The impact of three progressively introduced interventions on second wave daily COVID-19 case numbers in Melbourne, Australia

Priority populations' experiences of isolation, quarantine and distancing for COVID-19: protocol for a longitudinal cohort study (Optimise Study)

INTRODUCTION

Longitudinal studies can provide timely and accurate information to evaluate and inform COVID-19 control and mitigation strategies and future pandemic preparedness. The Optimise Study is a multidisciplinary research platform established in the Australian state of Victoria in September 2020 to collect epidemiological, social, psychological and behavioural data from priority populations. It aims to understand changing public attitudes, behaviours and experiences of COVID-19 and inform epidemic modelling and support responsive government policy.

METHODS AND ANALYSIS

This protocol paper describes the data collection procedures for the Optimise Study, an ongoing longitudinal cohort of ~1000 Victorian adults and their social networks. Participants are recruited using snowball sampling with a set of seeds and two waves of snowball recruitment. Seeds are purposively selected from priority groups, including recent COVID-19 cases and close contacts and people at heightened risk of infection and/or adverse outcomes of COVID-19 infection and/or public health measures. Participants complete a schedule of monthly quantitative surveys and daily diaries for up to 24 months, plus additional surveys annually for up to 48 months. Cohort participants are recruited for qualitative interviews at key time points to enable in-depth exploration of people's lived experiences. Separately, community representatives are invited to participate in community engagement groups, which review and interpret research findings to inform policy and practice recommendations.

ETHICS AND DISSEMINATION

The Optimise longitudinal cohort and qualitative interviews are approved by the Alfred Hospital Human Research Ethics Committee (# 333/20). The Optimise Study CEG is approved by the La Trobe University Human Ethics Committee (# HEC20532). All participants provide informed verbal consent to enter the cohort, with additional consent provided prior to any of the sub studies. Study findings will be disseminated through public website (https://optimisecovid.com.au/study-findings/) and through peer-reviewed publications.

TRIAL REGISTRATION NUMBER

NCT05323799.

Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model

Background

Forecast models have been essential in understanding COVID-19 transmission and guiding public health responses throughout the pandemic. This study aims to assess the effect of weather variability and Google data on COVID-19 transmission and develop multivariable time series AutoRegressive Integrated Moving Average (ARIMA) models for improving traditional predictive modelling for informing public health policy.

Methods

COVID-19 case notifications, meteorological factors and Google data were collected over the B.1.617.2 (Delta) outbreak in Melbourne, Australia from August to November 2021. Timeseries cross-correlation (TSCC) was used to evaluate the temporal correlation between weather factors, Google search trends, Google Mobility data and COVID-19 transmission. Multivariable time series ARIMA models were fitted to forecast COVID-19 incidence and Effective Reproductive Number (R _eff ) in the Greater Melbourne region. Five models were fitted to compare and validate predictive models using moving three-day ahead forecasts to test the predictive accuracy for both COVID-19 incidence and R _eff over the Melbourne Delta outbreak.

Results

Case-only ARIMA model resulted in an R squared (R²) value of 0.942, Root Mean Square Error (RMSE) of 141.59, and Mean Absolute Percentage Error (MAPE) of 23.19. The model including transit station mobility (TSM) and maximum temperature (Tmax) had greater predictive accuracy with R² 0.948, RMSE 137.57, and MAPE 21.26.

Conclusion

Multivariable ARIMA modelling for COVID-19 cases and R _eff was useful for predicting epidemic growth, with higher predictive accuracy for models including TSM and Tmax. These results suggest that TSM and Tmax would be useful for further exploration for developing weather-informed early warning models for future COVID-19 outbreaks with potential application for the inclusion of weather and Google data with disease surveillance in developing effective early warning systems for informing public health policy and epidemic response.

Inclusion of Cultural and Linguistic Diversity in COVID-19 Public Health Research: Research Design Adaptations to Seek Different Perspectives in Victoria, Australia

Participation of people from culturally and linguistically diverse (CALD) communities in public health research is often limited by challenges with recruitment, retention and second-language data collection. Consequently, people from CALD communities are at risk of their needs being marginalised in public health interventions. This paper presents intrinsic case analyses of two studies which were adapted to increase the cultural competence of research processes. Both cases were part of the Optimise study, a major mixed methods research study in Australia which provided evidence to inform the Victorian state government's decision-making about COVID-19 public health measures. Case study 1 involved the core Optimise longitudinal cohort study and Case study 2 was the CARE Victorian representative survey, an Optimise sub-study. Both case studies engaged cultural advisors and bilingual staff to adjust the survey measures and research processes to suit target CALD communities. Reflexive processes provided insights into the strengths and weaknesses of the inclusive strategies. Selected survey results are provided, demonstrating variation across CALD communities and in comparison to participants who reported speaking English at home. While in most cases a gradient of disadvantage was evident for CALD communities, some patterns were unexpected. The case studies demonstrate the challenge and value of investing in culturally competent research processes to ensure research guiding policy captures a spectrum of experiences and perspectives.

Immunogenicity and Effectiveness of Primary and Booster Vaccine Combination Strategies during Periods of SARS-CoV-2 Delta and Omicron Variants

In this study involving a cohort of employees of the National Airline company in Lebanon, we assessed humoral immunity levels and the effectiveness of two COVID-19 vaccines, Gam-COVID-Vac versus BNT162b2, after two doses and after a homologous and heterologous BNT162b2 booster, in addition to the impact of hybrid immunity. Vaccine effectiveness (VE) was retrospectively determined against laboratory-confirmed SARS-CoV-2 infection during the periods of Delta and Omicron variants' predominance, separately, and was calculated based on a case-control study design. The humoral immune response, measured by a SARS-CoV-2 anti-spike receptor-binding domain (RBD) IgG titer, was prospectively assessed after the aforementioned vaccination schemes at different time points. This study showed higher effectiveness of BNT162b2 after two doses (81%) compared to two doses of Gam-COVID-Vac (41.8%) against the Delta variant of SARS-CoV-2, which correlated with anti-spike antibody levels. Regarding the Omicron variant, protection against infection and antibody levels were severely compromised and the correlation between an anti-spike IgG titer and effectiveness was lost, unlike the situation during the Delta wave. Considering the booster vaccination schemes, a homologous BNT162b2 booster after a BNT162b2 primary vaccination induced a higher humoral immune response when compared to that induced by a heterologous BNT162b2 booster after a Gam-COVID-Vac primary vaccination. However, the VE of both booster regimens against the Omicron variant was almost equal (64% in the homologous regimen and 57% in heterologous regimen). Hybrid immunity evidenced a better humoral response and a greater and longer protection against Delta and Omicron infections compared to vaccination-induced immunity in COVID-19-naïve individuals. Finally, the findings show that VE waned with time during the same wave, highlighting the importance of reinforcing primary and booster COVID-19 vaccination mainly at the beginning of each wave during the surge of a new variant of concern.