{
  "abstract": "Introduction Mass testing strategies recognise the importance of rapidly identifying individuals infected with potentially asymptomatic pathogens to mitigate outbreaks. A pressing question is how to design testing strategies that navigate the delicate balance between reducing morbidity and minimising societal disruption and absenteeism among key workers while balancing associated logistical and financial considerations.Methods We use an individual-based model with explicit viral dynamics to explore the inherent trade-offs in testing regimen design and identify optimal regimens for different stages of a pandemic. Applying this framework to the SARS-CoV-2 pandemic in the UK, we evaluate the impact of testing and isolation protocols on two key objectives: protecting the vulnerable and reducing transmission. Each testing regimen is evaluated according to three outcomes: reduction in infectivity, testing efficiency and isolation efficiency.Results Our analysis shows that these outcomes are on orthogonal optimisation axes, indicating a sharp trade-off between strategic goals. This implies that transmission reduction strategies are inherently inefficient and that regimens with high testing efficiency will always have suboptimal isolation efficiency. By focusing on testing regimens to protect vulnerable groups, we map the predicted outcomes across different stages of the diagnostic development pipeline. We also examine how adherence to testing and isolation guidelines may influence the selection of regimens aimed at reducing overall transmission.Conclusions Based on these findings, we offer a framework to streamline the selection process for robust testing strategies spanning the duration of a pandemic, ensuring consistently high levels of adherence and thus impact.",
  "authors": [
    {
      "affiliations": [
        "Nuffield Department of Medicine, Oxford University, Oxford, UK"
      ],
      "name": "Ricardo Aguas"
    },
    {
      "affiliations": [
        "Biostatistics and Health Data Science, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK"
      ],
      "name": "Caroline Franco"
    },
    {
      "affiliations": [
        "Nuffield Department of Medicine, Oxford University, Oxford, UK"
      ],
      "name": "Bo Gao"
    },
    {
      "affiliations": [
        "Department of Statistical Sciences, University of Cape Town, Rondebosch, South Africa"
      ],
      "name": "Sheetal P Silal"
    },
    {
      "affiliations": [
        "Department of Statistical Sciences, University of Cape Town, Rondebosch, South Africa"
      ],
      "name": "Jared Norman"
    },
    {
      "affiliations": [
        "Department of Public and Ecosystem Health, Cornell University, Ithaca, New York, USA"
      ],
      "name": "Siyu Chen"
    },
    {
      "affiliations": [
        "Mathematical and Economic Modelling, Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand"
      ],
      "name": "Wirichada Pan-Ngum"
    },
    {
      "affiliations": [
        "UK Health Security Agency, London, UK"
      ],
      "name": "Susan Hopkins"
    },
    {
      "affiliations": [
        "UK Health Security Agency, London, UK",
        "Public Health Wales, Oxford, UK",
        "William Harvey Research Institute, Queen Mary University of London, London, UK"
      ],
      "name": "Tom Fowler"
    },
    {
      "affiliations": [
        "Department of Biology, University of Oxford, Oxford, UK"
      ],
      "name": "Lisa White"
    },
    {
      "affiliations": [],
      "name": "The EY-Oxford Health Analytics Consortium"
    }
  ],
  "title": "Informing optimal testing and isolation strategies across different stages of the diagnostic development pipeline using mathematical models: SARS-CoV-2 in the UK as a case study",
  "uid": "f278ce0d-11f5-534d-937a-4af68bd1d0d1"
}