scholarly journals Computational Modeling Framework for the Study of Infectious Disease Spread through Commercial Air-Travel

2020 ◽  
Author(s):  
Pierrot Derjany ◽  
Sirish Namilae ◽  
Dahai Liu ◽  
Ashok Srinivasan
Keyword(s):  
Infectious Disease ◽  
Computational Modeling ◽  
Air Travel ◽  
Disease Spread ◽  
Modeling Framework

scholarly journals Successive Approximation, Variational Iteration, and Multistage-Analytical Methods for a SEIR Model of Infectious Disease Involving Vaccination Strategy

2021 ◽  
Vol 3 (2) ◽  
pp. 114-126
Author(s):  
Sudi Mungkasi
Keyword(s):  
Infectious Disease ◽  
Successive Approximation ◽  
Time Domain ◽  
Analytical Method ◽  
Vaccination Strategy ◽  
Approximate Solutions ◽  
Disease Spread ◽  
Seir Model ◽  
Variational Iteration ◽  
Iteration Methods

We consider a SEIR model for the spread (transmission) of an infectious disease. The model has played an important role due to world pandemic disease spread cases. Our contributions in this paper are three folds. Our first contribution is to provide successive approximation and variational iteration methods to obtain analytical approximate solutions to the SEIR model. Our second contribution is to prove that for solving the SEIR model, the variational iteration and successive approximation methods are identical when we have some particular values of Lagrange multipliers in the variational iteration formulation. Third, we propose a new multistage-analytical method for solving the SEIR model. Computational experiments show that the successive approximation and variational iteration methods are accurate for small size of time domain. In contrast, our proposed multistage-analytical method is successful to solve the SEIR model very accurately for large size of time domain. Furthermore, the order of accuracy of the multistage-analytical method can be made higher simply by taking more number of successive iterations in the multistage evolution.

Employing Agent-based Modeling to Mitigate Infectious Disease Spread

2021 ◽  
Vol 65 (1) ◽  
pp. 390-394
Author(s):  
Michael Schwartz ◽  
Paul Oppold ◽  
Boniface Noyongoyo ◽  
Peter Hancock
Keyword(s):  
Infectious Disease ◽  
Viral Infections ◽  
Agent Based Modeling ◽  
Disease Spread ◽  
Effective Measure ◽  
Agent Based ◽  
Personal Learning ◽  
Business Operations ◽  
Interpersonal Factors ◽  
Reduce Risk

The current pandemic has tested systems in place as to how to fight infectious diseases in many countries. COVID-19 spreads quickly and is deadly. However, it can be controlled through different measures such as physical distancing. The current project examines through simulation model of the UCF Global building the potential spread of an infectious disease via AnyLogic Personal Learning Edition (PLE) 8.7.0 on a laptop running Windows 10. The goal is to determine the environmental and interpersonal factors that could be modified to reduce risk of illness while maintaining typical business operations. Multiple experiments were ran to see when there is a potential change in infection and spread rate. Results show that increases occur with density between 400 and 500. To curtail the spread it is therefore important to limit contact through physical distancing for it has been proven an effective measure for reducing the spread of viral infections.

scholarly journals Household members do not contact each other at random: implications for infectious disease modelling

2018 ◽  
Vol 285 (1893) ◽  
pp. 20182201 ◽  
Author(s):  
Nele Goeyvaerts ◽  
Eva Santermans ◽  
Gail Potter ◽  
Andrea Torneri ◽  
Kim Van Kerckhove ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Social Contact ◽  
Household Contact ◽  
Epidemic Models ◽  
Disease Spread ◽  
Contact Density ◽  
Contact Networks ◽  
Household Transmission ◽  
Household Members ◽  
Contact Survey

Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts, and thus easily spread within households. Epidemic models, used to gain insight into infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now, there has been no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) from 2010 to 2011. We analysed data from 318 households totalling 1266 individuals with household sizes ranging from two to seven members. Exponential-family random graph models (ERGMs) were fitted to the within-household contact networks to reveal the processes driving contact between household members, both on weekdays and weekends. The ERGMs showed a high degree of clustering and, specifically on weekdays, decreasing connectedness with increasing household size. Furthermore, we found that the odds of a contact between older siblings and between father and child are smaller than for any other pair. The epidemic simulation results suggest that within-household contact density is the main driver of differences in epidemic spread between complete and empirical-based household contact networks. The homogeneous mixing assumption may therefore be an adequate characterization of the within-household contact structure for the purpose of epidemic simulations. However, ignoring the contact density when inferring based on an epidemic model will result in biased estimates of within-household transmission rates. Further research regarding the implementation of within-household contact networks in epidemic models is necessary.

The Power of Inclusion

2021 ◽  
pp. 421-438
Author(s):  
Erasmos Charamba
Keyword(s):  
Infectious Disease ◽  
Electronic Mail ◽  
World Wide ◽  
Disease Spread ◽  
Educational Institutions ◽  
The Internet ◽  
Science Students ◽  
Wide Spread ◽  
The World ◽  
E Learning

The year 2019 saw the emergence of COVID-19, an infectious disease spread through human-to-human transmission. This resulted in the immediate worldwide suspension of contact classes as countries tried to contain the wide spread of the pandemic. Consequently, educational institutions were thus left with only one option: e-learning. E-learning is the delivery of learning experiences through the use of electronic mail, the internet, the world wide web, and it can either be synchronous or asynchronous. Through the translanguaging lens, this chapter reports on a qualitative study that sought to explore the crucial role language plays in the e-learning of multilingual science students at a secondary school in South Africa. The e-learning lessons were in the form of videos, multilingual glossaries, and narrated slides in English and isiZulu languages. Data was collected through lesson observations and interviews held via Microsoft Teams. This chapter suggests numerous cognitive and socio-cultural benefits of multilingual e-learning pedagogy and recommends its use in education.

Modeling Large-Scale Epidemics

2017 ◽  
Author(s):  
Sanjay Basu
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Markov Model ◽  
Simulation Modeling ◽  
Large Scale ◽  
Basic Feature ◽  
Risk Of Infection ◽  
Critical Aspect ◽  
Modeling Framework ◽  
Disease Epidemics

Previous chapters ignored a critical aspect of modeling some major diseases: the infectious nature of many diseases. For infectious diseases, the risk of getting the disease is related to how many people are infectious at a given time: the more infectious people in the area, the higher the risk of infection among susceptible people. In a typical Markov model, we can’t account for this basic feature of infectious diseases because the risk of moving from one state (healthy) to another state (diseased) is assumed to be constant. In this chapter, the author introduces a simulation modeling framework that has been used for decades to simulate infectious disease epidemics.

scholarly journals A practical guide to mechanistic systems modeling in biology using a logic-based approach

2020 ◽  
Author(s):  
Anna Niarakis ◽  
Tomáš Helikar
Keyword(s):  
Computational Modeling ◽  
Computational Models ◽  
Regulatory System ◽  
Mechanistic Modeling ◽  
Lac Operon ◽  
Modeling Framework ◽  
Validation Criteria ◽  
Hands On ◽  
Modeling Software ◽  
Logical Modeling

Abstract Mechanistic computational models enable the study of regulatory mechanisms implicated in various biological processes. These models provide a means to analyze the dynamics of the systems they describe, and to study and interrogate their properties, and provide insights about the emerging behavior of the system in the presence of single or combined perturbations. Aimed at those who are new to computational modeling, we present here a practical hands-on protocol breaking down the process of mechanistic modeling of biological systems in a succession of precise steps. The protocol provides a framework that includes defining the model scope, choosing validation criteria, selecting the appropriate modeling approach, constructing a model and simulating the model. To ensure broad accessibility of the protocol, we use a logical modeling framework, which presents a lower mathematical barrier of entry, and two easy-to-use and popular modeling software tools: Cell Collective and GINsim. The complete modeling workflow is applied to a well-studied and familiar biological process—the lac operon regulatory system. The protocol can be completed by users with little to no prior computational modeling experience approximately within 3 h.

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