scholarly journals When individual behaviour matters: homogeneous and network models in epidemiology

2007 ◽  
Vol 4 (16) ◽  
pp. 879-891 ◽  
Author(s):  
Shweta Bansal ◽  
Bryan T Grenfell ◽  
Lauren Ancel Meyers
Keyword(s):  
Compartmental Model ◽  
Network Models ◽  
Population Level ◽  
Disease Spread ◽  
Mixing Models ◽  
Epidemiological Models ◽  
Human Contact ◽  
Contact Patterns ◽  
Underlying Network ◽  
Simplifying Assumptions

Heterogeneity in host contact patterns profoundly shapes population-level disease dynamics. Many epidemiological models make simplifying assumptions about the patterns of disease-causing interactions among hosts. In particular, homogeneous-mixing models assume that all hosts have identical rates of disease-causing contacts. In recent years, several network-based approaches have been developed to explicitly model heterogeneity in host contact patterns. Here, we use a network perspective to quantify the extent to which real populations depart from the homogeneous-mixing assumption, in terms of both the underlying network structure and the resulting epidemiological dynamics. We find that human contact patterns are indeed more heterogeneous than assumed by homogeneous-mixing models, but are not as variable as some have speculated. We then evaluate a variety of methodologies for incorporating contact heterogeneity, including network-based models and several modifications to the simple SIR compartmental model. We conclude that the homogeneous-mixing compartmental model is appropriate when host populations are nearly homogeneous, and can be modified effectively for a few classes of non-homogeneous networks. In general, however, network models are more intuitive and accurate for predicting disease spread through heterogeneous host populations.

scholarly journals Inferring high-resolution human mixing patterns for disease modeling

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dina Mistry ◽  
Maria Litvinova ◽  
Ana Pastore y Piontti ◽  
Matteo Chinazzi ◽  
Laura Fumanelli ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Disease Transmission ◽  
Disease Modeling ◽  
Population Level ◽  
Effective Population ◽  
Human Contact ◽  
Contact Patterns ◽  
Data Driven Approach ◽  
Mixing Patterns ◽  
The Impact

AbstractMathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.

scholarly journals A Time-Dependent SEIRD Model for Forecasting the COVID-19 Transmission Dynamics

2020 ◽  
Author(s):  
Taarak Rapolu ◽  
Brahmani Nutakki ◽  
T. Sobha Rani ◽  
S. Durga Bhavani
Keyword(s):  
Mathematical Model ◽  
Time Dependent ◽  
Transmission Dynamics ◽  
Data Driven ◽  
Disease Spread ◽  
Main Principle ◽  
Epidemiological Models ◽  
Dynamic Nature ◽  
Human Contact ◽  
Medical Emergencies

AbstractThe spread of a disease caused by a virus can happen through human to human contact or could be from the environment. A mathematical model could be used to capture the dynamics of the disease spread to estimate the infections, recoveries, and fatalities that may result from the disease. An estimation is crucial to make policy decisions and for the alerts for the medical emergencies that may arise. Many epidemiological models are being used to make such an estimation. One major factor that is important in the forecasts using the models is the dynamic nature of the disease spread. Unless we can come up with a way of estimating the parameters that guide this dynamic spread, the models may not give accurate forecasts. The main principle is to keep the model generic while making minimal assumptions. In this work, we have derived a data-driven model from SEIRD, where we attempt to forecast Infected, Recovered and Deceased rates of COVID-19 up to a week. A method of estimating the parameters of the model is also discussed thoroughly in this work. The model is tested for India at a district level along with the most affected foreign cities like Lombardia from Italy and Moscow from Russia.The forecasts can help the governments in planning for emergencies such as ICU requirements, PPEs, hospitalizations, and so on as the infection is going to be prevalent for some time until a vaccine or cure is invented.

scholarly journals Evolving Nature of Human Contact Networks with Its Impact on Epidemic Processes

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Cong Li ◽  
Jing Li ◽  
Xiang Li
Keyword(s):  
State Transition ◽  
Diffusion Processes ◽  
Network Models ◽  
Temporal Network ◽  
Contact Networks ◽  
Human Contact ◽  
Contact Patterns ◽  
Activity State ◽  
The Individual ◽  
And Control

