MOOD publications

@article{nokey,

title = {Emergence of an early SARS-CoV-2 epidemic in the United States},

author = {Mark Zeller and Karthik Gangavarapu and Catelyn Anderson and Allison R. Smither and John A. Vanchiere and Rebecca Rose and Daniel J. Snyder and Gytis Dudas and Alexander Watts and Nathaniel L. Matteson and Refugio Robles-Sikisaka and Maximilian Marshall and Amy K. Feehan and Gilberto Sabino-Santos and Antoinette R. Bell-Kareem and Laura D. Hughes and Manar Alkuzweny and Patricia Snarski and Julia Garcia-Diaz and Rona S. Scott and Lilia I. Melnik and Raphaëlle Klitting and Michelle McGraw and Pedro Belda-Ferre and Peter DeHoff and Shashank Sathe and Clarisse Marotz and Nathan D. Grubaugh and David J. Nolan and Arnaud C. Drouin and Kaylynn J. Genemaras and Karissa Chao and Sarah Topol and Emily Spencer and Laura Nicholson and Stefan Aigner and Gene W. Yeo and Lauge Farnaes and Charlotte A. Hobbs and Louise C. Laurent and Rob Knight and Emma B. Hodcroft and Kamran Khan and Dahlene N. Fusco and Vaughn S. Cooper and Phillipe Lemey and Lauren Gardner and Susanna L. Lamers and Jeremy P. Kamil and Robert F. Garry and Marc A. Suchard and Kristian G. Andersen},

url = {https://www.sciencedirect.com/science/article/pii/S0092867421008898},

doi = {10.1016/j.cell.2021.07.030},

issn = {0092-8674},

year  = {2022},

date = {2022-09-16},

urldate = {2022-09-16},

journal = {Cell},

volume = {184},

issue = {19},

pages = {4939-4952.e15},

abstract = {The emergence of the COVID-19 epidemic in the United States (U.S.) went largely undetected due to inadequate testing. New Orleans experienced one of the earliest and fastest accelerating outbreaks, coinciding with Mardi Gras. To gain insight into the emergence of SARS-CoV-2 in the U.S. and how large-scale events accelerate transmission, we sequenced SARS-CoV-2 genomes during the first wave of the COVID-19 epidemic in Louisiana. We show that SARS-CoV-2 in Louisiana had limited diversity compared to other U.S. states and that one introduction of SARS-CoV-2 led to almost all of the early transmission in Louisiana. By analyzing mobility and genomic data, we show that SARS-CoV-2 was already present in New Orleans before Mardi Gras, and the festival dramatically accelerated transmission. Our study provides an understanding of how superspreading during large-scale events played a key role during the early outbreak in the U.S. and can greatly accelerate epidemics.},

keywords = {Covid-19 (Coronavirus), OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {SPREAD 4: online visualisation of pathogen phylogeographic reconstructions},

author = {Kanika D Nahata and Filip Bielejec and Juan Monetta and Simon Dellicour and Andrew Rambaut and Marc Suchard A and Guy Baele and Philippe Lemey},

url = {https://doi.org/10.1093/ve/veac088},

doi = {10.1093/ve/veac088},

isbn = {2057-1577},

year  = {2022},

date = {2022-09-01},

urldate = {2022-09-01},

journal = {Virus Evolution},

volume = {8},

number = {2},

abstract = {Phylogeographic analyses aim to extract information about pathogen spread from genomic data, and visualising spatio-temporal reconstructions is a key aspect of this process. Here we present SPREAD 4, a feature-rich web-based application that visualises estimates of pathogen dispersal resulting from Bayesian phylogeographic inference using BEAST on a geographic map, offering zoom-and-filter functionality and smooth animation over time. SPREAD 4 takes as input phylogenies with both discrete and continuous location annotation and offers customised visualisation as well as generation of publication-ready figures. SPREAD 4 now features account-based storage and easy sharing of visualisations by means of unique web addresses. SPREAD 4 is intuitive to use and is available online at https://spreadviz.org, with an accompanying web page containing answers to frequently asked questions at https://beast.community/spread4.},

