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.
21.
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.
22.
Eugenio Valdano Laura Di Domenico,; Colizza, Vittoria
Limited data on infectious disease distribution exposes ambiguity in epidemic modeling choices Journal Article
In: Physical review research, 2024.
Abstract | Links | BibTeX | Tags:
@article{Domenico2024d,
title = {Limited data on infectious disease distribution exposes ambiguity in epidemic modeling choices},
author = {Laura Di Domenico, Eugenio Valdano, and Vittoria Colizza},
doi = {https://doi.org/10.1103/PhysRevResearch.6.023265},
year = {2024},
date = {2024-06-10},
journal = {Physical review research},
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. },
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.
23.
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.
24.
Duerr R Gambaro F, Dimartino D
Emergence and dissemination of SARS-CoV-2 XBB.1.5 in New York Bachelor Thesis
2024.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus)
@bachelorthesis{F2024,
title = {Emergence and dissemination of SARS-CoV-2 XBB.1.5 in New York},
author = {Gambaro F, Duerr R, Dimartino D, Marier C, Iturrate E, Mulligan MJ, Heguy A, Dellicour S},
doi = {https://doi.org/10.1093/ve/veae035},
year = {2024},
date = {2024-05-07},
journal = {Virus Evolution},
volume = {10},
issue = {1},
abstract = {The recombinant SARS-CoV-2 Omicron XBB.1.5 variant was first detected in New York City (NYC) and rapidly became the predominant variant in the area by early 2023. The increased occurrence of circulating variants within the SARS-CoV-2 XBB-sublineage prompted the modification of COVID-19 mRNA vaccines by Moderna and Pfizer-BioNTech. This update, implemented in mid-September 2023, involved the incorporation of a monovalent XBB.1.5 component. Considering that NYC probably played a central role in the emergence of the XBB.1.5 variant, we conducted phylogeographic analysis to investigate the emergence and spread of this variant in the metropolitan area. Our analysis confirms that XBB.1.5 emerged within or near the NYC area and indicates that XBB.1.5 had a diffusion velocity similar to that of the variant Alpha in the same study area. Additionally, the analysis of 2,392 genomes collected in the context of the genomic surveillance program at NYU Langone Health system showed that there was no increased proportion of XBB.1.5, relative to all cocirculating variants, in the boosted compared to unvaccinated individuals. This study provides a comprehensive description of the emergence and dissemination of XBB.1.5.
},
keywords = {Covid-19 (Coronavirus)},
pubstate = {published},
tppubtype = {bachelorthesis}
}
The recombinant SARS-CoV-2 Omicron XBB.1.5 variant was first detected in New York City (NYC) and rapidly became the predominant variant in the area by early 2023. The increased occurrence of circulating variants within the SARS-CoV-2 XBB-sublineage prompted the modification of COVID-19 mRNA vaccines by Moderna and Pfizer-BioNTech. This update, implemented in mid-September 2023, involved the incorporation of a monovalent XBB.1.5 component. Considering that NYC probably played a central role in the emergence of the XBB.1.5 variant, we conducted phylogeographic analysis to investigate the emergence and spread of this variant in the metropolitan area. Our analysis confirms that XBB.1.5 emerged within or near the NYC area and indicates that XBB.1.5 had a diffusion velocity similar to that of the variant Alpha in the same study area. Additionally, the analysis of 2,392 genomes collected in the context of the genomic surveillance program at NYU Langone Health system showed that there was no increased proportion of XBB.1.5, relative to all cocirculating variants, in the boosted compared to unvaccinated individuals. This study provides a comprehensive description of the emergence and dissemination of XBB.1.5.
25.
Filippo Trentini Valeria d’Andrea, Valentina Marziano
Spatial spread of COVID-19 during the early pandemic phase in Italy Journal Article
In: BMC Infectious Diseases, no. 450, 2024.
