MOOD publications

@article{nokey,

title = {Sindbis virus outbreak and evidence for geographical expansion in Finland, 2021},

author = {Suvanto, Maija T and Uusitalo, Ruut and Otte im Kampe, Eveline and Vuorinen, Tytti and Kurkela, Satu and Vapalahti, Olli and Dub, Timothée and Huhtamo, Eili and Korhonen, Essi M},

url = {https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2022.27.31.2200580},

doi = {https://doi.org/10.2807/1560-7917.ES.2022.27.31.2200580},

year  = {2022},

date = {2022-08-04},

journal = {Eurosurveillance},

volume = {27},

number = {31},

abstract = {Sindbis virus (SINV) (Togaviridae family, Alphavirus genus) is the causative agent of Pogosta disease, a typically self-limited disease with common symptoms of rash, arthralgia, myalgia and fever [1,2]. In some cases, arthralgia and myalgia can persist from months to years and negatively affect quality of life [2,3]. While circulation of SINV has been reported in mosquitoes and birds globally, symptomatic human infection has almost exclusively been reported in Finland, Sweden, Russia and South Africa [4]. However, larger outbreaks and annual cases are reported only from Finland, where the SINV seroprevalence in the general population was 5.2% in the years 1999 to 2003 [5].



Laboratory diagnosis of Pogosta disease is done using ELISA, and paired samples are often needed because the antibody response against SINV develops slowly [6]. In Finland, SINV has been endemic since the 1960s, and the first epidemic occurred in 1974 [1,7]. A laboratory-confirmed case is defined as either detection of SINV IgM and IgG in a single serum specimen or seroconversion between paired specimens. The laboratory-confirmed cases have been notified to the National Infectious Diseases Register (NIDR) since its implementation in 1995 [8]. A total of 566 laboratory-confirmed cases were notified in 2021, compared with an average of 158 annual cases between 1995 and 2021, making it a notable outbreak year. Similarly, high incidence had previously been reported in 2002 with 597 laboratory-confirmed cases.



The aim of this rapid communication is to increase awareness of an upcoming SINV epidemic in 2022. The high SINV incidence in 2021 may precede a larger epidemic.},

keywords = {finland, infectious diseases, Pogosta disease, Sindbis virus},

pubstate = {published},

tppubtype = {article}

}

@article{@article{contreras2022impact,

title = {Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases},

author = {Contreras, Diego Andrés and Colosi, Elisabetta and Bassignana, Giulia and Colizza, Vittoria and Barrat, Alain},

url = {https://doi.org/10.1098/rsif.2022.0164},

doi = {10.1098/rsif.2022.0164},

year  = {2022},

date = {2022-06-22},

urldate = {2022-06-22},

journal = {Journal of the Royal Society Interface},

volume = {19},

number = {191},

pages = {20220164},

abstract = {Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation.},

keywords = {Computational models, COVID-19, infectious diseases, OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories},

author = {Ge, Yong; Zhang, Wen-Bin; Wu, Xilin; W Ruktanonchai, Corrine; Liu, Haiyan; Wang, Jianghao; Song, Yongze; Liu, Mengxiao; Yan, Wei; Yang, Juan; Cleary, Eimear; H Qader, Sarchil; Atuhaire, Fatumah; W Ruktanonchai, Nick; J Tatem, Andrew; Lai, Shengjie},

url = {https://www.nature.com/articles/s41467-022-30897-1#citeas},

doi = {10.1038/s41467-022-30897-1},

year  = {2022},

date = {2022-06-03},

urldate = {2022-06-03},

journal = {Nature Commun},

volume = {13},

number = {3106},

abstract = {Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42–62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants.},

keywords = {Applied mathematics, epidemiology, infectious diseases},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = { Meteorological and climatic variables predict the phenology of Ixodes ricinus nymph activity in France, accounting for habitat heterogeneity.},

author = {Phrutsamon Wongnak and Séverine Bord and Maude Jacquot and Albert Agoulon and Frédéric Beugnet and Laure Bournez and Nicolas Cèbe and Adélie Chevalier and Jean-François Cosson and Naïma Dambrine and Thierry Hoch and Frédéric Huard and Nathalie Korboulewsky and Isabelle Lebert and Aurélien Madouasse and Anders Mårell and Sara Moutailler and Olivier Plantard and Thomas Pollet and Valérie Poux and Magalie René-Martellet and Muriel Vayssier-Taussat and Hélène Verheyden and Gwenaël Vourc’h and Karine Chalvet-Monfray},

url = {https://www.nature.com/articles/s41598-022-11479-z},

doi = {https://doi.org/10.1038/s41598-022-11479-z},

year  = {2022},

date = {2022-05-12},

urldate = {2022-05-12},

journal = {Nature Scientific Reports},

volume = {7833},

issue = {12},

abstract = {Ixodes ricinus ticks (Acari: Ixodidae) are the most important vector for Lyme borreliosis in Europe. As climate change might affect their distributions and activities, this study aimed to determine the effects of environmental factors, i.e., meteorological, bioclimatic, and habitat characteristics on host-seeking (questing) activity of I. ricinus nymphs, an important stage in disease transmissions, across diverse climatic types in France over 8 years. Questing activity was observed using a repeated removal sampling with a cloth-dragging technique in 11 sampling sites from 7 tick observatories from 2014 to 2021 at approximately 1-month intervals, involving 631 sampling campaigns. Three phenological patterns were observed, potentially following a climatic gradient. The mixed-effects negative binomial regression revealed that observed nymph counts were driven by different interval-average meteorological variables, including 1-month moving average temperature, previous 3-to-6-month moving average temperature, and 6-month moving average minimum relative humidity. The interaction effects indicated that the phenology in colder climates peaked differently from that of warmer climates. Also, land cover characteristics that support the highest baseline abundance were moderate forest fragmentation with transition borders with agricultural areas. Finally, our model could potentially be used to predict seasonal human-tick exposure risks in France that could contribute to mitigating Lyme borreliosis risk.},

keywords = {Climate, climate change, disease ecology, France, infectious diseases, ixodes, ticks, vector},

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 = {epidemiology, infectious diseases, Model, Zika},

pubstate = {published},

tppubtype = {article}

}