note = {veac088},

keywords = {OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{@article{SCHAEFFER2022108317,,

title = {Labeled entities from social media data related to avian influenza disease},

author = {Camille Schaeffer and Roberto Interdonato and Renaud Lancelot and Mathieu Roche and Maguelonne Teisseire},

url = {https://www.sciencedirect.com/science/article/pii/S2352340922005194},

doi = {https://doi.org/10.1016/j.dib.2022.108317},

issn = {2352-3409},

year  = {2022},

date = {2022-08-01},

urldate = {2022-08-01},

journal = {Data in Brief},

volume = {43},

pages = {108317},

abstract = {This dataset is composed by spatial (e.g. location) and thematic (e.g. diseases, symptoms, virus) entities concerning avian influenza in social media (textual) data in English. It was created from three corpora: the first one includes 10 transcriptions of YouTube videos and 70 tweets manually annotated. The second corpus is composed by the same textual data but automatically annotated with Named Entity Recognition (NER) tools. These two corpora have been built to evaluate NER tools and apply them to a bigger corpus. The third corpus is composed of 100 YouTube transcriptions automatically annotated with NER tools. The aim of the annotation task is to recognize spatial information such as the names of the cities and epidemiological information such as the names of the diseases. An annotation guideline is provided in order to ensure a unified annotation and to help the annotators. This dataset can be used to train or evaluate Natural Language Processing (NLP) approaches such as specialized entity recognition.},

keywords = {HPAI (Avian Influenza), OpenDataSet, Text mining},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{@article{bernardsystematic,

title = {Systematic Review on Crimean–Congo Hemorrhagic Fever Enzootic Cycle and Factors Favoring Virus Transmission: Special Focus on France, an Apparently Free-Disease Area in Europe},

author = {Celia Bernard and Philippe Holzmuller and Madiou Thierno Bah and Matthieu Bastien and Benoit Combes and Ferran Jori and Vladimir Grosbois and Laurence Vial},

url = {https://www.frontiersin.org/articles/10.3389/fvets.2022.932304/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_Veterinary_Science&id=932304},

doi = {https://doi.org/10.3389/fvets.2022.932304},

year  = {2022},

date = {2022-07-19},

urldate = {2022-07-19},

journal = {Frontiers in Veterinary Science},

pages = {973},

abstract = {Crimean–Congo hemorrhagic fever (CCHF) is a viral zoonotic disease resulting in hemorrhagic syndrome in humans. Its causative agent is naturally transmitted by ticks to non-human vertebrate hosts within an enzootic sylvatic cycle. Ticks are considered biological vectors, as well as reservoirs for CCHF virus (CCHFV), as they are able to maintain the virus for several months or even years and to transmit CCHFV to other ticks. Although animals are not symptomatic, some of them can sufficiently replicate the virus, becoming a source of infection for ticks as well as humans through direct contact with contaminated body fluids. The recent emergence of CCHF in Spain indicates that tick–human interaction rates promoting virus transmission are changing and lead to the emergence of CCHF. In other European countries such as France, the presence of one of its main tick vectors and the detection of antibodies targeting CCHFV in animals, at least in Corsica and in the absence of human cases, suggest that CCHFV could be spreading silently. In this review, we study the CCHFV epidemiological cycle as hypothesized in the French local context and select the most likely parameters that may influence virus transmission among tick vectors and non-human vertebrate hosts. For this, a total of 1,035 articles dating from 1957 to 2021 were selected for data extraction. This study made it possible to identify the tick species that seem to be the best candidate vectors of CCHFV in France, but also to highlight the importance of the abundance and composition of local host communities on vectors' infection prevalence. Regarding the presumed transmission cycle involving Hyalomma marginatum, as it might exist in France, at least in Corsica, it is assumed that tick vectors are still weakly infected and the probability of disease emergence in humans remains low. The likelihood of factors that may modify this equilibrium is discussed.},

