scholarly journals Optimised prophylactic vaccination in metapopulations

2019 ◽  
Author(s):  
Mingmei Teo ◽  
Nigel Bean ◽  
Joshua V. Ross
Keyword(s):  
Infectious Disease ◽  
Optimal Allocation ◽  
Vaccination Strategy ◽  
Disease Spread ◽  
Vaccination Scheme ◽  
Prophylactic Vaccination ◽  
Metapopulation Models ◽  
Limited Supply ◽  
Computationally Intensive ◽  
Models Of Disease

AbstractA highly effective method for controlling the spread of an infectious disease is vaccination. However, there are many situations where vaccines are in limited supply. The ability to determine, under this constraint, a vaccination strategy which minimises the number of people that become infected over the course of a potential epidemic is essential. Two questions naturally arise: when is it best to allocate vaccines, and to whom should they be allocated? We address these questions in the context of metapopulation models of disease spread. We discover that it is optimal to distribute all vaccines prophylactically, rather than withholding until infection is introduced. For small metapopulations, we provide a method for determining the optimal allocation. As the optimal strategy becomes computationally intensive to obtain when the population size increases, we detail an approximation method to determine an approximately optimal vaccination scheme. Through comparisons with other strategies in the literature, we find that our approximate strategy is superior.

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.

scholarly journals An Economist’s Guide to Epidemiology Models of Infectious Disease

2020 ◽  
Vol 34 (4) ◽  
pp. 79-104
Author(s):  
Christopher Avery ◽  
William Bossert ◽  
Adam Clark ◽  
Glenn Ellison ◽  
Sara Fisher Ellison
Keyword(s):  
Infectious Disease ◽  
Political Economy ◽  
Disease Transmission ◽  
Sir Model ◽  
Disease Suppression ◽  
Disease Spread ◽  
High Profile ◽  
Models Of Disease ◽  
Susceptible Populations

We describe the structure and use of epidemiology models of disease transmission, with an emphasis on the susceptible/infected/recovered (SIR) model. We discuss high-profile forecasts of cases and deaths that have been based on these models, what went wrong with the early forecasts, and how they have adapted to the current COVID pandemic. We also offer three distinct areas where economists would be well positioned to contribute to or inform this epidemiology literature: modeling heterogeneity of susceptible populations in various dimensions, accommodating endogeneity of the parameters governing disease spread, and helping to understand the importance of political economy issues in disease suppression.

scholarly journals Impact of Network Topology on the Spread of Infectious Diseases

2020 ◽  
Vol 21 (1) ◽  
pp. 95
Author(s):  
Eduardo R. Pinto ◽  
Erivelton G. Nepomuceno ◽  
Andriana S. L. O. Campanharo
Keyword(s):  
Infectious Disease ◽  
Complex Network ◽  
Network Models ◽  
Vaccination Strategy ◽  
Small World ◽  
Disease Spread ◽  
Complex Network Theory ◽  
General Complex ◽  
The Individual ◽  
Complex Network Models

The complex network theory constitutes a natural support for the study of a disease propagation. In this work, we present a study of an infectious disease spread with the use of this theory in combination with the Individual Based Model. More specifically, we use several complex network models widely known in the literature to verify their topological effects in the propagation of the disease. In general, complex networks with different properties result in curves of infected individuals with different behaviors, and thus, the growth of a given disease is highly sensitive to the network model used. The disease eradication is observed when the vaccination strategy of 10% of the population is used in combination with the random, small world or modular network models, which opens an important space for control actions that focus on changing the topology of a complex network as a form of reduction or even elimination of an infectious disease.

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.

scholarly journals When past is not a prologue: Adapting informatics practice during a pandemic

2020 ◽  
Vol 27 (7) ◽  
pp. 1142-1146 ◽  
Author(s):  
Thomas G Kannampallil ◽  
Randi E Foraker ◽  
Albert M Lai ◽  
Keith F Woeltje ◽  
Philip R O Payne
Keyword(s):  
Predictive Models ◽  
Past History ◽  
Lessons Learned ◽  
Data Driven ◽  
Disease Spread ◽  
Delivery Of Care ◽  
The Past ◽  
History Of ◽  
Models Of Disease ◽  
Fundamental Shift

Abstract Data and information technology are key to every aspect of our response to the current coronavirus disease 2019 (COVID-19) pandemic—including the diagnosis of patients and delivery of care, the development of predictive models of disease spread, and the management of personnel and equipment. The increasing engagement of informaticians at the forefront of these efforts has been a fundamental shift, from an academic to an operational role. However, the past history of informatics as a scientific domain and an area of applied practice provides little guidance or prologue for the incredible challenges that we are now tasked with performing. Building on our recent experiences, we present 4 critical lessons learned that have helped shape our scalable, data-driven response to COVID-19. We describe each of these lessons within the context of specific solutions and strategies we applied in addressing the challenges that we faced.

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