pandemic in Romania, graphically, and that incorporates, unlike other COVID-19 statistical applications, a prediction of active cases evolution. The prediction is based on a neural network mathematical model, described from the architectural point of view.

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Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic Using Robust Multiobjective Optimization and Stochastic Fractal Search

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/9214159 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Fran Sérgio Lobato ◽

Gustavo Barbosa Libotte ◽

Gustavo Mendes Platt

Keyword(s):

Multiobjective Optimization ◽

Optimization Problem ◽

Search Algorithm ◽

Real Data ◽

The Novel ◽

Estimation Process ◽

Design Variables ◽

Novel Coronavirus ◽

Stochastic Fractal Search Algorithm ◽

Stochastic Fractal Search

Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead, and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multiobjective optimization problem is formulated, considering the minimization of uncertainties associated with the estimation process and the maximization of the robustness parameter. To solve this problem, the Multiobjective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.

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Hybrid Method for Simulation of a Fractional COVID-19 Model with Real Case Application

Axioms ◽

10.3390/axioms10040290 ◽

2021 ◽

Vol 10 (4) ◽

pp. 290

Author(s):

Anwarud Din ◽

Amir Khan ◽

Anwar Zeb ◽

Moulay Rchid Sidi Ammi ◽

Mouhcine Tilioua ◽

...

Keyword(s):

Hybrid Method ◽

Reproduction Number ◽

Real Data ◽

The Novel ◽

Order Analysis ◽

The Laplace Transform ◽

Case Application ◽

Novel Coronavirus ◽

Available Information ◽

Fractional Orders

In this research, we provide a mathematical analysis for the novel coronavirus responsible for COVID-19, which continues to be a big source of threat for humanity. Our fractional-order analysis is carried out using a non-singular kernel type operator known as the Atangana-Baleanu-Caputo (ABC) derivative. We parametrize the model adopting available information of the disease from Pakistan in the period 9 April to 2 June 2020. We obtain the required solution with the help of a hybrid method, which is a combination of the decomposition method and the Laplace transform. Furthermore, a sensitivity analysis is carried out to evaluate the parameters that are more sensitive to the basic reproduction number of the model. Our results are compared with the real data of Pakistan and numerical plots are presented at various fractional orders.

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Decentralized governance may lead to higher infection levels and sub-optimal releases of quarantines amid the COVID-19 pandemic

10.1101/2020.05.20.20108167 ◽

2020 ◽

Author(s):

Adam Lampert

Keyword(s):

Game Theory ◽

Theory Model ◽

The Novel ◽

Infection Level ◽

Low Level ◽

Decentralized Governance ◽

Novel Coronavirus ◽

Parameter Values ◽

Decentralized Government

AbstractThe outbreak of the novel Coronavirus (COVID-19) has lead countries worldwide to administer quarantine policies. However, each country or state decides independently what mobility restrictions to administer within its borders, while aiming to maximize its own citizens’ welfare. In turn, since individuals travel between countries and states, at least during periods when quarantines are less restrictive, the policy in one country may ultimately affect the infection level in other countries. Therefore, major questions are whether the policy dictated by a decentralized government is efficient, and if not, how the governments can coordinate a better policy. Here, we focus on the decision regarding the timing of releasing the quarantines. We consider a game theory model in which each of two governments decides when to switch from a restrictive to a non-restrictive quarantine and vice versa. We used parameter values driven by the literature and publically available data. We show that, if travel is sufficiently frequent during the non-restrictive quarantine periods, then the strategies are sub-optimal: Each governor tends to release the quarantine sooner, which ultimately leads to longer periods of restrictive quarantines and a higher prevalence of the disease. In turn, if the governments restrict international and interstate travel to a low level even when the quarantines are non-restrictive, the policy dictated by the decentralized governance may become optimal.

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Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling

10.1101/2020.12.29.20247833 ◽

2021 ◽

Author(s):

Miguel López ◽

Alberto Peinado ◽

Andrés Ortiz

Keyword(s):

