Monitoring outbreak events for disease surveillance in a data science context (MOOD)
WorldPop is one of 26 participants in this Horizon 2020 project, led by French agricultural research and international cooperation organisation, Cirad. The project aims to develop innovative tools and services for the early detection, assessment, and monitoring of current and potential infectious diseases threats in Europe in a context of global challenges, including climate change. Innovations arising from the project will increase the operational abilities of epidemic intelligence systems to combat new disease threats, including emerging diseases of known or unknown origin, and antimicrobial resistance pathogens.
These innovations will be designed to address the challenges of cross-sectorial data sharing and use in a One Health framework based on cross-sectorial collaboration for animal, human and environmental health. Expected end-users are the human and veterinary public-health agencies that are responsible for designing and implementing strategies to mitigate identified risks.
Our engagement with this project is to provide and tailor our work on population mapping and movement modelling to the development of innovative tools and services. We contribute to assessments of the risks of importation and spread of diseases from outside of the EU, provide insights of zoonotic and vector-borne disease transmission dynamics and share our learning from past outbreaks, through integrating geospatial datasets into phylodynamic analyses.
Our contributions include:
- Sourcing, acquiring, processing, and standardising of covariate data identified by disease profiles in collaboration with project partners, and sharing generic covariates as a resource for modelling a conceptualised threat: ‘Disease X’.
- Mapping of disease risk and analysis of trends and anomalies in space and time.
- Evaluating risk of importation of emerging infectious diseases, such as dengue and COVID-19.
MOOD output is being designed and developed with end users to assure their routine use during and beyond MOOD. They are tested and fine-tuned on air-borne, vector-borne, water-borne modelled diseases, including anti-microbial resistance. Extensive consultations with end users, studies into the barriers to data sharing, dissemination and training activities and studies on the cost-effectiveness of MOOD output are being undertaken to support sustainable user uptake.
- Effects of public-health measures for zeroing out different SARS-CoV-2 variants
- Combined and delayed impacts of epidemics and extreme weather on urban mobility recovery
- Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories (2022)
- Preliminary risk analysis of the international spread of new COVID-19 variants, lineage B.1.1.7, B.1.351 and P.1 (2021)
- Risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission Among Air Passengers in China (2021)
- Untangling introductions and persistence in COVID-19 resurgence in Europe (2021)
- Uncovering two phases of early intercontinental COVID-19 transmission dynamics (2020)
- Effect of non-pharmaceutical interventions to contain COVID-19 in China (2020)
- Assessing the impact of coordinated COVID-19 exit strategies across Europe (2020)
- Risk of Coronavirus Disease 2019 Transmission in Train Passengers: an Epidemiological and Modeling Study (2020)
Related WorldPop projects
- Exploring the Seasonality of COVID-19
- Human mobility models to forecast disease dynamics and the effectiveness of public health interventions (MIDAS)
- Covid-19 variants: Five things to know about how coronavirus is evolving in Horizon – The EU Research & Innovation Magazine
- MOnitoring Outbreaks for Disease surveillance in a data science context project website