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.
Kyla; Erazo Serres, Diana; Despréaux
Integrating indicator-based and event-based surveillance data for risk mapping of West Nile virus, Europe, 2006 to 2021 Journal Article
In: Eurosurveillance , vol. 29, iss. 44, 2024.
Abstract | Links | BibTeX | Tags:
@article{Serres2024,
title = {Integrating indicator-based and event-based surveillance data for risk mapping of West Nile virus, Europe, 2006 to 2021 },
author = {Serres, Kyla; Erazo, Diana; Despréaux, Garance; Vincenti-González, María F; Van Bortel, Wim; Arsevska, Elena; Dellicour, Simon},
url = {https://doi.org/10.2807/1560-7917.ES.2024.29.44.2400084},
year = {2024},
date = {2024-10-31},
journal = {Eurosurveillance },
volume = {29},
issue = {44},
abstract = {West Nile virus (WNV) has an enzootic cycle between birds and mosquitoes, humans being incidental dead-end hosts. Circulation of WNV is an increasing public health threat in Europe. While detection of WNV is notifiable in humans and animals in the European Union, surveillance based on human case numbers presents some limitations, including reporting delays.
Aim
We aimed to perform risk mapping of WNV circulation leading to human infections in Europe by integrating two types of surveillance systems: indicator-based and event-based surveillance.
Methods
For indicator-based surveillance, we used data on human case numbers reported to the European Centre for Disease Prevention and Control (ECDC), and for event-based data, we retrieved information from news articles collected through an automated biosurveillance platform. In addition to these data sources, we also used environmental data to train ecological niche models to map the risk of local WNV circulation leading to human infections.
Results
The ecological niche models based on both types of surveillance data highlighted new areas potentially at risk of WNV infection in humans, particularly in Spain, Italy, France and Greece.
Conclusion
Although event-based surveillance data do not constitute confirmed occurrence records, integrating both indicator-based and event-based surveillance data proved useful. These results underscore the potential for a more proactive and comprehensive strategy in managing the threat of WNV in Europe by combining indicator- and event-based and environmental data for effective surveillance and public health response.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
West Nile virus (WNV) has an enzootic cycle between birds and mosquitoes, humans being incidental dead-end hosts. Circulation of WNV is an increasing public health threat in Europe. While detection of WNV is notifiable in humans and animals in the European Union, surveillance based on human case numbers presents some limitations, including reporting delays.
Aim
We aimed to perform risk mapping of WNV circulation leading to human infections in Europe by integrating two types of surveillance systems: indicator-based and event-based surveillance.
Methods
For indicator-based surveillance, we used data on human case numbers reported to the European Centre for Disease Prevention and Control (ECDC), and for event-based data, we retrieved information from news articles collected through an automated biosurveillance platform. In addition to these data sources, we also used environmental data to train ecological niche models to map the risk of local WNV circulation leading to human infections.
Results
The ecological niche models based on both types of surveillance data highlighted new areas potentially at risk of WNV infection in humans, particularly in Spain, Italy, France and Greece.
Conclusion
Although event-based surveillance data do not constitute confirmed occurrence records, integrating both indicator-based and event-based surveillance data proved useful. These results underscore the potential for a more proactive and comprehensive strategy in managing the threat of WNV in Europe by combining indicator- and event-based and environmental data for effective surveillance and public health response.
Aim
We aimed to perform risk mapping of WNV circulation leading to human infections in Europe by integrating two types of surveillance systems: indicator-based and event-based surveillance.
Methods
For indicator-based surveillance, we used data on human case numbers reported to the European Centre for Disease Prevention and Control (ECDC), and for event-based data, we retrieved information from news articles collected through an automated biosurveillance platform. In addition to these data sources, we also used environmental data to train ecological niche models to map the risk of local WNV circulation leading to human infections.
Results
The ecological niche models based on both types of surveillance data highlighted new areas potentially at risk of WNV infection in humans, particularly in Spain, Italy, France and Greece.
Conclusion
Although event-based surveillance data do not constitute confirmed occurrence records, integrating both indicator-based and event-based surveillance data proved useful. These results underscore the potential for a more proactive and comprehensive strategy in managing the threat of WNV in Europe by combining indicator- and event-based and environmental data for effective surveillance and public health response.
3.
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.
4.
Laura; Goldberg Di Domenico, Yair; Colizza
Planning and adjusting the COVID-19 booster vaccination campaign to reduce disease burden Journal Article
In: Infectious Disease Modelling, vol. 10, iss. 1, pp. 150-162, 2024.
