Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK

Pietzonka, Patrick; Brorson, Erik; Bankes, William; Cates, Michael E.; Jack, Robert L.; Adhikari, R.

doi:10.1371/journal.pone.0258968

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…However, there does appear to have been a reduction in the IFR over the summer of 2020 matching some previous analysis [23]. There also appeared to be an upwards trend in the IFR and IHR into January 2021, this increase in lethality has been identified in other work [34] and is potentially driven by the increased severity of the Alpha variant [5], seasonality [35], or increased pressure on health services. However, due to the REACT-1 study not being in the field during December 2020 it is poorly placed to identify this as an estimate for the December peak in prevalence could not be made.…”

Section: Discussionsupporting

confidence: 78%

Quantifying changes in the IFR and IHR over 23 months of the SARS-CoV-2 pandemic in England

Eales

Haw

Wang

et al. 2022

Preprint

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Background: The relationship between prevalence of infection and severe outcomes such as hospitalisation and death changed over the course of the COVID-19 pandemic. The REal-time Assessment of Community Transmission -1 (REACT-1) study estimated swab positivity in England approximately monthly from May 2020 to 31 March 2022. This period covers widespread circulation of the original strain, the emergence of the Alpha, Delta and Omicron variants and the rollout of England's mass vaccination campaign. Methods: Here, we explore this changing relationship between prevalence of swab positivity and the infection fatality rate (IFR) and infection hospitalisation rate (IHR) over 23 months of the pandemic in England, using publicly available data for the daily number of deaths and hospitalisations, REACT-1 swab positivity data, time-delay models and Bayesian P-spline models. We analyse data for all age groups together, as well as in two sub-groups: those aged 65 and over and those aged 64 and under. Results: During 2020, we estimated the IFR to be 0.67% and the IHR to be 2.6%. By late-2021/early-2022 the IFR and IHR had both decreased to 0.097% and 0.76% respectively. Continuous estimates of the IFR and IHR of the virus were observed to increase during the periods of Alpha and Delta's emergence. During periods of vaccination rollout, and the emergence of the Omicron variant, the IFR and IHR of the virus decreased. During 2020, we estimated a time-lag of 19 days between hospitalisation and swab positivity, and 26 days between deaths and swab positivity. By late-2021/early-2022 these time-lags had decreased to 7 days for hospitalisations, and 18 days for deaths. Conclusion: Even though many populations have high levels of immunity to SARS-CoV-2 from vaccination and natural infection, waning of immunity and variant emergence will continue to be an upwards pressure on IHR and IFR. As investments in community surveillance are scaled back, alternative methods should be developed to accurately track the ever changing relationship between infection, hospitalisation and death.

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Section: Discussionsupporting

confidence: 78%

Quantifying changes in the IFR and IHR over 23 months of the SARS-CoV-2 pandemic in England

Eales

Haw

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, there does appear to have been a reduction in the IFR over the summer of 2020 as reported in previous analysis [ 23 ]. There also appears to have been an upwards trend in the IFR and IHR into January 2021, as previously reported [ 35 ], potentially driven by increased severity of the Alpha variant [ 5 ], seasonality [ 36 ], or increased pressure on health services. However, the REACT-1 study was not in the field for most of December 2020 so is poorly placed to identify this as an estimate for the December peak in prevalence could not be made.…”

Section: Discussionsupporting

confidence: 63%

Dynamics of SARS-CoV-2 infection hospitalisation and infection fatality ratios over 23 months in England

