Evaluating COVID-19 Lockdown and Reopening Scenarios For Georgia, Florida, and Mississippi

Killian, Jackson A.; Charpignon, Marie-Laure; Wilder, Bryan; Perrault, Andrew; Tambe, Milind; Majumder, Maimuna S.

doi:10.2139/ssrn.3598744

Cited by 11 publications

(11 citation statements)

References 10 publications

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“…Borg et al showed an average decrease of foot traffic in all businesses during the pandemic during pandemic 10 . Experts have cautioned of the potential resurgence of the virus if we open our economy prematurely [11][12][13][14][15][16] . However, business traffic patterns and the risk transmission after state reopening are still unknown.…”

Section: Introductionmentioning

confidence: 99%

Reopening businesses and risk of COVID-19 transmission

et al. 2021

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The true risk of a COVID-19 resurgence as states reopen businesses is unknown. In this paper, we used anonymized cell-phone data to quantify the potential risk of COVID-19 transmission in business establishments by building a Business Risk Index that measures transmission risk over time. The index was built using two metrics, visits per square foot and the average duration of visits, to account for both density of visits and length of time visitors linger in the business. We analyzed trends in traffic patterns to 1,272,260 businesses across eight states from January 2020 to June 2020. We found that potentially risky traffic behaviors at businesses decreased by 30% by April. Since the end of April, the risk index has been increasing as states reopen. There are some notable differences in trends across states and industries. Finally, we showed that the time series of the average Business Risk Index is useful for forecasting future COVID-19 cases at the county-level (P < 0.001). We found that an increase in a county’s average Business Risk Index is associated with an increase in positive COVID-19 cases in 1 week (IRR: 1.16, 95% CI: (1.1–1.26)). Our risk index provides a way for policymakers and hospital decision-makers to monitor the potential risk of COVID-19 transmission from businesses based on the frequency and density of visits to businesses. This can serve as an important metric as states monitor and evaluate their reopening strategies.

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Section: Introductionmentioning

confidence: 99%

Reopening businesses and risk of COVID-19 transmission

et al. 2021

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“…The modified SIR system (LVSIR) has a stable equilibrium point, an “energy” conservation property, and exhibits bounded cyclic behaviour similar to an LV system. This mapping permits a theoretical analysis of the control problem supporting some of the recent simulation-based studies [16, 29] that point to the benefits of periodic interventions. We use a control-Lyapunov approach to design adaptive control policies (CoSIR) to nudge the SIR model to the desired equilibrium that permits ready extensions to richer compartmental models.…”

mentioning

confidence: 56%

CoSIR: Optimal control of SIR epidemic dynamics by mapping to Lotka-Volterra System

Maheshwari¹,

Shetty²,

Bannur³

et al. 2020

Preprint

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Shaping an epidemic with an adaptive contact restriction policy that balances the disease and socioeconomic impact has been the holy grail during the COVID-19 pandemic. Most of the existing work on epidemiological models [40, 11, 17, 7] focuses on scenario-based forecasting via simulation but techniques for explicit control of epidemics via an analytical framework are largely missing. In this paper, we consider the problem of determining the optimal policy for transmission control assuming SIR dynamics [28], which is the most widely used epidemiological paradigm. We first demonstrate that the SIR model with infectious patients and susceptible contacts (i.e., product of transmission rate and susceptible population) interpreted as predators and prey respectively reduces to a Lotka-Volterra (LV) predator-prey model [8]. The modified SIR system (LVSIR) has a stable equilibrium point, an “energy” conservation property, and exhibits bounded cyclic behaviour similar to an LV system. This mapping permits a theoretical analysis of the control problem supporting some of the recent simulation-based studies [16, 29] that point to the benefits of periodic interventions. We use a control-Lyapunov approach to design adaptive control policies (CoSIR) to nudge the SIR model to the desired equilibrium that permits ready extensions to richer compartmental models. We also describe a practical implementation of this transmission control method by approximating the ideal control with a finite, but a time-varying set of restriction levels and provide simulation results to demonstrate its efficacy.

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“…The model assessed that imposing lockdown one week earlier in all the three states could have saved hundreds of lives from COVID-19. However, to reopen the economy even with a limited capacity, it is required to reduce the population contact down to 20-25% and implement strict social distancing measures along with the use of personal protective equipment [49] . Besides, a robust testing capacity would help the policymakers to estimate more precise reopening dates and it would be beneficial to detect and isolate asymptomatic carriers in a quicker manner after reopening.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Restarting the US economy could cause a rise in Coronavirus infections and the COVID-19 related mortality rate [48], and therefore it is necessary to manage reopening risks with due consideration for socioeconomic factors. A study using an agent-based Susceptible, Exposed, Infected, and Recovered (SEIR) model evaluated the impacts of alternative lockdown measures and reopening scenarios on coronavirus cases and deaths in Florida, Georgia, and Mississippi [49]. The model assessed that imposing lockdown one week earlier in all the three states could have saved hundreds of lives from COVID-19.…”

Section: Reopening Risksmentioning

confidence: 99%

Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data

Rahman

Ali

et al. 2021

Heliyon

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Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel patterns due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 50 States of the US and Washington D.C, the capital city of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of family members and high income are less interested in reopening the economy. The accuracy of the model is reasonable (i.e., the model can correctly classify 56.18% of the sentiments). The Pearson chi-squared test indicates that this model has high goodness-of-fit. This study provides clear insights for public and corporate policymakers on potential areas to allocate resources, and directional guidance on potential policy options they can undertake to improve socioeconomic conditions, to mitigate the impact of pandemic in the current situation, and in the future as well.

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Evaluating COVID-19 Lockdown and Reopening Scenarios For Georgia, Florida, and Mississippi

Cited by 11 publications

References 10 publications

Reopening businesses and risk of COVID-19 transmission

Reopening businesses and risk of COVID-19 transmission

CoSIR: Optimal control of SIR epidemic dynamics by mapping to Lotka-Volterra System

Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data

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