Comparative analysis of symptom profile and risk of death associated with infection by SARS-CoV-2 and its variants in Hong Kong (preprint)

Introduction: The recurrent multi-wave nature of COVID-19 necessitates updating its symptomatology. Before the omicron era, Hong Kong was relatively unscathed and had a low vaccine uptake rate among the old-old, giving us an opportunity to study the intrinsic severity of SARS-CoV-2 variants. A comparison of symptom patterns across variants and vaccination status in Hong Kong has yet to be undertaken. The intrinsic severity of variants and symptoms predictive of severe outcomes are also understudied as COVID-19 evolves. We therefore aim to characterize the effect of variants on symptom presentation, identify the symptoms predictive and protective of death, and quantify the effect of vaccination on symptom development. Methods: With the COVID-19 case series in Hong Kong from inception to 25 August 2022, an iterative multi-tier text-matching algorithm was developed to identify symptoms from free text. Cases were fully vaccinated if they completed two doses. Multivariate regression was used to measure associations between variants, symptom development, death and vaccination status. A least absolute shrinkage and selection operator technique was used to identify a parsimonious set of symptoms jointly associated with death. Results: Overall, 70.9% (54450/76762) of cases were symptomatic. We identified a wide spectrum of symptoms (n=102), with cough, fever, runny nose and sore throat being the most common (8.16-47.0%). Intrinsically, the wild-type and delta variant caused similar symptoms, with runny nose, sore throat, itchy throat and headache more frequent in the delta cohort; whereas symptoms were heterogeneous between the wild-type and omicron variant, with seven symptoms (fatigue, fever, chest pain, runny nose, sputum production, nausea/vomiting and sore throat) more frequent in the omicron cohort. With full vaccination, omicron was still more likely than delta to cause fever. Fever, blocked nose and shortness of breath were robustly jointly predictive of death as the virus evolved. Number of vaccine doses required for reduction in occurrence varied by symptoms. Discussion: This is the first large-scale study to evaluate the changing symptomatology by COVID-19 variants and vaccination status using free-text reporting by patients. We substantiate existing findings that omicron has a different clinical presentation compared to previous variants. Syndromic surveillance can be bettered with reduced reliance on symptom-based case identification, increased weighing on symptoms robustly predictive of mortality in outcome prediction, strengthened infection control in care homes through universal individual-based risk assessment to enable early risk stratification, adjusting the stockpile of medicine to tally with the changing symptom profiles across vaccine doses, and incorporating free-text symptom reporting by patients.

Quantifying the Risk of General Health and Early COVID-19 Spread in Residential Buildings with Deep Learning and Expert-augmented Machine Learning (preprint)

Buildings' built environment has been linked to their occupants' health. It remains unclear whether those elements that predisposed its residents to poor general health before the two SARS pandemics also put residents at risk of contracting COVID-19 during early outbreaks. Relevant research to uncover the associations is essential, but there lacks a systematic examination of the relative contributions of different elements in one's built environment and other non-environmental factors, singly or jointly. Hence, the current study developed a deep-learning approach with multiple input channels to capture the hierarchical relationships among an individual's socioecology's demographical, medical, behavioral, psychosocial, and built-environment levels. Our findings supported that 1) deep-learning models whose inputs were structured according to the hierarchy of one's socioecology outperformed plain models with one-layered input in predicting one's general health outcomes, with the model whose hierarchically structured input layers included one's built environment performed best; 2) built-environment features were more important to general health compared to features of one's sociodemographic and their health-related quality of life, behaviors, and service utilization; 3) a composite score representing built-environment features' statistical importance to general health significantly predicted building-level COVID-19 case counts; and 4) building configurations derived from the expert-augmented learning of granular built-environment features that were of high importance to the general health were also linked to building-level COVID-19 case counts of external samples. Specific built environments put residents at risk for poor general health and COVID-19 infections. Our machine-learning approach can benefit future quantitative research on sick buildings, health surveillance, and housing design.

