Post-Discharge Spirometry Evaluation in Patients Recovering from Moderate-to-Critical COVID-19: A Prospective Cohort Study (preprint)

To determine the prevalence and types of spirometry abnormalities among post-COVID-19 patients in Malaysia, with secondary objective focusing on associated factors. Conducted at the COVID-19 Research Clinic, Faculty of Medicine, University Technology MARA, from March 2021 to December 2022, this study included patients three months post-discharge from hospitals following moderate-to-critical COVID-19. Of 408 patients studied, abnormal spirometry was found in 46.8%, with 28.4% exhibiting a restrictive pattern, 17.4% showing preserved ratio impaired spirometry (PRISm), and 1.0% displaying an obstructive pattern. Factors independently associated with abnormal spirometry included older age (OR: 1.0, 95% CI: 1.01–1.04, p = 0.003), underlying cardiovascular disease (OR: 3.5, 95% CI: 1.19–10.47, p = 0.023), history of acute respiratory distress syndrome (p < 0.001), shorter discharge-to-follow-up interval (OR: 0.9, 95% CI: 1.00–1.02, p = 0.035), oxygen desaturation during 6-minute walk test (OR: 1.9, 95% CI: 1.20–3.06, p = 0.007), and presence of consolidation (OR: 8.1, 95% CI: 1.75–37.42, p = 0.008) or ground-glass opacity (OR: 2.6, 95% CI: 1.52–4.30, p < 0.001) on chest X-ray. This study highlights patients recovering from moderate-to-critical COVID-19 often exhibit abnormal spirometry, notably a restrictive pattern and PRISm. Routine spirometry screening for high-risk patients is recommended.

At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19 Patients Using Statistical and Machine Learning Methods: An International Cohort Study (preprint)

By September, 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients.

Waning COVID-19 Vaccine Effectiveness for BNT162b2 and CoronaVac in Malaysia: An Observational Study (preprint)

Evaluation of vaccine effectiveness over time against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or coronavirus disease 2019 (COVID-19) is important. Evidence on effectiveness over time for the CoronaVac vaccine is lacking despite its widespread use globally. In Malaysia, a diverse set-up of COVID-19 vaccines was rolled out nationwide, and the waning of vaccine protection is a concern. We aimed to investigate and compare waning vaccine effectiveness against COVID-19 infections, COVID-19 related ICU admission and COVID-19 related deaths for BNT162b2 and CoronaVac vaccines. In this observational study, we consolidated nationally representative data on COVID-19 vaccination and patients' outcomes. Data on all confirmed COVID-19 cases from 1 to 30 September 2021 were used to compare vaccine effectiveness between the 'early' group (fully vaccinated in April to June 2021) and the 'late' group (fully vaccinated in Jul to Aug 2021). We used a negative binomial regression model to estimate vaccine effectiveness against COVID-19 infections for both 'early' and 'late' groups, by comparing the rates of infection for individuals vaccinated in the two different periods relative to the unvaccinated. Among confirmed COVID-19 cases, we used logistic regression to estimate and compare vaccine effectiveness against ICU admission and deaths between the two different periods. For BNT162b2, vaccine effectiveness against COVID-19 infections declined from 90.8% (95% CI 89.4, 92.0) in the late group to 79.1% (95% CI 75.8, 81.9) in the late group. Vaccine effectiveness for BNT162b2 against ICU admission and deaths were comparable between the two different periods. For CoronaVac, vaccine effectiveness waned against COVID-19 infections from 74.4% in the late group (95% CI 209 70.4, 77.8) to 30.0% (95% CI 18.4, 39.9) in the early group. It also declined significantly against ICU admission, dropping from 56.1% (95% CI 51.4, 60.2) to 29.9% (95% CI 13.9, 43.0). For deaths, however, CoronaVac's effectiveness did not wane after three to five months of full vaccination. Vaccine effectiveness against COVID-19 infections waned after three to five months of full vaccination for both BNT162b2 and CoronaVac in Malaysia. Additionally, for CoronaVac, protection against ICU admission declined as well. Evidence on vaccine effectiveness over time informs evolving policy decisions on vaccination.

