COVID-19 healthcare demand and mortality in Sweden in response to non-pharmaceutical mitigation and suppression scenarios

Sjödin, Henrik; Johansson, Anders; Brännström, Åke; Farooq, Zia; Kriit, Hedi Katre; Wilder‐Smith, Annelies; Åström, Christofer; Thunberg, Johan; Söderquist, Mårten; Rocklöv, Joacim

doi:10.1093/ije/dyaa121

Cited by 54 publications

(56 citation statements)

References 20 publications

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“…A subsets of papers use a metapopulation approach accounting also for the age-stratification and contact matrices within each subpopulation. In this group we find papers that evaluate the risks of importation of cases from China in the early phases of the pandemic [22] , model the first wave [119] , [121] , [122] , [123] , [147] and investigate reopening scenarios [106] , [120] .…”

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%

“…Authors of Ref. [122] built an age-structured metapopulation model for Sweden at the municipality level. In doing so, they adopt the radiation model to estimate the mobility between subpopulations [154] before and during the pandemic.…”

Section: Epidemic Modelsmentioning

confidence: 99%

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

2021

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“…The relevance of sufficiently simple contagion dynamics of this kind cannot be underestimated, particularly in recent times. There has been a continued interest in this subject as shown by [ 6 – 8 ], but modern and ongoing research based on SIR models can be found in [ 9 – 11 ].…”

Section: Introductionmentioning

confidence: 99%

Exactly solvable SIR models, their extensions and their application to sensitive pandemic forecasting

Sadurní

Luna‐Acosta

2021

Nonlinear Dyn

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The classic SIR model of epidemic dynamics is solved completely by quadratures, including a time integral transform expanded in a series of incomplete gamma functions. The model is also generalized to arbitrary time-dependent infection rates and solved explicitly when the control parameter depends on the accumulated infections at time t . Numerical results are presented by way of comparison. Autonomous and non-autonomous generalizations of SIR for interacting regions are also considered, including non-separability for two or more interacting regions. A reduction of simple SIR models to one variable leads us to a generalized logistic model, Richards model, which we use to fit Mexico’s COVID-19 data up to day number 134. Forecasting scenarios resulting from various fittings are discussed. A critique to the applicability of these models to current pandemic outbreaks in terms of robustness is provided. Finally, we obtain the bifurcation diagram for a discretized version of Richards model, displaying period doubling bifurcation to chaos.

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“…S4. The time-series data of total cases, hospitalised people, people in ICU and dead people for Austria come from 7 . They are reported in Fig.…”

Section: S52 Austriamentioning

confidence: 99%

“…https://de.wikipedia.org/wiki/COVID-19-Pandemie_in_\unhbox\voidb@x\bgroup\accent127O\ protect\penalty\@M\hskip\z@skip\egroupsterreich (collection of various sources), accessed on 15/12/2020 7. https://covid19-dashboard.ages.at/dashboard_Hosp.html, accessed on 15/12/2020.…”

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confidence: 99%

Stages of COVID-19 pandemic and paths to herd immunity by vaccination: dynamical model comparing Austria, Luxembourg and Sweden

Kemp

Proverbio

Aalto

et al. 2021

Preprint

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BackgroundWorldwide more than 72 million people have been infected and 1.6 million died with SARS-CoV-2 by 15th December 2020. Non-pharmaceutical interventions which decrease social interaction have been implemented to reduce the spread of SARS-CoV-2 and to mitigate stress on healthcare systems and prevent deaths. The pandemic has been tackled with disparate strategies by distinct countries resulting in different epidemic dynamics. However, with vaccines now becoming available, the current urgent open question is how the interplay between vaccination strategies and social interaction will shape the pandemic in the next months.MethodsTo address this question, we developed an extended Susceptible-Exposed-Infectious-Removed (SEIR) model including social interaction, undetected cases and the progression of patients trough hospitals, intensive care units (ICUs) and death. We calibrated our model to data of Luxem-bourg, Austria and Sweden, until 15th December 2020. We incorporated the effect of vaccination to investigate under which conditions herd immunity would be achievable in 2021.ResultsThe model reveals that Sweden has the highest fraction of undetected cases, Luxembourg displays the highest fraction of infected population, and all three countries are far from herd immunity as of December 2020. The model quantifies the level of social interactions, and allows to assess the level which would keep Reff (t) below 1. In December 2020, this level is around 1/3 of what it was before the pandemic for all the three countries. The model allows to estimate the vaccination rate needed for herd immunity and shows that 2700 vaccinations/day are needed in Luxembourg to reach it by mid of April and 45,000 for Austria and Sweden. The model estimates that vaccinating the whole country’s population within 1 year could lead to herd immunity by July in Luxembourg and by August in Austria and Sweden.ConclusionThe model allows to shed light on the dynamics of the epidemics in different waves and countries. Our results emphasize that vaccination will help considerably but not immediately and therefore social measures will remain important for several months before they can be fully alleviated.

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COVID-19 healthcare demand and mortality in Sweden in response to non-pharmaceutical mitigation and suppression scenarios

Cited by 54 publications

References 20 publications

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

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

Exactly solvable SIR models, their extensions and their application to sensitive pandemic forecasting

Stages of COVID-19 pandemic and paths to herd immunity by vaccination: dynamical model comparing Austria, Luxembourg and Sweden

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