scholarly journals SAFE REOPENING STRATEGIES FOR EDUCATIONAL INSTITUTIONS DURING COVID-19: A DATA-DRIVEN AGENT-BASED APPROACH

2020 ◽  
Author(s):  
Ujjal K mukherjee ◽  
Subhonmesh Bose ◽  
Anton Ivanov ◽  
Sebastian Souyris ◽  
Sridhar Seshadri ◽  
...  
Keyword(s):  
Analytical Model ◽  
Contact Tracing ◽  
Educational Institutions ◽  
Model Parameters ◽  
Infection Rates ◽  
Agent Based ◽  
Social Distancing ◽  
Preventative Measures ◽  
Agent Based Simulations ◽  
Population Ratio

AbstractMany educational institutions have partially or fully closed all operations to cope with the challenges of the ongoing COVID-19 pandemic. In this paper, we explore strategies that such institutions can adopt to conduct safe reopening and resume operations during the pandemic. The research is motivated by the University of Illinois at Urbana-Champaign’s (UIUC’s) SHIELD program, (https://www.uillinois.edu/shield), which is a set of policies and strategies, including rapid saliva-based COVID-19 screening, for ensuring safety of students, faculty and staff to conduct in-person operations, at least partially. Specifically, we study how rapid bulk testing, contact tracing and preventative measures such as mask wearing, sanitization, and enforcement of social distancing can allow institutions to manage the epidemic spread.Research DesignThis work combines the power of analytical epidemic modeling, data analysis and agent-based simulations to derive policy insights. We develop an analytical model that takes into account the asymptomatic transmission of COVID-19, the effect of isolation via testing (both in bulk and through contact tracing) and the rate of contacts among people within and outside the institution. Next, we use data from the UIUC SHIELD program and 85 other universities to estimate parameters that describe the analytical model. Using the estimated parameters, we finally conduct agent-based simulations with various model parameters to evaluate testing and reopening strategies.ResultsThe parameter estimates from UIUC and other universities show similar trends. For example, infection rates at various institutions grow rapidly in certain months and this growth correlates positively with infection rates in counties where the universities are located. Infection rates are also shown to be negatively correlated with testing rates at the institutions. Through agent-based simulations, we demonstrate that the key to designing an effective reopening strategy is a combination of rapid bulk testing and effective preventative measures such as mask wearing and social distancing. Multiple other factors help to reduce infection load, such as efficient contact tracing, reduced delay between testing and result revelation, tests with less false negatives and targeted testing of high-risk class among others.ContributionsThis paper contributes to the nascent literature on combating the COVID-19 pandemic and is especially relevant for educational institutions and similarly large organizations. We contribute by providing an analytical model that can be used to estimate key parameters from data, which in turn can be used to simulate the effect of different strategies for reopening. We quantify the relative effect of different strategies such as bulk testing, contact tracing, reduced infectivity and contact rates in the context of educational institutions. Specifically, we show that for the estimated average base infectivity of 0.025 (R0 = 1.82), a daily number of tests to population ratio T/N of 0.2, i.e., once a week testing for all individuals, is a good indicative threshold. However, this test to population ratio is sensitive to external infectivities, internal and external mobilities, delay in getting results after testing, and measures related to mask wearing and sanitization, which affect the base infectivity.

scholarly journals Evaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ujjal K. Mukherjee ◽  
Subhonmesh Bose ◽  
Anton Ivanov ◽  
Sebastian Souyris ◽  
Sridhar Seshadri ◽  
...  
Keyword(s):  
Analytical Model ◽  
Contact Tracing ◽  
Educational Institutions ◽  
Model Parameters ◽  
Infection Rates ◽  
Agent Based ◽  
Social Distancing ◽  
Preventative Measures ◽  
Agent Based Simulations ◽  
Population Ratio

