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.
167.
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.
166.
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.
165.
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.
164.
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.
163.
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.
162.
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}
}
161.
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.
160.
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.
159.
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.
158.
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.
157.
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.
156.
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.
155.
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}
}
154.
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.
153.
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.
152.
Mencattelli, Giulia; Ndione, Marie Henriette Dior; Silverj, Andrea; Diagne, Moussa Moise; Curini, Valentina; Teodori, Liana; Domenico, Marco Di; Mbaye, Rassoul; Leone, Alessandra; Marcacci, Maurilia; Gaye, Alioune; Ndiaye, ElHadji; Diallo, Diawo; Ancora, Massimo; Secondini, Barbara; Lollo, Valeria Di; Mangone, Iolanda; Bucciacchio, Andrea; Polci, Andrea; Marini, Giovanni; Rosà, Roberto; Segata, Nicola; Fall, Gamou; Cammà, Cesare; Monaco, Federica; Diallo, Mawlouth; Rota-Stabelli, Omar; Faye, Oumar; & Giovanni Savini, Annapaola Rizzoli
Spatial and temporal dynamics of West Nile virus between Africa and Europe Journal Article
In: Nature Communications, 2023.
Links | BibTeX | Tags: OpenDataSet, WNV (West Nile Virus)
@article{nokey,
title = {Spatial and temporal dynamics of West Nile virus between Africa and Europe},
author = {Giulia Mencattelli and Marie Henriette Dior Ndione and Andrea Silverj and Moussa Moise Diagne and Valentina Curini and Liana Teodori and Marco Di Domenico and Rassoul Mbaye and Alessandra Leone and Maurilia Marcacci and Alioune Gaye and ElHadji Ndiaye and Diawo Diallo and Massimo Ancora and Barbara Secondini and Valeria Di Lollo and Iolanda Mangone and Andrea Bucciacchio and Andrea Polci and Giovanni Marini and Roberto Rosà and Nicola Segata and Gamou Fall and Cesare Cammà and Federica Monaco and Mawlouth Diallo and Omar Rota-Stabelli and Oumar Faye and Annapaola Rizzoli & Giovanni Savini},
doi = {https://doi.org/10.1038/s41467-023-42185-7},
year = {2023},
date = {2023-10-13},
urldate = {2023-10-13},
journal = {Nature Communications},
keywords = {OpenDataSet, WNV (West Nile Virus)},
pubstate = {published},
tppubtype = {article}
}
151.
Dias, Hélder; Guimarães, Artur; Martins, Bruno; Roche, Mathieu
Unsupervised Key-Phrase Extraction from Long Texts with Multilingual Sentence Transformers Journal Article
In: pp. 141–155, 2023.
Abstract | Links | BibTeX | Tags: Text mining
@article{nokey,
title = {Unsupervised Key-Phrase Extraction from Long Texts with Multilingual Sentence Transformers},
author = {Hélder Dias and Artur Guimarães and Bruno Martins and Mathieu Roche },
url = {https://link.springer.com/chapter/10.1007/978-3-031-45275-8_10},
doi = {https://doi.org/10.1007/978-3-031-45275-8_10},
year = {2023},
date = {2023-10-08},
pages = {141–155},
abstract = {Key-phrase extraction concerns retrieving a small set of phrases that encapsulate the core concepts of an input textual document. As in other text mining tasks, current methods often rely on pre-trained neural language models. Using these models, the state-of-the-art supervised systems for key-phrase extraction require large amounts of labelled data and generalize poorly outside the training domain, while unsupervised approaches generally present a lower accuracy. This paper presents a multilingual unsupervised approach to key-phrase extraction, improving upon previous methods in several ways (e.g., using representations from pre-trained Transformer models, while supporting the processing of long documents). Experimental results on datasets covering multiple languages and domains attest to the quality of the results.},
keywords = {Text mining},
pubstate = {published},
tppubtype = {article}
}
Key-phrase extraction concerns retrieving a small set of phrases that encapsulate the core concepts of an input textual document. As in other text mining tasks, current methods often rely on pre-trained neural language models. Using these models, the state-of-the-art supervised systems for key-phrase extraction require large amounts of labelled data and generalize poorly outside the training domain, while unsupervised approaches generally present a lower accuracy. This paper presents a multilingual unsupervised approach to key-phrase extraction, improving upon previous methods in several ways (e.g., using representations from pre-trained Transformer models, while supporting the processing of long documents). Experimental results on datasets covering multiple languages and domains attest to the quality of the results.