Impact of Pre-Event Testing and Quarantine On Reducing The Risk of COVID-19 Epidemic Rebound: A Modelling Study (preprint)

BACKGROUND With the evolving growth of the COVID-19 epidemic, travel restriction policies would need to be adjusted accordingly. Prohibition of mass event may be relaxed for social and economic benefits when virus transmission stops but could bear the risk of epidemic rebound. Against the background of the varied SARS-CoV-2 prevalence internationally, we modelled the potential impacts of pre-event interventions on epidemic risk of holding a mass event when COVID-19 is under control. METHODS We developed a mathematical model of SARS-CoV-2 transmission in Guangdong Province, China, where local virus transmission ceased to occur. A large-scale international trade fair was assumed to be held, with influx of people from overseas and rest of China over a short period of time, who participated for 2-week. Scenarios of pre-event intervention (none, quarantine arrangement and polymerase chain reaction (PCR) testing for participants) were compared. The influence of contact pattern, SARS-CoV-2 prevalence outside the province and China, and testing coverage were examined in sensitivity analyses. RESULTS In basecase scenario (no event), the epidemic has been under control since March 2020. The event would lead to the detection of 1% more confirmed cases by 31 July when community contact rate increases to pre-epidemic level. In event scenario without additional interventions, there would be 599 (93%) more new infections comparing with basecase scenario. To avert new infections, quarantining all participants before the event would be the most effective strategy, followed by quarantining all overseas participants and testing all other participants, and testing all participants before the event and on day 7. However, testing strategy is likely to be affected by the SARS-CoV-2 prevalence outside the event province.  CONCLUSIONS Pre-event interventions are effective for reducing the risk of epidemic rebound caused by an international large-scale event. Universal testing for participants is likely to be an effective and feasible intervention. 

Impact Of Pre-event Testing And Quarantine On Reducing The Risk Of COVID-19 Epidemic Rebound: A Modelling Study (preprint)

BACKGROUND With the evolving growth of the COVID-19 epidemic, travel restriction policies would need to be adjusted accordingly. Prohibition of mass event may be relaxed for social and economic benefits when virus transmission stops but could bear the risk of epidemic rebound. Against the background of the varied SARS-CoV-2 prevalence internationally, we modelled the potential impacts of pre-event interventions on epidemic risk of holding a mass event when COVID-19 is under control. METHODS We developed a mathematical model of SARS-CoV-2 transmission in Guangdong Province, China, where local virus transmission ceased to occur. A large-scale international trade fair was assumed to be held, with influx of people from overseas and rest of China over a short period of time, who participated for 2-week. Scenarios of pre-event intervention (none, quarantine arrangement and polymerase chain reaction (PCR) testing for participants) were compared. The influence of contact pattern, SARS-CoV-2 prevalence outside the province and China, and testing coverage were examined in sensitivity analyses. RESULTS In basecase scenario (no event), the epidemic has been under control since March 2020. The event would lead to the detection of 1% more confirmed cases by 31 July when community contact rate increased to pre-epidemic level. In event scenario without additional interventions, there would be 599 (93%) more new infections comparing with basecase scenario. To avert new infections, quarantining all participants before the event would be the most effective strategy, followed by quarantining all overseas participants and testing all other participants, and testing all participants before the event and on day 7. However, testing strategy is likely to be affected by the SARS-CoV-2 prevalence outside the event province.  CONCLUSIONS Pre-event interventions are effective for reducing the risk of epidemic rebound caused by an international large-scale event. Universal testing for participants is likely to be an effective and feasible intervention. 

Availability of workplace policy for prevention of coronavirus disease 2019 and its relationship with personal protection behaviours: A survey of employees (preprint)

Background: The evolving pandemic of coronavirus disease 2019 (COVID-19) has become a severe threat to public health, and the workplace presents high risks in terms of spreading the disease. Few studies have focused on the impact of workplace policy on individual behaviours. This study aimed to examine the relationship of workplace guidelines and measures with employees’ behaviours regarding COVID-19 prevention. Methods: A cross-sectional survey using an online questionnaire was conducted to gather employees’ access to workplace guidelines and measures as well as their personal protection behaviours. Statistical associations between these two factors in different occupations were examined using multiple ordinal logistic regressions. Results: A total of 1048 valid questionnaires across five occupational groups were analysed. Manual labourers reported lower availability of workplace guidelines and measures (76.9% vs. 89.9% for all, P = 0.003). Employees with available workplace guidelines and measures performed personal protection behaviours with higher frequency, and this association was more significant among managers/administrators and manual labourers. Conclusions: Awareness about the disease and pandemic among employers and administrators should be promoted, and resources should be allocated to publish guidelines and implement measures in the workplace. Manual labourers may require specific attention regarding accessibility of relevant information, given their poorer experience of workplace policy and their work nature. Governments should guide the establishment of appropriate policies and responses at the workplace level. Further studies are needed to test the effectiveness of specific workplace policies on COVID-19 prevention.