MOOD publications

@article{nokey,

title = {Assessing tick attachments to humans with citizen },

author = {"Lisa Bald

Nils Ratnaweera

Tomislav Hengl

Patrick Laube

Jürg Grunder

Werner Tischhauser

Netra Bhandari

Dirk Zeuss"},

doi = {https://doi.org/10.1186/s13071-024-06636-4},

year  = {2025},

date = {2025-01-23},

journal = {Parasites & Vectors},

abstract = {Ticks are the primary vectors of numerous zoonotic pathogens, transmitting more pathogens than any other blood-feeding arthropod. In the northern hemisphere, tick-borne disease cases in humans, such as Lyme borreliosis and tick-borne encephalitis, have risen in recent years, and are a significant burden on public healthcare systems. The spread of these diseases is further reinforced by climate change, which leads to expanding tick habitats. Switzerland is among the countries in which tick-borne diseases are a major public health concern, with increasing incidence rates reported in recent years.



},

keywords = {TBE (Tick Borne Encephalitis)},

pubstate = {published},

tppubtype = {article}

}

@article{Mäkelä2025,

title = {Knowledge, attitudes, and practices towards vector-borne diseases in changing climate in Finland},

author = {Henna Mäkelä, Timothée Dub, J. Pekka Nuorti, Jussi Sane},

doi = {https://doi.org/10.1017/s0950268824001468},

year  = {2025},

date = {2025-01-15},

urldate = {2025-01-15},

journal = {Epidemiology and Infection},

abstract = {With climate change, the geographic distribution of some VBDs has expanded, highlighting the need for adaptation, and managing the risks associated with emergence in new areas. We conducted a questionnaire survey on the knowledge, attitudes, and practices (KAP) about vector-borne diseases (VBDs) among sample of Finnish residents. The questions were scored and the level of KAP was determined based on scoring as poor, fair, good, or excellent. Binary logistic regression analysis was used to evaluate the associations of different KAP levels with sex, age, education, and possible previous VPD infection. },

keywords = {CHIK (Chikungunya), DEN (Dengue), Zika},

pubstate = {published},

tppubtype = {article}

}

@article{Parino2024,

title = {Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation},

author = {Francesco Parino, Emanuele Gustani-Buss, Trevor Bedford, Marc A. Suchard, N´ıdia Sequeira Trovao, Andrew Rambaut, Vittoria Colizza, Chiara Poletto, Philippe Lemey},

doi = {https://doi.org/10.1093/pnasnexus/pgae561},

year  = {2024},

date = {2024-12-17},

journal = {PNAS Nexus},

volume = {4},

issue = {1},

abstract = {Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological, and virological data, integrating different data sources. We propose a novel-combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic, and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across countries simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn–winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn–winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power.},

keywords = {HPAI (Avian Influenza)},

pubstate = {published},

tppubtype = {article}

}

@article{Calmon2024,

title = {Preserving friendships in school contacts: an algorithm to construct synthetic temporal networks for epidemic modelling},

author = {Lucille Calmon, Elisabetta Colosi, Giulia Bassignana, Alain Barrat, Vittoria Colizza},

editor = {Yamir Moreno, University of Zaragoza: Universidad de Zaragoza, SPAIN},

doi = {https://doi.org/10.1371/journal.pcbi.1012661},

year  = {2024},

date = {2024-12-09},

journal = {PloS Computational Biology},

abstract = {High-resolution temporal data on contacts between hosts provide crucial information on the mixing patterns underlying infectious disease transmission. Publicly available data sets of contact data are however typically recorded over short time windows with respect to the duration of an epidemic. To inform models of disease transmission, data are thus often repeated several times, yielding synthetic data covering long enough timescales. Looping over short term data to approximate contact patterns on longer timescales can lead to unrealistic transmission chains because of the deterministic repetition of all contacts, without any renewal of the contact partners of each individual between successive periods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Boudoua2024,

title = {EpiDCA: Adaptation and implementation of a danger theory algorithm for event-based epidemiological surveillance},

