Understanding COVID-19 testing behaviour in England through a sociodemographic lens (preprint)

BackgroundUnderstanding underlying mechanisms of heterogeneity in test-seeking and reporting behaviour can help to protect the vulnerable and guide equity-driven interventions. Using COVID-19 testing data for England and data from community prevalence surveillance surveys (REACT-1 and ONS-CIS) from October 2020 to March 2022, we investigated the relationship between sociodemographic factors and testing behaviours in England. MethodsWe used mass testing data for lateral flow device (LFD; data for 290 million tests performed and reported) and polymerase chain reaction (PCR) (data for 107 million tests performed and returned from the laboratory) tests made available for the general public, provided by date, self-reported age and ethnicity at lower tier local authority (LTLA) level. Using a mechanistic causal model to debias the PCR testing data, we obtained estimates of weekly SARS-CoV-2 prevalence by self-reported ethnic groups and age groups for LTLAs in England. This approach to debiasing the PCR (or LFD) testing data also estimated a testing bias parameter defined as the odds of testing in infected versus not infected individuals, which would be close to zero if the likelihood of test seeking (or seeking and reporting) was the same regardless of infection status. Using confirmatory PCR data, we estimated false positivity rates, sensitivity, specificity, and the rate of decline in detection probability by PCR by sociodemographic groups. We also estimated the daily incidence allowing us to determine the fraction of cases captured by the testing programme. FindingsFrom March 2021 onwards, individuals in the most deprived regions reported approximately half as many LFD tests per-capita than those in the least deprived areas (Median ratio [Inter quartile range, IQR]: 0{middle dot}50 [0{middle dot}44, 0{middle dot}54]). During October 2020 - June 2021, PCR testing patterns were in the opposite direction (Median ratio [IQR]: 1{middle dot}8 [1{middle dot}7, 1{middle dot}9]). Infection prevalences in Asian or Asian British communities were considerably higher than those of other ethnic groups during the Alpha and Omicron BA.1 waves. Our estimates indicate that the England COVID-19 testing program detected 26% - 40% of all cases (including asymptomatic cases) over the study period with no consistent differences by deprivation levels or ethnic groups. PCR testing biases were generally higher than for LFDs, which was in line with the general policy of symptomatic and asymptomatic use of these tests. During the invasion phases of the Delta and Omicron variants of concern, the PCR testing bias in the most deprived populations was roughly double (ratio: 2{middle dot}2 and 2{middle dot}7 respectively) that in the least. We also determined that ethnic minorities and older individuals were less likely to use confirmatory PCR tests through most of the pandemic and that there was possibly a longer delay in reporting a positive LFD test in the Black populations. InterpretationDifferences in testing behaviours across sociodemographic groups may be reflective of the relatively higher costs of self-isolation to vulnerable populations, differences in test accessibility, digital literacy, and differing perception about the utility of tests and risks posed by infection. Our work shows how mass testing data can be used in conjunction with surveillance surveys to identify gaps in the uptake of public health interventions at fine scale levels and by sociodemographic groups. It provides a framework for monitoring local interventions and yields valuable lessons for policy makers in ensuring an equitable response to future pandemics. FundingUK Health Security Agency.

Reconstructing the first COVID-19 pandemic wave with minimal data (preprint)

Early pandemic diagnostic tool is key important to provide timely pieces of evidence for public health decision making in the early pandemic where data was sparse. In this paper, we demonstrate how a published model can be used to reconstruct the first COVID-19 pandemic wave with minimal dataset.

Estimating the effectiveness of shielding during pregnancy against SARS-CoV-2 in New York City during the first year of the COVID-19 pandemic (preprint)

Pregnant patients have increased morbidity and mortality in the setting of SARS-CoV-2 infection. The exposure of pregnant patients in New York City to SARS-CoV-2 is not well understood due to early lack of access to testing and the presence of asymptomatic COVID-19 infections. Before the availability of vaccinations, preventative (shielding) measures, including but not limited to wearing a mask and quarantining at home to limit contact, were recommended for pregnant patients. Using universal testing data from 2196 patients who gave birth from April through December 2020 from one institution in New York City, and in comparison, with infection data of the general population in New York City, we estimated the exposure and real-world effectiveness of shielding in pregnant patients. Our Bayesian model shows that patients already pregnant at the onset of the pandemic had a 50% decrease in exposure compared to those who became pregnant after the onset of the pandemic and to the general population.

Levels of SARS-CoV-2 population exposure are considerably higher than suggested by seroprevalence surveys (preprint)

Accurate knowledge of accurate levels of prior population exposure has critical ramifications for preparedness plans of subsequent SARS-CoV-2 epidemic waves and vaccine prioritization strategies. Serological studies can be used to estimate levels of past exposure and thus position populations in their epidemic timeline. To circumvent biases introduced by decaying antibody titers over time, population exposure estimation methods should account for seroreversion, to reflect that changes in seroprevalence measures over time are the net effect of increases due to recent transmission and decreases due to antibody waning. Here, we present a new method that combines multiple datasets (serology, mortality, and virus positivity ratios) to estimate seroreversion time and infection fatality ratios and simultaneously infer population exposure levels. The results indicate that the average time to seroreversion is six months, and that true exposure may be more than double the current seroprevalence levels reported for several regions of England.

A population-level analysis of changes in diel rhythms and sleep and their association with negative emotions during the outbreak of COVID-19 in China (preprint)

Introduction: From December 2019, COVID-19 (novel coronavirus pneumonia) began spreading in China and has significantly affected the industrial economy and peoples’ daily lifestyle. Beginning on January 23, the public was asked to constantly stay at home for quarantine and community containment.Methods: To assess the effects of the changes in diel rhythms and sleep and their association with negative emotions during the COVID-19 outbreak, a questionnaire was administered to 451 responders for analysis between January 20, 2020, and January 31, 2020, in China.Results: We found that 34.6% of the participants reported diel rhythm disturbance. Moreover, 67.2% of the participants presented negative emotions regarding the pandemic situation, including worry, fear, downheartedness, anxiety, depression, and stupefaction; among them, worry was the most prevalent. Gender and age were significant factors for changes in the diel phases and emotions. There was a correlation between diel rhythm alterations and negative emotions. Three factors, i.e., the Spring Festival holiday, quarantines and concern regarding the pandemic situation, were associated with changes in diel rhythms, sleep, and negative emotions during the pandemic period. Holiday jet lag, quarantine (or community containment), and concerns regarding the pandemic situation had significant effects on diel rhythms, sleep and negative emotion in a substantial part of the population. Conclusions: Our findings suggest that diel rhythms and sleep and their association with negative emotions in COVID-19 patients and the normal population need to be considered. Moreover, the adjustment of diel rhythms could help relieve negative effects and improve the global health during the pandemic period.