Transmission dynamics reveal the impracticality of COVID-19 herd immunity strategies

Brett, Tobias; Rohani, Pejman

doi:10.1073/pnas.2008087117

Cited by 90 publications

(93 citation statements)

References 38 publications

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“…Several articles adopted SEIR-like models with age-structures to study the impact of different NPIs in countries [23] , [93] , [96] , [97] , [98] , [101] , reopening scenarios [95] , [103] , investigate herd immunity [94] , [104] , and consider particular contexts such as African countries [99] , [100] .…”

Section: Epidemic Modelsmentioning

confidence: 99%

“…Ref. [94] adopts an SEIR model with age structure and contact matrices fitted to the UK to investigate the feasibility of dynamic mitigation strategies that reduce the reproductive number in such a way to avoid overflowing the healthcare system. In other words, when the infections are decreasing some measures might be lifted until certain warning levels are reached.…”

Section: Epidemic Modelsmentioning

confidence: 99%

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Non-pharmaceutical interventions during the COVID-19 pandemic: A review

2021

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Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travels bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 348 articles written by more than 2518 of authors in the first 12 months of the emergency. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities.

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Section: Epidemic Modelsmentioning

confidence: 99%

Section: Epidemic Modelsmentioning

confidence: 99%

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

2021

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“…We use approximate Bayesian computation based on sequential Monte Carlo sampling (ABS-SMC) approach to estimate and 19 , 20 . Other parameters ( 16 , 17 , and 18 ) are taken from the literature.…”

Section: Resultsmentioning

confidence: 99%

“… States Type Transition Transition rate ( ) Inducer Source S (Susceptible) E (Exposed) I (Infected) C (Confirmed) (Quarantined-Susceptible) (Quarantined-Exposed) (Quarantined-Infected) R (Removed) Edge-based here, ; = average degree; = average weight Neighbors of state I in the contact-layer is estimated here, , we take Neighbors of state C in the tracing-layer Model Nodal 16 , 17 – – Model – Estimated – …”

Section: Methodsmentioning

confidence: 99%

Contact tracing evaluation for COVID-19 transmission in the different movement levels of a rural college town in the USA

Moon

Scoglio

2021

Sci Rep

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Contact tracing can play a key role in controlling human-to-human transmission of a highly contagious disease such as COVID-19. We investigate the benefits and costs of contact tracing in the COVID-19 transmission. We estimate two unknown epidemic model parameters (basic reproductive number $$R_0$$ R 0 and confirmed rate $$\delta _2$$ δ 2 ) by using confirmed case data. We model contact tracing in a two-layer network model. The two-layer network is composed by the contact network in the first layer and the tracing network in the second layer. In terms of benefits, simulation results show that increasing the fraction of traced contacts decreases the size of the epidemic. For example, tracing $$25\%$$ 25 % of the contacts is enough for any reopening scenario to reduce the number of confirmed cases by half. Considering the act of quarantining susceptible households as the contact tracing cost, we have observed an interesting phenomenon. The number of quarantined susceptible people increases with the increase of tracing because each individual confirmed case is mentioning more contacts. However, after reaching a maximum point, the number of quarantined susceptible people starts to decrease with the increase of tracing because the increment of the mentioned contacts is balanced by a reduced number of confirmed cases. The goal of this research is to assess the effectiveness of contact tracing for the containment of COVID-19 spreading in the different movement levels of a rural college town in the USA. Our research model is designed to be flexible and therefore, can be used to other geographic locations.

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“…A distinctive feature of the policy response to the management of the COVID-19 pandemic worldwide has been the role played by epidemiological modelling for evaluating the use of behavioural interventions exclusively for controlling epidemic outbreaks in populations [5][6][7][8][9][10][11][12] These mathematical models, based primarily thus far on extensions to the standard SEIR compartmental model, have enabled predictions of the course of the epidemic to warn policy-makers of the gravity of potential impacts, as well as help them in making comparisons of the various social measures proposed for suppressing viral transmission in exposed communities. For example, these tools have played critical roles for evaluating the comparative effects of locking down communities versus allowing a portion of the population to be exposed and develop immunity as alternate strategies for containing both rst and subsequent epidemic waves 13,14 .…”

Section: Introductionmentioning

confidence: 99%

Iterative near-term forecasting of the transmission and management of SARS-CoV-2/COVID-19 using social interventions at the county-level in the United States

Newcomb¹,

Smith²,

Donohue³

et al. 2020

Preprint

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The control of the initial outbreak and spread of SARS-CoV-2/COVID-19 by the implementation of unprecedented population-wide non-pharmaceutical mitigation measures has led to remarkable success in dampening the pandemic globally. With many countries easing or beginning to lift these measures to restart activities presently, concern is growing regarding the impacts that such reopening of societies could have on the subsequent transmission of the virus. While mathematical models of COVID-19 transmission have played important roles in evaluating the general population-level impacts of these measures for curbing virus transmission, a key need is for models that are able to effectively capture the effects of the spatial and social heterogeneities that drive the epidemic dynamics observed at the local community level. Iterative near-term forecasting that uses new incoming epidemiological and social behavioural data to sequentially update locally-applicable transmission models can overcome this gap, potentially leading to better predictions and intervention actions. Here, we present the development of one such data-driven iterative modelling tool based on publically-available data and an extended SEIR model for forecasting SARS-CoV-2 at the county level in the United States, and demonstrate, using data from the state of Florida, how this tool can be used to explore the outcomes of the social measures proposed for containing the course of the pandemic as a result of easing the initially imposed lockdown in the state. We provide comprehensive results showing the use of the locally identified models for accessing the impacts and societal tradeoffs of using specific strategies involving movement restriction, social distancing and mass testing, and conclude that while it is absolutely vital to continue with these measures over the near-term and likely to the end of March 2021 in all counties for containing the ongoing pandemic before less socially-disruptive vaccination strategies come into play, it could be possible to lift the more disruptive movement restriction/social distancing measures by end of December 2020 if these are accompanied by widespread testing and contact tracing. Our findings further show that such intensified social interventions could potentially also bring about the control of the epidemic in low and some medium incidence counties first, supporting the development and deployment of a geographically-phased approach to reopening the economy of Florida. We have made our data-driven forecasting system publicly available for policymakers and health officials to use in their own locales, with the hope that a more efficient coordinated strategy for controlling SARS-CoV-2 state-wide, based on effective control of viral transmission at the county level, can be developed and successfully implemented.

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Transmission dynamics reveal the impracticality of COVID-19 herd immunity strategies

Cited by 90 publications

References 38 publications

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

Contact tracing evaluation for COVID-19 transmission in the different movement levels of a rural college town in the USA

Iterative near-term forecasting of the transmission and management of SARS-CoV-2/COVID-19 using social interventions at the county-level in the United States

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