About: Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries

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About: Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries
Creator	Althaus, Christian Reidpath, Daniel Abbas Bhuiya, · Chowdhury, Rajiv Daisy Okonofua, · Franco, Oscar Goh, Gabriel Gonzalez-Jaramillo, Nathalia Gonzalez-Jaramillo, Valentina Heng, Kevin Md, · Ochoa-Rosales, Carolina Prathapan, Shamini Rahman, Shajedur Sara, ·
Source	Medline; PMC
abstract	To date, non-pharmacological interventions (NPI) have been the mainstay for controlling the coronavirus disease-2019 (COVID-19) pandemic. While NPIs are effective in preventing health systems overload, these long-term measures are likely to have significant adverse economic consequences. Therefore, many countries are currently considering to lift the NPIs—increasing the likelihood of disease resurgence. In this regard, dynamic NPIs, with intervals of relaxed social distancing, may provide a more suitable alternative. However, the ideal frequency and duration of intermittent NPIs, and the ideal “break” when interventions can be temporarily relaxed, remain uncertain, especially in resource-poor settings. We employed a multivariate prediction model, based on up-to-date transmission and clinical parameters, to simulate outbreak trajectories in 16 countries, from diverse regions and economic categories. In each country, we then modelled the impacts on intensive care unit (ICU) admissions and deaths over an 18-month period for following scenarios: (1) no intervention, (2) consecutive cycles of mitigation measures followed by a relaxation period, and (3) consecutive cycles of suppression measures followed by a relaxation period. We defined these dynamic interventions based on reduction of the mean reproduction number during each cycle, assuming a basic reproduction number (R(0)) of 2.2 for no intervention, and subsequent effective reproduction numbers (R) of 0.8 and 0.5 for illustrative dynamic mitigation and suppression interventions, respectively. We found that dynamic cycles of 50-day mitigation followed by a 30-day relaxation reduced transmission, however, were unsuccessful in lowering ICU hospitalizations below manageable limits. By contrast, dynamic cycles of 50-day suppression followed by a 30-day relaxation kept the ICU demands below the national capacities. Additionally, we estimated that a significant number of new infections and deaths, especially in resource-poor countries, would be averted if these dynamic suppression measures were kept in place over an 18-month period. This multi-country analysis demonstrates that intermittent reductions of R below 1 through a potential combination of suppression interventions and relaxation can be an effective strategy for COVID-19 pandemic control. Such a “schedule” of social distancing might be particularly relevant to low-income countries, where a single, prolonged suppression intervention is unsustainable. Efficient implementation of dynamic suppression interventions, therefore, confers a pragmatic option to: (1) prevent critical care overload and deaths, (2) gain time to develop preventive and clinical measures, and (3) reduce economic hardship globally. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10654-020-00649-w) contains supplementary material, which is available to authorized users.
has issue date	2020-05-19(xsd:dateTime)
bibo:doi	10.1007/s10654-020-00649-w
bibo:pmid	32430840
has license	cc-by
sha1sum (hex)	4fc427431c038f66a1c3a5b619dad279c92d4033
schema:url	https://doi.org/10.1007/s10654-020-00649-w
resource representing a document's title	Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries
has PubMed Central identifier	PMC7237242
has PubMed identifier	32430840
schema:publication	Eur J Epidemiol
resource representing a document's body	covid:4fc427431c038f66a1c3a5b619dad279c92d4033#body_text
is schema:about of	named entity 'settings' named entity 'social distancing' named entity 'capacities' named entity 'intensive care unit (ICU)' named entity 'resurgence' named entity 'Strategies' named entity 'basic reproduction number' named entity 'based' named entity 'trajectories' named entity 'period' named entity 'measures' named entity 'social distancing' named entity 'modelled' named entity 'Such' named entity 'outbreak' named entity 'preventive' named entity 'disease' named entity 'based' named entity 'critical care' named entity 'This' named entity 'overload' named entity 'break' named entity 'regions' named entity 'hardship' named entity 'disease' named entity 'day' named entity 'numbers' named entity 'social distancing' named entity 'coronavirus disease' named entity 'COVID-19 pandemic' named entity 'contrast' named entity 'relaxed' named entity 'measures' named entity 'global healthcare' named entity 'Mexico' named entity 'virus' named entity 'infection' named entity 'vaccines' named entity 'ICU' named entity 'dynamic model' named entity 'Western Europe' named entity 'epidemic' named entity 'Nigeria' named entity 'Open Access' named entity 'COVID-19' named entity 'ICU' named entity 'Horizon 2020' named entity 'compartmental model' named entity 'health systems' named entity 'economic status' named entity 'Demographic' named entity 'physical distancing' named entity 'European Union' named entity 'low income' named entity 'SARS-CoV-2' named entity 'SARS-CoV-2' named entity 'epidemic' named entity 'epidemic' named entity 'accounting' named entity 'social distancing' named entity 'South America' named entity 'social distancing' named entity 'epidemic' named entity 'IFRs' named entity 'incubation period' named entity 'ICU' named entity 'Netherlands'

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