AbstractMany educational institutions have partially or fully closed all operations to cope with the challenges of the ongoing COVID-19 pandemic. In this paper, we explore strategies that such institutions can adopt to conduct safe reopening and resume operations during the pandemic. The research is motivated by the University of Illinois at Urbana-Champaign’s (UIUC’s) SHIELD program, which is a set of policies and strategies, including rapid saliva-based COVID-19 screening, for ensuring safety of students, faculty and staff to conduct in-person operations, at least partially. Specifically, we study how rapid bulk testing, contact tracing and preventative measures such as mask wearing, sanitization, and enforcement of social distancing can allow institutions to manage the epidemic spread. This work combines the power of analytical epidemic modeling, data analysis and agent-based simulations to derive policy insights. We develop an analytical model that takes into account the asymptomatic transmission of COVID-19, the effect of isolation via testing (both in bulk and through contact tracing) and the rate of contacts among people within and outside the institution. Next, we use data from the UIUC SHIELD program and 85 other universities to estimate parameters that describe the analytical model. Using the estimated parameters, we finally conduct agent-based simulations with various model parameters to evaluate testing and reopening strategies. The parameter estimates from UIUC and other universities show similar trends. For example, infection rates at various institutions grow rapidly in certain months and this growth correlates positively with infection rates in counties where the universities are located. Infection rates are also shown to be negatively correlated with testing rates at the institutions. Through agent-based simulations, we demonstrate that the key to designing an effective reopening strategy is a combination of rapid bulk testing and effective preventative measures such as mask wearing and social distancing. Multiple other factors help to reduce infection load, such as efficient contact tracing, reduced delay between testing and result revelation, tests with less false negatives and targeted testing of high-risk class among others. This paper contributes to the nascent literature on combating the COVID-19 pandemic and is especially relevant for educational institutions and similarly large organizations. We contribute by providing an analytical model that can be used to estimate key parameters from data, which in turn can be used to simulate the effect of different strategies for reopening. We quantify the relative effect of different strategies such as bulk testing, contact tracing, reduced infectivity and contact rates in the context of educational institutions. Specifically, we show that for the estimated average base infectivity of 0.025 ($$R_0 = 1.82$$ R 0 = 1.82 ), a daily number of tests to population ratio T/N of 0.2, i.e., once a week testing for all individuals, is a good indicative threshold. However, this test to population ratio is sensitive to external infectivities, internal and external mobilities, delay in getting results after testing, and measures related to mask wearing and sanitization, which affect the base infection rate.

scholarly journals Quantifying the Effects of Social Distancing on the Spread of COVID-19

2020 ◽  
Author(s):  
Talal Daghriri ◽  
Ozlem Ozmen
Keyword(s):  
The United States ◽  
Contact Tracing ◽  
World Population ◽  
Human Society ◽  
Infection Rates ◽  
Agent Based ◽  
Social Distancing ◽  
Modeling Studies ◽  
The Social ◽  
The World

AbstractThis paper studies the interplay between the social distancing and the spread of COVID-19 disease—a widely spread pandemic that has affected nearly most of the world population. Starting in China, the virus has reached the United States of America with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, United Kingdom, Spain, India, Italy, and France. Even though it is not possible to eliminate the spread of the virus from the world or any other country, it might be possible to reduce its effect by decreasing the number of infected people. Implementing such policies needs a good understanding of the system’s dynamics, generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with complex and continuous feedback loops. As a result, we use agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise assisting decision-makers in managing the crisis through applying some policies such as social distancing, disease testing, contact tracing, home isolation, providing good emergency and hospitalization strategies, and preventing traveling would lead to reducing the infection rates. Based on imperial college modeling studies that prove increasing levels of interventions could slow down the spread of disease and infection cases as much as possible, and taking into account that social distancing policy is considered to be the most important factor that was recommended to follow. Our proposed model is designed to test if increasing the social distancing policies strictness can slow down the spread of disease significantly or not, and find out what is the required safe level of social distancing. So, the model was run six times, with six different percentages of social distancing with keeping the other parameters levels fixed for all experiments. The results of our study show that social distancing affects the spread of COVID19 significantly, where the spread of disease and infection rates decrease once social distancing procedures are implemented at higher levels. Also, the behavior space tool was used to run ten experiments with different levels of social distancing, which supported the previous results. We concluded that applying and increasing social distancing policy levels led to significantly reduced infection rates, which result in decreasing deaths. Both types of experiments prove that infection rates are reduced dramatically when the level of social distancing intervention is implemented between 80% to 100%.

scholarly journals Quantifying the Effects of Social Distancing on the Spread of COVID-19

2021 ◽  
Vol 18 (11) ◽  
pp. 5566
Author(s):  
Talal Daghriri ◽  
Ozlem Ozmen
Keyword(s):  
Complex Adaptive System ◽  
Linear Equations ◽  
The United States ◽  
The Other ◽  
Contact Tracing ◽  
Human Society ◽  
Infection Rates ◽  
Agent Based ◽  
Social Distancing ◽  
Modeling Studies

