Covid-19’s known unknowns

Smith, George Davey; Blastland, Michael; Munafò, Marcus R.

doi:10.1136/bmj.m3979

Cited by 42 publications

(31 citation statements)

References 12 publications

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“…Time-series data including only a limited amount of explanatory factors has to be interpreted with care and with respect to the many uncertainties which remain regarding COVID-19 [32].…”

Section: Discussionmentioning

confidence: 99%

Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points

et al. 2021

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We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.

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“…Time-series data including only a limited amount of explanatory factors has to be interpreted with care and with respect to the many uncertainties which remain regarding COVID-19 [32].…”

Section: Discussionmentioning

confidence: 99%

Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points

et al. 2021

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“…I agree with Davey Smith and colleagues about the expression of certainty in covid-19 communications and the effects on trust and public beliefs 1…”

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confidence: 52%

Covid-19’s known unknowns: population shared decision making

Cobain¹

2020

BMJ

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I agree with Davey Smith and colleagues about the expression of certainty in covid-19 communications and the effects on trust and public beliefs. 1 Certainty is reassuring for the general population at times of crisis. Certainty around severity increases perceived susceptibility, and clarity on resulting required behaviours provides a sense of control. But with declining certainty, we erode that trust. This is diametrically opposed to our clinical model of shared decision making, which helps patients weigh up the pros and cons of difficult health trade-offs so that decisions are made through discussion of values, preferences, and different likely outcomes. Current communication around covid-19 has led to polarisation and dismissal of rival positions. The evidence base usually acts as an impartial referee in shared decision making, but, as the authors rightly point out, the evidence is uncertain for a disease in its infancy. The presentation of personally changed positions is a fitting end to the article.

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“…However, given the re-emergence of the epidemic in the fall of 2020 at high test capacity and at relatively high temperatures shows that contact behavior is the major explanatory factor for virus activity. Nevertheless, any analysis of observational time series data including only a limited amount of explanatory factors has to be interpreted with care and with respect to the many uncertainties which remain regarding COVID-19 [27].…”

Section: Discussionmentioning

confidence: 99%

Analysis of the early Covid-19 epidemic curve in Germany by regression models with change points

Küchenhoff

Günther

Höhle

et al. 2020

Preprint

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We analyze the Covid-19 epidemic curve from March to end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analyzed by a Poisson trend regression model with change points. The change points are estimated directly from the data without further assumptions. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between March 9th and 13th for the time series of infections: from a strong increase to a stagnation or a slight decrease. Another change was found between March 24th and March 31st, where the decline intensified. These two major changes can be related to different governmental measures. On March, 11th, Chancellor Merkel appealed for social distancing in a press conference with the Robert Koch Institute (RKI) and a ban on major events with more than 1000 visitors (March 10th) was issued. The other change point at the end of March could be related to the shutdown in Germany. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases

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Covid-19’s known unknowns

Cited by 42 publications

References 12 publications

Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points

Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points

Covid-19’s known unknowns: population shared decision making

Analysis of the early Covid-19 epidemic curve in Germany by regression models with change points

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