The effect of social distancing on the reach of an epidemic in social networks

AbstractHow does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible–infected–removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the ‘global’ level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

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LONG-TERM MODEL AND MONTE CARLO SIMULATION OF THE PUBLIC HEALTH EXPENDITURE IN BULGARIA

Journal of IMAB - Annual Proceeding (Scientific Papers) ◽

10.5272/jimab.2021273.3911 ◽

2021 ◽

Vol 27 (3) ◽

pp. 3911-3918

Author(s):

Nikolay Atanasov ◽

Keyword(s):

Public Health ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Gross Domestic Product ◽

Health Expenditure ◽

Domestic Product ◽

Public Health Expenditure ◽

The Public ◽

Term Model

Purpose: The aim of the study is to build a long-term model and conduct a Monte Carlo simulation of the public health expenditure (PHE) of Bulgaria with the gross domestic product (GDP) as an independent variable. Material/Methods: Statistical models are used for modeling the long-term dependence between the macroeconomic dynamic rows, testing of hypotheses of stationarity (Augmented Dickey-Fuller tests), for serial autocorrelation and others. Results: There is a well-defined, statistically significant long-term relationship between public health expenditure and gross domestic product. The long-term model of health expenditure has an estimate of the cointegration constant of 1.023 (p-value < 0.05). Monte Carlo simulations are presented with 1 000, 2 000 and 3 000 experiments, generated based on the normal distribution of the input variable. Conclusions: In the period after the year 1990, a well-defined long-term relationship between public health expenditure and GDP exists. The Monte Carlo simulation can be regarded as a reliable instrument for studying the most likely fluctuations in health expenditure caused by the GDP.

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Quantifying the impact of individual and collective compliance with infection control measures for ethical public health policy

10.1101/2021.12.02.21267207 ◽

2021 ◽

Author(s):

Daniel Roberts ◽

Euzebiusz Jamrozik ◽

George S. Heriot ◽

Michael J. Selgelid ◽

Joel C. Miller

Keyword(s):

Public Health ◽

Infectious Disease ◽

Disease Transmission ◽

Health Benefit ◽

Control Measures ◽

Infectious Individual ◽

The Public ◽

Infection Control Measures ◽

Mathematical Techniques ◽

The Impact

AbstractCompliance with infectious disease control measures can benefit public health but be burdensome for individuals. This raises ethical questions regarding the value of the public health benefit created by individual and collective compliance. Answering such questions requires estimating the total benefit from an individual’s compliance, and how much of that benefit is experienced by others. This is complicated by “overdetermination” in infectious disease transmission: each susceptible person may have contact with more than one infectious individual, such that preventing one transmission may have no net effect if the same susceptible person is infected later. This article explores mathematical techniques enabling quantification of the impacts of individuals and groups complying with three types of public health measures: quarantine of arrivals, isolation of infected individuals, and vaccination/prophylaxis. The models presented suggest that these interventions all exhibit synergy: each intervention becomes more effective on a per-individual basis as the number complying increases, because overdetermination of outcomes is reduced, Thus additional compliance reduces transmission to a greater degree.

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Coping with uncertainties in integrated urban water management

Water Science & Technology ◽

10.2166/wst.1997.0677 ◽

1997 ◽

Vol 36 (8-9) ◽

pp. 265-269

Author(s):

Govert D. Geldof

Keyword(s):

Water Management ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Interdisciplinary Approach ◽

Urban Water ◽

Urban Water Management ◽

Integrated Water Management ◽

Good Design ◽

Management Techniques ◽

Simulation Results

In the practice of integrated water management we meet complexity, subjectivity and uncertainties. Uncertainties come into play when new urban water management techniques are applied. The art of a good design is not to reduce uncertainties as much as possible, but to find the middle course between cowardice and recklessness. This golden mean represents bravery. An interdisciplinary approach is needed to reach consensus. Calculating uncertainties by using Monte Carlo simulation results may be helpful.

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Cheating at Craps

ACM SIGMETRICS Performance Evaluation Review ◽

10.1145/3466826.3466845 ◽

2021 ◽

Vol 48 (4) ◽

pp. 53-61

Author(s):

Andrea Marin ◽

Carey Williamson

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Modeling And Simulation ◽

Analytical Model ◽

Quantitative Evaluation ◽

Mathematical Analysis ◽

Simulation Modeling ◽

Analytical Modeling ◽

The World ◽

Simulation Results

Craps is a simple dice game that is popular in casinos around the world. While the rules for Craps, and its mathematical analysis, are reasonably straightforward, this paper instead focuses on the best ways to cheat at Craps, by using loaded (biased) dice. We use both analytical modeling and simulation modeling to study this intriguing dice game. Our modeling results show that biasing a die away from the value 1 or towards the value 5 lead to the best (and least detectable) cheating strategies, and that modest bias on two loaded dice can increase the winning probability above 50%. Our Monte Carlo simulation results provide validation for our analytical model, and also facilitate the quantitative evaluation of other scenarios, such as heterogeneous or correlated dice.

