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COVID-19 vaccines against the earlier strains of SARS-CoV-2 are now available. However, breakthrough infections can still occur due to waning antibodies and immune escape by new variants. We assessed humoral immune responses to the mRNA (BNT162b2) and inactivated (CoronaVac) vaccines in our healthcare worker cohort (HCW). We recruited HCWs from public and private healthcare institutions across Hong Kong and collected blood samples at enrolment and every 6 months from June 2020 to June 2022. A subset of volunteers provided blood samples between 10 – 42 days after each dose of vaccine. Immune responses to vaccination were measured as SARS-CoV-2 binding antibodies detected by an enzyme-linked immunosorbent assay (ELISA) and SARS-CoV-2 neutralising antibodies by surrogate virus neutralization test (sVNT) and plaque reduction neutralization test (PRNT). Among the 1,736 HCWs enrolled in our cohort, 252 HCWs provided pre- and post-vaccination blood samples after each dose of either vaccine. Two doses of BNT162b2 generated levels of neutralizing antibodies (sVNT inhibition = 96.8%, range = 42.8%, 98.2%) comparable to those generated by natural infections in the first wave (sVNT inhibition = 84.0%, range = 32.9%, 93.8%). Similar levels were achieved with three doses of CoronaVac (sVNT inhibition = 95.3%, range = 64.7%, 98.3%) and heterologous vaccination with two doses of CoronaVac followed by a booster dose of BNT162b2 vaccine (sVNT inhibition = 97.0%, range = 85.8%, 97.7%). These antibody levels waned faster after second doses and slower after third doses for both vaccines. The BNT162b2 vaccine and CoronaVac vaccines can generate robust antibody responses comparable to natural infections. Three doses of the CoronaVac vaccine, or a heterologous boost with the BNT162b2 vaccine following two doses of the CoronaVac vaccine are required to achieve similar levels of neutralising antibodies in vaccinees who received two doses of the BNT162b2 vaccine. [ FROM AUTHOR] Copyright of International Journal of Infectious Diseases is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Intro: The number of PCR-confirmed cases may serve poorly as a surveillance data for a representative disease activity, due to its bias towards symptomatic cases or people having an identifiable risk exposure, and the high dependency on changing testing policy. We reported a large-scale ad-hoc community surveillance initiative, Daily Antigen Rapid Testing Surveillance (DARTS) System, using self-performed rapid antigen tests(RAT). Method(s): A representative cohort of 10000+ individuals was enrolled over the territory. Participants were divided into 7 sub-cohorts to achieve a rolling schedule with 1400+ individuals on a daily basis. Participant performed the RAT regularly irrespective of symptom or exposure history, with a self-sampled throat-and-nasal swab. RAT result and photo were reported on the same day of testing through an online platform. Daily point prevalence was disseminated on a real-time dashboard to inform the situation awareness(https://covid19.sph.hku.hk/dashboard). Finding(s): Since its launch during the peak of the fifth wave in March 2022, the system has tracked the changing trajectory of different phases of the Omicron pandemic, including the rapidly subsiding daily prevalence from an initial high value of 12.7% (8.4-18.7) in early March to a baseline of 0.6% (0.2-1.4) in early April, maintained with a non-zero baseline (0.1-0.3%) over May, and subsequent stepwise increase to 0.5% (0.2-1.2) in June. The reproduction number increased from 0.66(0.63,0.70) to 1.23(1.14,1.33) from March to June, signifying the gradual increase of residual Omicron transmission. Conclusion(s): Our DARTS system has demonstrated the feasibility of a participatory surveillance system using self-performed RAT, and its utility as an ad-hoc surveillance to timely reflect the rapidly changing epidemic trajectory. Regular testing irrespective of symptom and exposure risk helps to give more representative picture, including subclinical cases who also carried an implication of disease transmission. The use of RAT also helps to avoid the constraint of manpower and testing capacity, and has been quickly adopted for case definition.Copyright © 2023
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Background: Hong Kong followed a strict COVID-19 elimination strategy in 2020. We estimated the impact of the COVID-19 pandemic responses on all-cause and cause-specific hospitalizations and deaths in 2020. Methods: Interrupted time-series analysis using negative binomial regression accounting for seasonality and long-term trend was used on weekly 2010–2020 data to estimate the change in hospitalization risk and excess mortality occurring both within and out of hospitals. Findings: In 2020, as compared to a 2010–2019 baseline, we observed an overall reduction in all-cause hospitalizations, and a concurrent increase in deaths. The overall hospitalization reduction (per 100,000 population) was 4809 (95% CI: 4692, 4926) in 2020, with respiratory diseases (632, 95% CI: 607, 658) and cardiovascular diseases (275, 95% CI: 264, 286) contributing most. The overall excess mortality (per 100,000 population) was 25 (95% CI: 23, 27) in 2020, mostly among individuals with pre-existing cardiovascular diseases (12, 95% CI: 11, 13). A reduction in excess in-hospital mortality (−10 per 100,000, 95% CI: −12, −8) was accompanied by an increase in excess out-of-hospital mortality (32, 95% CI: 29, 34). Interpretation: The COVID-19 pandemic might have caused indirect impact on population morbidity and mortality likely through changed healthcare seeking particularly in youngest and oldest individuals and those with cardiovascular diseases. Better healthcare planning is needed during public health emergencies with disruptions in healthcare services. Funding: Health and Medical Research Fund, Collaborative Research Fund, AIR@InnoHK and RGC Senior Research Fellow Scheme, Hong Kong. © 2022 The Authors