keywords = {CCHF (Crimean Congo haemorrhagic fever virus), OpenDataSet},

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

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 = {OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{@article{2022,

title = {Investigating COVID-19 Vaccine Impact on the Risk of Hospitalisation through the Analysis of National Surveillance Data Collected in Belgium},

author = {Diana Erazo and Maria F. Vincenti-Gonzalez and Joris A. F. van Loenhout and Pierre Hubin and Mathil Vandromme and Piet Maes and Maxime Taquet and Johan Van Weyenbergh and Lucy Catteau and Simon Dellicour},

url = {https://www.mdpi.com/1999-4915/14/6/1315},

doi = {https://doi.org/10.3390/v14061315},

issn = {1999-4915},

year  = {2022},

date = {2022-06-16},

urldate = {2022-06-16},

journal = {Viruses},

volume = {14},

number = {6},

pages = {1315},

abstract = {The national vaccination campaign against SARS-CoV-2 started in January 2021 in Belgium. In the present study, we aimed to use national hospitalisation surveillance data to investigate the recent evolution of vaccine impact on the risk of COVID-19 hospitalisation. We analysed aggregated data from 27,608 COVID-19 patients hospitalised between October 2021 and February 2022, stratified by age category and vaccination status. For each period, vaccination status, and age group, we estimated risk ratios (RR) corresponding to the ratio between the probability of being hospitalised following SARS-CoV-2 infection if belonging to the vaccinated population and the same probability if belonging to the unvaccinated population. In October 2021, a relatively high RR was estimated for vaccinated people > 75 years old, possibly reflecting waning immunity within this group, which was vaccinated early in 2021 and invited to receive the booster vaccination at that time. In January 2022, a RR increase was observed in all age categories coinciding with the dominance of the Omicron variant. Despite the absence of control for factors like comorbidities, previous infections, or time since the last administered vaccine, we showed that such real-time aggregated data make it possible to approximate trends in vaccine impact over time.},

keywords = {Covid-19 (Coronavirus), OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{@article{10.7554/eLife.71703,

title = {Transmission networks of SARS-CoV-2 in Coastal Kenya during the first two waves: A retrospective genomic study},

author = {Charles N Agoti and Lynette Isabella Ochola-Oyier and Simon Dellicour and Khadija Said Mohammed and Arnold W Lambisia and Zaydah R de Laurent and John M Morobe and Maureen W Mburu and Donwilliams O Omuoyo and Edidah M Ongera and Leonard Ndwiga and Eric Maitha and Benson Kitole and Thani Suleiman and Mohamed Mwakinangu and John K Nyambu and John Otieno and Barke Salim and Jennifer Musyoki and Nickson Murunga and Edward Otieno and John N Kiiru and Kadondi Kasera and Patrick Amoth and Mercy Mwangangi and Rashid Aman and Samson Kinyanjui and George Warimwe and My Phan and Ambrose Agweyu and Matthew Cotten and Edwine Barasa and Benjamin Tsofa and D James Nokes and Philip Bejon Philip and George Githinji},

