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Public health in genetic spaces: a statistical framework to optimize cluster-based outbreak detection.

ABSTRACT: Genetic clustering is a popular method for characterizing variation in transmission rates for rapidly evolving viruses, and could potentially be used to detect outbreaks in 'near real time'. However, the statistical properties of clustering are poorly understood in this context, and there are no objective guidelines for setting clustering criteria. Here, we develop a new statistical framework to optimize a genetic clustering method based on the ability to forecast new cases. We analysed the pairwise Tamura-Nei (TN93) genetic distances for anonymized HIV-1 subtype B pol sequences from Seattle (n?=?1,653) and Middle Tennessee, USA (n?=?2,779), and northern Alberta, Canada (n?=?809). Under varying TN93 thresholds, we fit two models to the distributions of new cases relative to clusters of known cases: 1, a null model that assumes cluster growth is strictly proportional to cluster size, i.e. no variation in transmission rates among individuals; and 2, a weighted model that incorporates individual-level covariates, such as recency of diagnosis. The optimal threshold maximizes the difference in information loss between models, where covariates are used most effectively. Optimal TN93 thresholds varied substantially between data sets, e.g. 0.0104 in Alberta and 0.016 in Seattle and Tennessee, such that the optimum for one population would potentially misdirect prevention efforts in another. For a given population, the range of thresholds where the weighted model conferred greater predictive accuracy tended to be narrow (±0.005 units), and the optimal threshold tended to be stable over time. Our framework also indicated that variation in the recency of HIV diagnosis among clusters was significantly more predictive of new cases than sample collection dates (?AIC > 50). These results suggest that one cannot rely on historical precedence or convention to configure genetic clustering methods for public health applications, especially when translating methods between settings of low-level and generalized epidemics. Our framework not only enables investigators to calibrate a clustering method to a specific public health setting, but also provides a variable selection procedure to evaluate different predictive models of cluster growth.

SUBMITTER: Chato C

PROVIDER: S-EPMC7069216 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Public health in genetic spaces: a statistical framework to optimize cluster-based outbreak detection.

Chato Connor C Kalish Marcia L ML Poon Art F Y AFY

Virus evolution 20200101 1

Genetic clustering is a popular method for characterizing variation in transmission rates for rapidly evolving viruses, and could potentially be used to detect outbreaks in 'near real time'. However, the statistical properties of clustering are poorly understood in this context, and there are no objective guidelines for setting clustering criteria. Here, we develop a new statistical framework to optimize a genetic clustering method based on the ability to forecast new cases. We analysed the pair ...[more]

PMID: 32190349

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Project description:BackgroundNumerous determinants have been linked to public mental health; however, they have not been brought together in a comprehensive conceptual framework. The goal of this work was to bring together academic research, practitioner expertise, and public perspectives to create a public mental health conceptual framework.MethodsThe development process proceeded in four stages. First, we identified a comprehensive list of potential determinants through a state-of-the-art academic literature review, grey literature review, and created mind maps created by peer researchers. Next, we conducted in-person workshops, consultations, and an online survey with academics, practitioners, policy makers, and members of the public to review the potential determinants, nominate additional determinants, and prioritise determinants by importance for understanding public mental health. This iterative process resulted in the final list of determinants contained in the framework. We then conducted rapid reviews to define each determinant and to identify key research, interventions, and resources. Finally, we worked with a design team to visualise the conceptual framework as an online tool and printable infographic.ResultsWe found substantial overlap between sources reflecting a shared understanding of the key drivers of public mental health. The unique determinants that emerged from each data source highlighted the importance of using multiple sources to create a comprehensive model. 72 potential determinants were prioritised through stakeholder consultations, resulting in a final list of 55 determinants and organised into four levels: individual, family, community, and structural.ConclusionsThis is the most complete conceptual framework for public mental health to date, bringing together academic research, policy and practitioner views, and lived experience perspectives. The co-production processes and tools we used provides a template for researchers looking to include multiple perspectives in their research. The conceptual framework draws together current knowledge on each determinant, but also highlights areas where further research is needed to better understand the relationship between each factor and mental health, which can inform the research agenda. This online tool and infographic can be used by practitioners to identify interventions for promoting mental health, and by the general public as a resource to increase awareness of the broad factors which shape public mental health.

Project description:Genetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver. For these projects, monitoring will be required to detect unintended spread of gene drive mosquitoes beyond field sites, and the emergence of alternative alleles, such as drive-resistant alleles or non-functional effector genes, within intervention sites. This entails the need to distribute mosquito traps efficiently such that an allele of interest is detected as quickly as possible - ideally when remediation is still viable. Additionally, insecticide-based tools such as bednets are compromised by insecticide-resistance alleles for which there is also a need to detect as quickly as possible. To this end, we present MGSurvE (Mosquito Gene SurveillancE): a computational framework that optimizes trap placement for genetic surveillance of mosquito populations such that the time to detection of an allele of interest is minimized. A key strength of MGSurvE is that it allows important biological features of mosquitoes and the landscapes they inhabit to be accounted for, namely: i) resources required by mosquitoes (e.g., food sources and aquatic breeding sites) can be explicitly distributed through a landscape, ii) movement of mosquitoes may depend on their sex, the current state of their gonotrophic cycle (if female) and resource attractiveness, and iii) traps may differ in their attractiveness profile. Example MGSurvE analyses are presented to demonstrate optimal trap placement for: i) an Aedes aegypti population in a suburban landscape in Queensland, Australia, and ii) an Anopheles gambiae population on the island of São Tomé, São Tomé and Príncipe. Further documentation and use examples are provided in project's documentation. MGSurvE is freely available as an open-source Python package on pypi (https://pypi.org/project/MGSurvE/). It is intended as a resource for both field and computational researchers interested in mosquito gene surveillance.

Dataset Information

Public health in genetic spaces: a statistical framework to optimize cluster-based outbreak detection.

Publications

Public health in genetic spaces: a statistical framework to optimize cluster-based outbreak detection.

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