Growth Model ◽

Logistic Growth ◽

Data Series ◽

The Novel ◽

Short Term ◽

Growth Trend ◽

Global Pandemic ◽

First Case ◽

Logistic Growth Model ◽

Novel Coronavirus

AbstractSince the first case reported of SARS-CoV-2 the end of December 2019 in China, the number of cases quickly climbed following an exponential growth trend, demonstrating that a global pandemic is possible. As of December 3, 2020, the total number of cases reported are around 65,527,000 contagions worldwide, and 1,524,000 deaths affecting 218 countries and territories. In this scenario, Spain is one of the countries that has suffered in a hard way, the ongoing epidemic caused by the novel coronavirus SARS-CoV-2, namely COVID-19 disease. In this paper, we present the utilization of phenomenological epidemic models to characterize the two first outbreak waves of COVID-19 in Spain. The study is driven using a two-step phenomenological epidemic approach. First, we use a simple generalized growth model to fit the main parameters at the early epidemic phase; later, we apply our previous finding over a logistic growth model to that characterize both waves completely. The results show that even in the absence of accurate data series, it is possible to characterize the curves of case incidence, and even construct short-term forecast in the near time horizon.

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Controlling the cascade of corona

Open Science Journal ◽

10.23954/osj.v5i3.2529 ◽

2020 ◽

Vol 5 (3) ◽

Author(s):

Muneeba Azmat

Keyword(s):

Public Health ◽

The Novel ◽

Limited Data ◽

Press Releases ◽

The Public ◽

Health Measures ◽

First Case ◽

Pandemic Response ◽

Potential Impact ◽

Novel Coronavirus

The pandemic of the 2019 novel Coronavirus has seen unprecedented exponential growth. Within three months, 192 countries have been affected, crossing more than 1 million confirmed cases and over 60 thousand deaths until the first week of April. Decision making in such a pandemic becomes difficult due to limited data on the nature of the disease and its propagation, course, prevention, and treatment. The pandemic response has varied from country to country and has resulted in a heterogeneous timeline for novel Coronavirus propagation. We compared the public health measures taken by various countries and the potential impact on the spread. We studied 6 countries including China, Italy, South Korea, Singapore, United Kingdom(UK), United States(US), and the special administrative region of Hong Kong. All articles, press releases, and websites of government entities published over a five-month period were included. A comparison of the date of the first diagnosed case, the spread of disease, and time since the first case and major public health policy implemented for prevention and containment and current cases was done. An emphasis on early and aggressive border restriction and surveillance of travelers from infected areas, use of information technology, and social distancing is necessary for control of the novel pandemic. Moving forwards, improvement in infrastructure, and adequate preparedness for pandemics is required.

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Flexible Teaching and Learning Modalities in Undergraduate Science Amid the COVID-19 Pandemic

Frontiers in Education ◽

10.3389/feduc.2020.609703 ◽

2021 ◽

Vol 5 ◽

Author(s):

Ryan S. Anderton ◽

Julian Vitali ◽

Conner Blackmore ◽

Megan C. Bakeberg

Keyword(s):

Teaching And Learning ◽

Educational Institutions ◽

Learning Approaches ◽

The Novel ◽

Learning Modalities ◽

Alternative Teaching ◽

First Case ◽

Undergraduate Science ◽

Novel Coronavirus ◽

The Impact

Since the first case of the novel coronavirus emerged in late 2019 (COVID-19), it quickly spread beyond China, with reported cases in nearly all countries and territories. As these unprecedented times have resulted in significant social and economic disruption, educational institutions have been forced to implement alternative teaching and learning approaches, including a total transition to online learning. Given the dependence of undergraduate science units and degrees on practical and laboratory activities, students and academics are faced with significant hurdles regarding delivery, learning, and assessment. Therefore, this article considers the impact of COVID-19 and the approaches being utilized to facilitate undergraduate science learning during the evolving pandemic.

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An Evaluation of COVID-19 in Italy: A data-driven modeling analysis

10.21203/rs.3.rs-28146/v1 ◽

2020 ◽

Author(s):

Yongmei Ding ◽

Liyuan Gao

Keyword(s):

Real Data ◽

Data Driven ◽

The Novel ◽

State Variables ◽

Infected People ◽

Modeling Analysis ◽

Travel Restrictions ◽

Novel Coronavirus ◽

Data Driven Modeling ◽

Global Travel

Abstract The novel coronavirus (COVID-19) that has been spreading worldwide since December 2019 has sickened millions of people, shut down major cities and some countries, prompted unprecedented global travel restrictions. Real data-driven modeling is an effort to help evaluate and curb the spread of the novel virus. Lockdowns and the effectiveness of reduction in the contacts in Italy has been measured via our modified model, with the addition of auxiliary and state variables that represent contacts, contacts with infected, conversion rate, latent propagation. Results show the decrease in infected people due to stay-at-home orders and tracing quarantine intervention. The effect of quarantine and centralized medical treatment was also measured through numerical modeling analysis.

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