Sustaining effective COVID-19 control in Malaysia through large-scale vaccination (preprint)

Introduction As of 3rd June 2021, Malaysia is experiencing a resurgence of COVID-19 cases. In response, the federal government has implemented various non-pharmaceutical interventions (NPIs) under a series of Movement Control Orders and, more recently, a vaccination campaign to regain epidemic control. In this study, we assessed the potential for the vaccination campaign to control the epidemic in Malaysia and four high-burden regions of interest, under various public health response scenarios. Methods A modified susceptible-exposed-infectious-recovered compartmental model was developed that included two sequential incubation and infectious periods, with stratification by clinical state. The model was further stratified by age and incorporated population mobility to capture NPIs and micro-distancing (behaviour changes not captured through population mobility). Emerging variants of concern (VoC) were included as an additional strain competing with the existing wild-type strain. Several scenarios that included different vaccination strategies (i.e. vaccines that reduce disease severity and/or prevent infection, vaccination coverage) and mobility restrictions were implemented. Results The national model and the regional models all fit well to notification data but underestimated ICU occupancy and deaths in recent weeks, which may be attributable to increased severity of VoC or saturation of case detection. However, the true case detection proportion showed wide credible intervals, highlighting incomplete understanding of the true epidemic size. The scenario projections suggested that under current vaccination rates complete relaxation of all NPIs would trigger a major epidemic. The results emphasise the importance of micro-distancing, maintaining mobility restrictions during vaccination roll-out and accelerating the pace of vaccination for future control. Malaysia is particularly susceptible to a major COVID-19 resurgence resulting from its limited population immunity due to the historical success of the country in maintaining control throughout much of 2020.

Modelling infectious diseases with herd immunity in a randomly mixed population (preprint)

Background The conventional susceptible-infectious-recovered (SIR) model tends to overestimate the transmission dynamics of infectious diseases and ends up with total infections and total immunized population exceeding the threshold required for control and eradication of infectious diseases. The study aims to overcome the limitation by allowing the transmission rate of infectious disease to decline along with the reducing risk of contact infection.  Methods Two new SIR models were developed to mimic the declining transmission rate of infectious diseases at different stages of transmission. Model A mimicked the declining transmission rate along with the reducing risk of transmission following infection, while Model B mimicked the declining transmission rate following recovery. Then, the conventional SIR model, Model A and Model B were used to simulate an infectious disease with a basic reproduction number (r0) of 3.0 and a herd immunity threshold (HIT) of 0.667 with and without vaccination. The infectious disease was expected to be controlled or eradicated when the total immunized population either through infection or vaccination reached the level predicted by the HIT. Outcomes of simulations were assessed at the time when the total immunized population reached the level predicted by the HIT, and at the end of simulations. Findings All three models performed likewise at the beginning of the transmission when sizes of infectious and recovered were relatively small as compared with the population size. The infectious disease modelled using the conventional SIR model appeared completely out of control even when the HIT was achieved in all scenarios with and without vaccination. The infectious disease modelled using Model A appeared to be controlled at the level predicted by the HIT in all scenarios with and without vaccination. Model B projected the infectious disease to be controlled at the level predicted by the HIT only at high vaccination rates. At lower vaccination rates or without vaccination, the level at which the infectious disease was controlled cannot be accurately predicted by the HIT.   Conclusion Transmission dynamics of infectious diseases with herd immunity can accurately be modelled by allowing the transmission rate of infectious disease to decline along with the combined risk of contact infection. Model B provides a more credible framework for modelling infectious diseases with herd immunity in a randomly mixed population.   

