MOOD project is at the forefront of European research of infectious disease surveillance and modelling from a data science perspective, investigating the impact of global warming on disease outbreaks, and proposing innovations for building of One Health systems across Europe and the world.
In the table below all publications to which the MOOD project contributed are listed. Use the filter to select the most relevant articles.
1.
Cataldo, Claudia; Bellenghi, Maria; Masella, Roberta; Busani, Luca
In: One Health, vol. 19, pp. 100841, 2024, ISSN: 2352-7714.
Abstract | Links | BibTeX | Tags: OpenDataSet
@article{nokey,
title = {One Health and sex and gender-related perspective in the ecosystem: Interactions among drivers involved in the risk of leptospirosis in Europe. A scoping review},
author = {Claudia Cataldo and Maria Bellenghi and Roberta Masella and Luca Busani},
url = {https://www.sciencedirect.com/science/article/pii/S2352771424001678},
doi = {10.1016/j.onehlt.2024.100841},
issn = {2352-7714},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
journal = {One Health},
volume = {19},
pages = {100841},
abstract = {Leptospirosis has a complex transmission, involving rodents and many species of domestic and wild animals. Carrier animals spread leptospires, contaminating soil and water, the main sources of human infection. The risk of infection is modulated by socio-economic factors, environment and host animals and has changed, historically linked to agriculture but now prevalent in recreational environments. Leptospirosis also reveal gender-specific exposure patterns that determine infection risks. Emphasizing the interconnectedness of humans, animals, and the environment, the One Health approach highlights the ecosystem dynamics through which leptospires interact with hosts and abiotic factors, ensuring their survival and transmission.
We advocate for integrating gender considerations into the ecosystem dynamics of complex zoonoses, such as leptospirosis, through a One Health perspective. This approach, yet to be fully explored, may enhance our understanding of the infection and its modulating factors. A scoping review of the literature was conducted across Embase and Pubmed databases to collect information on sex and gender-specific drivers, sources of infections, environmental drivers, and related risks of leptospirosis. Quantitative data were extracted from the articles selected according to a list of criteria, and analyzed to discern sex and gender disparities and identify primary drivers of leptospirosis. We confirmed that the excess of male leptospirosis cases described in many parts of the world is also present in Europe. Furthermore, we identified environmental and sociocultural drivers and hypothesized their interactions between and within human, animal, and environmental sectors. These interactions modulate direct and indirect exposure to Leptospira, heightening infection risks across the ecosystem. Based on our findings, utilizing leptospirosis as a model, we advocate for integrating One Health and gender approaches in public health practices to better plan and implement more effective and timely intervention measures.},
keywords = {OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
Leptospirosis has a complex transmission, involving rodents and many species of domestic and wild animals. Carrier animals spread leptospires, contaminating soil and water, the main sources of human infection. The risk of infection is modulated by socio-economic factors, environment and host animals and has changed, historically linked to agriculture but now prevalent in recreational environments. Leptospirosis also reveal gender-specific exposure patterns that determine infection risks. Emphasizing the interconnectedness of humans, animals, and the environment, the One Health approach highlights the ecosystem dynamics through which leptospires interact with hosts and abiotic factors, ensuring their survival and transmission.
We advocate for integrating gender considerations into the ecosystem dynamics of complex zoonoses, such as leptospirosis, through a One Health perspective. This approach, yet to be fully explored, may enhance our understanding of the infection and its modulating factors. A scoping review of the literature was conducted across Embase and Pubmed databases to collect information on sex and gender-specific drivers, sources of infections, environmental drivers, and related risks of leptospirosis. Quantitative data were extracted from the articles selected according to a list of criteria, and analyzed to discern sex and gender disparities and identify primary drivers of leptospirosis. We confirmed that the excess of male leptospirosis cases described in many parts of the world is also present in Europe. Furthermore, we identified environmental and sociocultural drivers and hypothesized their interactions between and within human, animal, and environmental sectors. These interactions modulate direct and indirect exposure to Leptospira, heightening infection risks across the ecosystem. Based on our findings, utilizing leptospirosis as a model, we advocate for integrating One Health and gender approaches in public health practices to better plan and implement more effective and timely intervention measures.
We advocate for integrating gender considerations into the ecosystem dynamics of complex zoonoses, such as leptospirosis, through a One Health perspective. This approach, yet to be fully explored, may enhance our understanding of the infection and its modulating factors. A scoping review of the literature was conducted across Embase and Pubmed databases to collect information on sex and gender-specific drivers, sources of infections, environmental drivers, and related risks of leptospirosis. Quantitative data were extracted from the articles selected according to a list of criteria, and analyzed to discern sex and gender disparities and identify primary drivers of leptospirosis. We confirmed that the excess of male leptospirosis cases described in many parts of the world is also present in Europe. Furthermore, we identified environmental and sociocultural drivers and hypothesized their interactions between and within human, animal, and environmental sectors. These interactions modulate direct and indirect exposure to Leptospira, heightening infection risks across the ecosystem. Based on our findings, utilizing leptospirosis as a model, we advocate for integrating One Health and gender approaches in public health practices to better plan and implement more effective and timely intervention measures.
2.
Menya, Edmond; Interdonato, Roberto; Owuor, Dickson; Roche, Mathieu
Explainable epidemiological thematic features for event based disease surveillance Journal Article
In: ScienceDirect, vol. 250, 2024.
Abstract | Links | BibTeX | Tags: OpenDataSet, Text mining
@article{nokey,
title = {Explainable epidemiological thematic features for event based disease surveillance},
author = {Edmond Menya and Roberto Interdonato and Dickson Owuor and Mathieu Roche},
url = {https://www.sciencedirect.com/science/article/pii/S0957417424007607?via%3Dihub},
doi = {https://doi.org/10.1016/j.eswa.2024.123894},
year = {2024},
date = {2024-09-15},
urldate = {2024-09-15},
journal = {ScienceDirect},
volume = {250},
abstract = {Event based disease surveillance (EBS) systems are biosurveillance systems that have the ability to detect and alert on (re)-emerging infectious diseases by monitoring acute public or animal health event patterns from sources such as blogs, online news reports and curated expert accounts. These information rich sources, however, are largely unstructured text data requiring novel text mining techniques to achieve EBS goals such as epidemiological text classification. The main objective of this research was to improve epidemiological text classification by proposing a novel technique of enriching thematic features using a weak supervision approach. In our approach, we train and test a mixed domain language model named EpidBioELECTRA to first enrich thematic features which are then used to improve epidemiological text classification. We train EpidBioELECTRA on a large dataset which we create consisting of 70,700 annotated documents that includes 70,400 labeled thematic features. We empirically compare EpidBioELECTRA with both general purpose language models and domain specific language models in the task of epidemiological corpus classification. Our findings shows that epidemiological classification systems work best with language models pre-trained using both epidemiological and biomedical corpora with a continual pre-training strategy. EpidBioELECTRA improves epidemiological document classification by 19.2
score points as compared to its vanilla implementation BioELECTRA. We observe this by the comparison of BioELECTRA verses EpidBioELECTRA on our most challenging dataset PADI-Web
where our approach records 92.33 precision score, 94.62 recall score and 93.46
score. We also experiment the impact of increasing context length of train documents in epidemiological document classification and found out that this improves the classification task by 7.79
score points as recorded by EpidBioELECTRA’s performance. We also compute Almost Stochastic Order (ASO) scores to track EpidBioELECTRA’s statistical dominance. In addition, we carry out ablation studies on our proposed thematic feature enrichment approach using explainable AI techniques. We present explanations for the most critical thematic features and how they influence epidemiological classification task We found out that biomedical features (such as mentions of names of diseases and symptoms) are the most influential while spatio-temporal features (such as the mention of date of a given disease outbreak) are the least influential in epidemiological document classification. Our model can easily be extended to fit other domains.},
keywords = {OpenDataSet, Text mining},
pubstate = {published},
tppubtype = {article}
}
Event based disease surveillance (EBS) systems are biosurveillance systems that have the ability to detect and alert on (re)-emerging infectious diseases by monitoring acute public or animal health event patterns from sources such as blogs, online news reports and curated expert accounts. These information rich sources, however, are largely unstructured text data requiring novel text mining techniques to achieve EBS goals such as epidemiological text classification. The main objective of this research was to improve epidemiological text classification by proposing a novel technique of enriching thematic features using a weak supervision approach. In our approach, we train and test a mixed domain language model named EpidBioELECTRA to first enrich thematic features which are then used to improve epidemiological text classification. We train EpidBioELECTRA on a large dataset which we create consisting of 70,700 annotated documents that includes 70,400 labeled thematic features. We empirically compare EpidBioELECTRA with both general purpose language models and domain specific language models in the task of epidemiological corpus classification. Our findings shows that epidemiological classification systems work best with language models pre-trained using both epidemiological and biomedical corpora with a continual pre-training strategy. EpidBioELECTRA improves epidemiological document classification by 19.2
score points as compared to its vanilla implementation BioELECTRA. We observe this by the comparison of BioELECTRA verses EpidBioELECTRA on our most challenging dataset PADI-Web
where our approach records 92.33 precision score, 94.62 recall score and 93.46
score. We also experiment the impact of increasing context length of train documents in epidemiological document classification and found out that this improves the classification task by 7.79
score points as recorded by EpidBioELECTRA’s performance. We also compute Almost Stochastic Order (ASO) scores to track EpidBioELECTRA’s statistical dominance. In addition, we carry out ablation studies on our proposed thematic feature enrichment approach using explainable AI techniques. We present explanations for the most critical thematic features and how they influence epidemiological classification task We found out that biomedical features (such as mentions of names of diseases and symptoms) are the most influential while spatio-temporal features (such as the mention of date of a given disease outbreak) are the least influential in epidemiological document classification. Our model can easily be extended to fit other domains.
