MOOD publications

@article{@article{2022b,

title = {Two Years of Genomic Surveillance in Belgium during the SARS-CoV-2 Pandemic to Attain Country-Wide Coverage and Monitor the Introduction and Spread of Emerging Variants},

author = {Cuypers, Lize and Dellicour, Simon and Hong, Samuel L. and Potter, Barney I. and Verhasselt, Bruno and Vereecke, Nick and Lambrechts, Laurens and Durkin, Keith and Bours, Vincent and Klamer, Sofieke and Bayon-Vicente, Guillaume and Vael, Carl and Ariën, Kevin K. and De Mendonca, Ricardo and Soetens, Oriane and Michel, Charlotte and Bearzatto, Bertrand and Naesens, Reinout and Gras, Jeremie and Vankeerberghen, Anne and Matheeussen, Veerle and Martens, Geert and Obbels, Dagmar and Lemmens, Ann and Van den Poel, Bea and Van Even, Ellen and De Rauw, Klara and Waumans, Luc and Reynders, Marijke and Degosserie, Jonathan and Maes, Piet and André, Emmanuel and Baele, Guy},

url = {https://www.mdpi.com/1999-4915/14/10/2301},

doi = {https://doi.org/10.3390/v14102301},

issn = {1999-4915},

year  = {2022},

date = {2022-10-20},

urldate = {2022-10-20},

journal = {Viruses},

volume = {14},

number = {10},

pages = {2301},

abstract = {An adequate SARS-CoV-2 genomic surveillance strategy has proven to be essential for countries to obtain a thorough understanding of the variants and lineages being imported and successfully established within their borders. During 2020, genomic surveillance in Belgium was not structurally implemented but performed by individual research laboratories that had to acquire the necessary funds themselves to perform this important task. At the start of 2021, a nationwide genomic surveillance consortium was established in Belgium to markedly increase the country’s genomic sequencing efforts (both in terms of intensity and representativeness), to perform quality control among participating laboratories, and to enable coordination and collaboration of research projects and publications. We here discuss the genomic surveillance efforts in Belgium before and after the establishment of its genomic sequencing consortium, provide an overview of the specifics of the consortium, and explore more details regarding the scientific studies that have been published as a result of the increased number of Belgian SARS-CoV-2 genomes that have become available.},

keywords = {Belgium, COVID-19, genomic surveillance, next-generation sequencing, SARS-CoV-2, variants of concern},

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{@article{2022,

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

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

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, hospitalisation surveillance, risk ratio, SARS-CoV-2, vaccination impact},

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

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 = {Africa, COVID-19, epidemiology, genome, transmission},

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 = {COVID-19},

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

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

doi = {10.3390/v14030580},

issn = {1999-4915},

year  = {2022},

date = {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, genome, SARS-CoV-2, transmission},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {The effect of COVID-19 vaccination in Italy and perspectives for living with the virus},

author = {Marziano, Valentina; Guzzetta, Giorgio; Mammone, Alessia; Riccardo, Flavia; Poletti, Piero; Trentini, Filippo; Manica, Mattia; Siddu, Andrea; Bella, Antonino; Stefanelli, Paola; Pezzotti, Patrizio; Ajelli, Marco; Brusaferro, Silvio; Rezza, Giovanni; Merler, Stefano},

url = {https://www.nature.com/articles/s41467-021-27532-w#citeas},

doi = {10.1038/s41467-021-27532-w},

year  = {2021},

date = {2021-12-14},

urldate = {2021-12-14},

volume = {12},

number = {7272},

abstract = {COVID-19 vaccination is allowing a progressive release of restrictions worldwide. Using a mathematical model, we assess the impact of vaccination in Italy since December 27, 2020 and evaluate prospects for societal reopening after emergence of the Delta variant. We estimate that by June 30, 2021, COVID-19 vaccination allowed the resumption of about half of pre-pandemic social contacts. In absence of vaccination, the same number of cases is obtained by resuming only about one third of pre-pandemic contacts, with about 12,100 (95% CI: 6,600-21,000) extra deaths (+27%; 95% CI: 15–47%). Vaccination offset the effect of the Delta variant in summer 2021. The future epidemic trend is surrounded by substantial uncertainty. Should a pediatric vaccine (for ages 5 and older) be licensed and a coverage >90% be achieved in all age classes, a return to pre-pandemic society could be envisioned. Increasing vaccination coverage will allow further reopening even in absence of a pediatric vaccine.},