Abstract | Links | BibTeX | Tags:
@article{Domenico2024b,
title = {Planning and adjusting the COVID-19 booster vaccination campaign to reduce disease burden},
author = {Di Domenico, Laura; Goldberg, Yair; Colizza, Vittoria },
url = {https://doi.org/10.1016/j.idm.2024.09.002},
year = {2024},
date = {2024-09-12},
journal = {Infectious Disease Modelling},
volume = {10},
issue = {1},
pages = {150-162},
abstract = {As public health policies shifted in 2023 from emergency response to long-term COVID-19 disease management, immunization programs started to face the challenge of formulating routine booster campaigns in a still highly uncertain seasonal behavior of the COVID-19 epidemic. Mathematical models assessing past booster campaigns and integrating knowledge on waning of immunity can help better inform current and future vaccination programs. Focusing on the first booster campaign in the 2021/2022 winter in France, we used a multi-strain age-stratified transmission model to assess the effectiveness of the observed booster vaccination in controlling the succession of Delta, Omicron BA.1 and BA.2 waves. We explored counterfactual scenarios altering the eligibility criteria and inter-dose delay. Our study showed that the success of the immunization program in curtailing the Omicron BA.1 and BA.2 waves was largely dependent on the inclusion of adults among the eligible groups, and was highly sensitive to the inter-dose delay, which was changed over time. Shortening or prolonging this delay, even by only one month, would have required substantial social distancing interventions to curtail the hospitalization peak. Also, the time window for adjusting the delay was very short. Our findings highlight the importance of readiness and adaptation in the formulation of routine booster campaign in the current level of epidemiological uncertainty.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
As public health policies shifted in 2023 from emergency response to long-term COVID-19 disease management, immunization programs started to face the challenge of formulating routine booster campaigns in a still highly uncertain seasonal behavior of the COVID-19 epidemic. Mathematical models assessing past booster campaigns and integrating knowledge on waning of immunity can help better inform current and future vaccination programs. Focusing on the first booster campaign in the 2021/2022 winter in France, we used a multi-strain age-stratified transmission model to assess the effectiveness of the observed booster vaccination in controlling the succession of Delta, Omicron BA.1 and BA.2 waves. We explored counterfactual scenarios altering the eligibility criteria and inter-dose delay. Our study showed that the success of the immunization program in curtailing the Omicron BA.1 and BA.2 waves was largely dependent on the inclusion of adults among the eligible groups, and was highly sensitive to the inter-dose delay, which was changed over time. Shortening or prolonging this delay, even by only one month, would have required substantial social distancing interventions to curtail the hospitalization peak. Also, the time window for adjusting the delay was very short. Our findings highlight the importance of readiness and adaptation in the formulation of routine booster campaign in the current level of epidemiological uncertainty.
5.
Laetitia; Azé Viau, Jerome; Chen
Epid data explorer: A visualization tool for exploring and comparing spatio-temporal epidemiological data Journal Article
In: Health Informatics Journal, 2024.
Abstract | Links | BibTeX | Tags: Tools
@article{Viau2024,
title = {Epid data explorer: A visualization tool for exploring and comparing spatio-temporal epidemiological data},
author = {Viau, Laetitia; Azé, Jerome; Chen, Fati; Pompidor, Pierre; Poncelet, Pascal; Raveneau, Vincent; Rodriguez, Nancy; Sallaberry, Arnaud.},
url = {https://doi.org/10.1177/14604582241279720},
doi = {10.1177/14604582241279720},
year = {2024},
date = {2024-09-03},
urldate = {2024-09-03},
journal = {Health Informatics Journal},
abstract = {The analysis of large sets of spatio-temporal data is a fundamental challenge in epidemiological research. As the quantity and the complexity of such kind of data increases, automatic analysis approaches, such as statistics, data mining, machine learning, etc., can be used to extract useful information. While these approaches have proven effective, they require a priori knowledge of the information being sought, and some interesting insights into the data may be missed. To bridge this gap, information visualization offers a set of techniques for not only presenting known information, but also exploring data without having a hypothesis formulated beforehand. In this paper, we introduce Epid Data Explorer (EDE), a visualization tool that enables exploration of spatio-temporal epidemiological data. EDE allows easy comparisons of indicators and trends across different geographical areas and times. It facilitates this exploration through ready-to-use pre-loaded datasets as well as user-chosen datasets. The tool also provides a secure architecture for easily importing new datasets while ensuring confidentiality. In two use cases using data associated with the COVID-19 epidemic, we demonstrate the substantial impact of implemented lockdown measures on mobility and how EDE allows assessing correlations between the spread of COVID-19 and weather conditions.