score points as compared to its vanilla implementation BioELECTRA. We observe this by the comparison of BioELECTRA verses EpidBioELECTRA on our most challenging dataset PADI-Web
where our approach records 92.33 precision score, 94.62 recall score and 93.46
score. We also experiment the impact of increasing context length of train documents in epidemiological document classification and found out that this improves the classification task by 7.79
score points as recorded by EpidBioELECTRA’s performance. We also compute Almost Stochastic Order (ASO) scores to track EpidBioELECTRA’s statistical dominance. In addition, we carry out ablation studies on our proposed thematic feature enrichment approach using explainable AI techniques. We present explanations for the most critical thematic features and how they influence epidemiological classification task We found out that biomedical features (such as mentions of names of diseases and symptoms) are the most influential while spatio-temporal features (such as the mention of date of a given disease outbreak) are the least influential in epidemiological document classification. Our model can easily be extended to fit other domains.
3.
Bouyer, Fanny; Thiongane, Oumy; Hobeika, Alexandre; Arsevska, Elena; Binot, Aurélie; Corrèges, Déborah; Dub, Timothée; Mäkelä, Henna; van Kleef, Esther; Jori, Ferran; Lancelot, Renaud; Mercier, Alize; Fagandini, Francesca; Valentin, Sarah; Bortel, Wim Van; Ruault, Claire
Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance Journal Article
In: BMC Public Health, no. 973, 2024.
Abstract | Links | BibTeX | Tags: OpenDataSet
@article{nokey,
title = {Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance},
author = {Fanny Bouyer and Oumy Thiongane and Alexandre Hobeika and Elena Arsevska and Aurélie Binot and Déborah Corrèges and Timothée Dub and Henna Mäkelä and Esther van Kleef and Ferran Jori and Renaud Lancelot and Alize Mercier and Francesca Fagandini and Sarah Valentin and Wim Van Bortel and Claire Ruault },
url = {https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-024-18466-1},
doi = {https://doi.org/10.1186/s12889-024-18466-1},
year = {2024},
date = {2024-04-06},
journal = {BMC Public Health},
number = {973},
abstract = {Background
European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens.
Methods
We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software.
Results
Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics.
Conclusions
The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.},
keywords = {OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
Background
European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens.
Methods
We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software.
Results
Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics.
Conclusions
The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.
European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens.
Methods
We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software.
Results
Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics.
Conclusions
The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.
4.
Faucher, Benjamin; Sabbatini, Chiara E.; Czuppon, Peter; Kraemer, Moritz U. G.; Lemey, Philippe; Colizza, Vittoria; Blanquart, François; Boëlle, Pierre-Yves; Poletto, Chiara
Drivers and impact of the early silent invasion of SARS-CoV-2 Alpha Journal Article
In: Nature Communications, vol. 15, no. 2152, 2024.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus), OpenDataSet
@article{nokey,
title = {Drivers and impact of the early silent invasion of SARS-CoV-2 Alpha},
author = {Benjamin Faucher and Chiara E. Sabbatini and Peter Czuppon and Moritz U. G. Kraemer and Philippe Lemey and Vittoria Colizza and François Blanquart and Pierre-Yves Boëlle and Chiara Poletto},
url = {https://www.nature.com/articles/s41467-024-46345-1},
doi = {10.1038/s41467-024-46345-1},
year = {2024},
date = {2024-03-09},
journal = {Nature Communications},
volume = {15},
number = {2152},
abstract = {SARS-CoV-2 variants of concern (VOCs) circulated cryptically before being identified as a threat, delaying interventions. Here we studied the drivers of such silent spread and its epidemic impact to inform future response planning. We focused on Alpha spread out of the UK. We integrated spatio-temporal records of international mobility, local epidemic growth and genomic surveillance into a Bayesian framework to reconstruct the first three months after Alpha emergence. We found that silent circulation lasted from days to months and decreased with the logarithm of sequencing coverage. Social restrictions in some countries likely delayed the establishment of local transmission, mitigating the negative consequences of late detection. Revisiting the initial spread of Alpha supports local mitigation at the destination in case of emerging events.},
keywords = {Covid-19 (Coronavirus), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
SARS-CoV-2 variants of concern (VOCs) circulated cryptically before being identified as a threat, delaying interventions. Here we studied the drivers of such silent spread and its epidemic impact to inform future response planning. We focused on Alpha spread out of the UK. We integrated spatio-temporal records of international mobility, local epidemic growth and genomic surveillance into a Bayesian framework to reconstruct the first three months after Alpha emergence. We found that silent circulation lasted from days to months and decreased with the logarithm of sequencing coverage. Social restrictions in some countries likely delayed the establishment of local transmission, mitigating the negative consequences of late detection. Revisiting the initial spread of Alpha supports local mitigation at the destination in case of emerging events.
5.
Arsevska, Elena; Hengl, Tomislav; Singleton, David A.; Noble, Peter-John M.; Caminade, Cyril; Eneanya, Obiora; Jones, Philip H.; Hansford, Kayleigh; Medlock, Jolyon; Bonannella, Carmelo; Radford, Alan D.
Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021 Journal Article
In: Parasites and Vectors, 2024.
Links | BibTeX | Tags: OpenDataSet, TBE (Tick Borne Encephalitis)
@article{nokey,
title = {Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021},
author = {Elena Arsevska and Tomislav Hengl and David A. Singleton and Peter-John M. Noble and Cyril Caminade and Obiora Eneanya and Philip H. Jones and Kayleigh Hansford and Jolyon Medlock and Carmelo Bonannella and Alan D. Radford},
doi = {https://doi.org/10.1186/s13071-023-06094-4},
year = {2024},
date = {2024-01-22},
urldate = {2024-01-22},
journal = {Parasites and Vectors},
keywords = {OpenDataSet, TBE (Tick Borne Encephalitis)},
pubstate = {published},
tppubtype = {article}
}
6.
Mulchandani, Ranya; Zhao, Cheng; Tiseo, Katie; Pires, João; Boeckel, Thomas P. Van
Predictive Mapping of Antimicrobial Resistance for Escherichia coli, Salmonella, and Campylobacter in Food-Producing Animals, Europe, 2000–2021 Journal Article
In: Emerging Infectious Diseases, pp. 96-104, 2024.