author = {Bahdja Boudoua, Mathieu Roche, Maguelonne Teisseire, Annelise Tran},

doi = {https://doi.org/10.1016/j.compag.2024.109693},

year  = {2024},

date = {2024-12-04},

journal = {Computers and Electronics in Agriculture},

volume = {229},

abstract = {Amidst the overwhelming volume of health-related data available, diverse epidemiological surveillance strategies have been adopted to swiftly detect outbreak events. These strategies differ in terms of structure, type, and sources used. When combined, they offer a more comprehensive understanding of epidemiological events then when used alone. In this paper, we propose an unsupervised approach that allows epidemiological data to be combined with risk factors related to disease onset. We applied this method, named EpiDCA, to enhance the classification and early detection capabilities of Event-Based Surveillance (EBS) systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{S2024,

title = {How fast are viruses spreading in the wild?},

author = {Dellicour S, Bastide P, Rocu P, Fargette D, Hardy OJ, Suchard MA, Guindon S, Lemey P},

doi = {https://doi.org/10.1371/journal.pbio.3002914},

year  = {2024},

date = {2024-12-03},

journal = {PLoS Biology},

abstract = {Genomic data collected from viral outbreaks can be exploited to reconstruct the dispersal history of viral lineages in a two-dimensional space using continuous phylogeographic inference. These spatially explicit reconstructions can subsequently be used to estimate dispersal metrics that can be informative of the dispersal dynamics and the capacity to spread among hosts. Heterogeneous sampling efforts of genomic sequences can however impact the accuracy of phylogeographic dispersal metrics. While the impact of spatial sampling bias on the outcomes of continuous phylogeographic inference has previously been explored, the impact of sampling intensity (i.e., sampling size) when aiming to characterise dispersal patterns through continuous phylogeographic reconstructions has not yet been thoroughly evaluated. In our study, we use simulations to evaluate the robustness of 3 dispersal metrics — a lineage dispersal velocity, a diffusion coefficient, and an isolation-by-distance (IBD) signal metric — to the sampling intensity. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{nokey,

title = {MUST-AI: Multisource Surveillance Tool - Avian Influenza},

author = {"Carlene Trevennec, Pierre Pompidor, Samira Bououda, Julien Rabatel, Mathieu

Roche"},

doi = {https://doi.org/10.1016/j.procs.2024.09.718},

year  = {2024},

date = {2024-11-28},

journal = {Procedia Computer Science},

volume = {246},

pages = {3034-3043},

abstract = {The multisource surveillance tool (MUST) is a platform for collecting, gathering, and visualizing different sources of information related to health events and highly pathogenic avian influenza in mammals (HPAIM). MUST-AI constitutes the first part of the MUST tool, which centralizes health information relating to cases of HPAIM since January 1, 2021, and comes from 3 different notification sources, an official notification source confirmed by public health institutions (i.e., WAHIS) and two other alternative unofficial sources that collect events from online media (PADI-web) and expert networks (ProMED). Owing to the use of natural language processing (NLP) algorithms, HPAIM events are represented on an interactive map associated with a graph that represents their distribution over a given time interval. This paper presents new tools and approaches for data fusion and experiments for selecting data to integrate into MUST that are related to HPAIM events.

},

keywords = {HPAI (Avian Influenza)},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Quantifying West Nile virus circulation in the avian host population in Northern Italy},

author = {Alex De Nardi, Giovanni Marini, Ilaria Dorigatti, Roberto Rosà, Marco Tamba, Luca Gelmini, Alice Prosperi, Francesco Menegale, Piero Poletti, Mattia Calzolari, Andrea Pugliese},

doi = {https://doi.org/10.1016/j.idm.2024.12.009},

year  = {2024},

date = {2024-11-13},

journal = {Infectious Disease Modelling},

volume = {10},

issue = {2},

pages = {375-386},

abstract = {West Nile virus (WNV) is one of the most threatening mosquito-borne pathogens in Italy where hundreds of human cases were recorded during the last decade. Here, we estimated the WNV incidence in the avian population in the Emilia-Romagna region through a modelling framework which enabled us to eventually assess the fraction of birds that present anti-WNV antibodies at the end of each epidemiological season.