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A Comparison of Different Data-driven Procedures to Determine the Bunching Window

Public Finance Review ◽

10.1177/1091142121993055 ◽

2021 ◽

Vol 49 (2) ◽

pp. 262-293

Author(s):

Vincent Dekker ◽

Karsten Schweikert

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Income Distribution ◽

Prior Information ◽

Structural Breaks ◽

Data Driven ◽

Taxable Income ◽

Counterfactual Models ◽

Simulation Results ◽

Second Procedure

In this article, we compare three data-driven procedures to determine the bunching window in a Monte Carlo simulation of taxable income. Following the standard approach in the empirical bunching literature, we fit a flexible polynomial model to a simulated income distribution, excluding data in a range around a prespecified kink. First, we propose to implement methods for the estimation of structural breaks to determine a bunching regime around the kink. A second procedure is based on Cook’s distances aiming to identify outlier observations. Finally, we apply the iterative counterfactual procedure proposed by Bosch, Dekker, and Strohmaier which evaluates polynomial counterfactual models for all possible bunching windows. While our simulation results show that all three procedures are fairly accurate, the iterative counterfactual procedure is the preferred method to detect the bunching window when no prior information about the true size of the bunching window is available.

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Should a hotter paramagnet transform quicker to a ferromagnet? Monte Carlo simulation results for Ising model

Physical Chemistry Chemical Physics ◽

10.1039/d1cp00879j ◽

2021 ◽

Author(s):

Subir K Das ◽

Nalina Vadakkayil

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Ising Model ◽

Simulation Results ◽

Heating Up

For quicker formation of ice, before inserting inside a refrigerator, heating up of a body of water can be beneficial. We report first observation of a counterpart of this intriguing...

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Epidemiological modelling of the health and economic effects of COVID-19 control in Australia’s second wave

Journal of Public Health ◽

10.1007/s10389-021-01611-0 ◽

2021 ◽

Author(s):

R. Quentin Grafton ◽

John Parslow ◽

Tom Kompas ◽

Kathryn Glass ◽

Emily Banks

Keyword(s):

Public Health ◽

Health Outcomes ◽

Health Management ◽

Economic Effects ◽

Social Distancing ◽

The Public ◽

Long Duration ◽

Community Transmission ◽

Second Wave ◽

Detection Strategies

Abstract Background We investigated the public health and economy outcomes of different levels of social distancing to control a ‘second wave’ outbreak in Australia and identify implications for public health management of COVID-19. Methods Individual-based and compartment models were used to simulate the effects of different social distancing and detection strategies on Australian COVID-19 infections and the economy from March to July 2020. These models were used to evaluate the effects of different social distancing levels and the early relaxation of suppression measures, in terms of public health and economy outcomes. Results The models, fitted to observations up to July 2020, yielded projections consistent with subsequent cases and showed that better public health outcomes and lower economy costs occur when social distancing measures are more stringent, implemented earlier and implemented for a sufficiently long duration. Early relaxation of suppression results in worse public health outcomes and higher economy costs. Conclusions Better public health outcomes (reduced COVID-19 fatalities) are positively associated with lower economy costs and higher levels of social distancing; achieving zero community transmission lowers both public health and economy costs compared to allowing community transmission to continue; and early relaxation of social distancing increases both public health and economy costs.

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Monte Carlo Modeling and Design of Photon Energy Attenuation Layers for >10× Quantum Yield Enhancement in Si-Based Hard X-ray Detectors

Instruments ◽

10.3390/instruments5020017 ◽

2021 ◽

Vol 5 (2) ◽

pp. 17

Author(s):

Eldred Lee ◽

Kaitlin M. Anagnost ◽

Zhehui Wang ◽

Michael R. James ◽

Eric R. Fossum ◽

...

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Quantum Yield ◽

Photon Energy ◽

High Efficiency ◽

High Energy ◽

Yield Enhancement ◽

X Ray ◽

Simulation Results ◽

Conversion Efficiencies

High-energy (>20 keV) X-ray photon detection at high quantum yield, high spatial resolution, and short response time has long been an important area of study in physics. Scintillation is a prevalent method but limited in various ways. Directly detecting high-energy X-ray photons has been a challenge to this day, mainly due to low photon-to-photoelectron conversion efficiencies. Commercially available state-of-the-art Si direct detection products such as the Si charge-coupled device (CCD) are inefficient for >10 keV photons. Here, we present Monte Carlo simulation results and analyses to introduce a highly effective yet simple high-energy X-ray detection concept with significantly enhanced photon-to-electron conversion efficiencies composed of two layers: a top high-Z photon energy attenuation layer (PAL) and a bottom Si detector. We use the principle of photon energy down conversion, where high-energy X-ray photon energies are attenuated down to ≤10 keV via inelastic scattering suitable for efficient photoelectric absorption by Si. Our Monte Carlo simulation results demonstrate that a 10–30× increase in quantum yield can be achieved using PbTe PAL on Si, potentially advancing high-resolution, high-efficiency X-ray detection using PAL-enhanced Si CMOS image sensors.

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