Abstract | Links | BibTeX | Tags: AMR (Antimicrobial Resistance), OpenDataSet
@article{nokey,
title = {Predictive Mapping of Antimicrobial Resistance for Escherichia coli, Salmonella, and Campylobacter in Food-Producing Animals, Europe, 2000–2021},
author = {Ranya Mulchandani and Cheng Zhao and Katie Tiseo and João Pires and Thomas P. Van Boeckel},
url = {https://wwwnc.cdc.gov/eid/article/30/1/22-1450_article},
doi = {10.3201/eid3001.221450},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Emerging Infectious Diseases},
pages = {96-104},
abstract = {In Europe, systematic national surveillance of antimicrobial resistance (AMR) in food-producing animals has been conducted for decades; however, geographic distribution within countries remains unknown. To determine distribution within Europe, we combined 33,802 country-level AMR prevalence estimates with 2,849 local AMR prevalence estimates from 209 point prevalence surveys across 31 countries. We produced geospatial models of AMR prevalence in Escherichia coli, nontyphoidal Salmonella, and Campylobacter for cattle, pigs, and poultry. We summarized AMR trends by using the proportion of tested antimicrobial compounds with resistance >50% and generated predictive maps at 10 × 10 km resolution that disaggregated AMR prevalence. For E. coli, predicted prevalence rates were highest in southern Romania and southern/eastern Italy; for Salmonella, southern Hungary and central Poland; and for Campylobacter, throughout Spain. Our findings suggest that AMR distribution is heterogeneous within countries and that surveillance data from below the country level could help with prioritizing resources to reduce AMR.},
keywords = {AMR (Antimicrobial Resistance), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
In Europe, systematic national surveillance of antimicrobial resistance (AMR) in food-producing animals has been conducted for decades; however, geographic distribution within countries remains unknown. To determine distribution within Europe, we combined 33,802 country-level AMR prevalence estimates with 2,849 local AMR prevalence estimates from 209 point prevalence surveys across 31 countries. We produced geospatial models of AMR prevalence in Escherichia coli, nontyphoidal Salmonella, and Campylobacter for cattle, pigs, and poultry. We summarized AMR trends by using the proportion of tested antimicrobial compounds with resistance >50% and generated predictive maps at 10 × 10 km resolution that disaggregated AMR prevalence. For E. coli, predicted prevalence rates were highest in southern Romania and southern/eastern Italy; for Salmonella, southern Hungary and central Poland; and for Campylobacter, throughout Spain. Our findings suggest that AMR distribution is heterogeneous within countries and that surveillance data from below the country level could help with prioritizing resources to reduce AMR.
7.
Valdano, Eugenio; Colombi, Davide; Poletto, Chiara; Colizza, Vittoria
Epidemic graph diagrams as analytics for epidemic control in the data-rich era Journal Article
In: Nature Communications, 2023.
Abstract | Links | BibTeX | Tags: HPAI (Avian Influenza), OpenDataSet
@article{nokey,
title = {Epidemic graph diagrams as analytics for epidemic control in the data-rich era},
author = {Eugenio Valdano and Davide Colombi and Chiara Poletto and Vittoria Colizza},
url = {https://www.nature.com/articles/s41467-023-43856-1#citeas},
doi = {10.1038/s41467-023-43856-1},
year = {2023},
date = {2023-12-20},
urldate = {2023-12-20},
journal = {Nature Communications},
abstract = {COVID-19 highlighted modeling as a cornerstone of pandemic response. But it also revealed that current models may not fully exploit the high-resolution data on disease progression, epidemic surveillance and host behavior, now available. Take the epidemic threshold, which quantifies the spreading risk throughout epidemic emergence, mitigation, and control. Its use requires oversimplifying either disease or host contact dynamics. We introduce the epidemic graph diagrams to overcome this by computing the epidemic threshold directly from arbitrarily complex data on contacts, disease and interventions. A grammar of diagram operations allows to decompose, compare, simplify models with computational efficiency, extracting theoretical understanding. We use the diagrams to explain the emergence of resistant influenza variants in the 2007–2008 season, and demonstrate that neglecting non-infectious prodromic stages of sexually transmitted infections biases the predicted epidemic risk, compromising control. The diagrams are general, and improve our capacity to respond to present and future public health challenges.},
keywords = {HPAI (Avian Influenza), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
COVID-19 highlighted modeling as a cornerstone of pandemic response. But it also revealed that current models may not fully exploit the high-resolution data on disease progression, epidemic surveillance and host behavior, now available. Take the epidemic threshold, which quantifies the spreading risk throughout epidemic emergence, mitigation, and control. Its use requires oversimplifying either disease or host contact dynamics. We introduce the epidemic graph diagrams to overcome this by computing the epidemic threshold directly from arbitrarily complex data on contacts, disease and interventions. A grammar of diagram operations allows to decompose, compare, simplify models with computational efficiency, extracting theoretical understanding. We use the diagrams to explain the emergence of resistant influenza variants in the 2007–2008 season, and demonstrate that neglecting non-infectious prodromic stages of sexually transmitted infections biases the predicted epidemic risk, compromising control. The diagrams are general, and improve our capacity to respond to present and future public health challenges.
8.
Inward, Rhys P. D.; Jackson, Felix; Dasgupta, Abhishek; Lee, Graham; Battle, Anya Lindström; Parag, Kris V.; Kraemer, Moritz U. G.
Impact of spatiotemporal heterogeneity in COVID-19 disease surveillance on epidemiological parameters and case growth rates Journal Article
In: Epidemics, vol. 41, pp. 100627, 2023, ISSN: 1755-4365.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus), OpenDataSet
@article{nokey,
title = {Impact of spatiotemporal heterogeneity in COVID-19 disease surveillance on epidemiological parameters and case growth rates},
author = {Rhys P.D. Inward and Felix Jackson and Abhishek Dasgupta and Graham Lee and Anya Lindström Battle and Kris V. Parag and Moritz U.G. Kraemer},
url = {https://www.sciencedirect.com/science/article/pii/S1755436522000676},
doi = {10.1016/j.epidem.2022.100627},
issn = {1755-4365},
year = {2023},
date = {2023-12-01},
urldate = {2023-12-01},
journal = {Epidemics},
volume = {41},
pages = {100627},
abstract = {SARS-CoV-2 case data are primary sources for estimating epidemiological parameters and for modelling the dynamics of outbreaks. Understanding biases within case-based data sources used in epidemiological analyses is important as they can detract from the value of these rich datasets. This raises questions of how variations in surveillance can affect the estimation of epidemiological parameters such as the case growth rates. We use standardised line list data of COVID-19 from Argentina, Brazil, Mexico and Colombia to estimate delay distributions of symptom-onset-to-confirmation, -hospitalisation and -death as well as hospitalisation-to-death at high spatial resolutions and throughout time. Using these estimates, we model the biases introduced by the delay from symptom-onset-to-confirmation on national and state level case growth rates (rt) using an adaptation of the Richardson-Lucy deconvolution algorithm. We find significant heterogeneities in the estimation of delay distributions through time and space with delay difference of up to 19 days between epochs at the state level. Further, we find that by changing the spatial scale, estimates of case growth rate can vary by up to 0.13 d−1. Lastly, we find that states with a high variance and/or mean delay in symptom-onset-to-diagnosis also have the largest difference between the rt estimated from raw and deconvolved case counts at the state level. We highlight the importance of high-resolution case-based data in understanding biases in disease reporting and how these biases can be avoided by adjusting case numbers based on empirical delay distributions. Code and openly accessible data to reproduce analyses presented here are available.},
keywords = {Covid-19 (Coronavirus), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
SARS-CoV-2 case data are primary sources for estimating epidemiological parameters and for modelling the dynamics of outbreaks. Understanding biases within case-based data sources used in epidemiological analyses is important as they can detract from the value of these rich datasets. This raises questions of how variations in surveillance can affect the estimation of epidemiological parameters such as the case growth rates. We use standardised line list data of COVID-19 from Argentina, Brazil, Mexico and Colombia to estimate delay distributions of symptom-onset-to-confirmation, -hospitalisation and -death as well as hospitalisation-to-death at high spatial resolutions and throughout time. Using these estimates, we model the biases introduced by the delay from symptom-onset-to-confirmation on national and state level case growth rates (rt) using an adaptation of the Richardson-Lucy deconvolution algorithm. We find significant heterogeneities in the estimation of delay distributions through time and space with delay difference of up to 19 days between epochs at the state level. Further, we find that by changing the spatial scale, estimates of case growth rate can vary by up to 0.13 d−1. Lastly, we find that states with a high variance and/or mean delay in symptom-onset-to-diagnosis also have the largest difference between the rt estimated from raw and deconvolved case counts at the state level. We highlight the importance of high-resolution case-based data in understanding biases in disease reporting and how these biases can be avoided by adjusting case numbers based on empirical delay distributions. Code and openly accessible data to reproduce analyses presented here are available.
