Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data

Kulu, Hill; Dorey, Peter

doi:10.1016/j.healthplace.2020.102460

Cited by 42 publications

(32 citation statements)

References 36 publications

(39 reference statements)

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“…To do this we compared the number of cases reported each day in each LA with the number of hospitalisations and deaths 8 days and 21 days later, respectively, using the method outlined by Kulu and Dorey. 16 In brief we estimated the true weekly SARS-CoV-2 infection rate for each LA, using the age specific Infection Fatality Rate and Infection Hospitalisation Rate for the virus from Knock et al 17 along with the daily number of deaths and hospitalisations for COVID-19 in each LA. This gave two estimates of infection rates, one from hospitalisation data and one from mortality date.…”

Section: Methodsmentioning

confidence: 99%

“…The same process was applied using hospitalisations instead of deaths. We use the Infection Fatality and Infection Hospitalisation Rates estimated by Knock et al 17 In order to account for varying rates of morbidity between local authorities, following Kulu and Dorey 16 we apply a multiplier of chronic disease prevalence to the expected deaths and hospitalisation for each local authority. The idea being that in areas with higher rates of chronic disease, there would be expected to be higher COVID-19 Infection Fatality and Infection Hospitalisation Rates.…”

Section: Appendicesmentioning

confidence: 99%

“…We use a method proposed by Kulu and Dorey to calculate local authority specific infection rates from data on observed and expected deaths and hospitalisations from Covid-19. 16 The true infection rate was estimated from Covid-19 for each local authority as the number of observed deaths divided by the sum of the expected deaths for each age and sex sub-group, if everyone was infected. The expected deaths for each local authority was calculated by multiplying the population estimates for each age and sex group by the infection fatality rates for that sub-group.…”

Section: Appendicesmentioning

confidence: 99%

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Evaluating the impacts of tiered restrictions introduced in England, during October and December 2020 on COVID-19 cases: A synthetic control study

Zhang

Owen

Green

et al. 2021

Preprint

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Background In 2020, a second wave of COVID-19 cases unevenly affected places in England leading to the introduction of a tiered system of controls with different geographical areas subject to different levels of restrictions. Whilst previous research has examined the impact of national lockdowns on transmission, there has been limited research examining the marginal effect of differences in localised restrictions or how these effects vary between socioeconomic contexts. We therefore examined how Tier 3 restrictions in England implemented between October-December 2020, which included additional restrictions on the hospitality sector and people meeting outdoors affected COVID-19 case rates, compared to Tier 2 restrictions, and how these effects varied by level of deprivation. Methods We used data on weekly reported COVID-19 cases for 7201 neighbourhoods in England and adjusted these for changing case-detection rates to provide an estimate of weekly SARS-CoV-2 infections in each neighbourhood. We identified those areas that entered Tier 3 restrictions at two time points in October and December, and constructed a synthetic control group of similar places that had entered Tier 2 restrictions, using calibration weights to match them on a wide range of covariates that may influence transmission. We then compared the change in weekly infections between those entering Tier 3 to the synthetic control group to estimate the proportional reduction of cases resulting from Tier 3 restrictions compared to Tier 2 restrictions, over a 4-week period. We further used interaction analysis to estimate whether this effect differed based on the level of socioeconomic deprivation in each neighbourhood and whether effects were modified by the prevalence of a new more infectious variant of SARS-CoV-2 (B.1.1.7) in each area. Results The introduction of Tier 3 restrictions in October and December was associated with a 14% (95% CI 10% to 19%) and 20% (95% CI 13% to 29%) reduction in infections respectively, compared to the rates expected if only Tier 2 restrictions had been in place in those areas. We found that effects were similar across levels of deprivation and limited evidence that Tier 3 restrictions had a greater effect in areas where the new more infectious variant was more prevalent. Interpretation Additional restrictions on hospitality and meeting outdoors introduced in Tier 3 areas in England had a moderate effect on transmission and these restrictions did not appear to increase inequalities, having a similar impact across areas with differing levels of socioeconomic deprivation. Where transmission risks vary between geographical areas a tiered approach of local restrictions on outdoor mixing and hospitality can contribute to control of SARS-CoV-2 and is unlikely to increases inequalities in transmission.