keywords = {COVID-19, vaccination},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Adherence and sustainability of interventions informing optimal control against the COVID-19 pandemic},

author = {Di Domenico, Laura; E. Sabbatini, Chiara; Boëlle, Pierre-Yves; Poletto, Chiara; Crépey, Pascal; Paireau, Juliette; Cauchemez, Simon; Beck, François; Noel, Harold ; Lévy-Bruhl, Daniel; Colizza, Vittoria},

url = {https://www.nature.com/articles/s43856-021-00057-5},

doi = {10.1038/s43856-021-00057-5},

year  = {2021},

date = {2021-12-06},

urldate = {2021-12-06},

journal = {Nature Communications},

volume = {1},

number = {57},

abstract = {After one year of stop-and-go COVID-19 mitigation, in the spring of 2021 European countries still experienced sustained viral circulation due to the Alpha variant. As the prospect of entering a new pandemic phase through vaccination was drawing closer, a key challenge remained on how to balance the efficacy of long-lasting interventions and their impact on the quality of life.

We show that moderate interventions would require a much longer time to achieve the same result as high intensity lockdowns, with the additional risk of deteriorating control as adherence wanes. Shorter strict lockdowns are largely more effective than longer moderate lockdowns, for similar intermediate distress and infringement on individual freedom.},

keywords = {COVID-19, OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{@article{CEREDA2021100528,,

title = {The early phase of the COVID-19 epidemic in Lombardy, Italy},

author = {Danilo Cereda and Mattia Manica and Marcello Tirani and Francesca Rovida and Vittorio Demicheli and Marco Ajelli and Piero Poletti and Filippo Trentini and Giorgio Guzzetta and Valentina Marziano and Raffaella Piccarreta and Antonio Barone and Michele Magoni and Silvia Deandrea and Giulio Diurno and Massimo Lombardo and Marino Faccini and Angelo Pan and Raffaele Bruno and Elena Pariani and Giacomo Grasselli and Alessandra Piatti and Maria Gramegna and Fausto Baldanti and Alessia Melegaro and Stefano Merler},

url = {https://www.sciencedirect.com/science/article/pii/S1755436521000724},

doi = {https://doi.org/10.1016/j.epidem.2021.100528},

issn = {1755-4365},

year  = {2021},

date = {2021-12-01},

urldate = {2021-12-01},

journal = {Epidemics},

volume = {37},

pages = {100528},

abstract = {Background

In the night of February 20, 2020, the first epidemic of the novel coronavirus disease (COVID-19) outside Asia was uncovered by the identification of its first patient in Lombardy region, Italy. In the following weeks, Lombardy experienced a sudden increase in the number of ascertained infections and strict measures were imposed to contain the epidemic spread.

Methods

We analyzed official records of cases occurred in Lombardy to characterize the epidemiology of SARS-CoV-2 during the early phase of the outbreak. A line list of laboratory-confirmed cases was set up and later retrospectively consolidated, using standardized interviews to ascertained cases and their close contacts. We provide estimates of the serial interval, of the basic reproduction number, and of the temporal variation of the net reproduction number of SARS-CoV-2.

Results

Epidemiological investigations detected over 500 cases (median age: 69, IQR: 57–78) before the first COVID-19 diagnosed patient (February 20, 2020), and suggested that SARS-CoV-2 was already circulating in at least 222 out of 1506 (14.7%) municipalities with sustained transmission across all the Lombardy provinces. We estimated the mean serial interval to be 6.6 days (95% CrI, 0.7–19). Our estimates of the basic reproduction number range from 2.6 in Pavia (95% CI, 2.1–3.2) to 3.3 in Milan (95% CI, 2.9–3.8). A decreasing trend in the net reproduction number was observed following the detection of the first case.