},

keywords = {WNV (West Nile Virus)},

pubstate = {published},

tppubtype = {article}

}

@article{Serres2024,

title = {Integrating indicator-based and event-based surveillance data for risk mapping of West Nile virus, Europe, 2006 to 2021 },

author = {Serres, Kyla; Erazo, Diana; Despréaux, Garance; Vincenti-González, María F; Van Bortel, Wim; Arsevska, Elena; Dellicour, Simon},

url = {https://doi.org/10.2807/1560-7917.ES.2024.29.44.2400084},

year  = {2024},

date = {2024-10-31},

journal = {Eurosurveillance },

volume = {29},

issue = {44},

abstract = {West Nile virus (WNV) has an enzootic cycle between birds and mosquitoes, humans being incidental dead-end hosts. Circulation of WNV is an increasing public health threat in Europe. While detection of WNV is notifiable in humans and animals in the European Union, surveillance based on human case numbers presents some limitations, including reporting delays.



Aim

We aimed to perform risk mapping of WNV circulation leading to human infections in Europe by integrating two types of surveillance systems: indicator-based and event-based surveillance.



Methods

For indicator-based surveillance, we used data on human case numbers reported to the European Centre for Disease Prevention and Control (ECDC), and for event-based data, we retrieved information from news articles collected through an automated biosurveillance platform. In addition to these data sources, we also used environmental data to train ecological niche models to map the risk of local WNV circulation leading to human infections.



Results

The ecological niche models based on both types of surveillance data highlighted new areas potentially at risk of WNV infection in humans, particularly in Spain, Italy, France and Greece.



Conclusion

Although event-based surveillance data do not constitute confirmed occurrence records, integrating both indicator-based and event-based surveillance data proved useful. These results underscore the potential for a more proactive and comprehensive strategy in managing the threat of WNV in Europe by combining indicator- and event-based and environmental data for effective surveillance and public health response.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Menya2024,

title = {EpidGPT: A Combined Strategy to Discriminate Between Redundant and New Information for Epidemiological Surveillance Systems},

author = {Edmond Menya, Mathieu Roche, Roberto Interdonato and Dickson Owuor},

doi = {https://doi.org/10.1007/978-3-031-70239-6_30},

isbn = {978-3-031-70238-9},

year  = {2024},

date = {2024-09-20},

journal = {Natural Language Processing and Information Systems},

pages = {439–454},

abstract = {Textual documents such as online news articles have become a key source in epidemiological surveillance such as being used in the detection of new and re-emerging diseases. However, such sources suffer redundancies with the need to automate the process of identifying novel information. In this paper, we propose a framework for learning novel thematic information in epidemiological news documents. Our approach involves both extraction and classification of new, duplicate, additional and/or missing pieces of relevant information in epidemiological news documents. Firstly, we propose an initial step to solve the limited data problem where fewer gold labelled datasets exists for training text-based epidemiological surveillance systems. This initial step is built using extractive question answering technique whereby we automate the process of extracting relevant thematic features inclusive of disease and host names, location and date of reported events and reported number of cases in order to create a large silver labelled dataset. We then propose a main step where we build a novelty information classification model that is trained using our large silver labeled dataset. We then test our novelty classifier model alongside competitive ones on the challenge of detecting whether there is novel, redundant and/or missing information in a target epidemiological news article. We later carry out ablation studies on the most informative document segments in epidemiological news articles.},

keywords = {Text mining},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{Domenico2024c,

title = {Planning and adjusting the COVID-19 booster vaccination campaign to reduce disease burden},

author = {Laura Di Domenico, Yair Goldberg, Vittoria Colizza},

doi = {https://doi.org/10.1016/j.idm.2024.09.002},

year  = {2024},

date = {2024-09-12},

journal = {Infectious Disease Modelling},

volume = {10},

issue = {1},

abstract = {As public health policies shifted in 2023 from emergency response to long-term COVID-19 disease management, immunization programs started to face the challenge of formulating routine booster campaigns in a still highly uncertain seasonal behavior of the COVID-19 epidemic. Mathematical models assessing past booster campaigns and integrating knowledge on waning of immunity can help better inform current and future vaccination programs. Focusing on the first booster campaign in the 2021/2022 winter in France, we used a multi-strain age-stratified transmission model to assess the effectiveness of the observed booster vaccination in controlling the succession of Delta, Omicron BA.1 and BA.2 waves. We explored counterfactual scenarios altering the eligibility criteria and inter-dose delay. Our study showed that the success of the immunization program in curtailing the Omicron BA.1 and BA.2 waves was largely dependent on the inclusion of adults among the eligible groups, and was highly sensitive to the inter-dose delay, which was changed over time. Shortening or prolonging this delay, even by only one month, would have required substantial social distancing interventions to curtail the hospitalization peak. Also, the time window for adjusting the delay was very short. Our findings highlight the importance of readiness and adaptation in the formulation of routine booster campaign in the current level of epidemiological uncertainty.