},
keywords = {Tools},
pubstate = {published},
tppubtype = {article}
}
The analysis of large sets of spatio-temporal data is a fundamental challenge in epidemiological research. As the quantity and the complexity of such kind of data increases, automatic analysis approaches, such as statistics, data mining, machine learning, etc., can be used to extract useful information. While these approaches have proven effective, they require a priori knowledge of the information being sought, and some interesting insights into the data may be missed. To bridge this gap, information visualization offers a set of techniques for not only presenting known information, but also exploring data without having a hypothesis formulated beforehand. In this paper, we introduce Epid Data Explorer (EDE), a visualization tool that enables exploration of spatio-temporal epidemiological data. EDE allows easy comparisons of indicators and trends across different geographical areas and times. It facilitates this exploration through ready-to-use pre-loaded datasets as well as user-chosen datasets. The tool also provides a secure architecture for easily importing new datasets while ensuring confidentiality. In two use cases using data associated with the COVID-19 epidemic, we demonstrate the substantial impact of implemented lockdown measures on mobility and how EDE allows assessing correlations between the spread of COVID-19 and weather conditions.
6.
Andrea; Guzzetta Bizzotto, Giorgio; Marziano
Increasing situational awareness through nowcasting of the reproduction number Journal Article
In: Frontiers in Public Health, vol. 12, 2024.
Links | BibTeX | Tags: Covid-19 (Coronavirus)
@article{Bizzotto2024,
title = {Increasing situational awareness through nowcasting of the reproduction number},
author = {Bizzotto, Andrea; Guzzetta, Giorgio; Marziano, Valentina; Del Manso, Martina; Mateo Urdiales, Alberto; Petrone, Daniele; Cannone, Andrea; Sacco, Chiara; Poletti, Piero; Manica, Mattia; Zardini, Agenese; Trentini, Filippo; Fabiani, Massimo; Bella, Antonino; Riccardo, Flavia; Pezzotti, Patrizio; Ajelli, Marco & Merler, Stefano},
editor = {Joao Sollari Lopes, National Statistical Institute of Portugal, Portugal
},
doi = {doi: 10.3389/fpubh.2024.1430920},
year = {2024},
date = {2024-08-21},
journal = {Frontiers in Public Health},
volume = {12},
keywords = {Covid-19 (Coronavirus)},
pubstate = {published},
tppubtype = {article}
}
7.
Giovanni; Drakulovic Marini, Mitra; B. ; Jovanovic
Drivers and epidemiological patterns of West Nile virus in Serbia Journal Article
In: Frontiers in Public Health, vol. 12, 2024.
Abstract | Links | BibTeX | Tags:
@article{Marini2024,
title = {Drivers and epidemiological patterns of West Nile virus in Serbia},
author = {Marini, Giovanni; Drakulovic, Mitra; B.; Jovanovic, Verica; Dagostin, Francesca; Wint, Willy; Tagliapietra, Valentina; Vasic, Milena & Rizzoli, Annapaola},
url = {https://doi.org/10.3389/fpubh.2024.1429583},
year = {2024},
date = {2024-07-17},
journal = {Frontiers in Public Health},
volume = {12},
abstract = {West Nile virus (WNV) is an emerging mosquito-borne pathogen in Serbia, where it has been detected as a cause of infection in humans since 2012. We analyzed and modelled WNV transmission patterns in the country between 2012 and 2023.
Methods: We applied a previously developed modelling approach to quantify epidemiological parameters of interest and to identify the most important environmental drivers of the force of infection (FOI) by means of statistical analysis in the human population in the country.
Results: During the study period, 1,387 human cases were recorded, with substantial heterogeneity across years. We found that spring temperature is of paramount importance for WNV transmission, as FOI magnitude and peak timing are positively associated with it. Furthermore, FOI is also estimated to be greater in regions with a larger fraction of older adult people, who are at higher risk to develop severe infections.
Conclusion: Our results highlight that temperature plays a key role in shaping WNV outbreak magnitude in Serbia, confirming the association between spring climatic conditions and WNV human transmission risk and thus pointing out the importance of this factor as a potential early warning predictor for timely application of preventive and control measures.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
West Nile virus (WNV) is an emerging mosquito-borne pathogen in Serbia, where it has been detected as a cause of infection in humans since 2012. We analyzed and modelled WNV transmission patterns in the country between 2012 and 2023.
