A Meta-ontology Framework for Parameter Concepts of Disease Spread Simulation Models

Infectious disease epidemics have played and continue to play important roles in human populations. At different geographical levels, the spread of epidemics are affected by multiple demographic, social, cultural, political, economic, and other factors. Variation in these factors often produces different local or regional outcomes, so it is important for researchers to understand how individual behaviors and interactions can produce and explain larger patterns of disease spread. In small, traditional communities, important factors include settlement and household organization, daily behaviors, and relationships among residents. This research uses two computer simulation models to test the relative impact of these factors on disease spread in a small study community in Newfoundland and Labrador in the early 20th century, using data from the 1918 flu pandemic and other archival sources. In the agent-based model, which emphasizes movement to important social spaces, schoolchildren drive the size and timing of epidemics. In the social network model, which reflects important relationships among community residents, epidemics begun by adult women tend to be slower and smaller than epidemics begun by other types of individuals. These results demonstrate that, based on their roles in the community, members of different age and sex groups can strongly affect epidemic outcomes. Further, because simulation models are often used to develop or recommend public health policies or intervention strategies, the different results of the two models indicate the importance of selecting appropriate design features to ensure the best possible recommendations.

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Reducing spread of COVID-19 in closed environments: an outbreak investigation and modelling study in dormitory settings

10.21203/rs.3.rs-103301/v1 ◽

2020 ◽

Author(s):

Rachael Pung ◽

Bernard Lin ◽

Sebastian Maurer-Stroh ◽

Fernanda L Sirota ◽

Tze Minn Mak ◽

...

Keyword(s):

Attack Rate ◽

Simulation Models ◽

Housing Condition ◽

Control Measures ◽

Disease Spread ◽

Medical Attention ◽

Acute Infections ◽

Current Outbreak ◽

Living Environments ◽

Close Contacts

Abstract Starting with a handful of SARS-CoV-2 infections in dormitory residents in late March 2020, rapid tranmission in their dense living environments ensued and by October 2020, more than 50,000 acute infections were identified across various dormitories. Extensive epidemiological, serological and phylogentic investigations, supported by simulation models, helped to reveal the factors of transmission and impact of control measures in a dormitory. We find that asymptomatic cases and symptomatic cases who did not seek medical attention were major drivers of the outbreak. Furthermore, each resident has about 30 close contacts and each infected resident spread to 4.4 (IQR 3.5–5.3) others at the start of the outbreak. The final attack rate of the current outbreak was 76.2% (IQR 70.6%–98.0%) and could be reduced by further 10% under a modified dormitory housing condition. These findings are important when designing living environments in a post COVID-19 future to reduce disease spread and facilitate rapid implementation of outbreak control measures.

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Factors influencing SARS-CoV-2 transmission and outbreak control measures in densely populated settings

Scientific Reports ◽

10.1038/s41598-021-94463-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rachael Pung ◽

Bernard Lin ◽

Sebastian Maurer-Stroh ◽

Fernanda L. Sirota ◽

Tze Minn Mak ◽

...

Keyword(s):

Simulation Models ◽

Control Measures ◽

Disease Spread ◽

Medical Attention ◽

Outbreak Control ◽

Acute Infections ◽

Rapid Transmission ◽

Current Outbreak ◽

Living Environments ◽

The Impact

AbstractStarting with a handful of SARS-CoV-2 infections in dormitory residents in late March 2020, rapid transmission in their dense living environments ensued and by October 2020, more than 50,000 acute infections were identified across various dormitories in Singapore. The aim of the study is to identify combination of factors facilitating SARS-CoV-2 transmission and the impact of control measures in a dormitory through extensive epidemiological, serological and phylogenetic investigations, supported by simulation models. Our findings showed that asymptomatic cases and symptomatic cases who did not seek medical attention were major drivers of the outbreak. Furthermore, each resident had about 30 close contacts and each infected resident spread to 4.4 (IQR 3.5–5.3) others at the start of the outbreak. The final attack rate of the current outbreak was 76.2% (IQR 70.6–98.0%) and could be reduced by further 10% under a modified dormitory housing condition. These findings are important when designing living environments in a post COVID-19 future to reduce disease spread and facilitate rapid implementation of outbreak control measures.

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On the fallibility of simulation models in informing pandemic responses

10.31219/osf.io/rb8sd ◽

2020 ◽

Cited By ~ 1

Author(s):

Deepti Gurdasani ◽

Hisham Ziauddeen

Keyword(s):

Simulation Models ◽

Control Measures ◽

Disease Spread ◽

Contact Tracing ◽

Parameter Estimates ◽

Case Detection ◽

Rapid Testing ◽

Real World Data ◽

Effective Strategies ◽

Reliable Parameter

In the early stages of pandemics, mathematical models can provide invaluable insights into transmission dynamics, help predict disease spread, and evaluate control measures. However models are only valid within the limits of the parameters examined. As reliable parameter estimates are rarely available early in a new pandemic, best-guess estimates are used, which need to be constantly reviewed as new real-world data emerge. Estimating how sensitive the model is to changes in its parameters can provide useful information about validity when parameters are uncertain. Interpreting models without considering these factors can lead to flawed inferences, which can have far reaching effects when they inform public health policy. We illustrate this, here, using an example from the Hellewell et al. model published in Lancet Global Health, 2020. This model suggested that case detection and contact tracing was unlikely to be an effective strategy for pandemic control, and is likely to have informed UK government strategy to cease testing and contact tracing on the 12th March 2020. We show that this model is very sensitive to the parameter of delay between case detection and isolation. We demonstrate that when the delay scenario parameter is changed to a median of 1 day, which is very plausible in the context of current rapid testing, this model predicts a >80% probability of controlling the epidemic within 12 weeks, with relatively modest contact tracing. These results suggest that rapid testing, contact tracing and isolation could be effective strategies to control transmission.