Abstract | Links | BibTeX | Tags:
@article{d’Andread’Andrea2024,
title = {Spatial spread of COVID-19 during the early pandemic phase in Italy},
author = {Valeria d’Andrea, Filippo Trentini, Valentina Marziano, Agnese Zardini, Mattia Manica, Giorgio Guzzetta, Marco Ajelli, Daniele Petrone, Martina Del Manso, Chiara Sacco, Xanthi Andrianou, Antonino Bella, Flavia Riccardo, Patrizio Pezzotti, Piero Poletti & Stefano Merler },
doi = {https://doi.org/10.1186/s12879-024-09343-8},
year = {2024},
date = {2024-04-29},
journal = {BMC Infectious Diseases},
number = {450},
abstract = {Quantifying the potential spatial spread of an infectious pathogen is key to defining effective containment and control strategies. The aim of this study is to estimate the risk of SARS-CoV-2 transmission at different distances in Italy before the first regional lockdown was imposed, identifying important sources of national spreading. To do this, we leverage on a probabilistic model applied to daily symptomatic cases retrospectively ascertained in each Italian municipality with symptom onset between January 28 and March 7, 2020. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Quantifying the potential spatial spread of an infectious pathogen is key to defining effective containment and control strategies. The aim of this study is to estimate the risk of SARS-CoV-2 transmission at different distances in Italy before the first regional lockdown was imposed, identifying important sources of national spreading. To do this, we leverage on a probabilistic model applied to daily symptomatic cases retrospectively ascertained in each Italian municipality with symptom onset between January 28 and March 7, 2020.
26.
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.
27.
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.
28.
Elena ARSEVSKA Mehtab Alam SYED, Mathieu ROCHE
GeospaCy: A tool for extraction and geographical referencing of spatial expressions in textual data Proceedings
2024.
Abstract | Links | BibTeX | Tags: Text mining
@proceedings{nokey,
title = {GeospaCy: A tool for extraction and geographical referencing of spatial expressions in textual data},
author = {Mehtab Alam SYED, Elena ARSEVSKA, Mathieu ROCHE, Maguelonne TEISSEIRE},
url = {https://aclanthology.org/2024.eacl-demo.13},
year = {2024},
date = {2024-03-01},
journal = {Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations},
abstract = {Spatial information in text enables to understand the geographical context and relationships within text for better decision-making across various domains such as disease surveillance, disaster management and other location based services. Therefore, it is crucial to understand the precise geographical context for location-sensitive applications. In response to this necessity, we introduce the GeospaCy software tool, designed for the extraction and georeferencing of spatial information present in textual data. GeospaCy fulfils two primary objectives: 1) Geoparsing, which involves extracting spatial expressions, encompassing place names and associated spatial relations within the text data, and 2) Geocoding, which facilitates the assignment of geographical coordinates to the spatial expressions extracted during the Geoparsing task. Geoparsing is evaluated with a disease news article dataset consisting of event information, whereas a qualitative evaluation of geographical coordinates (polygons/geometries) of spatial expressions is performed by end-users for Geocoding task.
},
keywords = {Text mining},
pubstate = {published},
tppubtype = {proceedings}
}
Spatial information in text enables to understand the geographical context and relationships within text for better decision-making across various domains such as disease surveillance, disaster management and other location based services. Therefore, it is crucial to understand the precise geographical context for location-sensitive applications. In response to this necessity, we introduce the GeospaCy software tool, designed for the extraction and georeferencing of spatial information present in textual data. GeospaCy fulfils two primary objectives: 1) Geoparsing, which involves extracting spatial expressions, encompassing place names and associated spatial relations within the text data, and 2) Geocoding, which facilitates the assignment of geographical coordinates to the spatial expressions extracted during the Geoparsing task. Geoparsing is evaluated with a disease news article dataset consisting of event information, whereas a qualitative evaluation of geographical coordinates (polygons/geometries) of spatial expressions is performed by end-users for Geocoding task.
29.
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.
30.
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}
}
31.
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.
32.
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.
33.
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.
34.
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.
35.
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.
36.
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.
37.
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}
}
38.