This paper studies the interplay between social distancing and the spread of the COVID-19 disease—a global pandemic that has affected most of the world’s population. Our goals are to (1) to observe the correlation between the strictness of social distancing policies and the spread of disease and (2) to determine the optimal adoption level of social distancing policies. The earliest instances of the virus were found in China, and the virus has reached the United States with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, the United Kingdom, Spain, India, Italy, and France. Although it is impossible to stop it, it is possible to slow down its spread to reduce its impact on the society and economy. Governments around the world have deployed various policies to reduce the virus spread in response to the pandemic. To assess the effectiveness of these policies, the system’s dynamics of the society needs to be analyzed, which is generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with continuous feedback loops. Because of the challenges with the other methods, we chose agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise in assisting decision-makers in managing the crisis by applying policies such as social distancing, disease testing, contact tracing, home isolation, emergency hospitalization, and travel prevention to reduce infection rates. Based on modeling studies conducted in Imperial College, increasing levels of interventions could slow the spread of disease and infection. We ran the model with six different percentages of social distancing while keeping the other parameters constant. The results show that social distancing affects the spread of COVID-19 significantly, in turn decreasing the spread of disease and infection rates when implemented at higher levels. We also validated these results by using the behavior space tool with ten experiments with varying social distancing levels. We conclude that applying and increasing social distancing policy levels leads to a significant reduction in infection spread and the number of deaths. Both experiments show that infection rates are reduced drastically when social distancing intervention is implemented between 80% to 100%.

scholarly journals COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society

2021 ◽  
pp. 0272989X2110030
Author(s):  
Serin Lee ◽  
Zelda B. Zabinsky ◽  
Judith N. Wasserheit ◽  
Stephen M. Kofsky ◽  
Shan Liu
Keyword(s):  
Large City ◽  
Face Mask ◽  
School Closure ◽  
Contact Tracing ◽  
The Novel ◽  
Agent Based ◽  
Social Distancing ◽  
Variable Compliance ◽  
Novel Coronavirus ◽  
Nonpharmaceutical Interventions

As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.

scholarly journals How effective was Newfoundland & Labrador’s travel ban to prevent the spread of COVID-19? An agent-based analysis

2021 ◽  
Author(s):  
Dionne M. Aleman ◽  
Benjamin Z. Tham ◽  
Sean J. Wagner ◽  
Justin Semelhago ◽  
Asghar Mohammadi ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Spatial Location ◽  
Contact Tracing ◽  
Infection Rates ◽  
Case Scenario ◽  
Agent Based ◽  
Individual Level ◽  
Transmission Rates ◽  
Imported Cases ◽  
The Uk

AbstractBackgroundTo prevent the spread of COVID-19 in Newfoundland & Labrador (NL), NL implemented a wide travel ban in May 2020. We estimate the effectiveness of this travel ban using a customized agent-based simulation (ABS).MethodsWe built an individual-level ABS to simulate the movements and behaviors of every member of the NL population, including arriving and departing travellers. The model considers individual properties (spatial location, age, comorbidities) and movements between environments, as well as age-based disease transmission with pre-symptomatic, symptomatic, and asymptomatic transmission rates. We examine low, medium, and high travel volume, traveller infection rates, and traveller quarantine compliance rates to determine the effect of travellers on COVID spread, and the ability of contact tracing to contain outbreaks.ResultsInfected travellers increased COVID cases by 2-52x (8-96x) times and peak hospitalizations by 2-49x (8-94x), with (without) contact tracing. Although contact tracing was highly effective at reducing spread, it was insufficient to stop outbreaks caused by travellers in even the best-case scenario, and the likelihood of exceeding contact tracing capacity was a concern in most scenarios. Quarantine compliance had only a small impact on COVID spread; travel volume and infection rate drove spread.InterpretationNL’s travel ban was likely a critically important intervention to prevent COVID spread. Even a small number of infected travellers can play a significant role in introducing new chains of transmission, resulting in exponential community spread and significant increases in hospitalizations, while outpacing contact tracing capabilities. With the presence of more transmissible variants, e.g., the UK variant, prevention of imported cases is even more critical.

scholarly journals Using the LIST model to Estimate the Effects of Contact Tracing on COVID-19 Endemic Equilibria in England and its Regions

