COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care

Wood, Richard M.; McWilliams, Christopher; Thomas, Matthew; Bourdeaux, Christopher; Vasilakis, Christos

doi:10.1007/s10729-020-09511-7

Cited by 60 publications

(62 citation statements)

References 36 publications

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“…Managing healthcare capacity is of prime importance during a health disaster. While most research focused on ICU and conventional units [ 19 ], the place of downstream beds and rehabilitation units need more assessment [ 20 ]. We acknowledge that the choice of a log-normal distribution and the small number of young patients who died led to more uncertainty in the duration estimations.…”

Section: Discussionmentioning

confidence: 99%

Trajectories of Hospitalization in COVID-19 Patients: An Observational Study in France

Boëlle

Delory

Maynadier

et al. 2020

JCM

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Describing the characteristics of COVID-19 patients in the hospital is of importance to assist in the management of hospital capacity in the future. Here, we analyze the trajectories of 1321 patients admitted to hospitals in northern and eastern France. We found that the time from onset to hospitalization decreased with age, from 7.3 days in the 20–65 year-olds to 4.5 in the >80 year-olds (p < 0.0001). Overall, the length of stay in the hospital was 15.9 days, and the death rate was 20%. One patient out of four was admitted to the intensive care unit (ICU) for approximately one month. The characteristics of trajectories changed with age: fewer older patients were admitted to the ICU and the death rate was larger in the elderly. Admission shortly after onset was associated with increased mortality (odds-ratio (OR) = 1.8, Confidence Interval (CI) 95% [1.3, 2.6]) as well as male sex (OR = 2.1, CI 95% [1.5, 2.9]). Time from admission within the hospital to the transfer to ICU was short. The age- and sex-adjusted mortality rate decreased over the course of the epidemic, suggesting improvement in care over time. In the SARS-CoV-2 epidemic, the urgent need for ICU at admission and the prolonged length of stay in ICU are a challenge for bed management and organization of care.

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Section: Discussionmentioning

confidence: 99%

Trajectories of Hospitalization in COVID-19 Patients: An Observational Study in France

Boëlle

Delory

Maynadier

et al. 2020

JCM

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“…Ref. [347] aims to characterize the increase in ICU deaths linked to limited capacity. In the context of the UK, the modeling scenarios developed by the authors suggest that a significant impact could be obtained doubling beds and reducing length of stay by 25%.…”

Section: Quantifying the Effects On Npismentioning

confidence: 99%

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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“…Fighting the pandemic effectively is a complex challenge, since limited resources in the health care system and restrictions in everyday life need to be considered simultaneously [ 7 , 8 ]. Consequently, measures to control the pandemic have to be coordinated [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

The Role of Transparency, Trust, and Social Influence on Uncertainty Reduction in Times of Pandemics: Empirical Study on the Adoption of COVID-19 Tracing Apps

Oldeweme¹,

Märtins²,

Westmattelmann³

et al. 2021

J Med Internet Res

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Background Contact tracing apps are an essential component of an effective COVID-19 testing strategy to counteract the spread of the pandemic and thereby avoid overburdening the health care system. As the adoption rates in several regions are undesirable, governments must increase the acceptance of COVID-19 tracing apps in these times of uncertainty. Objective Building on the Uncertainty Reduction Theory (URT), this study aims to investigate how uncertainty reduction measures foster the adoption of COVID-19 tracing apps and how their use affects the perception of different risks. Methods Representative survey data were gathered at two measurement points (before and after the app’s release) and analyzed by performing covariance-based structural equation modeling (n=1003). Results We found that uncertainty reduction measures in the form of the transparency dimensions disclosure and accuracy, as well as social influence and trust in government, foster the adoption process. The use of the COVID-19 tracing app in turn reduced the perceived privacy and performance risks but did not reduce social risks and health-related COVID-19 concerns. Conclusions This study contributes to the mass adoption of health care technology and URT research by integrating interactive communication measures and transparency as a multidimensional concept to reduce different types of uncertainty over time. Furthermore, our results help to derive communication strategies to promote the mass adoption of COVID-19 tracing apps, thus detecting infection chains and allowing intelligent COVID-19 testing.

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COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care

Cited by 60 publications

References 36 publications

Trajectories of Hospitalization in COVID-19 Patients: An Observational Study in France

Trajectories of Hospitalization in COVID-19 Patients: An Observational Study in France

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

The Role of Transparency, Trust, and Social Influence on Uncertainty Reduction in Times of Pandemics: Empirical Study on the Adoption of COVID-19 Tracing Apps

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