Dataset Information

Phylodynamics reveals the role of human travel and contact tracing in controlling the first wave of COVID-19 in four island nations.

ABSTRACT: New Zealand, Australia, Iceland, and Taiwan all saw success in controlling their first waves of Coronavirus Disease 2019 (COVID-19). As islands, they make excellent case studies for exploring the effects of international travel and human movement on the spread of COVID-19. We employed a range of robust phylodynamic methods and genome subsampling strategies to infer the epidemiological history of Severe acute respiratory syndrome coronavirus 2 in these four countries. We compared these results to transmission clusters identified by the New Zealand Ministry of Health by contact tracing strategies. We estimated the effective reproduction number of COVID-19 as 1-1.4 during early stages of the pandemic and show that it declined below 1 as human movement was restricted. We also showed that this disease was introduced many times into each country and that introductions slowed down markedly following the reduction of international travel in mid-March 2020. Finally, we confirmed that New Zealand transmission clusters identified via standard health surveillance strategies largely agree with those defined by genomic data. We have demonstrated how the use of genomic data and computational biology methods can assist health officials in characterising the epidemiology of viral epidemics and for contact tracing.

SUBMITTER: Douglas J

PROVIDER: S-EPMC8344840 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Phylodynamics reveals the role of human travel and contact tracing in controlling the first wave of COVID-19 in four island nations.

Douglas Jordan J Mendes Fábio K FK Bouckaert Remco R Xie Dong D Jiménez-Silva Cinthy L CL Swanepoel Christiaan C de Ligt Joep J Ren Xiaoyun X Storey Matt M Hadfield James J Simpson Colin R CR Geoghegan Jemma L JL Drummond Alexei J AJ Welch David D

Virus evolution 20210608 2

New Zealand, Australia, Iceland, and Taiwan all saw success in controlling their first waves of Coronavirus Disease 2019 (COVID-19). As islands, they make excellent case studies for exploring the effects of international travel and human movement on the spread of COVID-19. We employed a range of robust phylodynamic methods and genome subsampling strategies to infer the epidemiological history of Severe acute respiratory syndrome coronavirus 2 in these four countries. We compared these results to ...[more]

PMID: 34527282

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Project description:BackgroundContact tracing (CT) represented one of the core activities for the prevention and control of COVID-19 in the early phase of the pandemic. Several guidance documents were developed by international public health agencies and national authorities on the organization of COVID-19 CT activities. While most research on CT focused on the use digital tools or relied on modelling techniques to estimate the efficacy of interventions, poor evidence is available on the real-world implementation of CT strategies and on the organizational models adopted during the initial phase of the emergency to set up CT activities.ObjectiveWe aimed to provide a comprehensive picture of the organizational aspects of CT activities during the first wave of the pandemic through the systematic identification and description of CT strategies used in different settings during the period from March to June 2020.MethodsA systematic review of published studies describing organizational models of COVID-19 CT strategies developed in real-world settings was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, Embase, and Cochrane Library were searched. Studies not providing a description of the organizational aspects of CT strategies and studies reporting or modelling theoretical strategies or focusing on the description of digital technologies' properties were excluded. Quality of reporting was assessed by using the Template for Intervention Description and Replication Checklist for Population Health and Policy. We developed a narrative synthesis, using a conceptual framework to map the extracted studies broken down by target population.ResultsWe retrieved a total of 1638 studies, of which 17 were included in the narrative synthesis; 7 studies targeted the general population and 10 studies described CT activities carried out in specific population subgroups. Our review identified some common elements across studies used to develop CT activities, including decentralization of CT activities, involvement of trained nonpublic health resources (eg, university students or civil servants), use of informatics tools for CT management, interagency collaboration, and community engagement. CT strategies implemented in the workplace envisaged a strong collaboration with occupational health services. Outreach activities were shown to increase CT efficiency in susceptible groups, such as people experiencing homelessness. Data on the effectiveness of CT strategies are scarce, with only few studies reporting on key performance indicators.ConclusionsDespite the lack of systematically collected data on CT effectiveness, our findings can provide some indication for the future planning and development of CT strategies for infectious disease control, mainly in terms of coordination mechanisms and the use of human and technical resources needed for the rapid development of CT activities. Further research on the organizational models of CT strategies during the COVID-19 pandemic would be required to contribute to a more robust evidence-making process.

Project description:The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting daily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as "pingdemic," may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users' infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.

Project description:To date, research on social capital in Indigenous contexts has been scarce. In this quantitative study, our objectives were to (1): Describe bonding social capital within four distinct First Nations communities in Canada, and (2) Explore the associations between bonding social capital and self-rated health in these communities. With community permission, cross-sectional data were drawn from the Canadian Alliance for Healthy Hearts and Minds study. Four reserve-based First Nations communities were included in the analysis, totaling 591 participants. Descriptive statistics were computed to examine levels of social capital among communities and logistic regression analyses were performed to identify social capital predictors of good self-rated health. Age, sex, education level, and community were controlled for in all models. Across the four communities in this study, areas of common social capital included frequent socialization among friends and large and interconnected family networks. Positive self-rated health was associated with civic engagement at federal or provincial levels (OR=1.65, p<0.05) and organizational membership (OR=1.60, p<0.05), but overall, sociodemographic variables were more significantly associated with self-rated health than social capital variables. Significant differences in social capital were found across the four communities and community of residence was a significant health outcomes predictor in all logistic regression models. In conclusion, this study represents one of the first efforts to quantitatively study First Nations social capital with respect to health in Canada. The results reflect significant differences in the social capital landscape across different First Nations communities and suggest the need for social capital measurement tools that may be adapted to unique Indigenous contexts. Further, the impact of social capital on health may be better explored and interpreted with more community-specific instruments and with supplementary qualitative inquiry.

Dataset Information

Phylodynamics reveals the role of human travel and contact tracing in controlling the first wave of COVID-19 in four island nations.

Publications

Phylodynamics reveals the role of human travel and contact tracing in controlling the first wave of COVID-19 in four island nations.

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