Browsing by Author "Binny RN"

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    Emerging advances in biosecurity to underpin human, animal, plant, and ecosystem health.
    (Elsevier B.V., 2023-09-15) Hulme PE; Beggs JR; Binny RN; Bray JP; Cogger N; Dhami MK; Finlay-Smits SC; French NP; Grant A; Hewitt CL; Jones EE; Lester PJ; Lockhart PJ
    One Biosecurity is an interdisciplinary approach to policy and research that builds on the interconnections between human, animal, plant, and ecosystem health to effectively prevent and mitigate the impacts of invasive alien species. To support this approach requires that key cross-sectoral research innovations be identified and prioritized. Following an interdisciplinary horizon scan for emerging research that underpins One Biosecurity, four major interlinked advances were identified: implementation of new surveillance technologies adopting state-of-the-art sensors connected to the Internet of Things, deployable handheld molecular and genomic tracing tools, the incorporation of wellbeing and diverse human values into biosecurity decision-making, and sophisticated socio-environmental models and data capture. The relevance and applicability of these innovations to address threats from pathogens, pests, and weeds in both terrestrial and aquatic ecosystems emphasize the opportunity to build critical mass around interdisciplinary teams at a global scale that can rapidly advance science solutions targeting biosecurity threats.
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    Practitioner perspectives on informing decisions in One Health sectors with predictive models
    (Springer Nature Limited, 2025-12-01) Pepin KM; Carlisle K; Chipman RB; Cole D; Anderson DP; Baker MG; Benschop J; Bunce M; Binny RN; French N; Greenhalgh S; O’Neale DRJ; McDougall S; Morgan FJ; Muellner P; Murphy E; Plank MJ; Tompkins DM; Hayman DTS
    The continued emergence of challenges in human, animal, and environmental health (One Health sectors) requires public servants to make management and policy decisions about system-level ecological and sociological processes that are complex, poorly understood, and change over time. Relying on intuition, evidence, and experience for robust decision-making is challenging without a formal assimilation of these elements (a model), especially when the decision needs to consider potential impacts if an action is or is not taken. Models can provide assistance to this challenge, but effective development and use of model-based evidence in decision-making (‘model-to-decision workflow’) can be challenging. To address this gap, we examined conditions that maximize the value of model-based evidence in decision-making in One Health sectors by conducting 41 semi-structured interviews of researchers, science advisors, operational managers, and policy decision-makers with direct experience in model-to-decision workflows (‘Practitioners’) in One Health sectors. Broadly, our interview guide was structured to understand practitioner perspectives about the utility of models in health policy or management decision-making, challenges and risks with using models in this capacity, experience with using models, factors that affect trust in model-based evidence, and perspectives about conditions that lead to the most effective model-to-decision workflow. We used inductive qualitative analysis of the interview data with iterative coding to identify key themes for maximizing the value of model-based evidence in One Health applications. Our analysis describes practitioner perspectives for improved collaboration among modelers and decision-makers in public service, and priorities for increasing accessibility and value of model-based evidence in One Health decision-making. Two emergent priorities include establishing different standards for development of model-based evidence before or after decisions are made, or in real-time versus preparedness phases of emergency response, and investment in knowledge brokers with modeling expertise working in teams with decision-makers.
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    Sensitivity of Reverse Transcription Polymerase Chain Reaction Tests for Severe Acute Respiratory Syndrome Coronavirus 2 Through Time
    (Oxford University Press on behalf of Infectious Diseases Society of America, 2023-01-01) Binny RN; Priest P; French NP; Parry M; Lustig A; Hendy SC; Maclaren OJ; Ridings KM; Steyn N; Vattiato G; Plank MJ
    BACKGROUND: Reverse transcription polymerase chain reaction (RT-PCR) tests are the gold standard for detecting recent infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Reverse transcription PCR sensitivity varies over the course of an individual's infection, related to changes in viral load. Differences in testing methods, and individual-level variables such as age, may also affect sensitivity. METHODS: Using data from New Zealand, we estimate the time-varying sensitivity of SARS-CoV-2 RT-PCR under varying temporal, biological, and demographic factors. RESULTS: Sensitivity peaks 4-5 days postinfection at 92.7% (91.4%-94.0%) and remains over 88% between 5 and 14 days postinfection. After the peak, sensitivity declined more rapidly in vaccinated cases compared with unvaccinated, females compared with males, those aged under 40 compared with over 40s, and Pacific peoples compared with other ethnicities. CONCLUSIONS: Reverse transcription PCR remains a sensitive technique and has been an effective tool in New Zealand's border and postborder measures to control coronavirus disease 2019. Our results inform model parameters and decisions concerning routine testing frequency.