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 Social Responsibilities in COVID-19: Pandemic

2021 ◽  
pp. 149-153
Author(s):  
Mayur B. Wanjari ◽  
Deeplata Mendhe ◽  
Pratibha Wankhede ◽  
Sagar Alwadkar
Keyword(s):  
Death Rate ◽  
Respiratory Infections ◽  
The United States ◽  
Social Distancing ◽  
Social Responsibilities ◽  
The Social ◽  
The World ◽  
Coronavirus Infection ◽  
Novel Coronavirus ◽  
Day By Day

Recent coronavirus discovered causes the coronavirus infection COVID-19 is also an infectious disease known to cause severe respiratory infections. This most recent virus and infection were unidentified until the epidemic in Wuhan in December 2019, China. Coronavirus has spread around the world and been declared a pandemic by the WHO. The disease has infected several nations, including Italy, Spain, and the United States, with brutality as the death rate rises day by day. The illness may transmit to cough or sneezes via small droplets. Therefore, social distancing is the only way to prevent the transmission as There is no vaccine available for prevention from thecoronavirus. One can reduce the chances of being infected by taking some social distancing measures which will reduce COVID-19 transmission. In the pandemic COVID-19, every individual’s responsibility is to follow all the social distancing measures, to follow the lockdown without being casual about the disease, to save our self, our family, community, and nation from novel coronavirus.

scholarly journals A path out of COVID-19 quarantine: an analysis of policy scenarios

2020 ◽  
Author(s):  
Lia Humphrey ◽  
Edward W. Thommes ◽  
Roie Fields ◽  
Naseem Hakim ◽  
Ayman Chit ◽  
...  
Keyword(s):  
Latent Infection ◽  
Average Rate ◽  
The United States ◽  
Contact Tracing ◽  
Brute Force ◽  
Social Distancing ◽  
Testing Rate ◽  
Universal Testing ◽  
The World ◽  
Brute Force Approach

In this work we present an analysis of the two major strategies currently implemented around the world in the fight against COVID-19: Social distancing & shelter-in-place measures to protect the susceptible, and testing & contact-tracing to identify, isolate and treat the infected. The majority of countries have principally relied on the former; we consider the examples of Italy, Canada and the United States. By fitting a disease transmission model to daily case report data, we infer that in each of the three countries, the current level of national shutdown is equivalent to about half the population being under quarantine. We demonstrate that in the absence of other measures, scaling back social distancing in such a way as to prevent a second wave will take prohibitively long. In contrast, South Korea, a country that has managed to control and suppress its outbreak principally through mass testing and contact tracing, and has only instated a partial shutdown. For all four countries, we estimate the level of testing which would be required to allow a complete exit from shutdown and a full lifting of social distancing measures, without a resurgence of COVID-19. We find that a “brute-force” approach of untargeted universal testing requires an average testing rate of once every 36 to 48 hours for every individual, depending on the country. If testing is combined with contact tracing, and/or if tests are able to identify latent infection, then an average rate of once every 4 to 5 days is sufficient.Significance StatementWe demonstrate how current quarantine measures can be lifted after the current pandemic wave by large-scale, frequent-testing and contact tracing on the remaining susceptible populations. We present an analysis of the two major strategies currently implemented around the world against COVID-19: Social distancing & shelter-in-place measures to protect the susceptible, and testing & contact-tracing to identify, isolate and treat the infected. We find that a “brute-force” approach of untargeted universal testing requires an average testing rate of once every 36 to 48 hours for every individual, depending on the country. If testing is combined with contact tracing, and/or if tests are able to identify latent infection, then an average rate of once every 4 to 5 days is sufficient.

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 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 Modeling Behavioural Evolution as Social Predictor for the Coronavirus Contagion and Immunization in Nigeria

2020 ◽  
Vol 3 (2) ◽  
pp. 135-144
Author(s):  
Arnold Adimabua Ojugo ◽  
Andrew Okonji Eboka
Keyword(s):  
United States Of America ◽  
The United States ◽  
Health Conditions ◽  
World Population ◽  
The Novel ◽  
Death Toll ◽  
The Social ◽  
The World ◽  
The Rich ◽  
Novel Coronavirus

Since the outbreak of the novel coronavirus (covid-19) pandemic from China in 2019, it has left the world leaders in great confusing due to its fast-paced propagation and spread that has left infected a world population of over Eleven Million persons with over five hundred and thirty four thousand deaths and counting with the United States of America, Brazil, Russia, India and Peru in the lead on these death toll. The pandemic whose increased mortality rate is targeted at ‘aged’ citizens, patients with low immunology as well as patients with chronic diseases and underlying health conditions. Study models covid-19 pandemic via a susceptible-infect-remove actor-based graph, with covid-19 virus as the innovation diffused within the social graph. We measure the rich connective patterns of the actor-based graph, and explore personal feats as they influence other nodes to adopt or reject an innovation. Results shows current triggers (lifting of inter-intra state migration bans) and shocks (exposure to covid-19 by migrants) will lead to late widespread majority adoption of 23.8-percent. At this, the death toll will climb from between 4.43-to-5.61-percent to over 12%.

