Elementary Effects Analysis of factors controlling COVID-19 infections
in computational simulation reveals the importance of Social Distancing and
Mask Usage
- URL: http://arxiv.org/abs/2011.11381v3
- Date: Sun, 28 Feb 2021 04:31:49 GMT
- Title: Elementary Effects Analysis of factors controlling COVID-19 infections
in computational simulation reveals the importance of Social Distancing and
Mask Usage
- Authors: Kelvin K.F. Li, Stephen A. Jarvis, Fayyaz Minhas
- Abstract summary: This paper investigates the effectiveness of masks, social distancing, lockdown and self-isolation for reducing the spread of SARS-CoV-2 infections.
Our findings show that whilst requiring a lockdown is widely believed to be the most efficient method to quickly reduce infection numbers, the practice of social distancing and the usage of surgical masks can potentially be more effective than requiring a lockdown.
- Score: 1.4653008985229614
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: COVID-19 was declared a pandemic by the World Health Organization (WHO) on
March 11th, 2020. With half of the world's countries in lockdown as of April
due to this pandemic, monitoring and understanding the spread of the virus and
infection rates and how these factors relate to behavioural and societal
parameters is crucial for effective policy making. This paper aims to
investigate the effectiveness of masks, social distancing, lockdown and
self-isolation for reducing the spread of SARS-CoV-2 infections. Our findings
based on agent-based simulation modelling show that whilst requiring a lockdown
is widely believed to be the most efficient method to quickly reduce infection
numbers, the practice of social distancing and the usage of surgical masks can
potentially be more effective than requiring a lockdown. Our multivariate
analysis of simulation results using the Morris Elementary Effects Method
suggests that if a sufficient proportion of the population wore surgical masks
and followed social distancing regulations, then SARS-CoV-2 infections can be
controlled without requiring a lockdown.
Related papers
- First 100 days of pandemic; an interplay of pharmaceutical, behavioral
and digital interventions -- A study using agent based modeling [14.192977334409104]
We simulate realistic pharmaceutical, behavioral, and digital interventions that mirror challenges in real-world policy adoption.
Our analysis reveals the pivotal role of the initial 100 days in dictating a pandemic's course.
arXiv Detail & Related papers (2024-01-09T19:38:59Z) - Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance
of Containment Measures [65.62256987706128]
Compartmental epidemiological models categorize individuals based on their disease status.
We propose an ABM architecture that combines an adapted SEIRD model with a decision-making model for citizens.
We illustrate the designed model by examining the progression of SARS-CoV-2 infections in A Coruna, Spain.
arXiv Detail & Related papers (2023-07-28T08:01:05Z) - A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable
Million-Agent Reinforcement Learning [7.653466578233261]
This paper proposes a deep-reinforcement-learning-powered microscopic model named Microscopic Pandemic Simulator (MPS)
By replacing rule-based agents with rational agents whose behaviors are driven to maximize rewards, the MPS provides a better approximation of real world dynamics.
This paper first calibrates the MPS against real-world data in Allegheny, US, then demonstratively evaluates two government strategies: information disclosure and quarantine.
arXiv Detail & Related papers (2021-08-14T17:07:25Z) - Reinforced Contact Tracing and Epidemic Intervention [8.141401074784406]
We develop an Individual-based Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart epidemic control strategies.
IDRLECA can suppress infections at a very low level and retain more than 95% of human mobility.
arXiv Detail & Related papers (2021-02-04T08:31:48Z) - Epidemic mitigation by statistical inference from contact tracing data [61.04165571425021]
We develop Bayesian inference methods to estimate the risk that an individual is infected.
We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic.
Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact.
arXiv Detail & Related papers (2020-09-20T12:24:45Z) - Controlling the Outbreak of COVID-19: A Noncooperative Game Perspective [61.558752620308134]
Isolation and social distancing seem to be effective preventive measures to control this pandemic.
We propose a noncooperative game that can provide an incentive for maintaining social distancing to prevent the spread of COVID-19.
Numerical results show that the individual incentive increases more than 85% with an increasing percentage of home isolation.
arXiv Detail & Related papers (2020-07-27T04:28:32Z) - Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US.
We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
arXiv Detail & Related papers (2020-06-23T03:36:19Z) - COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health
and Economic Effects of Social Distancing Interventions [0.0]
The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide.
This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics.
arXiv Detail & Related papers (2020-06-09T03:44:48Z) - COVI White Paper [67.04578448931741]
Contact tracing is an essential tool to change the course of the Covid-19 pandemic.
We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
arXiv Detail & Related papers (2020-05-18T07:40:49Z) - A Deep Q-learning/genetic Algorithms Based Novel Methodology For
Optimizing Covid-19 Pandemic Government Actions [63.669642197519934]
We use the SEIR epidemiological model to represent the evolution of the virus COVID-19 over time in the population.
The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system.
We prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses.
arXiv Detail & Related papers (2020-05-15T17:17:45Z) - Universal Masking is Urgent in the COVID-19 Pandemic: SEIR and Agent
Based Models, Empirical Validation, Policy Recommendations [0.0]
We present two models for the COVID-19 pandemic predicting the impact of universal face mask wearing upon the spread of SARS-CoV-2 virus.
We show a near perfect correlation between early universal masking and successful suppression of daily case growth rates.
We recommend immediate mask wearing recommendations, official guidelines for correct use, and awareness campaigns.
arXiv Detail & Related papers (2020-04-22T11:42:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.