9.
Cheng, Qu; Jing, Qinlong; Collender, Philip A.; Head, Jennifer R.; Li, Qi; Yu, Hailan; Li, Zhichao; Ju, Yang; Chen, Tianmu; Wang, Peng; Cleary, Eimear; Lai, Shengjie
In: Frontiers Public Health, vol. 11, 2023.
Abstract | Links | BibTeX | Tags: DEN (Dengue), OpenDataSet
@article{nokey,
title = {Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China},
author = {Qu Cheng and Qinlong Jing and Philip A. Collender and Jennifer R. Head and Qi Li and Hailan Yu and Zhichao Li and Yang Ju and Tianmu Chen and Peng Wang and Eimear Cleary and Shengjie Lai},
url = {https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1287678/full},
doi = {10.3389/fpubh.2023.1287678},
year = {2023},
date = {2023-12-01},
urldate = {2023-12-01},
journal = {Frontiers Public Health},
volume = {11},
abstract = {Introduction: Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier.
Methods: In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China.
Results: Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24–55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02–1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7–121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43–0.79), occurring at a lag of 45 days.
Discussion: These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.},
keywords = {DEN (Dengue), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
Introduction: Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier.
Methods: In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China.
Results: Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24–55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02–1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7–121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43–0.79), occurring at a lag of 45 days.
Discussion: These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.
Methods: In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China.
Results: Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24–55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02–1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7–121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43–0.79), occurring at a lag of 45 days.
Discussion: These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.
10.
SYED, Mehtab Alam; ARSEVSKA, Elena; ROCHE, Mathieu; TEISSEIRE, Maguelonne
GeospartRE: Extraction and Geocoding of spatial relation entities in textual documents Journal Article
In: Cartography and Geographic Information Science, 2023.
Links | BibTeX | Tags: OpenDataSet, Text mining
@article{nokey,
title = {GeospartRE: Extraction and Geocoding of spatial relation entities in textual documents},
author = {Mehtab Alam SYED and Elena ARSEVSKA and Mathieu ROCHE and Maguelonne TEISSEIRE},
doi = {https://doi.org/10.1080/15230406.2023.2264753},
year = {2023},
date = {2023-11-30},
urldate = {2023-11-30},
journal = {Cartography and Geographic Information Science},
keywords = {OpenDataSet, Text mining},
pubstate = {published},
tppubtype = {article}
}
11.
Mencattelli, Giulia; Ndione, Marie Henriette Dior; Silverj, Andrea; Diagne, Moussa Moise; Curini, Valentina; Teodori, Liana; Domenico, Marco Di; Mbaye, Rassoul; Leone, Alessandra; Marcacci, Maurilia; Gaye, Alioune; Ndiaye, ElHadji; Diallo, Diawo; Ancora, Massimo; Secondini, Barbara; Lollo, Valeria Di; Mangone, Iolanda; Bucciacchio, Andrea; Polci, Andrea; Marini, Giovanni; Rosà, Roberto; Segata, Nicola; Fall, Gamou; Cammà, Cesare; Monaco, Federica; Diallo, Mawlouth; Rota-Stabelli, Omar; Faye, Oumar; & Giovanni Savini, Annapaola Rizzoli
Spatial and temporal dynamics of West Nile virus between Africa and Europe Journal Article
In: Nature Communications, 2023.
Links | BibTeX | Tags: OpenDataSet, WNV (West Nile Virus)
@article{nokey,
title = {Spatial and temporal dynamics of West Nile virus between Africa and Europe},
author = {Giulia Mencattelli and Marie Henriette Dior Ndione and Andrea Silverj and Moussa Moise Diagne and Valentina Curini and Liana Teodori and Marco Di Domenico and Rassoul Mbaye and Alessandra Leone and Maurilia Marcacci and Alioune Gaye and ElHadji Ndiaye and Diawo Diallo and Massimo Ancora and Barbara Secondini and Valeria Di Lollo and Iolanda Mangone and Andrea Bucciacchio and Andrea Polci and Giovanni Marini and Roberto Rosà and Nicola Segata and Gamou Fall and Cesare Cammà and Federica Monaco and Mawlouth Diallo and Omar Rota-Stabelli and Oumar Faye and Annapaola Rizzoli & Giovanni Savini},
doi = {https://doi.org/10.1038/s41467-023-42185-7},
year = {2023},
date = {2023-10-13},
urldate = {2023-10-13},
journal = {Nature Communications},
keywords = {OpenDataSet, WNV (West Nile Virus)},
pubstate = {published},
tppubtype = {article}
}
12.
Messina, Jane Paula; Wint, William G R
The Spatial Distribution of Crimean–Congo Haemorrhagic Fever and Its Potential Vectors in Europe and Beyond Journal Article
In: Insects, vol. 14, no. 771, 2023, (This article is a version of the ECDC Technical report ‘The spatial distribution of Crimean-Congo haemorrhagic fever in Europe and neighbouring areas’ adapted for journal publication).
Abstract | Links | BibTeX | Tags: CCHF (Crimean Congo haemorrhagic fever virus), OpenDataSet
@article{nokey,
title = {The Spatial Distribution of Crimean–Congo Haemorrhagic Fever and Its Potential Vectors in Europe and Beyond},
author = {Jane Paula Messina and William G R Wint},
url = {https://www.mdpi.com/2075-4450/14/9/771},
doi = {10.3390/insects14090771},
year = {2023},
date = {2023-09-17},
urldate = {2023-09-17},
journal = {Insects},
volume = {14},
number = {771},
abstract = {Crimean–Congo haemorrhagic fever (CCHF) is considered to be spreading across the globe, with many countries reporting new human CCHF cases in recent decades including Georgia, Türkiye, Albania, and, most recently, Spain. We update a human CCHF distribution map produced in 2015 to include global disease occurrence records to June 2022, and we include the recent records for Europe. The predicted distributions are based on long-established spatial modelling methods and are extended to include all European countries and the surrounding areas. The map produced shows the environmental suitability for the disease, taking into account the distribution of the most important known and potential tick vectors Hyalomma marginatum and Hyalomma lusitanicum, without which the disease cannot occur. This limits the disease’s predicted distribution to the Iberian Peninsula, the Mediterranean seaboard, along with Türkiye and the Caucasus, with a more patchy suitability predicted for inland Greece, the southern Balkans, and extending north to north-west France and central Europe. These updated CCHF maps can be used to identify the areas with the highest probability of disease and to therefore target areas where mitigation measures should currently be focused.},
note = {This article is a version of the ECDC Technical report ‘The spatial distribution of Crimean-Congo haemorrhagic fever in Europe and neighbouring areas’ adapted for journal publication},
keywords = {CCHF (Crimean Congo haemorrhagic fever virus), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
Crimean–Congo haemorrhagic fever (CCHF) is considered to be spreading across the globe, with many countries reporting new human CCHF cases in recent decades including Georgia, Türkiye, Albania, and, most recently, Spain. We update a human CCHF distribution map produced in 2015 to include global disease occurrence records to June 2022, and we include the recent records for Europe. The predicted distributions are based on long-established spatial modelling methods and are extended to include all European countries and the surrounding areas. The map produced shows the environmental suitability for the disease, taking into account the distribution of the most important known and potential tick vectors Hyalomma marginatum and Hyalomma lusitanicum, without which the disease cannot occur. This limits the disease’s predicted distribution to the Iberian Peninsula, the Mediterranean seaboard, along with Türkiye and the Caucasus, with a more patchy suitability predicted for inland Greece, the southern Balkans, and extending north to north-west France and central Europe. These updated CCHF maps can be used to identify the areas with the highest probability of disease and to therefore target areas where mitigation measures should currently be focused.
13.