Methods: We applied a previously developed modelling approach to quantify epidemiological parameters of interest and to identify the most important environmental drivers of the force of infection (FOI) by means of statistical analysis in the human population in the country.
Results: During the study period, 1,387 human cases were recorded, with substantial heterogeneity across years. We found that spring temperature is of paramount importance for WNV transmission, as FOI magnitude and peak timing are positively associated with it. Furthermore, FOI is also estimated to be greater in regions with a larger fraction of older adult people, who are at higher risk to develop severe infections.
Conclusion: Our results highlight that temperature plays a key role in shaping WNV outbreak magnitude in Serbia, confirming the association between spring climatic conditions and WNV human transmission risk and thus pointing out the importance of this factor as a potential early warning predictor for timely application of preventive and control measures.
Methods: We applied a previously developed modelling approach to quantify epidemiological parameters of interest and to identify the most important environmental drivers of the force of infection (FOI) by means of statistical analysis in the human population in the country.
Results: During the study period, 1,387 human cases were recorded, with substantial heterogeneity across years. We found that spring temperature is of paramount importance for WNV transmission, as FOI magnitude and peak timing are positively associated with it. Furthermore, FOI is also estimated to be greater in regions with a larger fraction of older adult people, who are at higher risk to develop severe infections.
Conclusion: Our results highlight that temperature plays a key role in shaping WNV outbreak magnitude in Serbia, confirming the association between spring climatic conditions and WNV human transmission risk and thus pointing out the importance of this factor as a potential early warning predictor for timely application of preventive and control measures.
8.
D. Da Re, Marini
VectAbundance: a spatio-temporal database of Aedes mosquitoes observations Journal Article
In: Scientific Data , 2024.
Abstract | Links | BibTeX | Tags:
@article{Re2024,
title = {VectAbundance: a spatio-temporal database of Aedes mosquitoes observations},
author = {Da Re, D., Marini, G., Bonannella, C., Laurini, F., Manica, M., Anicic, N., Albieri, A., Angelini, P., Arnoldi, D., Blaha, M., Bertola, F., Caputo, B., De Liberato, C., Della Torre, A., Flacio, E., Franceschini, A., Gradoni, F., Kadriaj, P., Lencioni, V., Rosà, R.},
url = {https://doi.org/10.1038/s41597-024-03482-y},
year = {2024},
date = {2024-06-15},
journal = {Scientific Data },
abstract = {Modelling approaches play a crucial role in supporting local public health agencies by estimating and forecasting vector abundance and seasonality. However, the reliability of these models is contingent on the availability of standardized, high-quality data. Addressing this need, our study focuses on collecting and harmonizing egg count observations of the mosquito Aedes albopictus, obtained through ovitraps in monitoring and surveillance efforts across Albania, France, Italy, and Switzerland from 2010 to 2022. We processed the raw observations to obtain a continuous time series of ovitraps observations allowing for an extensive geographical and temporal coverage of Ae. albopictus population dynamics. The resulting post-processed observations are stored in the open-access database VectAbundance.This initiative addresses the critical need for accessible, high-quality data, enhancing the reliability of modelling efforts and bolstering public health preparedness.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Modelling approaches play a crucial role in supporting local public health agencies by estimating and forecasting vector abundance and seasonality. However, the reliability of these models is contingent on the availability of standardized, high-quality data. Addressing this need, our study focuses on collecting and harmonizing egg count observations of the mosquito Aedes albopictus, obtained through ovitraps in monitoring and surveillance efforts across Albania, France, Italy, and Switzerland from 2010 to 2022. We processed the raw observations to obtain a continuous time series of ovitraps observations allowing for an extensive geographical and temporal coverage of Ae. albopictus population dynamics. The resulting post-processed observations are stored in the open-access database VectAbundance.This initiative addresses the critical need for accessible, high-quality data, enhancing the reliability of modelling efforts and bolstering public health preparedness.
9.
Laura; Valdano Di Domenico, Eugenio; Colizza, Vittoria
Limited data on infectious disease distribution exposes ambiguity in epidemic modeling choices Journal Article
In: American Physical Society, vol. 6, iss. 2, 2024.