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Multi-species temporal network of livestock movements for disease spread

Applied Network Science ◽

10.1007/s41109-021-00354-x ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Anne-Sophie Ruget ◽

Gianluigi Rossi ◽

P. Theo Pepler ◽

Gaël Beaunée ◽

Christopher J. Banks ◽

...

Keyword(s):

Simulation Models ◽

Single Species ◽

Disease Spread ◽

Temporal Network ◽

Temporal Networks ◽

Central Question ◽

Disease Epidemiology ◽

Infectious Disease Epidemiology ◽

Node Importance ◽

Cattle And Sheep

Abstract Introduction The objective of this study is to show the importance of interspecies links and temporal network dynamics of a multi-species livestock movement network. Although both cattle and sheep networks have been previously studied, cattle-sheep multi-species networks have not generally been studied in-depth. The central question of this study is how the combination of cattle and sheep movements affects the potential for disease spread on the combined network. Materials and methods Our analysis considers static and temporal representations of networks based on recorded animal movements. We computed network-based node importance measures of two single-species networks, and compared the top-ranked premises with the ones in the multi-species network. We propose the use of a measure based on contact chains calculated in a network weighted with transmission probabilities to assess the importance of premises in an outbreak. To ground our investigation in infectious disease epidemiology, we compared this suggested measure with the results of disease simulation models with asymmetric probabilities of transmission between species. Results Our analysis of the temporal networks shows that the premises which are likely to drive the epidemic in this multi-species network differ from the ones in both the cattle and the sheep networks. Although sheep movements are highly seasonal, the estimated size of an epidemic is significantly larger in the multi-species network than in the cattle network, independently of the period of the year. Finally, we demonstrate that a measure based on contact chains allow us to identify around 30% of the key farms in a simulated epidemic, ignoring markets, whilst static network measures identify less than 10% of these farms. Conclusion Our results ascertain the importance of combining species networks, as well as considering layers of temporal livestock movements in detail for the study of disease spread.

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A Statistical Examination of Distinct Characteristics Influencing the Performance of Vector-Borne Epidemiological Agent-Based Simulation Models

Modelling ◽

10.3390/modelling2020009 ◽

2021 ◽

Vol 2 (2) ◽

pp. 166-196

Author(s):

Anna Paula Galvão Scheidegger ◽

Henrique dos Santos Maxir ◽

Amarnath Banerjee

Keyword(s):

Decision Making ◽

Data Quality ◽

Human Behavior ◽

Simulation Models ◽

Disease Spread ◽

Epidemiological Models ◽

Agent Based Simulation ◽

Single Strain ◽

Agent Based ◽

Vector Borne

The spread of infectious diseases is a complex system in which pathogens, humans, the environment, and sometimes vectors interact. Mathematical and simulation modelling is a suitable approach to investigate the dynamics of such complex systems. The 2019 novel coronavirus (COVID-19) pandemic reinforced the importance of agent-based simulation models to quickly and accurately provide information about the disease spread that would be otherwise hard or risky to obtain, and how this information can be used to support infectious disease control decisions. Due to the trade-offs between complexity, time, and accuracy, many assumptions are frequently made in epidemiological models. With respect to vector-borne diseases, these assumptions lead to epidemiological models that are usually bounded to single-strain and single-vector scenarios, where human behavior is modeled in a simplistic manner or ignored, and where data quality is usually not evaluated. In order to leverage these models from theoretical tools to decision-making support tools, it is important to understand how information quality, human behavior, multi-vector, and multi-strain affect the results. For this, an agent-based simulation model with different parameter values and different scenarios was considered. Its results were compared with the results of a traditional compartmental model with respect to three outputs: total number of infected individuals, duration of the epidemic, and number of epidemic waves. Paired t-test showed that, in most cases, data quality, human behavior, multi-vector, and multi-strain were characteristics that lead to statistically different results, while the computational costs to consider them were not high. Therefore, these characteristics should be investigated in more detail and be accounted for in epidemiological models in order to obtain more reliable results that can assist the decision-making process during epidemics.

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Correlation of Pore Structure and Permeability by SEM-Image Analysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100180896 ◽

1990 ◽

Vol 48 (1) ◽

pp. 428-429

Author(s):

C. A. Callender ◽

Wm. C. Dawson ◽

J. J. Funk

Keyword(s):

Image Analysis ◽

Pore Structure ◽

Imaging System ◽

Pore Space ◽

Simulation Models ◽

Image Analyzer ◽

Imaging Data ◽

Sem Images ◽

Thin Sections ◽

Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

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