“Stay nearby or get checked”: A Covid-19 control strategy

Brethouwer, Jan-Tino; Rijt, Arnout van de; Lindelauf, Roy; Fokkink, Robbert

doi:10.1016/j.idm.2020.10.013

Cited by 15 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…What we have not undertaken here, is to investigate or advocate any particular forms of changed behaviours that might be needed by those released, although understanding them can allow parameters (such as transmission rates) to be adjusted in our framework. Additional measures proposed include: reopening local connections before connecting cities further apart 26 , differential release times based on age 27–29 , on-going social distancing 9,30,31 , contact tracing using mobile applications 32 and behaviour monitoring 33 , case-finding 34 and cyclic schedules (e.g. short working weeks) 35,36 .…”

Section: Discussionmentioning

confidence: 99%

How and when to end the COVID-19 lockdown: an optimisation approach

Rawson

Brewer

Veltcheva

et al. 2020

Preprint

View full text Add to dashboard Cite

10Countries around the world are in a state of lockdown to help limit the spread of SARS-CoV-2. However, as the number of new daily confirmed cases begins to decrease, governments must decide how to release their populations from quarantine as efficiently as possible without overwhelming their health services. We applied an optimal control framework to an adapted Susceptible-Exposure-Infection-Recovery (SEIR) model framework to investigate the efficacy of two potential lockdown release strategies, focusing on the UK population as a test case. To limit recurrent spread, we find that ending quarantine for the entire population simultaneously is a high-risk strategy, and that a gradual re-integration approach would be more reliable.Furthermore, to increase the number of people that can be first released, lockdown should not be ended until the number of new daily confirmed cases reaches a sufficiently low threshold.We model a gradual release strategy by allowing different fractions of those in lockdown to re-enter the working non-quarantined population. Mathematical optimisation methods, combined with our adapted SEIR model, determine how to maximise those working while preventing the health service from being overwhelmed. The optimal strategy is broadly found to be to release approximately half the population two-to-four weeks from the end of an initial infection peak, then wait another three-to-four months to allow for a second peak before releasing everyone else. We also modelled an "on-off" strategy, of releasing everyone, but re-establishing lockdown if infections become too high. We conclude that the worst-case scenario of a gradual release is more manageable than the worst-case scenario of an on-off strategy, and caution against lockdown-release strategies based on a threshold-dependent on-off mechanism.The two quantities most critical in determining the optimal solution are transmission rate and the recovery rate, where the latter is defined as the fraction of infected people in any given day that then become classed as recovered. We suggest that the accurate identification of these values is of particular importance to the ongoing monitoring of the pandemic.

show abstract

Section: Discussionmentioning

confidence: 99%

How and when to end the COVID-19 lockdown: an optimisation approach

Rawson

Brewer

Veltcheva

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The issue with coming out of lockdown or relaxing scenario in between different periods of lockdown is an unexpected surge of transmission waves. [13][14][15] It challenges the medical infrastructure with overburdened staff and stressed supplies; and this seems to be true for almost every country. [16][17][18] The volatility analysis of wold economy suggests there will be a deep depression around the world in the year 2020; 3,19,20 and WHO recently stated the virus will be around for a while.…”

Section: Discussionmentioning

confidence: 99%

Cumulative Active and Recovery Rates Based Criterion for Gradual Lockdown Exit: A Global Observation of SARS Cov-2 Management

Raje¹,

Bajaj

Chakraborty³

et al. 2020

Preprint

View full text Add to dashboard Cite

On March 11, 2020 the World Health Organization (WHO) had declared SARS-CoV-2 pandemic; and at present there are over 6 million cases across the globe. Based on the interim guidelines of WHO, most of the countries opted for social distancing with lockdown as the only way to control the pandemic. This led to Manufacturing shut down, which acted as a spanner in the wheel for international supply chain leading to pressure on governments to review the protocols of the lockdown. We studied epidemiological parameters for 18 countries and obtained crossover time point referring to cumulative case active and case recovery rates and the time point for the peak positive confirmation rate in a time window of 92 days; and linked with the respective governmental decisions. For countries awaiting crossover, time series non-linear models could be used for predicting the crossover point. A sample study was carried out for India. The median time for reaching crossover for 12 countries was 37 days, while peak positive confirmation rate was 30 days after their first intervention. These countries enforced strict lockdown regulations and have shown constant improvement in their recovery rate even after crossover time point. A phase wise relaxation of lockdown is evident after crossover point in most of these countries. The crossover time point with the subsequent increasing recovery rate can be a strategy for lockdown relaxation as evident from the experiences of few countries. Also, we propose a criterion based on cumulative recovery and fatality rate for micro-management of lockdown.

show abstract

“…e advantage of this strategy is that it can better preserve the integrity of the network during the control of epidemics on the scale-free network [83]. e result of Brethouwer et al [136] showed that reductions of long-distance transmission are highly efficient for curbing the spread of COVID-19.…”

Section: Control Edge Strategymentioning

confidence: 99%

A Literature Review of Social Network Analysis in Epidemic Prevention and Control

Wang

Zhang

et al. 2021

Complexity

View full text Add to dashboard Cite

Studying the structure and evolution characteristics of social networks is of great significance in assessing and controlling the outbreak of infectious diseases. Therefore, it is necessary to find research trends in this field. In this study, 1,752 documents (2001–2020) related to the relationship between the social network and epidemic published from Scopus, WOS (Web of Science), and CNKI (China National Knowledge Infrastructure) databases were studied to provide a more comprehensive overview of the frontiers of research in this field, including epidemics in social networks, spread of disease, influence of different factors on the spread of the epidemic, prevention and control strategies of the epidemic, and comparison of various strategies. Besides, several new research directions in this field worthy of attention of researchers are discussed.

show abstract

“Stay nearby or get checked”: A Covid-19 control strategy

Cited by 15 publications

References 27 publications

How and when to end the COVID-19 lockdown: an optimisation approach

How and when to end the COVID-19 lockdown: an optimisation approach

Cumulative Active and Recovery Rates Based Criterion for Gradual Lockdown Exit: A Global Observation of SARS Cov-2 Management

A Literature Review of Social Network Analysis in Epidemic Prevention and Control

Contact Info

Product

Resources

About