Valentin, Sarah; Boudoua, Bahdja; Sewalk, Kara; Arınık, Nejat; Roche, Mathieu; Lancelot, Renaud; Arsevska, Elena
Dissemination of information in event-based surveillance, a case study of Avian Influenza Journal Article
In: PLoS ONE, 2023.
Abstract | Links | BibTeX | Tags: HPAI (Avian Influenza), OpenDataSet, Text mining
@article{nokey,
title = {Dissemination of information in event-based surveillance, a case study of Avian Influenza},
author = {Sarah Valentin and Bahdja Boudoua and Kara Sewalk and Nejat Arınık and Mathieu Roche and Renaud Lancelot and Elena Arsevska },
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0285341},
doi = {10.1371/journal.pone.0285341},
year = {2023},
date = {2023-09-05},
urldate = {2023-09-05},
journal = {PLoS ONE},
abstract = {Event-Based Surveillance (EBS) tools, such as HealthMap and PADI-web, monitor online news reports and other unofficial sources, with the primary aim to provide timely information to users from health agencies on disease outbreaks occurring worldwide. In this work, we describe how outbreak-related information disseminates from a primary source, via a secondary source, to a definitive aggregator, an EBS tool, during the 2018/19 avian influenza season. We analysed 337 news items from the PADI-web and 115 news articles from HealthMap EBS tools reporting avian influenza outbreaks in birds worldwide between July 2018 and June 2019. We used the sources cited in the news to trace the path of each outbreak. We built a directed network with nodes representing the sources (characterised by type, specialisation, and geographical focus) and edges representing the flow of information. We calculated the degree as a centrality measure to determine the importance of the nodes in information dissemination. We analysed the role of the sources in early detection (detection of an event before its official notification) to the World Organisation for Animal Health (WOAH) and late detection. A total of 23% and 43% of the avian influenza outbreaks detected by the PADI-web and HealthMap, respectively, were shared on time before their notification. For both tools, national and local veterinary authorities were the primary sources of early detection. The early detection component mainly relied on the dissemination of nationally acknowledged events by online news and press agencies, bypassing international reporting to the WAOH. WOAH was the major secondary source for late detection, occupying a central position between national authorities and disseminator sources, such as online news. PADI-web and HealthMap were highly complementary in terms of detected sources, explaining why 90% of the events were detected by only one of the tools. We show that current EBS tools can provide timely outbreak-related information and priority news sources to improve digital disease surveillance.
Figures},
keywords = {HPAI (Avian Influenza), OpenDataSet, Text mining},
pubstate = {published},
tppubtype = {article}
}
Event-Based Surveillance (EBS) tools, such as HealthMap and PADI-web, monitor online news reports and other unofficial sources, with the primary aim to provide timely information to users from health agencies on disease outbreaks occurring worldwide. In this work, we describe how outbreak-related information disseminates from a primary source, via a secondary source, to a definitive aggregator, an EBS tool, during the 2018/19 avian influenza season. We analysed 337 news items from the PADI-web and 115 news articles from HealthMap EBS tools reporting avian influenza outbreaks in birds worldwide between July 2018 and June 2019. We used the sources cited in the news to trace the path of each outbreak. We built a directed network with nodes representing the sources (characterised by type, specialisation, and geographical focus) and edges representing the flow of information. We calculated the degree as a centrality measure to determine the importance of the nodes in information dissemination. We analysed the role of the sources in early detection (detection of an event before its official notification) to the World Organisation for Animal Health (WOAH) and late detection. A total of 23% and 43% of the avian influenza outbreaks detected by the PADI-web and HealthMap, respectively, were shared on time before their notification. For both tools, national and local veterinary authorities were the primary sources of early detection. The early detection component mainly relied on the dissemination of nationally acknowledged events by online news and press agencies, bypassing international reporting to the WAOH. WOAH was the major secondary source for late detection, occupying a central position between national authorities and disseminator sources, such as online news. PADI-web and HealthMap were highly complementary in terms of detected sources, explaining why 90% of the events were detected by only one of the tools. We show that current EBS tools can provide timely outbreak-related information and priority news sources to improve digital disease surveillance.
Figures
Figures
14.
Marziano, Valentina; Guzzetta, Giorgio; Menegale, Francesco; Sacco, Chiara; Petrone, Daniele; Urdiales, Alberto Mateo; Manso, Martina Del; Bella, Antonino; Fabiani, Massimo; Vescio, Maria Fenicia; Riccardo, Flavia; Poletti, Piero; Manica, Mattia; Zardini, Agnese; d'Andrea, Valeria; Trentini, Filippo; Stefanelli, Paola; Rezza, Giovanni; Palamara, Anna Teresa; Brusaferro, Silvio; Ajelli, Marco; Pezzotti, Patrizio; Merler, Stefano
Estimating SARS-CoV-2 infections and associated changes in COVID-19 severity and fatality Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus), OpenDataSet
@article{nokey,
title = {Estimating SARS-CoV-2 infections and associated changes in COVID-19 severity and fatality},
author = {Valentina Marziano and Giorgio Guzzetta and Francesco Menegale and Chiara Sacco and Daniele Petrone and Alberto Mateo Urdiales and Martina Del Manso and Antonino Bella and Massimo Fabiani and Maria Fenicia Vescio and Flavia Riccardo and Piero Poletti and Mattia Manica and Agnese Zardini and Valeria d'Andrea and Filippo Trentini and Paola Stefanelli and Giovanni Rezza and Anna Teresa Palamara and Silvio Brusaferro and Marco Ajelli and Patrizio Pezzotti and Stefano Merler},
url = {https://onlinelibrary.wiley.com/doi/10.1111/irv.13181},
doi = {10.1111/irv.13181},
year = {2023},
date = {2023-08-16},
urldate = {2023-08-16},
abstract = {Background
The difficulty in identifying SARS-CoV-2 infections has not only been the major obstacle to control the COVID-19 pandemic but also to quantify changes in the proportion of infections resulting in hospitalization, intensive care unit (ICU) admission, or death.
Methods
We developed a model of SARS-CoV-2 transmission and vaccination informed by official estimates of the time-varying reproduction number to estimate infections that occurred in Italy between February 2020 and 2022. Model outcomes were compared with the Italian National surveillance data to estimate changes in the SARS-CoV-2 infection ascertainment ratio (IAR), infection hospitalization ratio (IHR), infection ICU ratio (IIR), and infection fatality ratio (IFR) in five different sub-periods associated with the dominance of the ancestral lineages and Alpha, Delta, and Omicron BA.1 variants.
Results
We estimate that, over the first 2 years of pandemic, the IAR ranged between 15% and 40% (range of 95%CI: 11%–61%), with a peak value in the second half of 2020. The IHR, IIR, and IFR consistently decreased throughout the pandemic with 22–44-fold reductions between the initial phase and the Omicron period. At the end of the study period, we estimate an IHR of 0.24% (95%CI: 0.17–0.36), IIR of 0.015% (95%CI: 0.011–0.023), and IFR of 0.05% (95%CI: 0.04–0.08).
Conclusions
Since 2021, changes in the dominant SARS-CoV-2 variant, vaccination rollout, and the shift of infection to younger ages have reduced SARS-CoV-2 infection ascertainment. The same factors, combined with the improvement of patient management and care, contributed to a massive reduction in the severity and fatality of COVID-19.},
keywords = {Covid-19 (Coronavirus), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
Background
The difficulty in identifying SARS-CoV-2 infections has not only been the major obstacle to control the COVID-19 pandemic but also to quantify changes in the proportion of infections resulting in hospitalization, intensive care unit (ICU) admission, or death.
Methods
We developed a model of SARS-CoV-2 transmission and vaccination informed by official estimates of the time-varying reproduction number to estimate infections that occurred in Italy between February 2020 and 2022. Model outcomes were compared with the Italian National surveillance data to estimate changes in the SARS-CoV-2 infection ascertainment ratio (IAR), infection hospitalization ratio (IHR), infection ICU ratio (IIR), and infection fatality ratio (IFR) in five different sub-periods associated with the dominance of the ancestral lineages and Alpha, Delta, and Omicron BA.1 variants.
