Modeling virus transport and dynamics in viscous flow medium.

In this paper, we developed a mathematical model to simulate virus transport through a viscous background flow driven by the natural pumping mechanism. Two types of respiratory pathogens viruses (SARS-Cov-2 and Influenza-A) are considered in this model. The Eulerian-Lagrangian approach is adopted to examine the virus spread in axial and transverse directions. The Basset-Boussinesq-Oseen equation is considered to study the effects of gravity, virtual mass, Basset force, and drag forces on the viruses transport velocity. The results indicate that forces acting on the spherical and non-spherical particles during the motion play a significant role in the transmission process of the viruses. It is observed that high viscosity is responsible for slowing the virus transport dynamics. Small sizes of viruses are found to be highly dangerous and propagate rapidly through the blood vessels. Furthermore, the present mathematical model can help to better understand the viruses spread dynamics in a blood flow.

Simulation Research on COVID-19 Transmission and Control Measures Based on SEIiRD Model

With the spread of the novel coronavirus pneumonia around the world, the data and transmission mechanism are analyzed. The SEIiRD model is constructed based on the existing SEIRD model, and the infected population is divided into asymptomatic infections, mild infections, severe infections and critical infections. The impact of the transmission rate of different infected people on the development of the epidemic was analyzed. Simulation experiments were carried out on the basis of fitting real data, and it was found that the main infected populations that affected the discovery of the epidemic were asymptomatic and mildly infected. On this basis, the transmission rate of different asymptomatic and mildly infected people was further analyzed. The impact of different intervention times on the number of infections and deaths was simulated. Results show that the model can effectively simulate the spread of COVID-19 and provide decision-making support to the departments to implement corresponding epidemic prevention and control strategies. © 2022 Acta Simulata Systematica Sinica. All rights reserved.

Analysis of epidemic spread dynamics using a PDE model and COVID-19 data from Hamilton County OH USA

We study the spatiotemporal dynamics of an epidemic spread using a compartmentalized PDE model. The model is validated using COVID-19 data from Hamilton County, Ohio, USA. The model parameters are estimated using a month of recorded data and then used to forecast the infection spread over the next ten days. The model is able to accurately estimate the key dynamic characteristics of COVID-19 spread in the county. Additionally, a stability analysis indicates that the model is robust to disturbances and perturbations which, for instance, could be used to represent the effects of super spreader events. We also use the modeling framework to analyse and discuss the impact of Non-pharmaceutical interventions (NPIs) for mitigation of infection. Our results suggest that such models can yield useful short and medium term predictive characterization of an epidemic spread in a restricted geographical region and also help formulate effective NPIs for mitigation. The results also signify the importance of further research into the accurate analytical representation of specific NPIs and hence their dampening effects on an infection spread. Copyright © 2021 The Authors.

How disease spread dynamics evolve over time

The recent outbreak of coronavirus disease has demonstrated that physical human interactions and modern movement paradigms are the principle drivers for the rapid spatial spread of infectious diseases. Modelling the impact of human mobility is crucial to understand the underlying dynamics of disease spread and consequently to develop effective containment and control strategies. While previous studies have investigated the impact of specific mobility profiles on the spreading dynamics of infectious diseases, they used either highly aggregated spatio-temporal data or portions of datasets that span a short period of time. These limitations do not allow to study how the influence of different mobility aspects on the spread changes as a disease outbreak progresses. In this paper we use large-scale comprehensive human mobility traces to study the impact of the latent period on the spreading dynamics of diseases. In addition, we provide a detailed analysis of how the spreading power of different mobility profiles changes over time. We propose an approach that analyses the behaviour of the individuals' spreading power as time progresses. Through extensive disease spread simulations we uncover a population influence homogeneity threshold, defined by a percentage of the population at which the identified mobility groups become equally influential to the spread. © 2021 ACM.

A FRACTAL-FRACTIONAL 2019-NCOV MODEL of MAJOR DISASTER for HUMAN LIFE

The purpose of this research is to explore the spread dynamics of a novel coronavirus outbreak, or 2019-nCOV via a fractional approach of type fractal-fractional (FF) derivative. We considered the FF approach in sense of the Atangana-Baleanu derivative for the system 2019-nCOV. In the FF operator, when we choose fractional-order one, we achieve the fractal model and when choosing fractal order one then we obtain a fractional model and while considering both the operators together we obtain the fractal-fractional model. The obtained results show via graphics for the different collections of fractal and fractional orders. The graphical results show the new operator impacts on a practical situation in a more visual way. © 2022

The Signature Features of COVID-19 Pandemic in a Hybrid Mathematical Model-Implications for Optimal Work-School Lockdown Policy.

The new COVID-19 pandemic has challenged policymakers on key issues. Most countries have adopted "lockdown" policies to reduce the spatial spread of COVID-19, but they have damaged the economic and moral fabric of society. Mathematical modeling in non-pharmaceutical intervention policy management has proven to be a major weapon in this fight due to the lack of an effective COVID-19 vaccine. A new hybrid model for COVID-19 dynamics using both an age-structured mathematical model based on the SIRD model and spatio-temporal model in silico is presented, analyzing the data of COVID-19 in Israel. Using the hybrid model, a method for estimating the reproduction number of an epidemic in real-time from the data of daily notification of cases is introduced. The results of the proposed model are confirmed by the Israeli Lockdown experience with a mean square error of 0.205 over 2 weeks. The use of mathematical models promises to reduce the uncertainty in the choice of "Lockdown" policies. The unique use of contact details from 2 classes (children and adults), the interaction of populations depending on the time of day, and several physical locations, allow a new look at the differential dynamics of the spread and control of infection.