Unveiling the real dynamics of transmission of COVID-19 in Malaysia without incarcerated clusters: a modelling study (preprint)

Introduction: The latest outbreak of COVID-19 in Malaysia emerged in early September and had two determinants. First, it involved incarcerated populations from four prisons located in Sabah, Kedah and Penang states. Second, the Sabah state by-election campaigns accelerated the spread of COVID-19 in the state and across the South China Sea into the west Malaysia. The emergence of multiple incarcerated clusters at different time points may shadow the real dynamics of transmission of COVID-19 in the community and lead to inaccurate interpretation and conclusion. The study aimed to reveal the real spreading pattern of COVID-19 by excluding incarcerated clusters in the modelling.Methodology: We extended the susceptible-infectious-removed (SIR) model to include an additional class for non-isolated active cases, which was assumed to impel the transmission of COVID-19 in the community. The model was fitted to actual total and removed cases for estimation of duration of transmission and hospitalization. The parameters were then applied to model the transmission for COVID-19 in the community.Results: The presence of incarcerated clusters shadowed the dynamics of transmission of COVID-19 with a lower reproduction number of 2.0. The proportion of non-isolated active cases increased slowly from 49.4% on 1 September 2020 to 60.3% on 8 October 2020. In the absence of incarcerated clusters, the dynamics of transmission of COVID-19 appeared differently with a higher reproduction number of 2.3. The proportion of non-isolated active cases increased tremendously from 22.1% on 1 September 2020 to 63.7% on 8 October 2020. The tremendous increase of non-isolated active cases impelled the dynamics of transmission of COVID-19 in the community following the Sabah state by-election campaigns and more inter-state travels.Conclusion: The inclusion of incarcerated clusters shadowed the dynamics of transmission of COVID-19 in the community with a lower transmission rate, which might lead to wrong interpretation of the dynamics of transmission in the community.

Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model (preprint)

The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I), and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, βt, and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily, and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4·7% each day with a decreased capacity of 40%. For 7–day and 14–day projections, the modified SIR model accurately predicted I total, I, and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19.

Serology surveillance of anti-SARS-CoV-2 antibodies among asymptomatic healthcare workers in Malaysian healthcare facilities designated for COVID-19 care (preprint)

IntroductionHealthcare workers (HCW) are presumed to be at increased risk of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection due to occupational exposure to infected patients. We aim to determine the prevalence of anti-SARS-CoV-2 antibodies among asymptomatic HCW.MethodsWe prospectively recruited HCW from the National Public Health Laboratory and two COVID-19 designated public hospitals in Klang Valley, Malaysia between April 13th and May 12th, 2020. Quota sampling was applied to ensure adequate representation of the HCW involved in provision of care for patients directly and indirectly. All participants had worked in the respective healthcare facility for at least 30 days prior study enrollment. HCW who were previously confirmed with COVID-19 infection or listed as “patient under investigation” were excluded. A self-administered questionnaire was used to capture sociodemographic information, history of contact with COVID-19 cases within the past month, clinical signs and symptoms and adherence to universal precautions. Blood samples were taken to test for anti-SARS-CoV-2 antibodies by surrogate virus neutralization test.ResultsA total of 400 HCW were recruited, comprising 154 (38.5%) nurses, 103 (25.8%) medical doctors, 47 (11.8%) laboratory technologists and others (23.9%). The mean age was 35±7.8 years, with females predominant (74%). A majority (68.9%) reported direct contact with COVID-19 patients, body fluids of COVID-19 patients and/or contaminated objects and surfaces in the past month within their respective workplaces. Nearly all claimed to adhere to personal protection equipment (PPE) guidelines (97%-100% adherence) and hand hygiene practice (91%-96% adherence). None (95% CI: 0, 0.0095) of the participants had anti-SARS-CoV-2 antibodies detected, despite 135 (33.8%) reporting respiratory symptoms one month prior to study recruitment. One hundred and fifteen (29%) participants claimed to have contact with known COVID-19 persons outside of the workplace.ConclusionOur finding of zero seroprevalence among asymptomatic HCW suggests a low risk of asymptomatic COVID-19 infection in our healthcare setting; which is at expected levels for a country with an incidence of 26 per 100,000. The adequacy of PPE equipment and strict adherence to infection prevention and control measures offers considerable protection during contact with COVID-19 cases and should be ensured to prevent future nosocomial transmission.