Abstract | Links | BibTeX | Tags:
@article{Domenico2024,
title = {Limited data on infectious disease distribution exposes ambiguity in epidemic modeling choices},
author = {Di Domenico, Laura; Valdano, Eugenio and Colizza, Vittoria
},
url = {https://doi.org/10.1103/PhysRevResearch.6.023265},
year = {2024},
date = {2024-06-10},
journal = {American Physical Society},
volume = {6},
issue = {2},
abstract = {Traditional disease transmission models assume that the infectious period is exponentially distributed with a recovery rate fixed in time and across individuals. This assumption provides analytical and computational advantages, however, it is often unrealistic when compared to empirical data. Current efforts in modeling nonexponentially distributed infectious periods are either limited to special cases or lead to unsolvable models. Also, the link between empirical data (the infectious period distribution) and the modeling needs (the definition of the corresponding recovery rates) lacks a clear understanding. Here we introduce a mapping of an arbitrary distribution of infectious periods into a distribution of recovery rates. Under the Markovian assumption to ensure analytical tractability, we show that the same infectious period distribution at the population level can be reproduced by two modeling schemes that we call host-based and population-based, depending on the individual response to the infection, and aggregated empirical data cannot easily discriminate the correct scheme. Besides being conceptually different, the two schemes also lead to different epidemic trajectories. Although sharing the same behavior close to the disease-free equilibrium, the host-based scheme deviates from the expected epidemic when reaching the endemic equilibrium of a susceptible-infectious-susceptible transmission model, while the population-based scheme turns out to be equivalent to assuming a homogeneous recovery rate. We show this through analytical computations and stochastic epidemic simulations on a contact network, using both generative network models and empirical contact data. It is therefore possible to reproduce heterogeneous infectious periods in network-based transmission models, however, the resulting prevalence is sensitive to the modeling choice for the interpretation of the empirically collected data on the length of the infectious period. In the absence of higher resolution data, studies should acknowledge such deviations in the epidemic predictions.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Traditional disease transmission models assume that the infectious period is exponentially distributed with a recovery rate fixed in time and across individuals. This assumption provides analytical and computational advantages, however, it is often unrealistic when compared to empirical data. Current efforts in modeling nonexponentially distributed infectious periods are either limited to special cases or lead to unsolvable models. Also, the link between empirical data (the infectious period distribution) and the modeling needs (the definition of the corresponding recovery rates) lacks a clear understanding. Here we introduce a mapping of an arbitrary distribution of infectious periods into a distribution of recovery rates. Under the Markovian assumption to ensure analytical tractability, we show that the same infectious period distribution at the population level can be reproduced by two modeling schemes that we call host-based and population-based, depending on the individual response to the infection, and aggregated empirical data cannot easily discriminate the correct scheme. Besides being conceptually different, the two schemes also lead to different epidemic trajectories. Although sharing the same behavior close to the disease-free equilibrium, the host-based scheme deviates from the expected epidemic when reaching the endemic equilibrium of a susceptible-infectious-susceptible transmission model, while the population-based scheme turns out to be equivalent to assuming a homogeneous recovery rate. We show this through analytical computations and stochastic epidemic simulations on a contact network, using both generative network models and empirical contact data. It is therefore possible to reproduce heterogeneous infectious periods in network-based transmission models, however, the resulting prevalence is sensitive to the modeling choice for the interpretation of the empirically collected data on the length of the infectious period. In the absence of higher resolution data, studies should acknowledge such deviations in the epidemic predictions.
10.
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.
11.
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.
12.
Erazo, Diana; Grant, Luke; Ghisbain, Guillaume; Marini, Giovanni; Colón-González, Felipe J.; Wint, William; Rizzoli, Annapaola; Bortel, Wim Van; Vogels, Chantal B. F.; Grubaugh, Nathan D.; Mengel, Matthias; Frieler, Katja; Thiery, Wim; Dellicour, Simon
Contribution of climate change to the spatial expansion of West Nile virus in Europe Journal Article
In: Nature Communications, vol. 15, no. 1196, 2024.
