Periodic Reporting for period 3 - VEO (Versatile Emerging infectious disease Observatory)
Reporting period: 2023-01-01 to 2024-06-30
VEO is being (co)designed and tested through five scenarios, reflecting main pathways of disease emergence, to attune developments to the needs of its intended users, and obtain proof-of-principle of utility, including ethical, legal and social implications. These scenarios represent emergence of a vector-borne (mosquito/tick) disease, a zoonotic disease following spillover from wild life, diseases caused by ecological impact of climate change, and global epidemics from previously localized problems (silent epidemics, for instance AMR). The last scenario is a Disease X scenario, bringing all tools together for a completely unknown emerging disease.
To meet these ambitious goals, VEO has outlined an interconnected workplan, on the one hand developing infrastructure and tools for data mobilization, linking and querying, and on the other hand, bringing together expert teams to work along one of the emergence pathways.
Despite the delays due to the pandemic, the use-case scenario work packages progressed well, with some adjustments to the plans. The mosquito-borne work took the observations of new detections of West Nile Virus (WNV) in Northern Europe. In the recently completed reporting period, integrated analysis of data on land use, bird migration data, climate variables and field data showed that in fact there are distinct dispersal patterns of these viruses in Europe, driven by different drivers. Land use change was listed as one of the key drivers. As information on mosquito presence and abundance is a key component of models predicting dispersal, the Mosquito Alert app was used to collect such information through citizens. In line with this broader engagement, a data challenge was called, in which more than 1000 data scientists from 100 teams from all over the world worked on improving the current models for mosquito identification.
Building from the same approach, the global epizootic of highly pathogenic avian influenza viruses was tracked, reconstructing from genomic data coupled with information of (migratory) birds what the most likely route of dispersal had been. The ongoing analyses also show where hotspots are for further mixing of genes of this evolving pathogen.
As Greenland is important in the ecology of wild birds and is one of the most affected ecosystems due to climate change, we were interested to study its role in global dissemination of bird-borne and vector-borne pathogens. Two expeditions to Greenland were organized to look into exposures to avian influenza but also other pathogens in birds, mosquitos and mammals in Greenland. This confirmed evidence of exposure to H5 influenza, but also made clear how variable the climate is, with large differences in environmental suitability for bird breeding in the two years. This work also was used for a cross consortium initiative to address risks and benefits of biosurveillance studies.
This work has continued with the spill over of H5N1 avian influenza into livestock (cattle) in North America, which was unexpected and unprecedented and raised new questions about genomic markers predicting host range and mammalian adaptation. A new activity in VEO has been the start of exploratory studies that aim to predict phenotypes from pathogen genomes, building from historic datasets and using different machine-learning approaches.
Part of the silent epidemic scenario is the exploration of the use of wastewater surveillance as an early warning indicator. This approach moved into the spotlight during the pandemic, where wastewater surveillance was established across Europe, including through a Joint Research Centre (JRC) coordinated effort. VEO is exploring how this could be expanded to any pathogen by using metagenomic sequencing, which generates data of a size and complexity that currently is difficult to host in commonly used databases in public health. Following initial publications on antimicrobial resistance genes in untreated sewage from 101 countries, a deeper analysis focused on tools for mining the large fraction of any metagenomic dataset that is not a known pathogen or resistance gene. This dark matter mining requires highly advanced bioinformatic analyses, which has been piloted in sequential samples from four cities in Europe.