Contact Network Model with Covert Infection

Abstract Objectives: Diseases such as SARS-CoV-2 have novel features that require modifications to the standard network-based stochastic SEIR model. In particular, we introduce modifications to this model to account for the potential changes in behavior patterns of individuals upon becoming symptomatic, as well as the tendency of a substantial proportion of those infected to remain asymptomatic. Methods: Using a generic network model where every potential contact exists with the same common probability, we conduct a simulation study in which we vary four key model parameters (transmission rate, probability of remaining asymptomatic, and the mean lengths of time spent in the exposed and infectious disease states) and examine the resulting impacts on various metrics of epidemic severity, including the effective reproduction number. We then consider the effects of a more complex network model. Results: We find that the mean length of time spent in the infectious state and the transmission rate are the most important model parameters, while the mean length of time spent in the exposed state and the probability of remaining asymptomatic are less important. We also find that the network structure has a significant impact on the dynamics of the disease spread. Conclusions: In this article, we present a modification to the network-based stochastic SEIR epidemic model which allows for modifications to the underlying contact network to account for the effects of quarantine. We also discuss the changes needed to the model to incorporate situations where some proportion of the individuals who are infected remain asymptomatic throughout the course of the disease.

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SH3 Domain Unfolding Pathway Studied by Protein Contact Network Model

International Journal of Digital Content Technology and its Applications ◽

10.4156/jdcta.vol4.issue4.18 ◽

2010 ◽

Vol 4 (4) ◽

pp. 182-192

Author(s):

HaiYan Li ◽

Jihua Wang

Keyword(s):

Network Model ◽

Sh3 Domain ◽

Contact Network ◽

Protein Contact Network

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Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case

BioMed Research International ◽

10.1155/2017/2483264 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 6

Author(s):

Guang Hu ◽

Luisa Di Paola ◽

Zhongjie Liang ◽

Alessandro Giuliani

Keyword(s):

Comparative Study ◽

Network Model ◽

Protein Complexes ◽

Protein Structures ◽

Contact Network ◽

Elastic Network Model ◽

Elastic Network ◽

Protein Contact Network ◽

Structural Systems Biology ◽

Allosteric Mechanisms

The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM) and Protein Contact Network (PCN) are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb) was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology.

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Modeling disease spreading on complex networks

Computer Science and Information Systems ◽

10.2298/csis110312061k ◽

2011 ◽

Vol 8 (4) ◽

pp. 1129-1141 ◽

Cited By ~ 2

Author(s):

Xiangjie Kong ◽

Yu Qi ◽

Xiumiao Song ◽

Guojiang Shen

Keyword(s):

Complex Network ◽

Network Model ◽

Virus Transmission ◽

H1n1 Influenza ◽

Contact Tracing ◽

Contact Network ◽

Cumulative Number ◽

Scale Free ◽

Disease Spreading ◽

Peak Value

Based on complex network approach, a contact network model with scale-free property is built. By analyzing the fact data of H1N1 influenza provided by Beijing Health Bureau, contact tracing mechanism is used to research H1N1 virus transmission dynamics with this model. Furthermore, the contact tracing coefficient and random checking coefficient are studied to analyze their impact on the peak value of new infections and cumulative number of infections. The simulation result fits well with the statistical data. It shows that the model built in this paper is valid and complex network model to simulate the epidemics of H1N1 influenza is feasible.

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Using the contact network model and Metropolis-Hastings sampling to reconstruct the COVID-19 spread on the “Diamond Princess”

Science Bulletin ◽

10.1016/j.scib.2020.04.043 ◽

2020 ◽

Vol 65 (15) ◽

pp. 1297-1305 ◽

Cited By ~ 9

Author(s):

Feng Liu ◽

Xin Li ◽

Gaofeng Zhu

Keyword(s):

Network Model ◽

Contact Network

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A stochastic contact network model for assessing outbreak risk of COVID-19 in workplaces

PLoS ONE ◽

10.1371/journal.pone.0262316 ◽

2022 ◽

Vol 17 (1) ◽

pp. e0262316

Author(s):

Xi Guo ◽

Abhineet Gupta ◽

Anand Sampat ◽

Chengwei Zhai

Keyword(s):

Network Model ◽

Cumulative Incidence ◽

First Generation ◽

Random Variable ◽

Source Model ◽

Control Measures ◽

Contact Network ◽

Risk Modeling ◽

Bernoulli Random Variable ◽

Model Code

The COVID-19 pandemic has drastically shifted the way people work. While many businesses can operate remotely, a large number of jobs can only be performed on-site. Moreover as businesses create plans for bringing workers back on-site, they are in need of tools to assess the risk of COVID-19 for their employees in the workplaces. This study aims to fill the gap in risk modeling of COVID-19 outbreaks in facilities like offices and warehouses. We propose a simulation-based stochastic contact network model to assess the cumulative incidence in workplaces. First-generation cases are introduced as a Bernoulli random variable using the local daily new case rate as the success rate. Contact networks are established through randomly sampled daily contacts for each of the first-generation cases and successful transmissions are established based on a randomized secondary attack rate (SAR). Modification factors are provided for SAR based on changes in airflow, speaking volume, and speaking activity within a facility. Control measures such as mask wearing are incorporated through modifications in SAR. We validated the model by comparing the distribution of cumulative incidence in model simulations against real-world outbreaks in workplaces and nursing homes. The comparisons support the model’s validity for estimating cumulative incidences for short forecasting periods of up to 15 days. We believe that the current study presents an effective tool for providing short-term forecasts of COVID-19 cases for workplaces and for quantifying the effectiveness of various control measures. The open source model code is made available at github.com/abhineetgupta/covid-workplace-risk.

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Determining whether a class of random graphs is consistent with an observed contact network

10.1101/168047 ◽

2017 ◽

Author(s):

Madhurima Nath ◽

Yihui Ren ◽

Yasamin Khorramzadeh ◽

Stephen Eubank

Keyword(s):

Network Model ◽

Network Structure ◽

Attack Rate ◽

Network Reliability ◽

Add Health ◽

Population Level ◽

Contact Network ◽

Dynamical Process ◽

Exponential Random Graph Models ◽

Disease Epidemiology

AbstractWe demonstrate a general method to analyze the sensitivity of attack rate in a network model of infectious disease epidemiology to the structure of the network. We use Moore and Shannon’s “network reliability” statistic to measure the epidemic potential of a network. A number of networks are generated using exponential random graph models based on the properties of the contact network structure of one of the Add Health surveys. The expected number of infections on the original Add Health network is significantly different from that on any of the models derived from it. Because individual-level transmissibility and network structure are not separately identifiable parameters given population-level attack rate data it is possible to re-calibrate the transmissibility to fix this difference. However, the temporal behavior of the outbreak remains significantly different. Hence any estimates of the effectiveness of time dependent interventions on one network are unlikely to generalize to the other. Moreover, we show that in one case even a small perturbation to the network spoils the re-calibration. Unfortunately, the set of sufficient statistics for specifying a contact network model is not yet known. Until it is, estimates of the outcome of a dynamical process on a particular network obtained from simulations on a different network are not reliable.

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