Jore, Solveig; Viljugrein, Hildegunn; Hjertqvist, Marika; Dub, Timothée; Mäkelä, Henna
Outdoor recreation, tick borne encephalitis incidence and seasonality in Finland, Norway and Sweden during the COVID-19 pandemic (2020/2021) Journal Article
In: Infection Ecology & Epidemiology, vol. 13, no. 1, pp. 2281055, 2023.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus), TBE (Tick Borne Encephalitis)
@article{nokey,
title = {Outdoor recreation, tick borne encephalitis incidence and seasonality in Finland, Norway and Sweden during the COVID-19 pandemic (2020/2021)},
author = {Solveig Jore and Hildegunn Viljugrein and Marika Hjertqvist and Timothée Dub and Henna Mäkelä},
url = {https://doi.org/10.1080/20008686.2023.2281055},
doi = {10.1080/20008686.2023.2281055},
year = {2023},
date = {2023-11-18},
urldate = {2023-11-18},
journal = {Infection Ecology & Epidemiology},
volume = {13},
number = {1},
pages = {2281055},
abstract = {During the pandemic outdoor activities were encouraged to mitigate transmission risk while providing safe spaces for social interactions. Human behaviour, which may favour or disfavour, contact rates between questing ticks and humans, is a key factor impacting tick-borne encephalitis (TBE) incidence. We analyzed annual and weekly TBE cases in Finland, Norway and Sweden from 2010 to 2021 to assess trend, seasonality, and discuss changes in human tick exposure imposed by COVID-19. We compared the pre-pandemic incidence (2010–2019) with the pandemic incidence (2020–2021) by fitting a generalized linear model (GLM) to incidence data. Pre-pandemic incidence was 1.0, 0.29 and 2.8 for Finland, Norway and Sweden, respectively, compared to incidence of 2.2, 1.0 and 3.9 during the pandemic years. However, there was an increasing trend for all countries across the whole study period. Therefore, we predicted the number of cases in 2020/2021 based on a model fitted to the annual cases in 2010–2019. The incidences during the pandemic were 1.3 times higher for Finland, 1.7 times higher for Norway and no difference for Sweden. When social restrictions were enforced to curb the spread of SARS-CoV-2 there were profound changes in outdoor recreational behavior. Future consideration of public health interventions that promote outdoor activities may increase exposure to vector-borne diseases. },
keywords = {Covid-19 (Coronavirus), TBE (Tick Borne Encephalitis)},
pubstate = {published},
tppubtype = {article}
}
During the pandemic outdoor activities were encouraged to mitigate transmission risk while providing safe spaces for social interactions. Human behaviour, which may favour or disfavour, contact rates between questing ticks and humans, is a key factor impacting tick-borne encephalitis (TBE) incidence. We analyzed annual and weekly TBE cases in Finland, Norway and Sweden from 2010 to 2021 to assess trend, seasonality, and discuss changes in human tick exposure imposed by COVID-19. We compared the pre-pandemic incidence (2010–2019) with the pandemic incidence (2020–2021) by fitting a generalized linear model (GLM) to incidence data. Pre-pandemic incidence was 1.0, 0.29 and 2.8 for Finland, Norway and Sweden, respectively, compared to incidence of 2.2, 1.0 and 3.9 during the pandemic years. However, there was an increasing trend for all countries across the whole study period. Therefore, we predicted the number of cases in 2020/2021 based on a model fitted to the annual cases in 2010–2019. The incidences during the pandemic were 1.3 times higher for Finland, 1.7 times higher for Norway and no difference for Sweden. When social restrictions were enforced to curb the spread of SARS-CoV-2 there were profound changes in outdoor recreational behavior. Future consideration of public health interventions that promote outdoor activities may increase exposure to vector-borne diseases.
39.
Dellicour S* Van Borm S*, Martin D
Complete genome reconstruction of the global and European regional dispersal history of lumpy skin disease virus Journal Article
In: Journal of Virology, 2023.