2020 ◽  
Author(s):  
Rosalyn J. Moran ◽  
Alexander J. Billig ◽  
Maell Cullen ◽  
Adeel Razi ◽  
Jean Daunizeau ◽  
...  
Keyword(s):  
Endemic Equilibrium ◽  
Contact Tracing ◽  
Model Parameters ◽  
Infection Rates ◽  
Stable States ◽  
Medium Term ◽  
The Stability ◽  
Lower Death Rate ◽  
The Impact ◽  
List Model

AbstractGovernments across Europe are preparing for the emergence from lockdown, in phases, to prevent a resurgence in cases of COVID-19. Along with social distancing (SD) measures, contact tracing – find, track, trace and isolate (FTTI) policies are also being implemented. Here, we investigate FTTI policies in terms of their impact on the endemic equilibrium. We used a generative model – the dynamic causal ‘Location’, ‘Infection’, ‘Symptom’ and ‘Testing’ (LIST) model to identify testing, tracing, and quarantine requirements. We optimised LIST model parameters based on time series of daily reported cases and deaths of COVID-19 in England— and based upon reported cases in the nine regions of England and in all 150 upper tier local authorities. Using these optimised parameters, we forecasted infection rates and the impact of FTTI for each area—national, regional, and local. Predicting data from early June 2020, we find that under conditions of medium-term immunity, a ‘40%’ FTTI policy (or greater), could reach a distinct endemic equilibrium that produces a significantly lower death rate and a decrease in ICU occupancy. Considering regions of England in isolation, some regions could substantially reduce death rates with 20% efficacy. We characterise the accompanying endemic equilibria in terms of dynamical stability, observing bifurcation patterns whereby relatively small increases in FTTI efficacy result in stable states with reduced overall morbidity and mortality. These analyses suggest that FTTI will not only save lives, even if only partially effective, and could underwrite the stability of any endemic steady-state we manage to attain.

scholarly journals Smart Investment of Virus RNA Testing Resources to Enhance Covid-19 Mitigation

2020 ◽  
Author(s):  
Hossein Gorji ◽  
Markus Arnoldini ◽  
David F. Jenny ◽  
Wolf-Dietrich Hardt ◽  
Patrick Jenny
Keyword(s):  
Reproduction Number ◽  
Economic Cost ◽  
Virus Antigen ◽  
Mitigation Strategies ◽  
Contact Tracing ◽  
Model Parameters ◽  
Two Stage ◽  
Social Distancing ◽  
Virus Rna ◽  
High Prevalence

AbstractCovid-19 mitigation commonly involves contact tracing (CT) and social distancing. Due to its high economic toll and its impact on personal freedom, we need to ease social distancing and deploy alternative measures, while preventing further waves of infections. While reliable mass testing (for virus RNA) would require too many resources to be effective, CT, which focuses on isolating symptomatic cases and their contacts, has been implemented in many countries. However, the latter approach has reduced efficiency when high numbers of positive patients are burdening the tracing centers. Moreover, CT misses transmissions by asymptomatic cases. Therefore, its effect in reducing the reproduction number has a theoretical limit.To improve effectiveness of contact tracing, we propose to complement it with a strategy relying on identifying and testing symptom free subgroups with a significantly higher than average virus prevalence. We call this smart testing (ST). By testing everybody in these subgroups, in addition to symptomatic cases, also large fractions of pre- and asymptomatic persons can be identified, which enhances the effectiveness of contact tracing. High prevalence subgroups can be found in different ways, which are discussed in this paper. A particularly efficient way is via preselection using cheap and fast virus antigen tests, as proposed recently. Mathematical modeling quantifies the potential reduction of the reproduction number by such a two-stage ST strategy. In addition to global scenarios, also more realistic local applications of two-stage ST have been investigated, that is, within counties, institutions, schools, companies, etc., where members have internal as well as external contacts. All involved model parameters have been varied within realistic ranges and results are presented with probabilities. Even with the most pessimistic parameter set, these results suggest that the effect of two-stage ST on the reproduction number would clearly outweigh its economic cost. Two-stage ST is technically and logistically feasible. Further, it is locally effective also when only applied within small local subpopulations. Thereby, two-stage ST efficiently complements the portfolio of mitigation strategies, which allow easing social distancing without compromising public health.Single Sentence SummaryIdentification of high prevalence groups within subpopulations to enhance detection rate of Covid-19 infections by virus RNA tests combined with subsequent isolation.

scholarly journals Analytical solution of equivalent SEIR and agent-based model of COVID-19; showing the bounds of contact tracing