Results
We estimate that, over the first 2 years of pandemic, the IAR ranged between 15% and 40% (range of 95%CI: 11%–61%), with a peak value in the second half of 2020. The IHR, IIR, and IFR consistently decreased throughout the pandemic with 22–44-fold reductions between the initial phase and the Omicron period. At the end of the study period, we estimate an IHR of 0.24% (95%CI: 0.17–0.36), IIR of 0.015% (95%CI: 0.011–0.023), and IFR of 0.05% (95%CI: 0.04–0.08).
Conclusions
Since 2021, changes in the dominant SARS-CoV-2 variant, vaccination rollout, and the shift of infection to younger ages have reduced SARS-CoV-2 infection ascertainment. The same factors, combined with the improvement of patient management and care, contributed to a massive reduction in the severity and fatality of COVID-19.
The difficulty in identifying SARS-CoV-2 infections has not only been the major obstacle to control the COVID-19 pandemic but also to quantify changes in the proportion of infections resulting in hospitalization, intensive care unit (ICU) admission, or death.
Methods
We developed a model of SARS-CoV-2 transmission and vaccination informed by official estimates of the time-varying reproduction number to estimate infections that occurred in Italy between February 2020 and 2022. Model outcomes were compared with the Italian National surveillance data to estimate changes in the SARS-CoV-2 infection ascertainment ratio (IAR), infection hospitalization ratio (IHR), infection ICU ratio (IIR), and infection fatality ratio (IFR) in five different sub-periods associated with the dominance of the ancestral lineages and Alpha, Delta, and Omicron BA.1 variants.
Results
We estimate that, over the first 2 years of pandemic, the IAR ranged between 15% and 40% (range of 95%CI: 11%–61%), with a peak value in the second half of 2020. The IHR, IIR, and IFR consistently decreased throughout the pandemic with 22–44-fold reductions between the initial phase and the Omicron period. At the end of the study period, we estimate an IHR of 0.24% (95%CI: 0.17–0.36), IIR of 0.015% (95%CI: 0.011–0.023), and IFR of 0.05% (95%CI: 0.04–0.08).
Conclusions
Since 2021, changes in the dominant SARS-CoV-2 variant, vaccination rollout, and the shift of infection to younger ages have reduced SARS-CoV-2 infection ascertainment. The same factors, combined with the improvement of patient management and care, contributed to a massive reduction in the severity and fatality of COVID-19.
15.
Kafando, Rodrique; Decoupes, Rémy; Roche, Mathieu; Teisseire, Maguelonne
SNEToolkit: Spatial Named Entities disambiguation Toolkit Journal Article
In: SoftwareX, 2023.
Abstract | Links | BibTeX | Tags: OpenDataSet
@article{nokey,
title = {SNEToolkit: Spatial Named Entities disambiguation Toolkit},
author = {Rodrique Kafando and Rémy Decoupes and Mathieu Roche and Maguelonne Teisseire},
url = {https://www.softxjournal.com/article/S2352-7110(23)00176-0/fulltext},
doi = {10.1016/j.softx.2023.101480},
year = {2023},
date = {2023-07-31},
urldate = {2023-07-31},
journal = {SoftwareX},
abstract = {“Can you tell me where San Jose is located?” “Uh! Do you know that there are more than 1700 locations named San Jose in the world?” The official name of a location is often not the name with which we are familiar. Spatial named entity (SNE) disambiguation is the process of identifying and assigning precise coordinates to a place name that can be identified in a text. This task is not always straightforward, especially when the place name in question is ambiguous for various reasons. In this context, we are interested in the disambiguation of spatial named entities that can be identified in a textual document on a country level. The solution that we propose is based on a set of techniques that allow us to disambiguate the spatial entity considering the context in which it is mentioned from a certain number of characteristics that are specific to it. The solution uses as input a textual document and extricates the named entities identified therein while associating them with the correct coordinates. SNE disambiguation is designed to support the process of fast exploration of spatiotemporal data analysis, most often for event tracking. The proposed approach was tested on 1360 SNEs extracted from the GeoVirus dataset. The results show that SNEToolkit outperformed the baseline, the standard Geonames geocoder, with a recall value of 0.911 against a recall value of 0.871 for the baseline. A flexible Python package is provided for end users.
},
keywords = {OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
“Can you tell me where San Jose is located?” “Uh! Do you know that there are more than 1700 locations named San Jose in the world?” The official name of a location is often not the name with which we are familiar. Spatial named entity (SNE) disambiguation is the process of identifying and assigning precise coordinates to a place name that can be identified in a text. This task is not always straightforward, especially when the place name in question is ambiguous for various reasons. In this context, we are interested in the disambiguation of spatial named entities that can be identified in a textual document on a country level. The solution that we propose is based on a set of techniques that allow us to disambiguate the spatial entity considering the context in which it is mentioned from a certain number of characteristics that are specific to it. The solution uses as input a textual document and extricates the named entities identified therein while associating them with the correct coordinates. SNE disambiguation is designed to support the process of fast exploration of spatiotemporal data analysis, most often for event tracking. The proposed approach was tested on 1360 SNEs extracted from the GeoVirus dataset. The results show that SNEToolkit outperformed the baseline, the standard Geonames geocoder, with a recall value of 0.911 against a recall value of 0.871 for the baseline. A flexible Python package is provided for end users.
16.
joseph l.-h. tsui,; john t. mccrone,; ben lambert,; sumali bajaj,; rhys p. d. inward,; paolo bosetti,; rosario evans pena,; houriiyah tegally,; verity hill,; alexander e. zarebski,; thomas p. peacock,; luyang liu,; neo wu,; megan davis,; isaac i. bogoch,; kamran khan,; meaghan kall,; nurin iwani binti abdul aziz,; rachel colquhoun,; áine o’toole,; ben jackson,; abhishek dasgupta,; eduan wilkinson,; tulio de oliveira,; the covid-19 genomics uk (cog-uk) consortium,; thomas r. connor,; nicholas j. loman,; vittoria colizza,; christophe fraser,; erik volz,; xiang ji,; bernardo gutierrez,; meera chand,; simon dellicour,; simon cauchemez,; jayna raghwani,; marc a. suchard,; philippe lemey,; andrew rambaut,; oliver g. pybus,; moritz u. g. kraemer,
Genomic assessment of invasion dynamics of SARS-CoV-2 Omicron BA.1 Journal Article
In: Science, vol. 381, iss. 6655, pp. 336-343, 2023.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus), OpenDataSet
@article{nokey,
title = {Genomic assessment of invasion dynamics of SARS-CoV-2 Omicron BA.1},
author = {joseph l.-h. tsui and john t. mccrone and ben lambert and sumali bajaj and rhys p. d. inward and paolo bosetti and rosario evans pena and houriiyah tegally and verity hill and alexander e. zarebski and thomas p. peacock and luyang liu and neo wu and megan davis and isaac i. bogoch and kamran khan and meaghan kall and nurin iwani binti abdul aziz and rachel colquhoun and áine o’toole and ben jackson and abhishek dasgupta and eduan wilkinson and tulio de oliveira and the covid-19 genomics uk (cog-uk) consortium and thomas r. connor and nicholas j. loman and vittoria colizza and christophe fraser and erik volz and xiang ji and bernardo gutierrez and meera chand and simon dellicour and simon cauchemez and jayna raghwani and marc a. suchard and philippe lemey and andrew rambaut and oliver g. pybus and moritz u. g. kraemer},
url = {https://www.science.org/doi/10.1126/science.adg6605#supplementary-materials},
doi = {10.1126/science.adg660},
year = {2023},
date = {2023-07-20},
urldate = {2023-07-20},
journal = {Science},
volume = {381},
issue = {6655},
pages = {336-343},
abstract = {Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) now arise in the context of heterogeneous human connectivity and population immunity. Through a large-scale phylodynamic analysis of 115,622 Omicron BA.1 genomes, we identified >6,000 introductions of the antigenically distinct VOC into England and analyzed their local transmission and dispersal history. We find that six of the eight largest English Omicron lineages were already transmitting when Omicron was first reported in southern Africa (22 November 2021). Multiple datasets show that importation of Omicron continued despite subsequent restrictions on travel from southern Africa as a result of export from well-connected secondary locations. Initiation and dispersal of Omicron transmission lineages in England was a two-stage process that can be explained by models of the country’s human geography and hierarchical travel network. Our results enable a comparison of the processes that drive the invasion of Omicron and other VOCs across multiple spatial scales.},
keywords = {Covid-19 (Coronavirus), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) now arise in the context of heterogeneous human connectivity and population immunity. Through a large-scale phylodynamic analysis of 115,622 Omicron BA.1 genomes, we identified >6,000 introductions of the antigenically distinct VOC into England and analyzed their local transmission and dispersal history. We find that six of the eight largest English Omicron lineages were already transmitting when Omicron was first reported in southern Africa (22 November 2021). Multiple datasets show that importation of Omicron continued despite subsequent restrictions on travel from southern Africa as a result of export from well-connected secondary locations. Initiation and dispersal of Omicron transmission lineages in England was a two-stage process that can be explained by models of the country’s human geography and hierarchical travel network. Our results enable a comparison of the processes that drive the invasion of Omicron and other VOCs across multiple spatial scales.
