Time varying association between deprivation, ethnicity and SARS-CoV-2 infections in England: a space-time study (preprint)

Background: Ethnically diverse and socio-economically deprived communities have been differentially affected by the COVID-19 pandemic in the UK. Method: Using a multilevel regression model we assess the time-varying association between SARS-CoV-2 infections and areal level deprivation and ethnicity. We separately consider weekly test positivity rate and estimated unbiased prevalence at the Lower Tier Local Authority (LTLA) level, adjusting for confounders and spatio-temporal correlation structure. Findings: Comparing the least deprived and predominantly White areas with most deprived and predominantly non-White areas over the whole study period, the weekly positivity rate increases by 13% from 2.97% to 3.35%. Similarly, prevalence increases by 10% from 0.37% to 0.41%. Deprivation has a stronger effect until October 2020, while the effect of ethnicity becomes more pronounced at the peak of the second wave and then again in May-June 2021. In the second wave of the pandemic, LTLAs with large South Asian populations were the most affected, whereas areas with large Black populations did not show increased values for either outcome during the entire period under analysis. Interpretation: IMD and BAME% are both associated with an increased COVID-19 burden in terms of disease spread and monitoring, and the strength of association varies over the course of the pandemic. The consistency of results across the two outcomes suggests that deprivation and ethnicity have a differential impact on disease exposure or susceptibility rather than testing access and habits.

Interoperability of statistical models in pandemic preparedness: principles and reality (preprint)

We present "interoperability" as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring spatial-temporal coronavirus disease 2019 (COVID-19) prevalence and reproduction numbers in England.

Local prevalence of transmissible SARS-CoV-2 infection : an integrative causal model for debiasing fine-scale targeted testing data (preprint)

Targeted surveillance testing schemes for SARS-CoV-2 focus on certain subsets of the population, such as individuals experiencing one or more of a prescribed list of symptoms. These schemes have routinely been used to monitor the spread of SARS-CoV-2 in countries across the world. The number of positive tests in a given region can provide local insights into important epidemiological parameters, such as prevalence and effective reproduction number. Moreover, targeted testing data has been used inform the deployment of localised non-pharmaceutical interventions. However, surveillance schemes typically suffer from ascertainment bias; the individuals who are tested are not necessarily representative of the wider population of interest. Here, we show that data from randomised testing schemes, such as the REACT study in the UK, can be used to debias fine-scale targeted testing data in order to provide accurate localised estimates of the number of infectious individuals. We develop a novel, integrative causal framework that explicitly models the process underlying the selection of individuals for targeted testing. The output from our model can readily be incorporated into longitudinal analyses to provide local estimates of the reproduction number. We apply our model to characterise the size of the infectious population in England between June 2020 and January 2021. Our local estimates of the effective reproduction number are predictive of future changes in positive case numbers. We also capture local increases in both prevalence and effective reproductive number in the South East from November 2020 to December 2020, reflecting the spread of the Kent variant. Our results illustrate the complementary roles of randomised and targeted testing schemes. Preparations for future epidemics should ensure the rapid deployment of both types of schemes to accurately monitor the spread of emerging and ongoing infectious diseases.

Long-term exposure to air-pollution and COVID-19 mortality in England: a hierarchical spatial analysis (preprint)

Background: Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design, based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 deaths up to June 30, 2020 in England using high geographical resolution. Methods: We included 38 573 COVID-19 deaths up to June 30, 2020 at the Lower Layer Super Output Area level in England (n=32 844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014-2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. Findings: We find a 0.5% (95% credible interval: -0.2%-1.2%) and 1.4% (-2.1%-5.1%) increase in COVID-19 mortality rate for every 1g/m3 increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect of 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease during the first wave of the epidemic. Interpretation: Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain. Funding: Medical Research Council, Wellcome Trust, Environmental Protection Agency and National Institutes of Health.