Abstract | Links | BibTeX | Tags: WNV (West Nile Virus)
@article{nokey,
title = {Contribution of climate change to the spatial expansion of West Nile virus in Europe},
author = {Diana Erazo and Luke Grant and Guillaume Ghisbain and Giovanni Marini and Felipe J. Colón-González and William Wint and Annapaola Rizzoli and Wim Van Bortel and Chantal B. F. Vogels and Nathan D. Grubaugh and Matthias Mengel and Katja Frieler and Wim Thiery and Simon Dellicour},
url = {https://link.springer.com/article/10.1038/s41467-024-45290-3?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20240208&utm_content=10.1038/s41467-024-45290-3#article-info},
doi = {https://doi.org/10.1038/s41467-024-45290-3},
year = {2024},
date = {2024-02-08},
journal = {Nature Communications},
volume = {15},
number = {1196},
abstract = {West Nile virus (WNV) is an emerging mosquito-borne pathogen in Europe where it represents a new public health threat. While climate change has been cited as a potential driver of its spatial expansion on the continent, a formal evaluation of this causal relationship is lacking. Here, we investigate the extent to which WNV spatial expansion in Europe can be attributed to climate change while accounting for other direct human influences such as land-use and human population changes. To this end, we trained ecological niche models to predict the risk of local WNV circulation leading to human cases to then unravel the isolated effect of climate change by comparing factual simulations to a counterfactual based on the same environmental changes but a counterfactual climate where long-term trends have been removed. Our findings demonstrate a notable increase in the area ecologically suitable for WNV circulation during the period 1901–2019, whereas this area remains largely unchanged in a no-climate-change counterfactual. We show that the drastic increase in the human population at risk of exposure is partly due to historical changes in population density, but that climate change has also been a critical driver behind the heightened risk of WNV circulation in Europe.},
keywords = {WNV (West Nile Virus)},
pubstate = {published},
tppubtype = {article}
}
West Nile virus (WNV) is an emerging mosquito-borne pathogen in Europe where it represents a new public health threat. While climate change has been cited as a potential driver of its spatial expansion on the continent, a formal evaluation of this causal relationship is lacking. Here, we investigate the extent to which WNV spatial expansion in Europe can be attributed to climate change while accounting for other direct human influences such as land-use and human population changes. To this end, we trained ecological niche models to predict the risk of local WNV circulation leading to human cases to then unravel the isolated effect of climate change by comparing factual simulations to a counterfactual based on the same environmental changes but a counterfactual climate where long-term trends have been removed. Our findings demonstrate a notable increase in the area ecologically suitable for WNV circulation during the period 1901–2019, whereas this area remains largely unchanged in a no-climate-change counterfactual. We show that the drastic increase in the human population at risk of exposure is partly due to historical changes in population density, but that climate change has also been a critical driver behind the heightened risk of WNV circulation in Europe.
13.
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}
}
14.
Sabbatini, Chiara E.; Pullano, Giulia; Domenico, Laura Di; Rubrichi, Stefania; & Vittoria Colizza, Shweta Bansal
The impact of spatial connectivity on NPIs effectiveness Journal Article
In: BMC Infectious Diseases, no. 21, 2024.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus)
@article{nokey,
title = {The impact of spatial connectivity on NPIs effectiveness},
author = {Chiara E. Sabbatini and Giulia Pullano and Laura Di Domenico and Stefania Rubrichi and Shweta Bansal & Vittoria Colizza},
url = {https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-023-08900-x},
doi = {10.1186/s12879-023-08900-x},
year = {2024},
date = {2024-01-02},
urldate = {2024-01-02},
journal = {BMC Infectious Diseases},
number = {21},
abstract = {Background
France implemented a combination of non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic between September 2020 and June 2021. These included a lockdown in the fall 2020 – the second since the start of the pandemic – to counteract the second wave, followed by a long period of nighttime curfew, and by a third lockdown in the spring 2021 against the Alpha wave. Interventions have so far been evaluated in isolation, neglecting the spatial connectivity between regions through mobility that may impact NPI effectiveness.
Methods
Focusing on September 2020–June 2021, we developed a regionally-based epidemic metapopulation model informed by observed mobility fluxes from daily mobile phone data and fitted the model to regional hospital admissions. The model integrated data on vaccination and variants spread. Scenarios were designed to assess the impact of the Alpha variant, characterized by increased transmissibility and risk of hospitalization, of the vaccination campaign and alternative policy decisions.
Results
The spatial model better captured the heterogeneity observed in the regional dynamics, compared to models neglecting inter-regional mobility. The third lockdown was similarly effective to the second lockdown after discounting for immunity, Alpha, and seasonality (51% vs 52% median regional reduction in the reproductive number R0, respectively). The 6pm nighttime curfew with bars and restaurants closed, implemented in January 2021, substantially reduced COVID-19 transmission. It initially led to 49% median regional reduction of R0, decreasing to 43% reduction by March 2021. In absence of vaccination, implemented interventions would have been insufficient against the Alpha wave. Counterfactual scenarios proposing a sequence of lockdowns in a stop-and-go fashion would have reduced hospitalizations and restriction days for low enough thresholds triggering and lifting restrictions.