},

keywords = {Covid-19 (Coronavirus)},

pubstate = {published},

tppubtype = {article}

}

@article{Domenico2024b,

title = {Planning and adjusting the COVID-19 booster vaccination campaign to reduce disease burden},

author = {Di Domenico, Laura; Goldberg, Yair; Colizza, Vittoria },

url = {https://doi.org/10.1016/j.idm.2024.09.002},

year  = {2024},

date = {2024-09-12},

journal = {Infectious Disease Modelling},

volume = {10},

issue = {1},

pages = {150-162},

abstract = {As public health policies shifted in 2023 from emergency response to long-term COVID-19 disease management, immunization programs started to face the challenge of formulating routine booster campaigns in a still highly uncertain seasonal behavior of the COVID-19 epidemic. Mathematical models assessing past booster campaigns and integrating knowledge on waning of immunity can help better inform current and future vaccination programs. Focusing on the first booster campaign in the 2021/2022 winter in France, we used a multi-strain age-stratified transmission model to assess the effectiveness of the observed booster vaccination in controlling the succession of Delta, Omicron BA.1 and BA.2 waves. We explored counterfactual scenarios altering the eligibility criteria and inter-dose delay. Our study showed that the success of the immunization program in curtailing the Omicron BA.1 and BA.2 waves was largely dependent on the inclusion of adults among the eligible groups, and was highly sensitive to the inter-dose delay, which was changed over time. Shortening or prolonging this delay, even by only one month, would have required substantial social distancing interventions to curtail the hospitalization peak. Also, the time window for adjusting the delay was very short. Our findings highlight the importance of readiness and adaptation in the formulation of routine booster campaign in the current level of epidemiological uncertainty.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Viau2024b,

title = {Epid Data Explorer: A Visualization Tool for Exploring and Comparing Spatio-Temporal Epidemiological Data},

author = {Laetitia Viau, Jérôme Azé, Fati Chen, Pierre Pompidor, Pascal Poncelet, Vincent Raveneau, Nancy Rodriguez and Arnaud Sallaberry},

doi = {https://doi.org/10.1177/14604582241279720},

year  = {2024},

date = {2024-09-03},

journal = {Health Informatics Journal},

abstract = {The analysis of large sets of spatio-temporal data is a fundamental challenge in epidemiological research. As the quantity and the complexity of such kind of data increases, automatic analysis approaches, such as statistics, data mining, machine learning, etc., can be used to extract useful information. While these approaches have proven effective, they require a priori knowledge of the information being sought, and some interesting insights into the data may be missed. To bridge this gap, information visualization offers a set of techniques for not only presenting known information, but also exploring data without having a hypothesis formulated beforehand. In this paper, we introduce Epid Data Explorer (EDE), a visualization tool that enables exploration of spatio-temporal epidemiological data},

keywords = {Text mining},

pubstate = {published},

tppubtype = {article}

}

@article{Viau2024,

title = {Epid data explorer: A visualization tool for exploring and comparing spatio-temporal epidemiological data},

author = {Viau, Laetitia; Azé, Jerome; Chen, Fati; Pompidor, Pierre; Poncelet, Pascal; Raveneau, Vincent; Rodriguez, Nancy; Sallaberry, Arnaud.},

url = {https://doi.org/10.1177/14604582241279720},

doi = {10.1177/14604582241279720},

year  = {2024},

date = {2024-09-03},

urldate = {2024-09-03},

journal = {Health Informatics Journal},

abstract = {The analysis of large sets of spatio-temporal data is a fundamental challenge in epidemiological research. As the quantity and the complexity of such kind of data increases, automatic analysis approaches, such as statistics, data mining, machine learning, etc., can be used to extract useful information. While these approaches have proven effective, they require a priori knowledge of the information being sought, and some interesting insights into the data may be missed. To bridge this gap, information visualization offers a set of techniques for not only presenting known information, but also exploring data without having a hypothesis formulated beforehand. In this paper, we introduce Epid Data Explorer (EDE), a visualization tool that enables exploration of spatio-temporal epidemiological data. EDE allows easy comparisons of indicators and trends across different geographical areas and times. It facilitates this exploration through ready-to-use pre-loaded datasets as well as user-chosen datasets. The tool also provides a secure architecture for easily importing new datasets while ensuring confidentiality. In two use cases using data associated with the COVID-19 epidemic, we demonstrate the substantial impact of implemented lockdown measures on mobility and how EDE allows assessing correlations between the spread of COVID-19 and weather conditions.

