i-sense: EPSRC IRC in Agile Early Warning Sensing Systems for Infectious Diseases and Antimicrobial Resistance (i-sense COVID-19: Harnessing digital and diagnostic technologies for COVID-19)

  • Funded by UK Research and Innovation (UKRI)
  • Total publications:48 publications

Grant number: EP/R00529X/1

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Key facts

  • Disease

    COVID-19
  • Known Financial Commitments (USD)

    $490,800
  • Funder

    UK Research and Innovation (UKRI)
  • Principal Investigator

    Pending
  • Research Location

    United Kingdom
  • Lead Research Institution

    University College London
  • Research Priority Alignment

    N/A
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Diagnostics

  • Special Interest Tags

    N/A

  • Study Subject

    Non-Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

Additional funding for the I-sense IRC to address the COVID-19 Pandemic. To harness the multidisciplinary expertise within the i-sense EPSRC IRC track and test Flagship programmes to address the key challenges associated with the COVID-19 pandemic; namely early identification of infection in the community through online data sources and point-of-care diagnostic tests (PoCT) linked to national health systems.

Publicationslinked via Europe PMC

Estimating the household secondary attack rate and serial interval of COVID-19 using social media.

Ultrasensitive Dual ELONA/SERS-RPA Multiplex Diagnosis of Antimicrobial Resistance.

A large-scale and PCR-referenced vocal audio dataset for COVID-19.

Multiplex detection of the big five carbapenemase genes using solid-phase recombinase polymerase amplification.

Enhanced Antimalarial and Antisequestration Activity of Methoxybenzenesulfonate-Modified Biopolymers and Nanoparticles for Tackling Severe Malaria.

Long-term Sudan virus Ebola survivors maintain multiple antiviral defense mechanisms.

Cohort Profile: Virus Watch-understanding community incidence, symptom profiles and transmission of COVID-19 in relation to population movement and behaviour.

Neural network models for influenza forecasting with associated uncertainty using Web search activity trends.

CRISPR-assisted test for Schistosoma haematobium.