Conclusions
Spatial connectivity induced by mobility impacted the effectiveness of interventions especially in regions with higher mobility rates. Early evening curfew with gastronomy sector closed allowed authorities to delay the third wave. Stop-and-go lockdowns could have substantially lowered both healthcare and societal burdens if implemented early enough, compared to the observed application of lockdown-curfew-lockdown, but likely at the expense of several labor sectors. These findings contribute to characterize the effectiveness of implemented strategies and improve pandemic preparedness.},
keywords = {Covid-19 (Coronavirus)},
pubstate = {published},
tppubtype = {article}
}
Background
France implemented a combination of non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic between September 2020 and June 2021. These included a lockdown in the fall 2020 – the second since the start of the pandemic – to counteract the second wave, followed by a long period of nighttime curfew, and by a third lockdown in the spring 2021 against the Alpha wave. Interventions have so far been evaluated in isolation, neglecting the spatial connectivity between regions through mobility that may impact NPI effectiveness.
Methods
Focusing on September 2020–June 2021, we developed a regionally-based epidemic metapopulation model informed by observed mobility fluxes from daily mobile phone data and fitted the model to regional hospital admissions. The model integrated data on vaccination and variants spread. Scenarios were designed to assess the impact of the Alpha variant, characterized by increased transmissibility and risk of hospitalization, of the vaccination campaign and alternative policy decisions.
Results
The spatial model better captured the heterogeneity observed in the regional dynamics, compared to models neglecting inter-regional mobility. The third lockdown was similarly effective to the second lockdown after discounting for immunity, Alpha, and seasonality (51% vs 52% median regional reduction in the reproductive number R0, respectively). The 6pm nighttime curfew with bars and restaurants closed, implemented in January 2021, substantially reduced COVID-19 transmission. It initially led to 49% median regional reduction of R0, decreasing to 43% reduction by March 2021. In absence of vaccination, implemented interventions would have been insufficient against the Alpha wave. Counterfactual scenarios proposing a sequence of lockdowns in a stop-and-go fashion would have reduced hospitalizations and restriction days for low enough thresholds triggering and lifting restrictions.
Conclusions
Spatial connectivity induced by mobility impacted the effectiveness of interventions especially in regions with higher mobility rates. Early evening curfew with gastronomy sector closed allowed authorities to delay the third wave. Stop-and-go lockdowns could have substantially lowered both healthcare and societal burdens if implemented early enough, compared to the observed application of lockdown-curfew-lockdown, but likely at the expense of several labor sectors. These findings contribute to characterize the effectiveness of implemented strategies and improve pandemic preparedness.
France implemented a combination of non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic between September 2020 and June 2021. These included a lockdown in the fall 2020 – the second since the start of the pandemic – to counteract the second wave, followed by a long period of nighttime curfew, and by a third lockdown in the spring 2021 against the Alpha wave. Interventions have so far been evaluated in isolation, neglecting the spatial connectivity between regions through mobility that may impact NPI effectiveness.
Methods
Focusing on September 2020–June 2021, we developed a regionally-based epidemic metapopulation model informed by observed mobility fluxes from daily mobile phone data and fitted the model to regional hospital admissions. The model integrated data on vaccination and variants spread. Scenarios were designed to assess the impact of the Alpha variant, characterized by increased transmissibility and risk of hospitalization, of the vaccination campaign and alternative policy decisions.
Results
The spatial model better captured the heterogeneity observed in the regional dynamics, compared to models neglecting inter-regional mobility. The third lockdown was similarly effective to the second lockdown after discounting for immunity, Alpha, and seasonality (51% vs 52% median regional reduction in the reproductive number R0, respectively). The 6pm nighttime curfew with bars and restaurants closed, implemented in January 2021, substantially reduced COVID-19 transmission. It initially led to 49% median regional reduction of R0, decreasing to 43% reduction by March 2021. In absence of vaccination, implemented interventions would have been insufficient against the Alpha wave. Counterfactual scenarios proposing a sequence of lockdowns in a stop-and-go fashion would have reduced hospitalizations and restriction days for low enough thresholds triggering and lifting restrictions.
Conclusions
Spatial connectivity induced by mobility impacted the effectiveness of interventions especially in regions with higher mobility rates. Early evening curfew with gastronomy sector closed allowed authorities to delay the third wave. Stop-and-go lockdowns could have substantially lowered both healthcare and societal burdens if implemented early enough, compared to the observed application of lockdown-curfew-lockdown, but likely at the expense of several labor sectors. These findings contribute to characterize the effectiveness of implemented strategies and improve pandemic preparedness.
15.
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.
16.
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.
17.
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.
18.