et al. 2023

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The relationship between prevalence of infection and severe outcomes such as hospitalisation and death changed over the course of the COVID-19 pandemic. Reliable estimates of the infection fatality ratio (IFR) and infection hospitalisation ratio (IHR) along with the time-delay between infection and hospitalisation/death can inform forecasts of the numbers/timing of severe outcomes and allow healthcare services to better prepare for periods of increased demand. The REal-time Assessment of Community Transmission-1 (REACT-1) study estimated swab positivity for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in England approximately monthly from May 2020 to March 2022. Here, we analyse the changing relationship between prevalence of swab positivity and the IFR and IHR over this period in England, using publicly available data for the daily number of deaths and hospitalisations, REACT-1 swab positivity data, time-delay models, and Bayesian P-spline models. We analyse data for all age groups together, as well as in 2 subgroups: those aged 65 and over and those aged 64 and under. Additionally, we analysed the relationship between swab positivity and daily case numbers to estimate the case ascertainment rate of England’s mass testing programme. During 2020, we estimated the IFR to be 0.67% and the IHR to be 2.6%. By late 2021/early 2022, the IFR and IHR had both decreased to 0.097% and 0.76%, respectively. The average case ascertainment rate over the entire duration of the study was estimated to be 36.1%, but there was some significant variation in continuous estimates of the case ascertainment rate. Continuous estimates of the IFR and IHR of the virus were observed to increase during the periods of Alpha and Delta’s emergence. During periods of vaccination rollout, and the emergence of the Omicron variant, the IFR and IHR decreased. During 2020, we estimated a time-lag of 19 days between hospitalisation and swab positivity, and 26 days between deaths and swab positivity. By late 2021/early 2022, these time-lags had decreased to 7 days for hospitalisations and 18 days for deaths. Even though many populations have high levels of immunity to SARS-CoV-2 from vaccination and natural infection, waning of immunity and variant emergence will continue to be an upwards pressure on the IHR and IFR. As investments in community surveillance of SARS-CoV-2 infection are scaled back, alternative methods are required to accurately track the ever-changing relationship between infection, hospitalisation, and death and hence provide vital information for healthcare provision and utilisation.

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“…Elevated temperatures inhibit the spread of SARS-CoV-2 [ 19 , 20 ]. For example, Shaman et al [ 21 ] discovered that an increase in autumn mortality due to SARS-CoV-2 in the UK in 2020 was associated with seasonality. However, Briz-Redon and Serrano-Aroca [ 22 ] reported controversial findings regarding the effect of temperature, possibly reflecting spatial heterogeneity in the relationship between temperature and SARS-CoV-2 transmission.…”

Section: Introductionmentioning

confidence: 99%

Impact of climate change on SARS-CoV-2 epidemic in China

Wang

Tan

et al. 2023

PLoS ONE

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The outbreak and prevalence of SARS-CoV-2 have severely affected social security. Physical isolation is an effective control that affects the short-term human-to-human transmission of the epidemic, although weather presents a long-term effect. Understanding the effect of weather on the outbreak allow it to be contained at the earliest possible. China is selected as the study area, and six weather factors that receive the most attention from January 20, 2020 to April 30, 2020 are selected to investigate the correlation between weather and SARS-CoV-2 to provide a theoretical basis for long-term epidemic prevention and control. The results show that (1) the average growth rate (GR) of SARS-CoV-2 in each province is logarithmically distributed with a mean value of 5.15%. The GR of the southeastern region is higher than that of the northwestern region, which is consistent with the Hu Line. (2) The specific humidity, 2-m temperature (T), ultraviolet (UV) radiation, and wind speed (WS) adversely affect the GR. By contrast, the total precipitation (TP) and surface pressure (SP) promote the GR. (3) For every 1 unit increase in UV radiation, the GR decreases by 0.30% in 11 days, and the UV radiation in China is higher than that worldwide (0.92% higher per day). Higher population aggregation and urbanization directly affect the epidemic, and weather is an indirect factor.

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Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK

Cited by 4 publications

References 25 publications

Quantifying changes in the IFR and IHR over 23 months of the SARS-CoV-2 pandemic in England

Quantifying changes in the IFR and IHR over 23 months of the SARS-CoV-2 pandemic in England

Dynamics of SARS-CoV-2 infection hospitalisation and infection fatality ratios over 23 months in England

Impact of climate change on SARS-CoV-2 epidemic in China

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