Conclusions

At the time of first case notification, COVID-19 was already widespread in the entire Lombardy region. This may explain the large number of critical cases experienced by this region in a very short timeframe. The slight decrease of the reproduction number observed in the early days after February 20, 2020 might be due to increased population awareness and early interventions implemented before the regional lockdown imposed on March 8, 2020.},

keywords = {Coronavirus, COVID-19, epidemiology, Lombardy outbreak, reproduction number, SARS-CoV-2, Transmission dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{kraemer2021monitoring,

title = {Monitoring key epidemiological parameters of SARS-CoV-2 transmission},

author = {Moritz U.G. Kraemer and Oliver G. Pybusand Fraser and Christophe Cauchemez and Simon Rambaut and Andrew Cowling and J. Benjamin},

doi = {https://doi.org/10.1038/s41591-021-01545-w},

year  = {2021},

date = {2021-11-08},

urldate = {2021-11-08},

journal = {Nature Medicine},

volume = {27},

number = {1854–1855 },

pages = {1--2},

abstract = {Control of the SARS-CoV-2 pandemic requires targeted interventions, which in turn require precise estimates of quantities that describe transmission. Per-capita transmission rates are influenced by four quantities: (1) the latent period (time from infection to becoming infectious); (2) individual variability in infectiousness (defined by variation in intrinsic transmissibility and contact rate); (3) the incubation period (time from infection to symptom onset); and (4) the serial interval (time between symptom onset of an infector and an infected) (Fig. 1).},

keywords = {COVID-19, epidemiology, transmission},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Spatial opinion mining from COVID-19 twitter data},

author = {Syed, Mehtab Alam and Decoupes, Rémy and Arsevska, Elena and Roche, Mathieu and Teisseire, Maguelonne},

url = {https://www.ijidonline.com/article/S1201-9712(21)00957-7/pdf},

doi = {https://doi.org/10.1016/j.ijid.2021.12.065},

year  = {2021},

date = {2021-11-06},

journal = {International Journal of Infectious Diseases},

volume = {116},

issue = {549},

pages = {527},

abstract = {: In the first quarter of 2020, World Health Organization (WHO) declared COVID-19 as a public health emergency around the globe. Therefore, different users from all over the world shared their thoughts about COVID-19 on social media platforms i.e., Twitter, Facebook etc. So, it is important to analyze public opinions about COVID-19 from different regions over different period of time. To fulfill the spatial analysis issue, a previous work called H-TF-IDF (Hierarchy-based measure for tweet analysis) for term extraction from tweet data has been proposed. In this work, we focus on the sentiment analysis performed on terms selected by H-TFIDF for spatial tweets groups to know local situations during the ongoing epidemic COVID-19 over different time frames.},

keywords = {COVID-19, public opinion, social networks, Text mining},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil},

author = {Nuno R. Faria and Thomas A. Mellan and Charles Whittaker and Ingra M. Claro and Darlan da S. Candido and Swapnil Mishra and Myuki A. E. Crispim},

url = {https://www.science.org/doi/abs/10.1126/science.abh2644},

doi = {10.1126/science.abh2644},

year  = {2021},

date = {2021-11-01},

urldate = {2021-11-01},

journal = {Science},

volume = {372},

number = {6544},

pages = {815-821},

abstract = {Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Manaus, Brazil, resurged in late 2020 despite previously high levels of infection. Genome sequencing of viruses sampled in Manaus between November 2020 and January 2021 revealed the emergence and circulation of a novel SARS-CoV-2 variant of concern. Lineage P.1 acquired 17 mutations, including a trio in the spike protein (K417T, E484K, and N501Y) associated with increased binding to the human ACE2 (angiotensin-converting enzyme 2) receptor. Molecular clock analysis shows that P.1 emergence occurred around mid-November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.7- to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.1 that it provides against non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness.},

keywords = {Brazil, COVID-19, epidemiology, OpenDataSet, phylogenetic},

pubstate = {published},

tppubtype = {article}

}

@article{@article{10.1093/cid/ciab836,,

title = {Risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission Among Air Passengers in China },