2020 ◽  
Author(s):  
Huseyin Tunc ◽  
Fatma Zehra Sari ◽  
Busra Nur Darendeli ◽  
Ramin Nashebi ◽  
Murat Sari ◽  
...  
Keyword(s):  
Growth Rate ◽  
Numerical Solutions ◽  
Transmission Rate ◽  
Contact Tracing ◽  
Analytical Relationship ◽  
Model Parameters ◽  
Agent Based Model ◽  
Agent Based ◽  
Hidden Parameters ◽  
Sensitive Parameters

AbstractMathematical models not only forecast the possible future but also is used to find hidden parameters of the COVID-19 pandemic. Numerical estimates can inform us of both goals. Still, the interdependencies of parameters stay obscure. Many numerical solutions have been proposed so far; however, the analytical relationship between the outbreak growth, decay and equilibrium are much less studied. In this study, we have employed both an equivalent agent-based model and a Susceptible-Exposed-Infected-Recovered (SEIR)-like model to prove that the growth rate can be determined analytically in terms of other model parameters, including contact tracing rate. We identify the most sensitive parameters as undocumented transmission rate and documentation ratio. Unfortunately, these are the parameters we have the least knowledge. We derived an identity that predicts the effectiveness of contact tracing in a country from observable parameters. We underline an unavoidable dilemma: that even in the case of high contact tracing, we cannot bring the outbreak to stalemate without applying substantial quarantine; however, some countries are benefiting from contact tracing. Besides, we have shown that the seemingly same parameters of the SEIR models and agent-based models are not equivalent. We propose a correction to bridge both models.

scholarly journals POINT CLOUD BASED 3D MODELS FOR AGENT BASED SIMULATIONS IN SOCIAL DISTANCING AND EVACUATION

2021 ◽  
Vol V-4-2021 ◽  
pp. 113-120
Author(s):  
S. Nikoohemat ◽  
P. Godoy ◽  
N. Valkhoff ◽  
M. Wouters - van Leeuwen ◽  
R. Voûte ◽  
...  
Keyword(s):  
Point Clouds ◽  
3D Models ◽  
Raw Material ◽  
Agent Based ◽  
Social Distancing ◽  
Information Models ◽  
Mapping System ◽  
Agent Based Simulations ◽  
Building Information ◽  
Mobile Mapping System

Abstract. Point clouds serve as the raw material for various models, such as Building Information Models (BIM). In this work, we investigate the reconstruction steps needed to create models that can be utilized directly for agent-based simulations. The input data for the reconstruction is captured with an indoor mobile mapping system. To show the prominence of this idea, we run social distancing and evacuation simulations on the reconstructed models. The simulations are run with multiple agents using a vision-based pedestrian model and A*-based path finding algorithm. The limitations of this approach are discussed. The video of the simulation is shared with the audience.Link to the video: https://youtu.be/r2D3IxXt7Ls

scholarly journals Modeling the systemic risks of COVID-19 on the wildland firefighting workforce

2021 ◽  
Author(s):  
Erin J Belval ◽  
Jude Bayham ◽  
Matthew P Thompson ◽  
Jacob Dilliott ◽  
Andrea G. Buchwald
Keyword(s):  
Wildland Fire ◽  
Control Measures ◽  
Infection Rates ◽  
Shared Resources ◽  
Wildfire Management ◽  
Agent Based ◽  
Social Distancing ◽  
Systemic Risks ◽  
The Us ◽  
Wildland Firefighting

AbstractWildfire management in the US relies on a complex nationwide network of shared resources that are allocated based on regional need. While this network bolsters firefighting capacity, it may also provide pathways for COVID-19 transmission between fire sites. We develop an agent-based model of COVID-19 built on historical wildland fire assignments using detailed dispatch data from 2016-2018, which form a network of firefighters dispersed spatially and temporally across the US. We use this model to simulate SARS-CoV-2 transmission under several intervention scenarios including vaccination and social distancing. We find vaccination and social distancing are effective at reducing transmission at fire incidents. Under a scenario assuming High Compliance with recommended mitigations (including vaccination), infection rates, number of outbreaks, and worker days missed are effectively negligible. Under a contrasting Low Compliance scenario, it is possible for cascading outbreaks to emerge leading to relatively high numbers of worker days missed. The current set of interventions in place successfully mitigate the risk of cascading infections between fires, and off-assignment infection may be the dominant infection concern in the 2021 season. COVID-19 control measures in place in wildfire management are highly beneficial at decreasing both the health and resource impacts of the ongoing pandemic.

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