Eco-Epidemiological Intelligence for early Warning and response to mosquito-borne disease risk in Endemic and Emergence settings

Grant number: 101086640

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Key facts

  • Disease

    N/A

  • Start & end year

    2023
    2026
  • Known Financial Commitments (USD)

    $4,368,306.03
  • Funder

    European Commission
  • Principal Investigator

    BARTUMEUS Frederic
  • Research Location

    Spain
  • Lead Research Institution

    AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS
  • Research Priority Alignment

    N/A
  • Research Category

    Animal and environmental research and research on diseases vectors

  • Research Subcategory

    Animal source and routes of transmission

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

Mosquito-borne diseases place a heavy burden on society, causing widespread suffering and driving poverty. They are increasing in prevalence, geographical distribution and severity, representing a growing threat worldwide. Hence, there is a need for better disease intelligence, capable of anticipating and identifying eco-epidemiological risks leading to explosive epidemics and emergence in previously unaffected areas. The basis of such intelligence stems from a deep understanding of the factors that drive disease circulation, emergence and spread. This requires insights into the complex interplay between humans, pathogen-carrying mosquitoes, pathogen reservoirs (e.g. birds), and a changing environment. The E4Warning consortium brings together interdisciplinary, innovative, and open science to contribute to the One Health paradigm shift that is required to tackle the spread and transmission of zoonotic deadly pathogens, and harness this shift to nowcast and forecast mosquito-borne disease risk in a constantly changing and globally connected environment. Our work aims to disrupt disease transmission pathways connecting humans, mosquitoes, and birds through innovative eco-epidemiological modelling tools and intelligent digital solutions, co-designed and implemented by public health administrations. Open innovation strategies and big data tools are the cornerstone of the next-level One Health Early Warning Systems required in the face of mounting mosquito-borne disease threats.

Publicationslinked via Europe PMC

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Toward optimal disease surveillance with graph-based active learning.

Automated classification of mixed populations of Aedes aegypti and Culex quinquefasciatus mosquitoes under field conditions.