Long-term spatial patterns in COVID-19 booster vaccine uptake

Sanchez

Bessell

et al. 2022

Preprint

Self Cite

For a disease such as COVID-19, it is important to identify individuals in a population at heightened risk of infection, as well as broader patterns of infection spread. This is both to estimate burden on healthcare systems (given substantial variation in disease severity from person to person), and to better control the spread of infection. In Scotland, the circulation of SARS-CoV-2 continues to place sustained pressure on healthcare systems, even after a comprehensive vaccination programme and earlier strict non-pharmaceutical interventions. To better understand individuals at heightened risk, we analyse the spatio-temporal distribution of over 450,000 cases of COVID-19 registered in Scotland in the waves of the B.1.1.529 Omicron lineage from November 2021, and an earlier wave of the B.1.617.2 Delta lineage from May 2021. These cases are taken from a uniquely fine scale national data set specifying individual tests. We use random forest regression on local case numbers, informing the model with measures relating to local geography, demographics, deprivation, COVID-19 testing and vaccination coverage. We can then identify broader risk factors indicative of higher case numbers. Despite the Delta and Omicron waves occurring around six months apart, with different control measures and immunity from vaccination and prior infection, the overall risk factors remained broadly similar for both. We find that finer details and clusters in the case distribution are only adequately explained when incorporating a combination of all these factors, implying that variation in COVID-19 cases results from a complex interplay of individual-level behaviour, existing immunity, and willingness to test for COVID-19 at all. On comparing testing patterns to subsequent COVID-19 hospitalisations, we conjecture that the distribution of cases may not be representative of the wider pattern of infection, particularly with respect to local deprivation.

Section: Discussionmentioning

confidence: 99%

Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data

Sanchez

Bessell

et al. 2022

Preprint

Self Cite

“…The programme of free community testing in Scotland has allowed the spread of the virus to be tracked at remarkable spatio-temporal resolution [28, 29, 30, 2, 31, 32, 33]. Despite prevalence remaining high throughout 2022 [34], Scotland has since entered a phase where testing is no longer mandatory, nor generally free of charge.…”

Section: Discussionmentioning

confidence: 99%

Time intervals between COVID-19 cases, and more severe outcomes

Kao

2022

Preprint

Self Cite

A critical factor in infectious disease control is the risk of an outbreak overwhelming local healthcare capacity. The overall demand on healthcare services will depend on disease severity, but the precise timing and size of peak demand also depends on the time interval (or clinical time delay) between initial infection, and development of severe disease. A broader distribution of intervals may draw that demand out over a longer period, but have a lower peak demand. These interval distributions are therefore important in modelling trajectories of e.g. hospital admissions, given a trajectory of incidence. Conversely, as testing rates decline, an incidence trajectory may need to be inferred through the delayed, but relatively unbiased signal of hospital admissions. Healthcare demand has been extensively modelled during the COVID-19 pandemic, where localised waves of infection have imposed severe stresses on healthcare services. While the initial acute threat posed by this disease has since subsided from immunity buildup from vaccination and prior infection, prevalence remains high and waning immunity may lead to substantial pressures for years to come. In this work, then, we present a set of interval distributions, for COVID-19 cases and subsequent severe outcomes; hospital admission, ICU admission, and death. These may be used to model more realistic scenarios of hospital admissions and occupancy, given a trajectory of infections or cases. We present a method for obtaining empirical distributions using COVID-19 outcomes data from Scotland between September 2020 and January 2022 (N = 31724 hospital admissions, N = 3514 ICU admissions, N = 8306 mortalities). We present separate distributions for individual age, sex, and deprivation of residing community. We show that, while the risk of severe disease following COVID-19 infection is substantially higher for the elderly or those residing in areas of high deprivation, the length of stay shows no strong dependence, suggesting that severe outcomes are equally severe across risk groups. As Scotland and other countries move into a phase where testing is no longer abundant, these intervals may be of use for retrospective modelling of patterns of infection, given data on severe outcomes.

“…The programme of free community testing in Scotland has allowed the spread of the virus to be tracked at remarkable spatio-temporal resolution [2,[32][33][34][35][36][37]. Despite prevalence remaining high throughout 2022 and early 2023 per randomised testing [38], Scotland has since entered a phase where testing is no longer mandatory, nor generally free of charge.…”

Section: Plos Onementioning

confidence: 99%

Empirical distributions of time intervals between COVID-19 cases and more severe outcomes in Scotland

Kao

2023

PLoS ONE

Self Cite

A critical factor in infectious disease control is the risk of an outbreak overwhelming local healthcare capacity. The overall demand on healthcare services will depend on disease severity, but the precise timing and size of peak demand also depends on the time interval (or clinical time delay) between initial infection, and development of severe disease. A broader distribution of intervals may draw that demand out over a longer period, but have a lower peak demand. These interval distributions are therefore important in modelling trajectories of e.g. hospital admissions, given a trajectory of incidence. Conversely, as testing rates decline, an incidence trajectory may need to be inferred through the delayed, but relatively unbiased signal of hospital admissions. Healthcare demand has been extensively modelled during the COVID-19 pandemic, where localised waves of infection have imposed severe stresses on healthcare services. While the initial acute threat posed by this disease has since subsided with immunity buildup from vaccination and prior infection, prevalence remains high and waning immunity may lead to substantial pressures for years to come. In this work, then, we present a set of interval distributions, for COVID-19 cases and subsequent severe outcomes; hospital admission, ICU admission, and death. These may be used to model more realistic scenarios of hospital admissions and occupancy, given a trajectory of infections or cases. We present a method for obtaining empirical distributions using COVID-19 outcomes data from Scotland between September 2020 and January 2022 (N = 31724 hospital admissions, N = 3514 ICU admissions, N = 8306 mortalities). We present separate distributions for individual age, sex, and deprivation of residing community. While the risk of severe disease following COVID-19 infection is substantially higher for the elderly and those residing in areas of high deprivation, the length of stay shows no strong dependence, suggesting that severe outcomes are equally severe across risk groups. As Scotland and other countries move into a phase where testing is no longer abundant, these intervals may be of use for retrospective modelling of patterns of infection, given data on severe outcomes.