Impact of Social Distancing Measures on Coronavirus Disease Healthcare Demand, Central Texas, USA

Wang, Xutong; Pasco, Remy; Du, Zhanwei; Petty, Michaela; Fox, Spencer J.; Galvani, Alison P.; Pignone, Michael; Johnston, S. Claiborne; Meyers, Lauren Ancel

doi:10.3201/eid2610.201702

Cited by 98 publications

(97 citation statements)

References 26 publications

(29 reference statements)

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“…This agrees with Wang et al [18] who use a stochastic age-and risk-structured susceptible-exposed-asymptomaticsymptomatic-hospitalized-recovered (SEAYHR) model to considered the effect of various levels of social distancing.…”

Section: Discussionsupporting

confidence: 89%

State-level tracking of COVID-19 in the United States

Unwin¹,

Mishra²,

Bradley³

et al. 2020

Preprint

122

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As of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly modelled the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We used changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. Nationally, we estimated 3.7% [3.4%-4.0%] of the population had been infected by 1st June 2020, with wide variation between states, and approximately 0.01% of the population was infectious. We also demonstrated that good model forecasts of deaths for the next 3 weeks with low error and good coverage of our credible intervals.

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Section: Discussionsupporting

confidence: 89%

State-level tracking of COVID-19 in the United States

Unwin¹,

Mishra²,

Bradley³

et al. 2020

Preprint

122

View full text Add to dashboard Cite

show abstract

“…We find that magnitude of the reductions in average mobility, and the resulting increases in residential mobility, are important in determining the size of reduction in R t . This agrees with Wang et al 19 who use a stochastic age-and risk-structured susceptible-exposed-asymptomatic-symptomatic-hospitalised-recovered (SEAYHR) model to considered the effect of various levels of social distancing. They found that social distancing measures, which reduced nonhousehold contacts by <50%, would not prevent a healthcare crisis and that only their 75% and 90% contact reduction scenarios were projected to enable metropolitan areas to remain within healthcare levels.…”

Section: Discussionsupporting

confidence: 89%

“…We would also like to thank David Joerg and Jacob Steinhardt for their comments through Open Review. This research was partly funded by the Imperial College COVID- 19…”

Section: Acknowledgementsmentioning

confidence: 99%

State-level tracking of COVID-19 in the United States

et al. 2020

View full text Add to dashboard Cite

As of 1st June 2020, the US Centres for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. We estimate that Rt was only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%–4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.

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“…By implementing this prevention and protection measure endorsed in a place of work, it will potentially improve to defeat this COVID-19 pandemic [38]. Governments must consequently rapidly take accurate and inclusive measures to prevent the spread of the epidemic [39].…”

Section: Prevention Measures and Covid-19mentioning

confidence: 99%

Correlation of COVID-19 Pandemic with Healthcare System Response and Prevention Measures in Saudi Arabia

Adly

AlJahdali

Garout

et al. 2020

IJERPH

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Background: The Saudi government has taken the decision to prevent the entrance of about 2.5 million international pilgrims seeking to perform hajj in order to protect the world from a catastrophic widespread of disease. Moreover, health systems in Saudi Arabia are offering free testing for residents whether Saudi and non-Saudi. Objective: This study aimed to evaluate the spread of COVID-19 associated with preventive measures taken in Saudi Arabia and to develop a detailed COVID-19 prevention strategy as a framework for the Saudi Arabia community. Methodology: Population size and age distributions among the country of Saudi Arabia were taken from the 2020 World Population Prospects. Contact patterns were measured using the Saudi Arabia Ministry of Health Statistical Annual Report. Conclusions: Our study demonstrates that performing screening tests as early as possible to facilitate the rapid detection of infected cases, fast treatment, and instant isolation for suspected cases is the most definitive rejoinder for public health. Moreover, our study revealed the significance of performing preventive measures in reducing infection and death rates around Saudi Arabia by 27%, while in other countries, it reduced the death rate ranging from 10–73%. This study provides an achievable strategy for prevention and early detection of COVID-19 spread.

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Impact of Social Distancing Measures on Coronavirus Disease Healthcare Demand, Central Texas, USA

Cited by 98 publications

References 26 publications

State-level tracking of COVID-19 in the United States

State-level tracking of COVID-19 in the United States

State-level tracking of COVID-19 in the United States

Correlation of COVID-19 Pandemic with Healthcare System Response and Prevention Measures in Saudi Arabia

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