Human contact networks constitute a multitude of individuals and pairwise contacts among them. However, the dynamic nature, which generates the evolution of human contact networks, of contact patterns is unknown yet. Here, we analyse three empirical datasets and identify two crucial mechanisms of the evolution of temporal human contact networks, i.e., the activity state transition laws for an individual to be socially active and the contact establishment mechanism that active individuals adopt. We consider both of the two mechanisms to propose a temporal network model, the so-called memory-driven (MD) model, of human contact networks. Then, we study the susceptible-infected (SI) spreading processes on empirical human contact networks as well as four corresponding temporal network models and compare the full prevalence time of SI processes with various infection rates on the networks. The full prevalence time of SI processes in the MD model is the same as that in real-world human contact networks. Moreover, we find that the individual activity state transition promotes the spreading process, while the contact establishment of active individuals suppresses the prevalence. Besides, we observe that individuals who establish social ties with a small exploring rate are still able to induce an endemic which prevails in the networks. The study offers new insights to predict and control the diffusion processes on networks.

scholarly journals Identification of effective spreaders in contact networks using dynamical influence

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ruaridh A. Clark ◽  
Malcolm Macdonald
Keyword(s):  
Disease Transmission ◽  
Average Rate ◽  
Laplacian Matrix ◽  
Disease Spread ◽  
Eigenvector Centrality ◽  
Contact Networks ◽  
Human Contact ◽  
Spread Dynamics ◽  
Definition Of ◽  
High Degree

AbstractContact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. However, metrics that are essentially agnostic to the network’s structure, such as weighted degree (strength) centrality and its variants, perform near-optimally in selecting effective spreaders. These degree-based metrics outperform eigenvector centrality, despite disease spread over a network being a random walk process. This paper improves eigenvector-based spreader selection by introducing the non-linear relationship between contact time and the probability of disease transmission into the assessment of network dynamics. This approximation of disease spread dynamics is achieved by altering the Laplacian matrix, which in turn highlights why nodes with a high degree are such influential disease spreaders. From this approach, a trichotomy emerges on the definition of an effective spreader where, for susceptible-infected simulations, eigenvector-based selections can either optimise the initial rate of infection, the average rate of infection, or produce the fastest time to full infection of the network. Simulated and real-world human contact networks are examined, with insights also drawn on the effective adaptation of ant colony contact networks to reduce pathogen spread and protect the queen ant.

WSBen

2011 ◽  
pp. 739-758 ◽  
Author(s):  
Seog-Chan Oh ◽  
Dongwon Lee
Keyword(s):  
Statistical Analysis ◽  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Network Models ◽  
Web Service Composition ◽  
Ai Planning ◽  
Underlying Network ◽  
Test Sets ◽  
Diverse Data

In this article, a novel benchmark toolkit, WSBen, for testing web services discovery and composition algorithms is presented. The WSBen includes: (1) a collection of synthetically generated web services files in WSDL format with diverse data and model characteristics; (2) queries for testing discovery and composition algorithms; (3) auxiliary files to do statistical analysis on the WSDL test sets; (4) converted WSDL test sets that conventional AI planners can read; and (5) a graphical interface to control all these behaviors. Users can finetune the generated WSDL test files by varying underlying network models. To illustrate the application of the WSBen, in addition, we present case studies from three domains: (1) web service composition; (2) AI planning; and (3) the laws of networks in Physics community. It is our hope that WSBen will provide useful insights in evaluating the performance of web services discovery and composition algorithms. The WSBen toolkit is available at: http://pike.psu.edu/sw/wsben/.

scholarly journals Optimal Interplay between Synaptic Strengths and Network Structure Enhances Activity Fluctuations and Information Propagation in Hierarchical Modular Networks

2020 ◽  
Vol 10 (4) ◽  
pp. 228
Author(s):  
Rodrigo F. O. Pena ◽  
Vinicius Lima ◽  
Renan O. Shimoura ◽  
João Paulo Novato ◽  
Antonio C. Roque
Keyword(s):  
Network Structure ◽  
Network Models ◽  
Population Level ◽  
Synaptic Strength ◽  
Network Size ◽  
Strength Parameter ◽  
Information Propagation ◽  
Topological Features ◽  
Maximum Value ◽  
Parameter Ranges

In network models of spiking neurons, the joint impact of network structure and synaptic parameters on activity propagation is still an open problem. Here, we use an information-theoretical approach to investigate activity propagation in spiking networks with a hierarchical modular topology. We observe that optimized pairwise information propagation emerges due to the increase of either (i) the global synaptic strength parameter or (ii) the number of modules in the network, while the network size remains constant. At the population level, information propagation of activity among adjacent modules is enhanced as the number of modules increases until a maximum value is reached and then decreases, showing that there is an optimal interplay between synaptic strength and modularity for population information flow. This is in contrast to information propagation evaluated among pairs of neurons, which attains maximum value at the maximum values of these two parameter ranges. By examining the network behavior under the increase of synaptic strength and the number of modules, we find that these increases are associated with two different effects: (i) the increase of autocorrelations among individual neurons and (ii) the increase of cross-correlations among pairs of neurons. The second effect is associated with better information propagation in the network. Our results suggest roles that link topological features and synaptic strength levels to the transmission of information in cortical networks.