150.
Messina, Jane Paula; Wint, William G R
The Spatial Distribution of Crimean–Congo Haemorrhagic Fever and Its Potential Vectors in Europe and Beyond Journal Article
In: Insects, vol. 14, no. 771, 2023, (This article is a version of the ECDC Technical report ‘The spatial distribution of Crimean-Congo haemorrhagic fever in Europe and neighbouring areas’ adapted for journal publication).
Abstract | Links | BibTeX | Tags: CCHF (Crimean Congo haemorrhagic fever virus), OpenDataSet
@article{nokey,
title = {The Spatial Distribution of Crimean–Congo Haemorrhagic Fever and Its Potential Vectors in Europe and Beyond},
author = {Jane Paula Messina and William G R Wint},
url = {https://www.mdpi.com/2075-4450/14/9/771},
doi = {10.3390/insects14090771},
year = {2023},
date = {2023-09-17},
urldate = {2023-09-17},
journal = {Insects},
volume = {14},
number = {771},
abstract = {Crimean–Congo haemorrhagic fever (CCHF) is considered to be spreading across the globe, with many countries reporting new human CCHF cases in recent decades including Georgia, Türkiye, Albania, and, most recently, Spain. We update a human CCHF distribution map produced in 2015 to include global disease occurrence records to June 2022, and we include the recent records for Europe. The predicted distributions are based on long-established spatial modelling methods and are extended to include all European countries and the surrounding areas. The map produced shows the environmental suitability for the disease, taking into account the distribution of the most important known and potential tick vectors Hyalomma marginatum and Hyalomma lusitanicum, without which the disease cannot occur. This limits the disease’s predicted distribution to the Iberian Peninsula, the Mediterranean seaboard, along with Türkiye and the Caucasus, with a more patchy suitability predicted for inland Greece, the southern Balkans, and extending north to north-west France and central Europe. These updated CCHF maps can be used to identify the areas with the highest probability of disease and to therefore target areas where mitigation measures should currently be focused.},
note = {This article is a version of the ECDC Technical report ‘The spatial distribution of Crimean-Congo haemorrhagic fever in Europe and neighbouring areas’ adapted for journal publication},
keywords = {CCHF (Crimean Congo haemorrhagic fever virus), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
Crimean–Congo haemorrhagic fever (CCHF) is considered to be spreading across the globe, with many countries reporting new human CCHF cases in recent decades including Georgia, Türkiye, Albania, and, most recently, Spain. We update a human CCHF distribution map produced in 2015 to include global disease occurrence records to June 2022, and we include the recent records for Europe. The predicted distributions are based on long-established spatial modelling methods and are extended to include all European countries and the surrounding areas. The map produced shows the environmental suitability for the disease, taking into account the distribution of the most important known and potential tick vectors Hyalomma marginatum and Hyalomma lusitanicum, without which the disease cannot occur. This limits the disease’s predicted distribution to the Iberian Peninsula, the Mediterranean seaboard, along with Türkiye and the Caucasus, with a more patchy suitability predicted for inland Greece, the southern Balkans, and extending north to north-west France and central Europe. These updated CCHF maps can be used to identify the areas with the highest probability of disease and to therefore target areas where mitigation measures should currently be focused.
149.
Manica, Mattia; Marini, Giovanni; Solimini, Angelo; Guzzetta, Giorgio; Poletti, Piero; Scognamiglio, Paola; Virgillito, Chiara; della Torre, Alessandra; Merler, Stefano; Rosà, Roberto; Vairo, Francesco; Caputo, Beniamino
Reporting delays of chikungunya cases during the 2017 outbreak in Lazio region, Italy Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags: CHIK (Chikungunya)
@article{nokey,
title = {Reporting delays of chikungunya cases during the 2017 outbreak in Lazio region, Italy},
author = {Mattia Manica and Giovanni Marini and Angelo Solimini and Giorgio Guzzetta and Piero Poletti and Paola Scognamiglio and Chiara Virgillito and Alessandra della Torre and Stefano Merler and Roberto Rosà and Francesco Vairo and Beniamino Caputo
},
url = {https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0011610},
doi = {10.1371/journal.pntd.0011610},
year = {2023},
date = {2023-09-14},
urldate = {2023-09-14},
abstract = {Background
Emerging arboviral diseases in Europe pose a challenge due to difficulties in detecting and diagnosing cases during the initial circulation of the pathogen. Early outbreak detection enables public health authorities to take effective actions to reduce disease transmission. Quantification of the reporting delays of cases is vital to plan and assess surveillance and control strategies. Here, we provide estimates of reporting delays during an emerging arboviral outbreak and indications on how delays may have impacted onward transmission.
Methodology/principal findings
Using descriptive statistics and Kaplan-Meyer curves we analyzed case reporting delays (the period between the date of symptom onset and the date of notification to the public health authorities) during the 2017 Italian chikungunya outbreak. We further investigated the effect of outbreak detection on reporting delays by means of a Cox proportional hazard model. We estimated that the overall median reporting delay was 15.5 days, but this was reduced to 8 days after the notification of the first case. Cases with symptom onset after outbreak detection had about a 3.5 times higher reporting rate, however only 3.6% were notified within 24h from symptom onset. Remarkably, we found that 45.9% of identified cases developed symptoms before the detection of the outbreak.
Conclusions/significance
These results suggest that efforts should be undertaken to improve the early detection and identification of arboviral cases, as well as the management of vector species to mitigate the impact of long reporting delays.},
keywords = {CHIK (Chikungunya)},
pubstate = {published},
tppubtype = {article}
}
Background
Emerging arboviral diseases in Europe pose a challenge due to difficulties in detecting and diagnosing cases during the initial circulation of the pathogen. Early outbreak detection enables public health authorities to take effective actions to reduce disease transmission. Quantification of the reporting delays of cases is vital to plan and assess surveillance and control strategies. Here, we provide estimates of reporting delays during an emerging arboviral outbreak and indications on how delays may have impacted onward transmission.
Methodology/principal findings
Using descriptive statistics and Kaplan-Meyer curves we analyzed case reporting delays (the period between the date of symptom onset and the date of notification to the public health authorities) during the 2017 Italian chikungunya outbreak. We further investigated the effect of outbreak detection on reporting delays by means of a Cox proportional hazard model. We estimated that the overall median reporting delay was 15.5 days, but this was reduced to 8 days after the notification of the first case. Cases with symptom onset after outbreak detection had about a 3.5 times higher reporting rate, however only 3.6% were notified within 24h from symptom onset. Remarkably, we found that 45.9% of identified cases developed symptoms before the detection of the outbreak.
Conclusions/significance
These results suggest that efforts should be undertaken to improve the early detection and identification of arboviral cases, as well as the management of vector species to mitigate the impact of long reporting delays.
Emerging arboviral diseases in Europe pose a challenge due to difficulties in detecting and diagnosing cases during the initial circulation of the pathogen. Early outbreak detection enables public health authorities to take effective actions to reduce disease transmission. Quantification of the reporting delays of cases is vital to plan and assess surveillance and control strategies. Here, we provide estimates of reporting delays during an emerging arboviral outbreak and indications on how delays may have impacted onward transmission.
Methodology/principal findings
Using descriptive statistics and Kaplan-Meyer curves we analyzed case reporting delays (the period between the date of symptom onset and the date of notification to the public health authorities) during the 2017 Italian chikungunya outbreak. We further investigated the effect of outbreak detection on reporting delays by means of a Cox proportional hazard model. We estimated that the overall median reporting delay was 15.5 days, but this was reduced to 8 days after the notification of the first case. Cases with symptom onset after outbreak detection had about a 3.5 times higher reporting rate, however only 3.6% were notified within 24h from symptom onset. Remarkably, we found that 45.9% of identified cases developed symptoms before the detection of the outbreak.
Conclusions/significance
These results suggest that efforts should be undertaken to improve the early detection and identification of arboviral cases, as well as the management of vector species to mitigate the impact of long reporting delays.
