Modeling the effects of intervention strategies on COVID-19 transmission dynamics

Kennedy, Deanna M.; Zambrano, Gustavo José; Wang, Yiyu; Neto, Osmar Pinto

doi:10.1016/j.jcv.2020.104440

Cited by 65 publications

(99 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mortality rates associated with COVID-19 may rise when hospitals become overwhelmed and have fewer resources to treat patients with life-threatening symptoms (https://www.hopkinsmedicine.org/coronavirus). This result provides additional support for the notion that personal protection is critical for maintaining control over the COVID-19 pandemic [15][16][17][18][19].…”

Section: Avoiding a Second Peakmentioning

confidence: 52%

See 1 more Smart Citation

Mathematical model of COVID-19 intervention scenarios for São Paulo- Brazil

Neto

Reis²,

Brizzi

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

An epidemiological compartmental model was used to simulate social distancing strategies to contain the COVID-19 pandemic and prevent a second wave in São Paulo, Brazil. Optimization using genetic algorithm was used to determine the optimal solutions. Our results suggest the best-case strategy for São Paulo is to maintain or increase the current magnitude of social distancing for at least 60 more days and increase the current levels of personal protection behaviors by a minimum of 10% (e.g., wearing facemasks, proper hand hygiene and avoid agglomeration). Followed by a long-term oscillatory level of social distancing with a stepping-down approach every 80 days over a period of two years with continued protective behavior.

show abstract

Section: Avoiding a Second Peakmentioning

confidence: 52%

“…high rates of testing have been emphasized to mitigating the spread of COVID-19 in addition to SD [15][16][17][18][19] .…”

Section: Controlling Rst Peakmentioning

confidence: 99%

Mathematical model of COVID-19 intervention scenarios for São Paulo- Brazil

Neto

Reis²,

Brizzi

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The duration to achieve zero new cases/deaths is similar to the rst scenario, but the total number of cases are very small ( Figure 2). Figure 3 shows the effect of each percent increasing in social distancing level from 85% to 90% on the cumulative number of cases according to equation (2) when the N 0 =1000 and WGR 0 =40 at time of intervention up to 8 weeks. It is clear that in a country with a population of 100 million 1% is equal to 1 million individuals being exposed to infection.…”

Section: Resultsmentioning

confidence: 99%

“…Various studies have been conducted in the eld of modeling intervention methods such as isolating patients and their contacts, personal protection (physical distancing, face masks, hand washing), and extensive or smart quarantine of cities to prevent COVID-19 and control outbreaks. But most of these models are complex and require many parameters [2][3][4][5][6][7] . In the present study, a simple experimental rule has been introduced that can predict the time required to control the pandemic, the number of cases in consecutive weeks, and the total number of cases based on the level of intervention (social distancing).…”

Section: Introductionmentioning

confidence: 99%

An Empirical Model to Estimate the Effect of Social Distance Levels on COVID-19 Outbreak

Tabesh

Saki

2020

Preprint

View full text Add to dashboard Cite

The implementation of social distancing measures for controlling the outbreak of coronavirus disease 2019 (COVID‑19) in different countries has not been at the same level due to differences in policies, economies, and cultures. Hence, the effect of this pandemic on different aspects of societies varies depending on several factors.In this study, we found that the speed of disease transmission is directly related to the odds of being exposed to the disease in the society, that is directly related to the level of practicing social distancing measures. We introduced the weekly growth ratio (WGR) index and proposed an experimental rule model to monitor the current level of social distancing and predict the levels of intervention so as to reduce the WGR and control the sequential peaks. Our model showed that the minimum level of practicing social distancing in the community should be 80% to control the first peak of the disease; but after controlling the first peak, maintaining 50% of social distancing policies continuously keeps the number of cases/deaths constant and prevents the occurrence of a new peak.

show abstract

“…A simple SIRD (where D denotes deaths) model [49], fit to data up to 30 May 2020 predicts that there will be 3.8 million infected and 244 420 deaths by 1 September 2020. A SEIR-type model with additional compartments including unsusceptible (to take into account social distancing), hospitalised and critical populations was proposed by Kennedy et al [26]. They took into account social distancing by removing individuals between the susceptible and unsusceptible compartments at a timedependent rate.…”

Section: Epidemiology and Infectionmentioning

confidence: 99%

A predictive model for Covid-19 spread – with application to eight US states and how to end the pandemic

2020

View full text Add to dashboard Cite

A compartmental model is proposed to predict the coronavirus 2019 (Covid-19) spread. It considers: detected and undetected infected populations, social sequestration, release from sequestration, plus reinfection. This model, consisting of seven coupled equations, has eight coefficients which are evaluated by fitting data for eight US states that make up 43% of the US population. The evolution of Covid-19 is fairly similar among the states: variations in contact and undetected recovery rates remain below 5%; however, variations are larger in recovery rate, death rate, reinfection rate, sequestration adherence and release rate from sequestration. Projections based on the current situation indicate that Covid-19 will become endemic. If lockdowns had been kept in place, the number of deaths would most likely have been significantly lower in states that opened up. Additionally, we predict that decreasing contact rate by 10%, or increasing testing by approximately 15%, or doubling lockdown compliance (from the current ~15% to ~30%) will eradicate infections in Texas within a year. Extending our fits for all of the US states, we predict about 11 million total infections (including undetected), and 8 million cumulative confirmed cases by 1 November 2020.

show abstract

Modeling the effects of intervention strategies on COVID-19 transmission dynamics

Cited by 65 publications

References 11 publications

Mathematical model of COVID-19 intervention scenarios for São Paulo- Brazil

Mathematical model of COVID-19 intervention scenarios for São Paulo- Brazil

An Empirical Model to Estimate the Effect of Social Distance Levels on COVID-19 Outbreak

A predictive model for Covid-19 spread – with application to eight US states and how to end the pandemic

Contact Info

Product

Resources

About