MOOD publications

@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{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}

}

@article{10.1093/trstmh/trab009,

title = {A review of models applied to the geographic spread of Zika virus},

author = {Sabrina L Li and Jane P Messina and Oliver G Pybus and Moritz U G Kraemer and Lauren Gardner},

url = {https://doi.org/10.1093/trstmh/trab009},

doi = {10.1093/trstmh/trab009},

issn = {0035-9203},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Transactions of The Royal Society of Tropical Medicine and Hygiene},

volume = {115},

number = {9},

pages = {956-964},

abstract = {In recent years, Zika virus (ZIKV) has expanded its geographic range and in 2015–2016 caused a substantial epidemic linked to a surge in developmental and neurological complications in newborns. Mathematical models are powerful tools for assessing ZIKV spread and can reveal important information for preventing future outbreaks. We reviewed the literature and retrieved modelling studies that were developed to understand the spatial epidemiology of ZIKV spread and risk. We classified studies by type, scale, aim and applications and discussed their characteristics, strengths and limitations. We examined the main objectives of these models and evaluated the effectiveness of integrating epidemiological and phylogeographic data, along with socioenvironmental risk factors that are known to contribute to vector–human transmission. We also assessed the promising application of human mobility data as a real-time indicator of ZIKV spread. Lastly, we summarised model validation methods used in studies to ensure accuracy in models and modelled outcomes. Models are helpful for understanding ZIKV spread and their characteristics should be carefully considered when developing future modelling studies to improve arbovirus surveillance.},

keywords = {Text mining, Zika},

pubstate = {published},

tppubtype = {article}

}

@article{oidtman2021trade,

title = {Trade-offs between individual and ensemble forecasts of an emerging infectious disease},

author = {Rachel J Oidtman and Elisa Omodei and Moritz UG Kraemer and Carlos A Casteneda-Orjuela and Erica Cruz-Rivera and Sandra Misnaza-Castrillon and Myriam Patricia Cifuentes and Luz Emilse Rincon and Viviana Canon and Pedro Alarcon and others},

url = {https://www.nature.com/articles/s41467-021-25695-0},

doi = {https://doi.org/10.1038/s41467-021-25695-0},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Nature Communications},

volume = {12},

number = {5379 },

publisher = {Cold Spring Harbor Laboratory Press},

abstract = {Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.},

keywords = {OpenDataSet, Zika},

pubstate = {published},

tppubtype = {article}

}