editor = {Grabowski, Mary Kate and van der Meer, Jos W},},

url = {https://doi.org/10.7554/eLife.71703},

doi = {10.7554/eLife.71703},

issn = {2050-084X},

year  = {2022},

date = {2022-06-14},

urldate = {2022-06-14},

journal = {eLife},

volume = {11},

pages = {e71703},

abstract = {Detailed understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) regional transmission networks within sub-Saharan Africa is key for guiding local public health interventions against the pandemic. textbf{Methods:} Here, we analysed 1139 SARS-CoV-2 genomes from positive samples collected between March 2020 and February 2021 across six counties of Coastal Kenya (Mombasa, Kilifi, Taita Taveta, Kwale, Tana River, and Lamu) to infer virus introductions and local transmission patterns during the first two waves of infections. Virus importations were inferred using ancestral state reconstruction, and virus dispersal between counties was estimated using discrete phylogeographic analysis. textbf{Results:} During Wave 1, 23 distinct Pango lineages were detected across the six counties, while during Wave 2, 29 lineages were detected; 9 of which occurred in both waves and 4 seemed to be Kenya specific (B.1.530, B.1.549, B.1.596.1, and N.8). Most of the sequenced infections belonged to lineage B.1 (n = 723, 63%), which predominated in both Wave 1 (73%, followed by lineages N.8 [6%] and B.1.1 [6%]) and Wave 2 (56%, followed by lineages B.1.549 [21%] and B.1.530 [5%]). Over the study period, we estimated 280 SARS-CoV-2 virus importations into Coastal Kenya. Mombasa City, a vital tourist and commercial centre for the region, was a major route for virus imports, most of which occurred during Wave 1, when many Coronavirus Disease 2019 (COVID-19) government restrictions were still in force. In Wave 2, inter-county transmission predominated, resulting in the emergence of local transmission chains and diversity. textbf{Conclusions:} Our analysis supports moving COVID-19 control strategies in the region from a focus on international travel to strategies that will reduce local transmission. textbf{Funding:} This work was funded by The Wellcome (grant numbers: 220985, 203077/Z/16/Z, 220977/Z/20/Z, and 222574/Z/21/Z) and the National Institute for Health and Care Research (NIHR), project references: 17/63/and 16/136/33 using UK Aid from the UK government to support global health research, The UK Foreign, Commonwealth and Development Office. The views expressed in this publication are those of the author(s) and not necessarily those of the funding agencies.},},

keywords = {Covid-19 (Coronavirus), OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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 = {Covid-19 (Coronavirus), OpenDataSet},

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 = {OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{@article{COLOSI2022101047,

title = {Modelling COVID-19 in school settings to evaluate prevention and control protocols},

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

url = {https://www.sciencedirect.com/science/article/pii/S2352556822000285},

doi = {https://doi.org/10.1016/j.accpm.2022.101047},

issn = {2352-5568},

year  = {2022},

date = {2022-04-01},

urldate = {2022-04-01},

journal = {Anaesthesia Critical Care & Pain Medicine},

volume = {41},

number = {2},

pages = {101047},

keywords = {Covid-19 (Coronavirus), OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{@article{colosi2022screening,,

title = {Screening and vaccination against COVID-19 to minimise school closure: a modelling study},

author = {Elisabetta Colosi and Giulia Bassignana and Diego Andrés Contreras and Canelle Poirier and Pierre-Yves Boëlle and Simon Cauchemez and Yazdan Yazdanpanah and Bruno Lina and Arnaud Fontanet and Alain Barrat and others},

url = {https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(22)00138-4/fulltext},

doi = {https://doi.org/10.1016/S1473-3099(22)00138-4},

year  = {2022},

date = {2022-04-01},

urldate = {2022-04-01},

journal = {The Lancet Infectious Diseases},

volume = {2},

issue = {7},

pages = {Pages 977-989},

keywords = {Covid-19 (Coronavirus), OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Agent-based modelling of reactive vaccination of workplaces and schools against COVID-19},

author = {Benjamin Faucher and Rania Assab and Jonathan Roux and Daniel Levy-Bruhl and Cécile Tran Kiem and Simon Cauchemez and Laura Zanetti and Vittoria Colizza and Pierre-Yves Boëlle and Chiara Poletto},