Abstract | Links | BibTeX | Tags:
@article{S*2023,
title = {Complete genome reconstruction of the global and European regional dispersal history of lumpy skin disease virus},
author = {Van Borm S*, Dellicour S*, Martin D, Lemey P, Agianniotaki E, Chondrokouki E, Vidanovic D, Vaskovic N, Petrović T, Lazić S, Koleci X, Vodica A, Djadjovski I, Krstevski K, Vandenbussche F, Haegeman A, De Clercq K, Mathijs E},
doi = {https://doi.org/10.1128/jvi.01394-23},
year = {2023},
date = {2023-10-31},
journal = {Journal of Virology},
abstract = {Lumpy skin disease virus (LSDV) causes a disease of economic importance affecting cattle. Its global epidemiology is complex due to the combination of vector-borne and anthropogenic spread, the circulation of vaccine-like recombinants, and the use of vaccines. The slow molecular evolution of its DNA genome limits the utility of genetic variation for accurate tracing based on evolutionary analyses, but this limitation has not yet been formally assessed. Furthermore, until present, whole genome sequencing in affected areas has remained patchy. This study combines the first fine-grained sampling of LSDV whole genomes from a time-constrained (2015–2017) southeastern European (SEE) LSDV outbreak, which we analyze along with curated public genomes to investigate the global and regional viral dispersal dynamics. First, haplotype networks visualizing the limited genetic variability associated with the SEE LSDV outbreak show intense intermixing between countries. We also assess at which spatial scale a correlation between genetic and geographic distances can be detected for LSDV. On a global scale, we show the importance of accounting for recombination events that can impact phylogenetic and phylogeographic reconstructions. Following the assessment of the temporal signal in the recombination-free alignment, our time-scaled continuous phylogeographic analysis of Kenya-like and recent wild-type viruses confirms the origin and global dissemination history of LSDV. Our analyses highlight the importance of careful selection and application of phylodynamic approaches to DNA viruses, as well as the importance of whole genome sampling in endemic and outbreak areas to improve our understanding of the evolution, epidemiology, and transmission dynamics of DNA viruses.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lumpy skin disease virus (LSDV) causes a disease of economic importance affecting cattle. Its global epidemiology is complex due to the combination of vector-borne and anthropogenic spread, the circulation of vaccine-like recombinants, and the use of vaccines. The slow molecular evolution of its DNA genome limits the utility of genetic variation for accurate tracing based on evolutionary analyses, but this limitation has not yet been formally assessed. Furthermore, until present, whole genome sequencing in affected areas has remained patchy. This study combines the first fine-grained sampling of LSDV whole genomes from a time-constrained (2015–2017) southeastern European (SEE) LSDV outbreak, which we analyze along with curated public genomes to investigate the global and regional viral dispersal dynamics. First, haplotype networks visualizing the limited genetic variability associated with the SEE LSDV outbreak show intense intermixing between countries. We also assess at which spatial scale a correlation between genetic and geographic distances can be detected for LSDV. On a global scale, we show the importance of accounting for recombination events that can impact phylogenetic and phylogeographic reconstructions. Following the assessment of the temporal signal in the recombination-free alignment, our time-scaled continuous phylogeographic analysis of Kenya-like and recent wild-type viruses confirms the origin and global dissemination history of LSDV. Our analyses highlight the importance of careful selection and application of phylodynamic approaches to DNA viruses, as well as the importance of whole genome sampling in endemic and outbreak areas to improve our understanding of the evolution, epidemiology, and transmission dynamics of DNA viruses.
40.
Dagostin, Francesca; Tagliapietra, Valentina; Marini, Giovanni; Cataldo, Claudia; Bellenghi, Maria; Pizzarelli, Scilla; Cammarano, Rosaria Rosanna; Wint, William; Alexander, Neil S; Neteler, Markus; Haas, Julia; Dub, Timothée; Busani, Luca; Rizzoli, Annapaola
Ecological and environmental factors affecting the risk of tick-borne encephalitis in Europe, 2017 to 2021 Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags: TBE (Tick Borne Encephalitis)
@article{nokey,
title = {Ecological and environmental factors affecting the risk of tick-borne encephalitis in Europe, 2017 to 2021},
author = {Francesca Dagostin and Valentina Tagliapietra and Giovanni Marini and Claudia Cataldo and Maria Bellenghi and Scilla Pizzarelli and Rosaria Rosanna Cammarano and William Wint and Neil S Alexander and Markus Neteler and Julia Haas and Timothée Dub and Luca Busani and Annapaola Rizzoli
},
url = {https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2023.28.42.2300121},
doi = {10.2807/1560-7917.ES.2023.28.42.2300121},
year = {2023},
date = {2023-10-19},
urldate = {2023-10-19},
abstract = {Background
Tick-borne encephalitis (TBE) is a disease which can lead to severe neurological symptoms, caused by the TBE virus (TBEV). The natural transmission cycle occurs in foci and involves ticks as vectors and several key hosts that act as reservoirs and amplifiers of the infection spread. Recently, the incidence of TBE in Europe has been rising in both endemic and new regions.