17.
Decoupes, Rémy; Roche, Mathieu; Teisseire, Maguelonne
GeoNLPlify: A spatial data augmentation enhancing text classification for crisis monitoring Journal Article
In: Intelligent Data Analysis, pp. 1-25, 2023.
Abstract | Links | BibTeX | Tags: OpenDataSet, Text mining
@article{nokey,
title = {GeoNLPlify: A spatial data augmentation enhancing text classification for crisis monitoring},
author = {Rémy Decoupes and Mathieu Roche and Maguelonne Teisseire},
url = {https://content.iospress.com/articles/intelligent-data-analysis/ida230040},
doi = {10.3233/IDA-230040},
year = {2023},
date = {2023-07-06},
urldate = {2023-07-06},
journal = {Intelligent Data Analysis},
pages = {1-25},
abstract = {Crises such as natural disasters and public health emergencies generate vast amounts of text data, making it challenging to classify the information into relevant categories. Acquiring expert-labeled data for such scenarios can be difficult, leading to limited training datasets for text classification by fine-tuning BERT-like models. Unfortunately, traditional data augmentation techniques only slightly improve F1-scores. How can data augmentation be used to obtain better results in this applied domain? In this paper, using neural network explicability methods, we aim to highlight that fine-tuned BERT-like models on crisis corpora give too much importance to spatial information to make their predictions. This overfitting of spatial information limits their ability to generalize especially when the event which occurs in a place has evolved and changed since the training dataset has been built. To reduce this bias, we propose GeoNLPlify,1
a novel data augmentation technique that leverages spatial information to generate new labeled data for text classification related to crises. Our approach aims to address overfitting without necessitating modifications to the underlying model architecture, distinguishing it from other prevalent methods employed to combat overfitting. Our results show that GeoNLPlify significantly improves F1-scores, demonstrating the potential of the spatial information for data augmentation for crisis-related text classification tasks. In order to evaluate the contribution of our method, GeoNLPlify is applied to three public datasets (PADI-web, CrisisNLP and SST2) and compared with classical natural language processing data augmentations.},
keywords = {OpenDataSet, Text mining},
pubstate = {published},
tppubtype = {article}
}
Crises such as natural disasters and public health emergencies generate vast amounts of text data, making it challenging to classify the information into relevant categories. Acquiring expert-labeled data for such scenarios can be difficult, leading to limited training datasets for text classification by fine-tuning BERT-like models. Unfortunately, traditional data augmentation techniques only slightly improve F1-scores. How can data augmentation be used to obtain better results in this applied domain? In this paper, using neural network explicability methods, we aim to highlight that fine-tuned BERT-like models on crisis corpora give too much importance to spatial information to make their predictions. This overfitting of spatial information limits their ability to generalize especially when the event which occurs in a place has evolved and changed since the training dataset has been built. To reduce this bias, we propose GeoNLPlify,1
a novel data augmentation technique that leverages spatial information to generate new labeled data for text classification related to crises. Our approach aims to address overfitting without necessitating modifications to the underlying model architecture, distinguishing it from other prevalent methods employed to combat overfitting. Our results show that GeoNLPlify significantly improves F1-scores, demonstrating the potential of the spatial information for data augmentation for crisis-related text classification tasks. In order to evaluate the contribution of our method, GeoNLPlify is applied to three public datasets (PADI-web, CrisisNLP and SST2) and compared with classical natural language processing data augmentations.
a novel data augmentation technique that leverages spatial information to generate new labeled data for text classification related to crises. Our approach aims to address overfitting without necessitating modifications to the underlying model architecture, distinguishing it from other prevalent methods employed to combat overfitting. Our results show that GeoNLPlify significantly improves F1-scores, demonstrating the potential of the spatial information for data augmentation for crisis-related text classification tasks. In order to evaluate the contribution of our method, GeoNLPlify is applied to three public datasets (PADI-web, CrisisNLP and SST2) and compared with classical natural language processing data augmentations.
18.
Cuypers, Lize; Keyaerts, Els; Hong, Samuel Leandro; Gorissen, Sarah; Menezes, Soraya Maria; Starick, Marick; Elslande, Jan Van; Weemaes, Matthias; Wawina-Bokalanga, Tony; Marti-Carreras, Joan; Vanmechelen, Bert; Holm, Bram Van; Bloemen, Mandy; Dogne, Jean-Michel; Dufrasne, François; Durkin, Keith; Ruelle, Jean; Mendonca, Ricardo De; Wollants, Elke; Vermeersch, Pieter; Consortium, COVID-19 Genomics Belgium; Boulouffe, Caroline; Djiena, Achille; Broucke, Caroline; Catry, Boudewijn; Lagrou, Katrien; Ranst, Marc Van; Neyts, Johan; Baele, Guy; Maes, Piet; André, Emmanuel; Dellicour, Simon; Weyenbergh, Johan Van
Immunovirological and environmental screening reveals actionable risk factors for fatal COVID-19 during post-vaccination nursing home outbreaks Journal Article
In: Nature Aging , vol. 3, pp. 722–733, 2023.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus), OpenDataSet
@article{nokey,
title = {Immunovirological and environmental screening reveals actionable risk factors for fatal COVID-19 during post-vaccination nursing home outbreaks},
author = {Lize Cuypers and Els Keyaerts and Samuel Leandro Hong and Sarah Gorissen and Soraya Maria Menezes and Marick Starick and Jan Van Elslande and Matthias Weemaes and Tony Wawina-Bokalanga and Joan Marti-Carreras and Bert Vanmechelen and Bram Van Holm and Mandy Bloemen and Jean-Michel Dogne and François Dufrasne and Keith Durkin and Jean Ruelle and Ricardo De Mendonca and Elke Wollants and Pieter Vermeersch and COVID-19 Genomics Belgium Consortium and Caroline Boulouffe and Achille Djiena and Caroline Broucke and Boudewijn Catry and Katrien Lagrou and Marc Van Ranst and Johan Neyts and Guy Baele and Piet Maes and Emmanuel André and Simon Dellicour and Johan Van Weyenbergh},
url = {https://www.nature.com/articles/s43587-023-00421-1#citeas},
doi = {10.1038/s43587-023-00421-1},
year = {2023},
date = {2023-05-22},
urldate = {2023-05-22},
journal = {Nature Aging },
volume = {3},
pages = {722–733},
abstract = {Coronavirus Disease 2019 (COVID-19) vaccination has resulted in excellent protection against fatal disease, including in older adults. However, risk factors for post-vaccination fatal COVID-19 are largely unknown. We comprehensively studied three large nursing home outbreaks (20–35% fatal cases among residents) by combining severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) aerosol monitoring, whole-genome phylogenetic analysis and immunovirological profiling of nasal mucosa by digital nCounter transcriptomics. Phylogenetic investigations indicated that each outbreak stemmed from a single introduction event, although with different variants (Delta, Gamma and Mu). SARS-CoV-2 was detected in aerosol samples up to 52 d after the initial infection. Combining demographic, immune and viral parameters, the best predictive models for mortality comprised IFNB1 or age, viral ORF7a and ACE2 receptor transcripts. Comparison with published pre-vaccine fatal COVID-19 transcriptomic and genomic signatures uncovered a unique IRF3 low/IRF7 high immune signature in post-vaccine fatal COVID-19 outbreaks. A multi-layered strategy, including environmental sampling, immunomonitoring and early antiviral therapy, should be considered to prevent post-vaccination COVID-19 mortality in nursing homes.},
keywords = {Covid-19 (Coronavirus), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
Coronavirus Disease 2019 (COVID-19) vaccination has resulted in excellent protection against fatal disease, including in older adults. However, risk factors for post-vaccination fatal COVID-19 are largely unknown. We comprehensively studied three large nursing home outbreaks (20–35% fatal cases among residents) by combining severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) aerosol monitoring, whole-genome phylogenetic analysis and immunovirological profiling of nasal mucosa by digital nCounter transcriptomics. Phylogenetic investigations indicated that each outbreak stemmed from a single introduction event, although with different variants (Delta, Gamma and Mu). SARS-CoV-2 was detected in aerosol samples up to 52 d after the initial infection. Combining demographic, immune and viral parameters, the best predictive models for mortality comprised IFNB1 or age, viral ORF7a and ACE2 receptor transcripts. Comparison with published pre-vaccine fatal COVID-19 transcriptomic and genomic signatures uncovered a unique IRF3 low/IRF7 high immune signature in post-vaccine fatal COVID-19 outbreaks. A multi-layered strategy, including environmental sampling, immunomonitoring and early antiviral therapy, should be considered to prevent post-vaccination COVID-19 mortality in nursing homes.