url = {https://www.nature.com/articles/s41467-022-29015-y#Abs1},

doi = {10.1038/s41467-022-29015-y},

year  = {2022},

date = {2022-03-17},

urldate = {2022-03-17},

journal = {Nature Communications},

volume = {13},

number = {1414},

abstract = {With vaccination against COVID-19 stalled in some countries, increasing vaccine accessibility and distribution could help keep transmission under control. Here, we study the impact of reactive vaccination targeting schools and workplaces where cases are detected, with an agent-based model accounting for COVID-19 natural history, vaccine characteristics, demographics, behavioural changes and social distancing. In most scenarios, reactive vaccination leads to a higher reduction in cases compared with non-reactive strategies using the same number of doses. The reactive strategy could however be less effective than a moderate/high pace mass vaccination program if initial vaccination coverage is high or disease incidence is low, because few people would be vaccinated around each case. In case of flare-ups, reactive vaccination could better mitigate spread if it is implemented quickly, is supported by enhanced test-trace-isolate and triggers an increased vaccine uptake. These results provide key information to plan an adaptive vaccination rollout.},

keywords = {OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {On the Origin and Propagation of the COVID-19 Outbreak in the Italian Province of Trento, a Tourist Region of Northern Italy},

author = {Luca Bianco and Mirko Moser and Andrea Silverjand and Diego Micheletti and Giovanni Lorenzin and Lucia Collini and Mattia Barbareschi and Paolo Lanzafame and Nicola Segata and Massimo Pindo and Pietro Franceschi and Omar Rota-Stabelli and Annapaola Rizzoli and Paolo Fontana and Claudio Donati},

url = {https://www.mdpi.com/1999-4915/14/3/580},

doi = {10.3390/v14030580},

issn = {1999-4915},

year  = {2022},

date = {2022-03-11},

urldate = {2022-03-11},

journal = {Viruses},

volume = {14},

number = {580},

issue = {3},

abstract = {Background: Trentino is an Italian province with a tourism-based economy, bordering the regions of Lombardy and Veneto, where the two earliest and largest outbreaks of COVID-19 occurred in Italy. The earliest cases in Trentino were reported in the first week of March 2020, with most of the cases occurring in the winter sport areas in the Dolomites mountain range. The number of reported cases decreased over the summer months and was followed by a second wave in the autumn and winter of 2020. Methods: we performed high-coverage Oxford Nanopore sequencing of 253 positive SARS-CoV-2 swabs collected in Trentino between March and December 2020. Results: in this work, we analyzed genome sequences to trace the routes through which the virus entered the area, and assessed whether the autumnal resurgence could be attributed to lineages persisting undetected during summer, or as a consequence of new introductions. Conclusions: Comparing the draft genomes analyzed with a large selection of European sequences retrieved from GISAID we found that multiple introductions of the virus occurred at the early stage of the epidemics; the two epidemic waves were unrelated; the second wave was due to reintroductions of the virus in summer when traveling restrictions were uplifted.},

keywords = {Covid-19 (Coronavirus), OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{@article{10.1093/ve/veac015,