scholarly journals The Effect of Vaccination Rates on the Infection of COVID-19 under the Vaccination Rate below the Herd Immunity Threshold

2021 ◽  
Vol 18 (14) ◽  
pp. 7491
Author(s):  
Yi-Tui Chen
Keyword(s):  
Infection Rate ◽  
Herd Immunity ◽  
The United States ◽  
Vaccination Rate ◽  
Infection Rates ◽  
Vaccination Rates ◽  
The United Kingdom ◽  
The World ◽  
Almost All ◽  
The Impact

Although vaccination is carried out worldwide, the vaccination rate varies greatly. As of 24 May 2021, in some countries, the proportion of the population fully vaccinated against COVID-19 has exceeded 50%, but in many countries, this proportion is still very low, less than 1%. This article aims to explore the impact of vaccination on the spread of the COVID-19 pandemic. As the herd immunity of almost all countries in the world has not been reached, several countries were selected as sample cases by employing the following criteria: more than 60 vaccine doses per 100 people and a population of more than one million people. In the end, a total of eight countries/regions were selected, including Israel, the UAE, Chile, the United Kingdom, the United States, Hungary, and Qatar. The results find that vaccination has a major impact on reducing infection rates in all countries. However, the infection rate after vaccination showed two trends. One is an inverted U-shaped trend, and the other is an L-shaped trend. For those countries with an inverted U-shaped trend, the infection rate begins to decline when the vaccination rate reaches 1.46–50.91 doses per 100 people.

scholarly journals Exploring the effectiveness of a COVID-19 contact tracing app using an agent-based model

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jonatan Almagor ◽  
Stefano Picascia
Keyword(s):  
High Rate ◽  
Contact Tracing ◽  
Agent Based Model ◽  
Agent Based ◽  
The Social ◽  
Behavioural Factors ◽  
The Impact ◽  
Different Levels ◽  
User Rate

AbstractA contact-tracing strategy has been deemed necessary to contain the spread of COVID-19 following the relaxation of lockdown measures. Using an agent-based model, we explore one of the technology-based strategies proposed, a contact-tracing smartphone app. The model simulates the spread of COVID-19 in a population of agents on an urban scale. Agents are heterogeneous in their characteristics and are linked in a multi-layered network representing the social structure—including households, friendships, employment and schools. We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the impact on the epidemic. Results suggest that a contact tracing app can contribute substantially to reducing infection rates in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing, a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive. This points to the crucial role of an efficient testing policy and the necessity to upscale testing capacity.

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 Engaging for Puerto Rico: #RickyRenuncia (and #RickySeQueda) during El Verano del 19 and digital identities

2019 ◽  
Author(s):  
Danilo T Perez-Rivera ◽  
Christopher Torres Lugo ◽  
Alexis R Santos-Lozada
Keyword(s):  
Social Media ◽  
Puerto Rico ◽  
Social Activism ◽  
The United States ◽  
The Political ◽  
Digital Revolution ◽  
The People ◽  
The Social ◽  
The World ◽  
Puerto Rican Diaspora

Between July 13-24, 2019 the people of Puerto Rico took the streets after a series of corruption scandals shocked the political establishment. The social uprising resulted in the ousting of the Governor of Puerto Rico (Dr. Ricardo Rosselló, Ricky), the resignation of the majority of his staff something unprecedented in the history of Puerto Rico; this period has been called El Verano del 19 (Summer of 19). Social media played a crucial role in both the organization and dissemination of the protests, marches, and other activities that occurred within this period. Puerto Ricans in the island and around the world engaged in this social movement through the digital revolution mainly under the hashtag #RickyRenuncia (Ricky Resign), with a small counter movement under the hashtag #RickySeQueda (Ricky will stay). The purpose of this study is to illustrate the magnitude and grass roots nature of the political movement’s social media presence, as well as their characteristics of the population of both movements and their structures. We found that #RickyRenuncia was used approximately one million times in the period of analysis while #RickySeQueda barely reached 6,000 tweets. Particularly, the pervasiveness of cliques in the #RickySeQueda show concentrations of authority dedicated to its propagation, whilst the #RickyRenuncia propagation was much more distributed and decentralized with little to no interaction between significant nodes of authority. Noteworthy was the role of the Puerto Rican diaspora in the United States of America and around the world, contributing close to 40% of all geo-located tweets. Finally, we found that the Twitter followers of the former governor had indicators of being composed of two distinct populations: 1) those active in social media and 2) those who follow the account but who are not active participants of the social network. We discuss the implications of these findings on the interpretation of emergence, structure and dissemination of social activism and countermovement to these activities in the context of Puerto Rico.

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