19.
Marini, Giovanni; Tagliapietra, Valentina; Cristofolini, Fabiana; Cristofori, Antonella; Dagostin, Francesca; Zuccali, Maria Grazia; Molinaro, Silvia; Gottardini, Elena; Rizzoli, Annapaola
Correlation between airborne pollen data and the risk of tick-borne encephalitis in northern Italy Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags: OpenDataSet, TBE (Tick Borne Encephalitis)
@article{nokey_42,
title = {Correlation between airborne pollen data and the risk of tick-borne encephalitis in northern Italy},
author = {Giovanni Marini and Valentina Tagliapietra and Fabiana Cristofolini and Antonella Cristofori and Francesca Dagostin and Maria Grazia Zuccali and Silvia Molinaro and Elena Gottardini and Annapaola Rizzoli },
url = {https://www.nature.com/articles/s41598-023-35478-w#article-info},
doi = {10.1038/s41598-023-35478-w},
year = {2023},
date = {2023-05-22},
urldate = {2023-05-22},
abstract = {Tick-borne encephalitis (TBE) is caused by a flavivirus that infects animals including humans. In Europe, the TBE virus circulates enzootically in natural foci among ticks and rodent hosts. The abundance of ticks depends on the abundance of rodent hosts, which in turn depends on the availability of food resources, such as tree seeds. Trees can exhibit large inter-annual fluctuations in seed production (masting), which influences the abundance of rodents the following year, and the abundance of nymphal ticks two years later. Thus, the biology of this system predicts a 2-year time lag between masting and the incidence of tick-borne diseases such as TBE. As airborne pollen abundance is related to masting, we investigated whether inter-annual variation in pollen load could be directly correlated with inter-annual variation in the incidence of TBE in human populations with a 2-year time lag. We focused our study on the province of Trento (northern Italy), where 206 TBE cases were notified between 1992 and 2020. We tested the relationship between TBE incidence and pollen load collected from 1989 to 2020 for 7 different tree species common in our study area. Through univariate analysis we found that the pollen quantities recorded two years prior for two tree species, hop-hornbeam (Ostrya carpinifolia) and downy oak (Quercus pubescens), were positively correlated with TBE emergence (R2 = 0.2) while a multivariate model with both tree species better explained the variation in annual TBE incidence (R2 = 0.34). To the best of our knowledge, this is the first attempt at quantifying the correlation between pollen quantities and the incidence of TBE in human populations. As pollen loads are collected by widespread aerobiological networks using standardized procedures, our study could be easily replicated to test their potential as early warning system for TBE and other tick-borne diseases.},
keywords = {OpenDataSet, TBE (Tick Borne Encephalitis)},
pubstate = {published},
tppubtype = {article}
}
Tick-borne encephalitis (TBE) is caused by a flavivirus that infects animals including humans. In Europe, the TBE virus circulates enzootically in natural foci among ticks and rodent hosts. The abundance of ticks depends on the abundance of rodent hosts, which in turn depends on the availability of food resources, such as tree seeds. Trees can exhibit large inter-annual fluctuations in seed production (masting), which influences the abundance of rodents the following year, and the abundance of nymphal ticks two years later. Thus, the biology of this system predicts a 2-year time lag between masting and the incidence of tick-borne diseases such as TBE. As airborne pollen abundance is related to masting, we investigated whether inter-annual variation in pollen load could be directly correlated with inter-annual variation in the incidence of TBE in human populations with a 2-year time lag. We focused our study on the province of Trento (northern Italy), where 206 TBE cases were notified between 1992 and 2020. We tested the relationship between TBE incidence and pollen load collected from 1989 to 2020 for 7 different tree species common in our study area. Through univariate analysis we found that the pollen quantities recorded two years prior for two tree species, hop-hornbeam (Ostrya carpinifolia) and downy oak (Quercus pubescens), were positively correlated with TBE emergence (R2 = 0.2) while a multivariate model with both tree species better explained the variation in annual TBE incidence (R2 = 0.34). To the best of our knowledge, this is the first attempt at quantifying the correlation between pollen quantities and the incidence of TBE in human populations. As pollen loads are collected by widespread aerobiological networks using standardized procedures, our study could be easily replicated to test their potential as early warning system for TBE and other tick-borne diseases.
20.
de Meijere, Giulia; Valdano, Eugenio; Castellano, Claudio; Debin, Marion; Kengne-Kuetche, Charly; Turbelin, Clément; Noël, Harold; Weitz, Joshua S.; Paolotti, Daniela; Hermans, Lisa; Hens, Niel; Colizza, Vittoria
In: 2023.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus), OpenDataSet
@article{nokey,
title = {Attitudes towards booster, testing and isolation, and their impact on COVID-19 response in winter 2022/2023 in France, Belgium, and Italy: a cross-sectional survey and modelling study},
author = {Giulia de Meijere and Eugenio Valdano and Claudio Castellano and Marion Debin and Charly Kengne-Kuetche and Clément Turbelin and Harold Noël and Joshua S. Weitz and Daniela Paolotti and Lisa Hermans and Niel Hens and Vittoria Colizza
},
url = {https://www.sciencedirect.com/science/article/pii/S2666776223000327?via%3Dihub},
doi = {10.1016/j.lanepe.2023.100614},
year = {2023},
date = {2023-05-11},
urldate = {2023-05-11},
abstract = {The vast majority of survey participants (N = 4594) was willing to adhere to testing (>91%) and rapid isolation (>88%) across the three countries. Pronounced differences emerged in the declared senior adherence to booster vaccination (73% in France, 94% in Belgium, 86% in Italy). Epidemic model results estimate that testing and isolation protocols would confer significant benefit in reducing transmission (17–24% reduction, from R = 1.6 to R = 1.3 in France and Belgium, to R = 1.2 in Italy) with declared adherence. Achieving a mitigating level similar to the French protocol, the Belgian protocol would require 35% fewer tests (from 1 test to 0.65 test per infected person) and avoid the long isolation periods of the Italian protocol (average of 6 days vs. 11). A cost barrier to test would significantly decrease adherence in France and Belgium, undermining protocols’ effectiveness.},
keywords = {Covid-19 (Coronavirus), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
The vast majority of survey participants (N = 4594) was willing to adhere to testing (>91%) and rapid isolation (>88%) across the three countries. Pronounced differences emerged in the declared senior adherence to booster vaccination (73% in France, 94% in Belgium, 86% in Italy). Epidemic model results estimate that testing and isolation protocols would confer significant benefit in reducing transmission (17–24% reduction, from R = 1.6 to R = 1.3 in France and Belgium, to R = 1.2 in Italy) with declared adherence. Achieving a mitigating level similar to the French protocol, the Belgian protocol would require 35% fewer tests (from 1 test to 0.65 test per infected person) and avoid the long isolation periods of the Italian protocol (average of 6 days vs. 11). A cost barrier to test would significantly decrease adherence in France and Belgium, undermining protocols’ effectiveness.