Interoperability of statistical models in pandemic preparedness: principles and reality.

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 and characterising spatial-temporal prevalence and reproduction numbers of SARS-CoV-2 infections in England.

Time varying association between deprivation, ethnicity and SARS-CoV-2 infections in England: A population-based ecological study.

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 assessed the time-varying association between SARS-CoV-2 infections and areal level deprivation and ethnicity from 1st of June 2020 to the 19th of September 2021. We separately considered weekly test positivity rate and estimated debiased 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 from 2·977% (95% CrI 2.913%-3.029%) to 3·347% (95% CrI 3.300%-3.402%). Similarly, prevalence increases from 0·369% (95% CrI 0.361%-0.375%) to 0·405% (95% CrI 0.399%-0.412%). 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: Deprivation and proportion of non-White populations are both associated with an increased COVID-19 burden in terms of disease spread and monitoring, but the strength of association varies over the course of the pandemic and for different ethnic subgroups. 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. FUNDINGS: EPSRC, MRC, The Alan Turing Institute, NIH, UKHSA, DHSC.

Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework.

Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.

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

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 (number of positive tests over the total number of tests) and estimated unbiased prevalence (proportion of individuals in the population who would test positive) at the Lower Tier Local Authority (LTLA) level. The model also adjusts for age, urbanicity, vaccine uptake 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 297% to 335%. Similarly, prevalence increases by 10% from 037% to 041%. Deprivation has a stronger effect until October 2020, while the effect of ethnicity becomes slightly more pronounced at the peak of the second wave and then again in May-June 2021. Not all BAME groups were equally affected: 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: At the area level, IMD and BAME% are both associated with an increased COVID-19 burden in terms of prevalence (disease spread) and test positivity (disease monitoring), and the strength of association varies over the course of the pandemic. The consistency of results across the two outcome measures suggests that community level characteristics such as deprivation and ethnicity have a differential impact on disease exposure or susceptibility rather than testing access and habits. FUNDINGS: EPSRC, MRC, The Alan Turing Institute, NIH, UKHSA, DHSC, NIHR.