title = {Phycova — a tool for exploring covariates of pathogen spread},

author = {Tim Blokker and Guy Baele and Philippe Lemey and Simon Dellicour},

url = {https://academic.oup.com/ve/article/8/1/veac015/6530450},

doi = {https://doi.org/10.1093/ve/veac015},

issn = {2057-1577},

year  = {2022},

date = {2022-02-18},

urldate = {2022-02-18},

journal = {Virus Evolution},

volume = {8},

issue = {1},

abstract = {Genetic analyses of fast-evolving pathogens are frequently undertaken to test the impact of covariates on their dispersal. In particular, a popular approach consists of parameterizing a discrete phylogeographic model as a generalized linear model to identify and analyse the predictors of the dispersal rates of viral lineages among discrete locations. However, such a full probabilistic inference is often computationally demanding and time-consuming. In the face of the increasing amount of viral genomes sequenced in epidemic outbreaks, there is a need for a fast exploration of covariates that might be relevant to consider in formal analyses. We here present PhyCovA (short for ‘Phylogeographic Covariate Analysis’), a web-based application allowing users to rapidly explore the association between candidate covariates and the number of phylogenetically informed transition events among locations. Specifically, PhyCovA takes as input a phylogenetic tree with discrete state annotations at the internal nodes, or reconstructs those states if not available, to subsequently conduct univariate and multivariate linear regression analyses, as well as an exploratory variable selection analysis. In addition, the application can also be used to generate and explore various visualizations related to the regression analyses or to the phylogenetic tree annotated by the ancestral state reconstruction. PhyCovA is freely accessible at https://evolcompvir-kuleuven.shinyapps.io/PhyCovA/ and also distributed in a dockerized form obtainable from https://hub.docker.com/repository/docker/timblokker/phycova. The source code and tutorial are available from the GitHub repository https://github.com/TimBlokker/PhyCovA.},

note = {veac015},

keywords = {OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Model-based evaluation of alternative reactive class closure strategies against COVID-19.},

author = {Quan-Hui Liu and Juanjuan Zhang and Cheng Peng and Maria Litvinova and Shudong Huang and Piero Poletti and Filippo Trentini and Giorgio Guzzetta and Valentina Marziano and Tao Zhou and Cecile Viboud and Ana I. Bento and Jiancheng Lv and Alessandro Vespignani and Stefano Merler and Hongjie Yu and Marco Ajelli 

},

url = {https://www.nature.com/articles/s41467-021-27939-5#article-info},

doi = {10.1038/s41467-021-27939-5},

year  = {2022},

date = {2022-01-14},

urldate = {2022-01-14},

abstract = {There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, we developed a data-driven computational model of SARS-CoV-2 transmission. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 17.3% (95%CI: 8.0–26.8%), due to the low probability of timely identification of infections in the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Depending on the contribution of schools to transmission, this strategy can greatly reduce COVID-19 burden even when school contribution to transmission and immunity in the population is low. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to be rolled out.



},

keywords = {Covid-19 (Coronavirus), OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Epidemiology of West Nile virus in Africa: An underestimated threat},

author = {Giulia Mencattelli and Marie Henriette Dior Ndione and Roberto Rosà and Giovanni Marini and Cheikh Tidiane Diagne and Moise Moussa and Gamou Fall and Ousmane Faye and Mawlouth Diallo and Oumar Faye and Giovanni Savini and Annapaola Rizzoli},

url = {https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0010075#abstract0},

doi = {10.1371/journal.pntd.0010075},

year  = {2022},

date = {2022-01-10},

urldate = {2022-01-10},

journal = {PLOS Neglected Tropical Diseases},

volume = {16},

number = {1},

pages = {1-31},

abstract = {Background West Nile virus is a mosquito-borne flavivirus which has been posing continuous challenges to public health worldwide due to the identification of new lineages and clades and its ability to invade and establish in an increasing number of countries. Its current distribution, genetic variability, ecology, and epidemiological pattern in the African continent are only partially known despite the general consensus on the urgency to obtain such information for quantifying the actual disease burden in Africa other than to predict future threats at global scale.   Methodology and principal findings References were searched in PubMed and Google Scholar electronic databases on January 21, 2020, using selected keywords, without language and date restriction. Additional manual searches of reference list were carried out. Further references have been later added accordingly to experts’ opinion. We included 153 scientific papers published between 1940 and 2021. This review highlights: (i) the co-circulation of WNV-lineages 1, 2, and 8 in the African continent; (ii) the presence of diverse WNV competent vectors in Africa, mainly belonging to the Culex genus; (iii) the lack of vector competence studies for several other mosquito species found naturally infected with WNV in Africa; (iv) the need of more competence studies to be addressed on ticks; (iv) evidence of circulation of WNV among humans, animals and vectors in at least 28 Countries; (v) the lack of knowledge on the epidemiological situation of WNV for 19 Countries and (vii) the importance of carrying out specific serological surveys in order to avoid possible bias on WNV circulation in Africa.   Conclusions This study provides the state of art on WNV investigation carried out in Africa, highlighting several knowledge gaps regarding i) the current WNV distribution and genetic diversity, ii) its ecology and transmission chains including the role of different arthropods and vertebrate species as competent reservoirs, and iii) the real disease burden for humans and animals. This review highlights the needs for further research and coordinated surveillance efforts on WNV in Africa.},