Zortman, Iyonna; de Garine-Wichatitsky, Michel; Arsevska, Elena; Dub, Timothée; Bortel, Wim Van; Lefrançois, Estelle; Vial, Laurence; Pollet, Thomas; Aurélie Binot,
In: One Health, vol. 17, pp. 100630, 2023, ISSN: 2352-7714.
Abstract | Links | BibTeX | Tags: TBE (Tick Borne Encephalitis)
@article{nokey,
title = {A social-ecological systems approach to tick bite and tick-borne disease risk management: Exploring collective action in the Occitanie region in southern France},
author = {Iyonna Zortman and Michel de Garine-Wichatitsky and Elena Arsevska and Timothée Dub and Wim Van Bortel and Estelle Lefrançois and Laurence Vial and Thomas Pollet and Aurélie Binot,},
url = {https://www.sciencedirect.com/science/article/pii/S2352771423001507},
doi = {https://doi.org/10.1016/j.onehlt.2023.100630},
issn = {2352-7714},
year = {2023},
date = {2023-12-01},
urldate = {2023-12-01},
journal = {One Health},
volume = {17},
pages = {100630},
abstract = {Ticks are amongst the most important zoonotic disease vectors affecting human and animal health worldwide. Tick-borne diseases (TBDs) are rapidly expanding geographically and in incidence, most notably in temperate regions of Europe where ticks are considered the principal zoonotic vector of Public Health relevance, as well as a major health and economic preoccupation in agriculture and equine industries. Tick-borne pathogen (TBP) transmission is contingent on complex, interlinked vector-pathogen-host dynamics, environmental and ecological conditions and human behavior. Tackling TBD therefore requires a better understanding of the interconnected social and ecological variables (i.e., the social-ecological system) that favor disease (re)-emergence. The One Health paradigm recognizes the interdependence of human, animal and environmental health and proposes an integrated approach to manage TBD. However, One Health interventions are limited by significant gaps in our understanding of the complex, systemic nature of TBD risk, in addition to a lack of effective, universally accepted and environmentally conscious tick control measures. Today individual prevention gestures are the most effective strategy to manage TBDs in humans and animals, making local communities important actors in TBD detection, prevention and management. Yet, how they engage and collaborate within a multi-actor TBD network has not yet been explored. Here, we argue that transdisciplinary collaborations that go beyond research, political and medical stakeholders, and extend to local community actors can aid in identifying relevant social-ecological risk indicators key for informing multi-level TBD detection, prevention and management measures. This article proposes a transdisciplinary social-ecological systems framework, based on participatory research approaches, to better understand the necessary conditions for local actor engagement to improve TBD risk. We conclude with perspectives for implementing this methodological framework in a case study in the south of France (Occitanie region), where multi-actor collaborations are mobilized to stimulate multi-actor collective action and identify relevant social-ecological indicators of TBD risk.},
keywords = {TBE (Tick Borne Encephalitis)},
pubstate = {published},
tppubtype = {article}
}
Ticks are amongst the most important zoonotic disease vectors affecting human and animal health worldwide. Tick-borne diseases (TBDs) are rapidly expanding geographically and in incidence, most notably in temperate regions of Europe where ticks are considered the principal zoonotic vector of Public Health relevance, as well as a major health and economic preoccupation in agriculture and equine industries. Tick-borne pathogen (TBP) transmission is contingent on complex, interlinked vector-pathogen-host dynamics, environmental and ecological conditions and human behavior. Tackling TBD therefore requires a better understanding of the interconnected social and ecological variables (i.e., the social-ecological system) that favor disease (re)-emergence. The One Health paradigm recognizes the interdependence of human, animal and environmental health and proposes an integrated approach to manage TBD. However, One Health interventions are limited by significant gaps in our understanding of the complex, systemic nature of TBD risk, in addition to a lack of effective, universally accepted and environmentally conscious tick control measures. Today individual prevention gestures are the most effective strategy to manage TBDs in humans and animals, making local communities important actors in TBD detection, prevention and management. Yet, how they engage and collaborate within a multi-actor TBD network has not yet been explored. Here, we argue that transdisciplinary collaborations that go beyond research, political and medical stakeholders, and extend to local community actors can aid in identifying relevant social-ecological risk indicators key for informing multi-level TBD detection, prevention and management measures. This article proposes a transdisciplinary social-ecological systems framework, based on participatory research approaches, to better understand the necessary conditions for local actor engagement to improve TBD risk. We conclude with perspectives for implementing this methodological framework in a case study in the south of France (Occitanie region), where multi-actor collaborations are mobilized to stimulate multi-actor collective action and identify relevant social-ecological indicators of TBD risk.
19.
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.
20.
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}
}