},

keywords = {Tools},

pubstate = {published},

tppubtype = {article}

}

@article{Bizzotto2024,

title = {Increasing situational awareness through nowcasting of the reproduction number},

author = {Bizzotto, Andrea; Guzzetta, Giorgio; Marziano, Valentina; Del Manso, Martina; Mateo Urdiales, Alberto; Petrone, Daniele; Cannone, Andrea; Sacco, Chiara; Poletti, Piero; Manica, Mattia; Zardini, Agenese; Trentini, Filippo; Fabiani, Massimo; Bella, Antonino; Riccardo, Flavia; Pezzotti, Patrizio; Ajelli, Marco & Merler, Stefano},

editor = {Joao Sollari Lopes, National Statistical Institute of Portugal, Portugal

},

doi = {doi: 10.3389/fpubh.2024.1430920},

year  = {2024},

date = {2024-08-21},

journal = {Frontiers in Public Health},

volume = {12},

keywords = {Covid-19 (Coronavirus)},

pubstate = {published},

tppubtype = {article}

}

@article{Marini2024,

title = {Drivers and epidemiological patterns of West Nile virus in Serbia},

author = {Marini, Giovanni; Drakulovic, Mitra; B.; Jovanovic, Verica; Dagostin, Francesca; Wint, Willy; Tagliapietra, Valentina; Vasic, Milena & Rizzoli, Annapaola},

url = {https://doi.org/10.3389/fpubh.2024.1429583},

year  = {2024},

date = {2024-07-17},

journal = {Frontiers in Public Health},

volume = {12},

abstract = {West Nile virus (WNV) is an emerging mosquito-borne pathogen in Serbia, where it has been detected as a cause of infection in humans since 2012. We analyzed and modelled WNV transmission patterns in the country between 2012 and 2023.



Methods: We applied a previously developed modelling approach to quantify epidemiological parameters of interest and to identify the most important environmental drivers of the force of infection (FOI) by means of statistical analysis in the human population in the country.



Results: During the study period, 1,387 human cases were recorded, with substantial heterogeneity across years. We found that spring temperature is of paramount importance for WNV transmission, as FOI magnitude and peak timing are positively associated with it. Furthermore, FOI is also estimated to be greater in regions with a larger fraction of older adult people, who are at higher risk to develop severe infections.



Conclusion: Our results highlight that temperature plays a key role in shaping WNV outbreak magnitude in Serbia, confirming the association between spring climatic conditions and WNV human transmission risk and thus pointing out the importance of this factor as a potential early warning predictor for timely application of preventive and control measures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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, Maria Bellenghi, Roberta Masella, Luca Busani},

doi = {https://doi.org/10.1016/j.onehlt.2024.100841},

year  = {2024},

date = {2024-06-21},

journal = {One health},

volume = {19},

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.

},

keywords = {Leptospirosis},

pubstate = {published},

tppubtype = {article}

}

@article{Re2024b,

title = {VectAbundance: a spatio-temporal database of vector observations},

author = {Daniele Da Re, Giovanni Marini, Carmelo Bonannella, Fabrizio Laurini, Mattia Manica, Nikoleta Anicic, Alessandro Albieri, Paola Angelini, Daniele Arnoldi, Marharyta Blaha, Federica Bertola, Beniamino Caputo, Claudio De Liberato, Alessandra Della Torre, Enkelejda Velo, Eleonora Flacio, Alessandra Franceschini, Francesco Gradoni, Përparim Kadriaj, Valeria Lencioni, Irene Del Lesto, Francesco La Russa, Riccardo Paolo Lia, Fabrizio Montarsi, Domenico Otranto, Gregory L’Ambert, Annapaola Rizzoli, Pasquale Rombolà, Federico Romiti, Gionata Stancher, Alessandra Torina, Chiara Virgillito, Fabiana Zandonai, Roberto Rosà},

doi = {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 = {CHIK (Chikungunya), DEN (Dengue), Zika},

pubstate = {published},

tppubtype = {article}

}