author = {Hu, Maogui and Wang, Jinfeng and Lin, Hui and Ruktanonchai, Corrine W and Xu, Chengdong and Meng, Bin and Zhang, Xin and Carioli, Alessandra and Feng, Yuqing and Yin, Qian and Floyd, Jessica R and Ruktanonchai, Nick W and Li, Zhongjie and Yang, Weizhong and Tatem, Andrew J and Lai, Shengjie},

url = {https://academic.oup.com/cid/article/75/1/e234/6373518},

doi = {https://doi.org/10.1093/cid/ciab836},

issn = {1058-4838},

year  = {2021},

date = {2021-09-21},

journal = {Clinical Infectious Diseases},

volume = {75},

issue = {1},

pages = {e234–e240},

abstract = {Modern transportation plays a key role in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and new variants. However, little is known about the exact transmission risk of the virus on airplanes.Using the itinerary and epidemiological data of coronavirus disease 2019 (COVID-19) cases and close contacts on domestic airplanes departing from Wuhan city in China before the lockdown on 23 January 2020, we estimated the upper and lower bounds of overall transmission risk of COVID-19 among travelers.In total, 175 index cases were identified among 5797 passengers on 177 airplanes. The upper and lower attack rates (ARs) of a seat were 0.60\% (34/5622, 95\% confidence interval [CI] .43–.84\%) and 0.33\% (18/5400, 95\% CI .21–.53\%), respectively. In the upper- and lower-bound risk estimates, each index case infected 0.19 (SD 0.45) and 0.10 (SD 0.32) cases, respectively. The seats immediately adjacent to the index cases had an AR of 9.2\% (95\% CI 5.7–14.4\%), with a relative risk 27.8 (95\% CI 14.4–53.7) compared to other seats in the upper limit estimation. The middle seat had the highest AR (0.7\%, 95\% CI .4\%–1.2\%). The upper-bound AR increased from 0.7\% (95\% CI 0.5\%–1.0\%) to 1.2\% (95\% CI .4–3.3\%) when the co-travel time increased from 2.0 hours to 3.3 hours.The ARs among travelers varied by seat distance from the index case and joint travel time, but the variation was not significant between the types of aircraft. The overall risk of SARS-CoV-2 transmission during domestic travel on planes was relatively low. These findings can improve our understanding of COVID-19 spread during travel and inform response efforts in the pandemic.},

keywords = {airplanes, attack rate, COVID-19, SARS-CoV-2, transmission},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Risk of Symptomatic Infection During a Second Coronavirus Disease 2019 Wave in Severe Acute Respiratory Syndrome Coronavirus 2–Seropositive Individuals },

author = {Manica, Mattia and Pancheri, Serena and Poletti, Piero and Giovanazzi, Giulia and Guzzetta, Giorgio and Trentini, Filippo and Marziano, Valentina and Ajelli, Marco and Grazia Zuccali, Maria and Benetollo, Pier Paolo and Merler, Stefano and Ferro, Antonio},

url = {https://academic.oup.com/cid/article/74/5/893/6301134},

doi = {10.1093/cid/ciab556},

issn = {1058-4838},

year  = {2021},

date = {2021-06-16},

urldate = {2021-06-16},

journal = {Clinical Infectious Diseases},

volume = {74},

number = {5},

pages = {893-896},

abstract = {We analyzed 221 coronavirus disease 2019 cases identified between June 2020 and January 2021 in 6074 individuals screened for immunoglobulin G antibodies in May 2020, representing 77% of residents of 5 Italian municipalities. The relative risk of developing symptomatic infection in seropositive participants was 0.055 (95% confidence interval, .014–.220)},

keywords = {COVID-19, naturak immunity, reinfection risk, SARS-CoV-2, serological screening},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence—Belgium as a study case},

author = {Simon Dellicour, Catherine Linard, Nina Van Goethem, Daniele Da Re, Jean Artois, Jérémie Bihin, Pierre Schaus, François Massonnet, Herman Van Oyen, Sophie O. Vanwambeke, Niko Speybroeck, Marius Gilbert },

url = {https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-021-00281-1},

doi = {https://doi.org/10.1186/s12942-021-00281-1},

issn = {1476-072X},

year  = {2021},

date = {2021-06-14},

urldate = {2021-06-14},

journal = {International Journal of Health Geographics},

number = {29},

issue = {20},

abstract = {Background

The COVID-19 pandemic is affecting nations globally, but with an impact exhibiting significant spatial and temporal variation at the sub-national level. Identifying and disentangling the drivers of resulting hospitalisation incidence at the local scale is key to predict, mitigate and manage epidemic surges, but also to develop targeted measures. However, this type of analysis is often not possible because of the lack of spatially-explicit health data and spatial uncertainties associated with infection.