keywords = {OpenDataSet, WNV (West Nile Virus)},

pubstate = {published},

tppubtype = {article}

}

@article{@article{10.1371/journal.pntd.0010019,,

title = {Mapping environmental suitability of Haemagogus and Sabethes spp. mosquitoes to understand sylvatic transmission risk of yellow fever virus in Brazil},

author = {Sabrina L. Li and André L. Acosta and Sarah C. Hill and Oliver J. Brady and Marco A.B.de Almeida and Jader da C. Cardoso and Arran Hamlet and Luis F. Mucci and Juliana Telles de Deus and Felipe C. Iani and Neil S. Alexander and William G. R. Wint and Oliver G. Pybus and Moritz Kraemer and Nuno R. Fariaand Jane P. Messina},

editor = {Public Library of Science},

url = {https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0010019},

doi = {https://doi.org/10.1371/journal.pntd.0010019},

year  = {2022},

date = {2022-01-07},

urldate = {2022-01-07},

journal = {PLOS Neglected Tropical Diseases},

volume = {16},

pages = {1-21},

abstract = {Background Yellow fever (YF) is an arboviral disease which is endemic to Brazil due to a sylvatic transmission cycle maintained by infected mosquito vectors, non-human primate (NHP) hosts, and humans. Despite the existence of an effective vaccine, recent sporadic YF epidemics have underscored concerns about sylvatic vector surveillance, as very little is known about their spatial distribution. Here, we model and map the environmental suitability of YF’s main vectors in Brazil, Haemagogus spp. and Sabethes spp., and use human population and NHP data to identify locations prone to transmission and spillover risk.   Methodology/Principal findings We compiled a comprehensive set of occurrence records on Hg. janthinomys, Hg. leucocelaenus, and Sabethes spp. from 1991–2019 using primary and secondary data sources. Linking these data with selected environmental and land-cover variables, we adopted a stacked regression ensemble modelling approach (elastic-net regularized GLM, extreme gradient boosted regression trees, and random forest) to predict the environmental suitability of these species across Brazil at a 1 km x 1 km resolution. We show that while suitability for each species varies spatially, high suitability for all species was predicted in the Southeastern region where recent outbreaks have occurred. By integrating data on NHP host reservoirs and human populations, our risk maps further highlight municipalities within the region that are prone to transmission and spillover.   Conclusions/Significance Our maps of sylvatic vector suitability can help elucidate potential locations of sylvatic reservoirs and be used as a tool to help mitigate risk of future YF outbreaks and assist in vector surveillance. Furthermore, at-risk regions identified from our work could help disease control and elucidate gaps in vaccination coverage and NHP host surveillance.},

keywords = {OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa},

author = {Raquel Viana and Sikhulile Moyo and Daniel G Amoako and Houriiyah Tegally and Cathrine Scheepers and Christian L Althaus and Ugochukwu J Anyaneji and Phillip A Bester and Maciej F Boni and Mohammed Chand and others},

url = {https://www.nature.com/articles/s41586-022-04411-y#citeas},

doi = {10.1038/s41586-022-04411-y},

year  = {2022},

date = {2022-01-07},

urldate = {2022-01-07},

journal = {Nature},

volume = {603},

number = {7902},

pages = {679-686},

abstract = {The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) variants, respectively1,2,3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron, B.1.1.529) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function4. Here we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.},

keywords = {Covid-19 (Coronavirus), OpenDataSet},

pubstate = {published},

tppubtype = {article}

}