Aim
In this study we want to provide comprehensive understanding of the main ecological and environmental factors that affect TBE spread across Europe.
Methods
We searched available literature on covariates linked with the circulation of TBEV in Europe. We then assessed the best predictors for TBE incidence in 11 European countries by means of statistical regression, using data on human infections provided by the European Surveillance System (TESSy), averaged between 2017 and 2021.
Results
We retrieved data from 62 full-text articles and identified 31 different covariates associated with TBE occurrence. Finally, we selected eight variables from the best model, including factors linked to vegetation cover, climate, and the presence of tick hosts.
Discussion
The existing literature is heterogeneous, both in study design and covariate types. Here, we summarised and statistically validated the covariates affecting the variability of TBEV across Europe. The analysis of the factors enhancing disease emergence is a fundamental step towards the identification of potential hotspots of viral circulation. Hence, our results can support modelling efforts to estimate the risk of TBEV infections and help decision-makers implement surveillance and prevention campaigns.},
keywords = {TBE (Tick Borne Encephalitis)},
pubstate = {published},
tppubtype = {article}
}
Background
Tick-borne encephalitis (TBE) is a disease which can lead to severe neurological symptoms, caused by the TBE virus (TBEV). The natural transmission cycle occurs in foci and involves ticks as vectors and several key hosts that act as reservoirs and amplifiers of the infection spread. Recently, the incidence of TBE in Europe has been rising in both endemic and new regions.
Aim
In this study we want to provide comprehensive understanding of the main ecological and environmental factors that affect TBE spread across Europe.
Methods
We searched available literature on covariates linked with the circulation of TBEV in Europe. We then assessed the best predictors for TBE incidence in 11 European countries by means of statistical regression, using data on human infections provided by the European Surveillance System (TESSy), averaged between 2017 and 2021.
Results
We retrieved data from 62 full-text articles and identified 31 different covariates associated with TBE occurrence. Finally, we selected eight variables from the best model, including factors linked to vegetation cover, climate, and the presence of tick hosts.
Discussion
The existing literature is heterogeneous, both in study design and covariate types. Here, we summarised and statistically validated the covariates affecting the variability of TBEV across Europe. The analysis of the factors enhancing disease emergence is a fundamental step towards the identification of potential hotspots of viral circulation. Hence, our results can support modelling efforts to estimate the risk of TBEV infections and help decision-makers implement surveillance and prevention campaigns.
Tick-borne encephalitis (TBE) is a disease which can lead to severe neurological symptoms, caused by the TBE virus (TBEV). The natural transmission cycle occurs in foci and involves ticks as vectors and several key hosts that act as reservoirs and amplifiers of the infection spread. Recently, the incidence of TBE in Europe has been rising in both endemic and new regions.
Aim
In this study we want to provide comprehensive understanding of the main ecological and environmental factors that affect TBE spread across Europe.
Methods
We searched available literature on covariates linked with the circulation of TBEV in Europe. We then assessed the best predictors for TBE incidence in 11 European countries by means of statistical regression, using data on human infections provided by the European Surveillance System (TESSy), averaged between 2017 and 2021.
Results
We retrieved data from 62 full-text articles and identified 31 different covariates associated with TBE occurrence. Finally, we selected eight variables from the best model, including factors linked to vegetation cover, climate, and the presence of tick hosts.
Discussion
The existing literature is heterogeneous, both in study design and covariate types. Here, we summarised and statistically validated the covariates affecting the variability of TBEV across Europe. The analysis of the factors enhancing disease emergence is a fundamental step towards the identification of potential hotspots of viral circulation. Hence, our results can support modelling efforts to estimate the risk of TBEV infections and help decision-makers implement surveillance and prevention campaigns.