The effect of interventions on COVID-19

Soltesz, Kristian; Gustafsson, Fredrik; Timpka, Toomas; Jaldén, Joakim; Jidling, Carl; Heimerson, Albin; Schön, Thomas B.; Spreco, Armin; Ekberg, Joakim; Dahlström, Örjan; Carlson, Fredrik Bagge; Jöud, Anna; Bernhardsson, Bo

doi:10.1038/s41586-020-3025-y

Cited by 128 publications

(94 citation statements)

References 5 publications

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“…The check indicated that the choice of countries and definition of NPIs has a larger influence on the estimated effects than the detailed choices in modeling. A re-analysis of the original study by Flaxman et al could also demonstrate sensitivity of the results with respect to specific choices of the definition of NPIs, but also found sensitivity to specific modeling choices [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave

et al. 2021

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The novel coronavirus (SARS-CoV-2) has rapidly developed into a global epidemic. To control its spread, countries have implemented non-pharmaceutical interventions (NPIs), such as school closures, bans of small gatherings, or even stay-at-home orders. Here we study the effectiveness of seven NPIs in reducing the number of new infections, which was inferred from the reported cases of COVID-19 using a semi-mechanistic Bayesian hierarchical model. Based on data from the first epidemic wave of n = 20 countries (i.e., the United States, Canada, Australia, the EU-15 countries, Norway, and Switzerland), we estimate the relative reduction in the number of new infections attributed to each NPI. Among the NPIs considered, bans of large gatherings were most effective, followed by venue and school closures, whereas stay-at-home orders and work-from-home orders were least effective. With this retrospective cross-country analysis, we provide estimates regarding the effectiveness of different NPIs during the first epidemic wave.

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Section: Discussionmentioning

confidence: 99%

Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave

et al. 2021

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“…The Re changes through time and reflects changes in the immune status of the population, policy, climate, and/or individual behaviours (1,2). It can thus be used to estimate the effectiveness of non-pharmaceutical interventions in disease control (5,(8)(9)(10)(11). However, Rcc estimates have some notable drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Wastewater-based estimation of the effective reproductive number of SARS-CoV-2

Huisman

Scire

Caduff

et al. 2021

Preprint

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The effective reproductive number, Re, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, Re estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. Here we show that the dynamics of SARS-CoV-2 RNA in wastewater can be used to estimate Re in near real-time, independent of clinical data and without associated biases stemming from clinical testing and reporting strategies. The method to estimate Re from wastewater is robust and applicable to data from different countries and wastewater matrices. The resulting estimates are as similar to the Re estimates from case report data as Re estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer Re. To our knowledge, this is the first time Re has been estimated from wastewater. This method provides a low cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens.

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“…Observing variables in fixed time intervals, like the daily numbers of COVID-19 incidences and related deaths in each country, naturally produces time series. In the ongoing research focussing on these time series, one can distinguish three main directions: First, the temporal dynamics of COVID-19 incidences and related deaths at the onset of the pandemic in a specific country [ 6 , 7 , 8 , 9 ]; second, the correlation between COVID-19 incidences and another factor of interest, ranging from air pollution [ 10 ] through social capital [ 11 ] to stroke admissions [ 12 ]; third, the effect of political measures—from social distancing to complete lockdown of public life—on COVID-19 incidence and related death numbers [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Nine Months of COVID-19 Pandemic in Europe: A Comparative Time Series Analysis of Cases and Fatalities in 35 Countries

Meintrup

Nowak-Machen

Borgmann

2021

IJERPH

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(1) Background: to describe the dynamic of the pandemic across 35 European countries over a period of 9 months. (2) Methods: a three-phase time series model was fitted for 35 European countries, predicting deaths based on SARS-CoV-2 incidences. Hierarchical clustering resulted in three clusters of countries. A multiple regression model was developed predicting thresholds for COVID-19 incidences, coupled to death numbers. (3) Results: The model showed strongly connected deaths and incidences during the waves in spring and fall. The corrected case-fatality rates ranged from 2% to 20.7% in the first wave, and from 0.5% to 4.2% in the second wave. If the incidences stay below a threshold, predicted by the regression model (R2=85.0%), COVID-19 related deaths and incidences were not necessarily coupled. The clusters represented different regions in Europe, and the corrected case-fatality rates in each cluster flipped from high to low or vice versa. Severely and less severely affected countries flipped between the first and second wave. (4) Conclusions: COVID-19 incidences and related deaths were uncoupled during the summer but coupled during two waves. Once a country-specific threshold of infections is reached, death numbers will start to rise, allowing health care systems and countries to prepare.

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The effect of interventions on COVID-19

Cited by 128 publications

References 5 publications

Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave

Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave

Wastewater-based estimation of the effective reproductive number of SARS-CoV-2

Nine Months of COVID-19 Pandemic in Europe: A Comparative Time Series Analysis of Cases and Fatalities in 35 Countries

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