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.
Li, Sabrina L; Messina, Jane P; Pybus, Oliver G; Kraemer, Moritz U G; Gardner, Lauren
A review of models applied to the geographic spread of Zika virus Journal Article
In: Transactions of The Royal Society of Tropical Medicine and Hygiene, vol. 115, no. 9, pp. 956-964, 2021, ISSN: 0035-9203.
Abstract | Links | BibTeX | Tags: Text mining, Zika
@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}
}
Oidtman, Rachel J; Omodei, Elisa; Kraemer, Moritz UG; Casteneda-Orjuela, Carlos A; Cruz-Rivera, Erica; Misnaza-Castrillon, Sandra; Cifuentes, Myriam Patricia; Rincon, Luz Emilse; Canon, Viviana; Alarcon, Pedro; others,
Trade-offs between individual and ensemble forecasts of an emerging infectious disease Journal Article
In: Nature Communications, vol. 12, no. 5379 , 2021.
Abstract | Links | BibTeX | Tags: OpenDataSet, Zika
@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}
}