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Section: Methodsmentioning

confidence: 99%

Section: Appendicesmentioning

confidence: 99%

Section: Appendicesmentioning

confidence: 99%

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Evaluating the impacts of tiered restrictions introduced in England, during October and December 2020 on COVID-19 cases: A synthetic control study

Zhang

Owen

Green

et al. 2021

Preprint

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“…Following the emergence of COVID-19, the disease rapidly spread through the human population, due to effective human-to-human transmission. Several studies have demonstrated that significant risk factors for acquiring SARS-CoV-2 infection are related to human-to-human contact rates, including high population density, living in large urban areas, mobility, and low socioeconomic status [ 83 , 84 , 85 ].…”

Section: Potentially Favourable Conditions For the Emergence Of Sars-cov-2mentioning

confidence: 99%

Current Status of Putative Animal Sources of SARS-CoV-2 Infection in Humans: Wildlife, Domestic Animals and Pets

et al. 2021

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SARS-CoV-2 is currently considered to have emerged from a bat coronavirus reservoir. However, the real natural cycle of this virus remains to be elucidated. Moreover, the COVID-19 pandemic has led to novel opportunities for SARS-CoV-2 transmission between humans and susceptible animal species. In silico and in vitro evaluation of the interactions between the SARS-CoV-2 spike protein and eucaryotic angiotensin-converting enzyme 2 (ACE2) receptor have tentatively predicted susceptibility to SARS-CoV-2 infection of several animal species. Although useful, these data do not always correlate with in vivo data obtained in experimental models or during natural infections. Other host biological properties may intervene such as the body temperature, level of receptor expression, co-receptor, restriction factors, and genetic background. The spread of SARS-CoV-2 also depends on the extent and duration of viral shedding in the infected host as well as population density and behaviour (group living and grooming). Overall, current data indicate that the most at-risk interactions between humans and animals for COVID-19 infection are those involving certain mustelids (such as minks and ferrets), rodents (such as hamsters), lagomorphs (especially rabbits), and felines (including cats). Therefore, special attention should be paid to the risk of SARS-CoV-2 infection associated with pets.

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“…Like many pathogens that cause respiratory diseases ( 1-3 ), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appears to be transmitted more effectively in densely populated areas ( 4-6 ). The increased disease rates reported among high-density populations ( 4, 5, 7, 8 ) may, however, be an artefact of confounders, such as the higher proportion of individuals of lower socioeconomic status or from minority ethnic groups in urban areas ( 9 ). Using COVID-19 associated mortality data from the Office for National Statistics, we aimed to assess the evidence for density dependence.…”

Section: Introductionmentioning

confidence: 99%

The importance of saturating density dependence for predicting SARS-CoV-2 resurgence

Nightingale

Brady

Yakob

2020

Preprint

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is transmitted more effectively in densely populated areas and omitting this phenomenon from epidemiological models may substantially affect projections of spread and control. Adjusting for deprivation, proportion of ethnic minority population and proportion of key workers among the working population, mortality data from England show good evidence for an increasing trend with population density until a saturating level. Projections from a mathematical model that accounts for this observation deviate markedly from the current status quo for SARS-CoV-2 models which either assume linearity between density and transmission (30% of models) or no relationship at all (70%). Respectively, these standard model structures over- and under-estimate the delay in infection resurgence following the release of lockdown. Models have had a prominent role in SARS-CoV-2 intervention strategy; identifying saturation points for given populations and including transmission terms that account for this feature will improve model utility.

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Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data

Cited by 42 publications

References 36 publications

Evaluating the impacts of tiered restrictions introduced in England, during October and December 2020 on COVID-19 cases: A synthetic control study

Evaluating the impacts of tiered restrictions introduced in England, during October and December 2020 on COVID-19 cases: A synthetic control study

Current Status of Putative Animal Sources of SARS-CoV-2 Infection in Humans: Wildlife, Domestic Animals and Pets

The importance of saturating density dependence for predicting SARS-CoV-2 resurgence

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