scholarly journals Rate of intersexual interactions affects injury likelihood in Tasmanian devil contact networks

2019 ◽  
Vol 30 (4) ◽  
pp. 1087-1095 ◽  
Author(s):  
David G Hamilton ◽  
Menna E Jones ◽  
Elissa Z Cameron ◽  
Hamish McCallum ◽  
Andrew Storfer ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Cancer Cell Line ◽  
Mate Guarding ◽  
Disease Spread ◽  
Tasmanian Devil ◽  
Mating Season ◽  
Wildlife Diseases ◽  
Individual Level ◽  
Contact Patterns ◽  
Bite Wounds

Abstract Identifying the types of contacts that result in disease transmission is important for accurately modeling and predicting transmission dynamics and disease spread in wild populations. We investigated contacts within a population of adult Tasmanian devils (Sarcophilus harrisii) over a 6-month period and tested whether individual-level contact patterns were correlated with accumulation of bite wounds. Bite wounds are important in the spread of devil facial tumor disease, a clonal cancer cell line transmitted through direct inoculation of tumor cells when susceptible and infected individuals bite each other. We used multimodel inference and network autocorrelation models to investigate the effects of individual-level contact patterns, identities of interacting partners, and position within the social network on the propensity to be involved in bite-inducing contacts. We found that males were more likely to receive potentially disease-transmitting bite wounds than females, particularly during the mating season when males spend extended periods mate-guarding females. The number of bite wounds individuals received during the mating season was unrelated to any of the network metrics examined. Our approach illustrates the necessity for understanding which contact types spread disease in different systems to assist the management of this and other infectious wildlife diseases.

scholarly journals Using network properties to predict disease dynamics on human contact networks

2011 ◽  
Vol 278 (1724) ◽  
pp. 3544-3550 ◽  
Author(s):  
Gregory M. Ames ◽  
Dylan B. George ◽  
Christian P. Hampson ◽  
Andrew R. Kanarek ◽  
Cayla D. McBee ◽  
...  
Keyword(s):  
Degree Distribution ◽  
Stochastic Simulations ◽  
Disease Spread ◽  
Contact Structures ◽  
Contact Network ◽  
Contact Networks ◽  
Human Contact ◽  
Differential Equation Models ◽  
Network Properties ◽  
Intermediate Density

Recent studies have increasingly turned to graph theory to model more realistic contact structures that characterize disease spread. Because of the computational demands of these methods, many researchers have sought to use measures of network structure to modify analytically tractable differential equation models. Several of these studies have focused on the degree distribution of the contact network as the basis for their modifications. We show that although degree distribution is sufficient to predict disease behaviour on very sparse or very dense human contact networks, for intermediate density networks we must include information on clustering and path length to accurately predict disease behaviour. Using these three metrics, we were able to explain more than 98 per cent of the variation in endemic disease levels in our stochastic simulations.

Characterizing pairwise contact patterns in human contact networks

2012 ◽  
Vol 10 (3) ◽  
pp. 524-535 ◽  
Author(s):  
Lintao Yang ◽  
Hao Jiang ◽  
Sai Wang ◽  
Lin Wang ◽  
Yuan Fang
Keyword(s):  
Contact Networks ◽  
Human Contact ◽  
Contact Patterns

scholarly journals Assessing Breastfeeding Practices Objectively Using Stable Isotope Techniques

2019 ◽  
Vol 75 (2) ◽  
pp. 109-113
Author(s):  
Christine Slater ◽  
Pernille Kaestel ◽  
Lisa Houghton
Keyword(s):  
Human Milk ◽  
Compartmental Model ◽  
Deuterium Oxide ◽  
Population Level ◽  
World Health ◽  
Objective Method ◽  
Breastfeeding Practices ◽  
Nutritional Components ◽  
Lactating Mothers ◽  
Health Organization

An objective method of assessing breastfeeding practices is required to evaluate progress toward the World Health Organization Global Target 2025: to increase exclusive breastfeeding (EBF) rates in the first 6 months to at least 50% by 2025. Currently, assessment of EBF at the population level is based on mother or caregiver reporting, which risks recall and social desirability bias. A more objective method is the deuterium oxide dose to mother (DTM) technique, in which lactating mothers are given a small amount of deuterium-labeled water. The infant receives deuterium during breastfeeding, and a compartmental model is used to determine the amount of human milk consumed by the infant, and the exclusivity of breastfeeding practices. If the amount of human milk consumed by an infant is determined using the DTM technique and the concentration of nutritional components or potentially toxic contaminants is measured, then the infant’s intake of essential nutrients or environmental contaminants can be ascertained.

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