148.
Valentin, Sarah; Boudoua, Bahdja; Sewalk, Kara; Arınık, Nejat; Roche, Mathieu; Lancelot, Renaud; Arsevska, Elena
Dissemination of information in event-based surveillance, a case study of Avian Influenza Journal Article
In: PLoS ONE, 2023.
Abstract | Links | BibTeX | Tags: HPAI (Avian Influenza), OpenDataSet, Text mining
@article{nokey,
title = {Dissemination of information in event-based surveillance, a case study of Avian Influenza},
author = {Sarah Valentin and Bahdja Boudoua and Kara Sewalk and Nejat Arınık and Mathieu Roche and Renaud Lancelot and Elena Arsevska },
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0285341},
doi = {10.1371/journal.pone.0285341},
year = {2023},
date = {2023-09-05},
urldate = {2023-09-05},
journal = {PLoS ONE},
abstract = {Event-Based Surveillance (EBS) tools, such as HealthMap and PADI-web, monitor online news reports and other unofficial sources, with the primary aim to provide timely information to users from health agencies on disease outbreaks occurring worldwide. In this work, we describe how outbreak-related information disseminates from a primary source, via a secondary source, to a definitive aggregator, an EBS tool, during the 2018/19 avian influenza season. We analysed 337 news items from the PADI-web and 115 news articles from HealthMap EBS tools reporting avian influenza outbreaks in birds worldwide between July 2018 and June 2019. We used the sources cited in the news to trace the path of each outbreak. We built a directed network with nodes representing the sources (characterised by type, specialisation, and geographical focus) and edges representing the flow of information. We calculated the degree as a centrality measure to determine the importance of the nodes in information dissemination. We analysed the role of the sources in early detection (detection of an event before its official notification) to the World Organisation for Animal Health (WOAH) and late detection. A total of 23% and 43% of the avian influenza outbreaks detected by the PADI-web and HealthMap, respectively, were shared on time before their notification. For both tools, national and local veterinary authorities were the primary sources of early detection. The early detection component mainly relied on the dissemination of nationally acknowledged events by online news and press agencies, bypassing international reporting to the WAOH. WOAH was the major secondary source for late detection, occupying a central position between national authorities and disseminator sources, such as online news. PADI-web and HealthMap were highly complementary in terms of detected sources, explaining why 90% of the events were detected by only one of the tools. We show that current EBS tools can provide timely outbreak-related information and priority news sources to improve digital disease surveillance.
Figures},
keywords = {HPAI (Avian Influenza), OpenDataSet, Text mining},
pubstate = {published},
tppubtype = {article}
}
Event-Based Surveillance (EBS) tools, such as HealthMap and PADI-web, monitor online news reports and other unofficial sources, with the primary aim to provide timely information to users from health agencies on disease outbreaks occurring worldwide. In this work, we describe how outbreak-related information disseminates from a primary source, via a secondary source, to a definitive aggregator, an EBS tool, during the 2018/19 avian influenza season. We analysed 337 news items from the PADI-web and 115 news articles from HealthMap EBS tools reporting avian influenza outbreaks in birds worldwide between July 2018 and June 2019. We used the sources cited in the news to trace the path of each outbreak. We built a directed network with nodes representing the sources (characterised by type, specialisation, and geographical focus) and edges representing the flow of information. We calculated the degree as a centrality measure to determine the importance of the nodes in information dissemination. We analysed the role of the sources in early detection (detection of an event before its official notification) to the World Organisation for Animal Health (WOAH) and late detection. A total of 23% and 43% of the avian influenza outbreaks detected by the PADI-web and HealthMap, respectively, were shared on time before their notification. For both tools, national and local veterinary authorities were the primary sources of early detection. The early detection component mainly relied on the dissemination of nationally acknowledged events by online news and press agencies, bypassing international reporting to the WAOH. WOAH was the major secondary source for late detection, occupying a central position between national authorities and disseminator sources, such as online news. PADI-web and HealthMap were highly complementary in terms of detected sources, explaining why 90% of the events were detected by only one of the tools. We show that current EBS tools can provide timely outbreak-related information and priority news sources to improve digital disease surveillance.
Figures
Figures