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The Australian government swift interventions and actions early to the COVID 19 epidemic included enforced quarantine, isolation, varying degrees of social and physical distancing measures, travel restrictions, community level testing and enhanced contact tracing models which effected the trajectory of the epidemic impact. While the search for effective therapeutics and vaccines continues, it is important to understand how to effectively implement and optimise the current public health interventions available; application of traditional contact tracing , contributions of new contact tracing mobile phone applications, community level testing and use of specific fit for purpose diagnostic tests; to screen, detect and provide evidence of infection clearance.
While the suppression measures have been effective on disease transmission rates, it has had economic, social and non COVID-19 health costs impacts. As community restrictions change it will be important to monitor and evaluate the effectiveness of these key interventions. This is a longitudinal study that will follow the experience and behaviors of 2 key risk populations impacted by COVID-19 transmission containment measures.
This is a mixed methods longitudinal study of a cohort of 1000 participants for a 18 month period. Victorian adults from three key risk populations will be recruited into two longitudinal cohort groups. It will follow the experiences and behaviors of the groups; Recruitment group 1/Seed set 1 - recently diagnosed 'COVID-19 cases' (Group 1) and their social network with up to 2 waves of /Key people, will be followed up for 1 month initially with specific monitoring tools before they will transition into ongoing follow up and monitoring as recruitment group 2 Recruitment group 2/Seed set 2 - people from the general community that are not currently infected with COVID19 'Group 2' that are practicing physical distancing and represent specific key risk groups, and their social network with up to 2 waves of Key people.
Over sampling of vulnerable populations including people with disabilities, single parents and people living alone will be conducted across all groups. Individuals may be invited to participate in more than one cohort when they change between risk population definitions.
The project aims to meet the following objectives using a longitudinal cohort design and a mixed method of qualitative and quantitative tools to enable us to assess changes within individuals over time and to assess the influence of social networks on their health, well-being, attitudes and perceptions.
Objectives of the study are:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Group 1/Seed set 1 - recently diagnosed 'COVID-19 cases | (Group1) and their social network with up to 2 waves of /Key people, will be followed up for 1 month initially with specific monitoring tools before they will transition into ongoing follow up and monitoring as recruitment group 2 | ||
| Group 2/Seed set 2 - people from the general community that are not currently infected with COVID19 | 'Group 2' that are practicing physical distancing and represent specific key risk groups, and their social network with up to 2 waves of Key people |
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| Measure | Description | Time Frame |
|---|---|---|
| Change from response in baseline questionnaires to Adherence (non-adherence) with COVID19 transmission containment measures | Participants will complete questionnaires and diaries as binary daily measure of outcomes of compliance/nonadherence for each individual, determined by their ability to adhere to government regulations | 18 months |
| Change from response in baseline questionnaires depicting what are the unintended health, social and economic consequences of the government response to control COVID19 transmission | Using data from baseline and follow up questionnaires the researchers will calculate both an individual and a composite variable to assess the following unintended consequence, including:
| 18 months |
| What are the social contacts and mixing patterns of COVID-19 cases | The questionnaire participants will be ask to complete include nominating the number of contacts per day and the time will permit collation of social network data. Using data from the daily diaries (retrospective and prospective) the researchers will calculate:
| 18 months |
| Measure | Description | Time Frame |
|---|---|---|
| Collect and collate empirical data of COVID19 cases, contacts and key vulnerable groups | The researchers will integrate data from adherence outcomes, social contacts and transmission outcomes from diaries and questioners into key analyses; cross sectional social network models, structural equation modelling & social network analyses. Researchers will use empirical data regarding transmission dynamics, social contacts and mixing patterns of COVID19 cases, their contacts and key vulnerable groups to develop and refine mathematical models to improve precision and timeliness of dynamic transmission estimate. The researchers will use social contacts and transmission outcomes to update deterministic compartmental epidemic model to estimate the effect of testing and surveillance on the spread of COVID-19 in Australia. |
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Inclusion Criteria:
Exclusion Criteria:
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Volunteer participants over the age of 18 years in Victoria, Australia
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| Name | Affiliation | Role |
|---|---|---|
| Margaret Hellard | The Macfarlane Burnet Institute for Medical Research and Public Health | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The Macfarlane Burnet Institute for Medical Research and Public Health | Melbourne | Victoria | 3004 | Australia |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38216183 | Derived | Pedrana A, Bowring A, Heath K, Thomas AJ, Wilkinson A, Fletcher-Lartey S, Saich F, Munari S, Oliver J, Merner B, Altermatt A, Nguyen T, Nguyen L, Young K, Kerr P, Osborne D, Kwong EJL, Corona MV, Ke T, Zhang Y, Eisa L, Al-Qassas A, Malith D, Davis A, Gibbs L, Block K, Horyniak D, Wallace J, Power R, Vadasz D, Ryan R, Shearer F, Homer C, Collie A, Meagher N, Danchin M, Kaufman J, Wang P, Hassani A, Sadewo GRP, Robins G, Gallagher C, Matous P, Roden B, Karkavandi MA, Coutinho J, Broccatelli C, Koskinen J, Curtis S, Doyle JS, Geard N, Hill S, Coelho A, Scott N, Lusher D, Stoove MA, Gibney KB, Hellard M. Priority populations' experiences of isolation, quarantine and distancing for COVID-19: protocol for a longitudinal cohort study (Optimise Study). BMJ Open. 2024 Jan 12;14(1):e076907. doi: 10.1136/bmjopen-2023-076907. |
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| ID | Term |
|---|---|
| D000086382 | COVID-19 |
| ID | Term |
|---|---|
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
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| What are the types of social contacts and mixing patterns of COVID-19 cases | The questionnaire participants will be ask to complete will include nominating the relationship with their contacts per day(i.e. partner, child, friend), and individuals COVID test status, providing detailed specifics of the projects social network data | 18 months |
| Measure of the efficacy of government interventions | Participants responses to questionnaires, focus group discussions will be used as binary measurers; The researchers will not be using a scale, reporting will be in the format:
| 18 months |
| 18 months |
| Assess the effectiveness of government interventions to reduce COVID-19 transmission from questionnaire responses from cohort participants | Including but not limited to the number/proportion of participants with accurate knowledge of government recommendations and the number/proportion of participants using COVID-19 prevention measures as recommended. The researchers will analyse adherence outcomes using serial cross-sectional approach to estimate a adherence level for participant groups of a) isolation, b) quarantine and c) physical distancing. The researchers will assess changes in adherences over time within participant groups a) isolation, b) quarantine, using chi-square tests of proportions. The researchers will assess changes in adherence to government requirements within participant group c) physical distancing over time, using time to event analysis, such as proportional hazards tests or Cox regression. Researchers are not using individual assessments for analyses. | 18 months |
| D014777 |
| Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |