SCoVMod – a spatially explicit mobility and deprivation adjusted model of first wave COVID-19 transmission dynamics

Banks, Christopher J.; Colman, Ewan; Doherty, T.; Tearne, Oliver; Arnold, Mark; Atkins, Katherine E.; Balaz, Daniel; Beaunée, Gaël; Bessell, Paul R.; Enright, Jessica; Kleczkowski, A.; Rossi, Gianluigi; Ruget, Anne-Sophie; Kao, Rowland R.

doi:10.12688/wellcomeopenres.17716.1

Cited by 6 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, living in a deprived community was suggested from early on [50] and has since also emerged as a risk factor for severe COVID-19 disease [51, 52, 53, 54, 55, 56]. However, the corresponding ALEs for the variables associated with deprivation are small.…”

Section: Discussionmentioning

confidence: 99%

“…Individuals are equally infectious throughout the entire infection. In our calculations we estimate 1 /λ = 6.26 days, using the posterior mean duration of infectiousness obtained from the SCoVMod compartmental model (for more detail see Refer-ence [56]).…”

Section: Methodsmentioning

confidence: 99%

“…Throughout the epidemic, cases have not been evenly distributed amongst the population [9, 10, 11]. The main contribution of this work is to explain variation at the resolution of neighbourhoods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Wood

Sanchez

Bessell

et al. 2022

Preprint

Self Cite

View full text Add to dashboard 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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Wood

Sanchez

Bessell

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

Wood

Kao

2022

Preprint

View full text Add to dashboard 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.

show abstract

“…Finally, living in a deprived community was suggested from early on [51] and has since also emerged as a risk factor for severe COVID-19 disease [52][53][54][55][56][57]. However, the corresponding ALEs for the variables associated with deprivation are small.…”

Section: Risk Factorsmentioning

confidence: 99%

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

Wood,

Sanchez,

Bessell

et al. 2023

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

For the long term control of an infectious disease such as COVID-19, it is crucial to identify the most likely individuals to become infected and the role that differences in demographic characteristics play in the observed patterns of infection. As high-volume surveillance winds down, testing data from earlier periods are invaluable for studying risk factors for infection in detail. Observed changes in time during these periods may then inform how stable the pattern will be in the long term. To this end we analyse the distribution of cases of COVID-19 across Scotland in 2021, where the location (census areas of order 500–1,000 residents) and reporting date of cases are known. We consider over 450,000 individually recorded cases, in two infection waves triggered by different lineages: B.1.1.529 (“Omicron”) and B.1.617.2 (“Delta”). We use random forests, informed by measures of geography, demography, testing and vaccination. We show that the distributions are only adequately explained when considering multiple explanatory variables, implying that case heterogeneity arose from a combination of individual behaviour, immunity, and testing frequency. Despite differences in virus lineage, time of year, and interventions in place, we find the risk factors remained broadly consistent between the two waves. Many of the observed smaller differences could be reasonably explained by changes in control measures.

show abstract

SCoVMod – a spatially explicit mobility and deprivation adjusted model of first wave COVID-19 transmission dynamics

Cited by 6 publications

References 29 publications

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

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

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

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

Contact Info

Product

Resources

About