Methods

To overcome these limitations, we propose an analytical framework to investigate potential drivers of the spatio–temporal heterogeneity in COVID-19 hospitalisation incidence when data are only available at the hospital level. Specifically, the approach is based on the delimitation of hospital catchment areas, which allows analysing associations between hospitalisation incidence and spatial or temporal covariates. We illustrate and apply our analytical framework to Belgium, a country heavily impacted by two COVID-19 epidemic waves in 2020, both in terms of mortality and hospitalisation incidence.



Results

Our spatial analyses reveal an association between the hospitalisation incidence and the local density of nursing home residents, which confirms the important impact of COVID-19 in elderly communities of Belgium. Our temporal analyses further indicate a pronounced seasonality in hospitalisation incidence associated with the seasonality of weather variables. Taking advantage of these associations, we discuss the feasibility of predictive models based on machine learning to predict future hospitalisation incidence.



Conclusion

Our reproducible analytical workflow allows performing spatially-explicit analyses of data aggregated at the hospital level and can be used to explore potential drivers and dynamic of COVID-19 hospitalisation incidence at regional or national scales.},

keywords = {Belgium, Boosted regression trees, COVID-19, Hospitalisation incidence, Spatial covariates, Temporal covariates},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {H-TFIDF: What makes areas specific over time in the massive flow of tweets related to the covid pandemic?},

author = {Rémy Decoupes and Kafando Rodrique and Mathieu Roche and Maguelonne Teisseire },

doi = {10.5194/agile-giss-2-2-2021},

year  = {2021},

date = {2021-06-01},

urldate = {2020-01-01},

journal = {AGILE: GIScience Series},

volume = {2},

number = {4},

pages = {1-8},

publisher = {European Centre for Disease Prevention and Control},

abstract = {Data produced by social networks may contain weak signals of possible epidemic outbreaks. In this paper, we focus on Twitter data during the waiting period before the appearance of COVID-19 first cases outside China. Among the huge flow of tweets that reflects a global growing concern in all countries, we propose to analyze such data with an adaptation of the TF-IDF measure. It allows the users to extract the discriminant vocabularies used across time and space. The results are then discussed to show how the specific spatio-temporal anchoring of the extracted terms make it possible to follow the crisis dynamics on different scales of time and space.},

keywords = {COVID-19, EBS, media extraction, Model, population dynamics, Public Health, Twitter},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {Impact of January 2021 curfew measures on SARS-CoV-2 B.1.1.7 circulation in France},

author = {Laura Di Domenico and Chiara E. Sabbatini and Giulia Pullano and Daniel Lévy-Bruhl and Vittoria Colizza},

url = {https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2021.26.15.2100272},

doi = { 10.2807/1560-7917.ES.2021.26.15.2100272},

year  = {2021},

date = {2021-04-15},

urldate = {2021-03-10},

journal = {Eurosurveillance},

volume = {26},

number = {2},

issue = {15},

publisher = {Cold Spring Harbor Laboratory Press},

abstract = {Facing B.1.1.7 variant, social distancing was strengthened in France in January 2021. Using a 2-strain mathematical model calibrated on genomic surveillance, we estimated that curfew measures allowed hospitalizations to plateau, by decreasing transmission of the historical strain while B.1.1.7 continued to grow. School holidays appear to have further slowed down progression in February. Without progressively strengthened social distancing, a rapid surge of hospitalizations is expected, despite the foreseen increase in vaccination rhythm.},

keywords = {COVID-19, epidemiology, Europe, Public Health},

pubstate = {published},

tppubtype = {article}

}