MOOD publications

@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{nokey,

title = {Estimation of the incubation period and generation time of SARS-CoV-2 Alpha and Delta variants from contact tracing data},

author = {Mattia Manica and Maria Litvinova and Alfredo De Bellis and Giorgio Guzzetta and Pamela Mancuso and Massimo Vicentini and Francesco Venturelli and Eufemia Bisaccia and Ana I. Bento and Piero Poletti and Valentina Marziano and Agnese Zardini and Valeria d'Andrea and Filippo Trentini and Antonino Bella and Flavia Riccardo and Patrizio Pezzotti and Marco Ajelli and Paolo Giorgi Rossi and Stefano Merler and the Reggio Emilia COVID-19 Working Group},

url = {https://arxiv.org/abs/2203.07063},

doi = {https://doi.org/10.48550/arXiv.2203.07063},

year  = {2022},

date = {2022-03-11},

urldate = {2022-03-11},

journal = {arXiv},

abstract = {Background. During 2021, the COVID-19 pandemic was characterized by the emergence of lineages with increased fitness. For most of these variants, quantitative information is scarce on epidemiological quantities such as the incubation period and generation time, which are critical for both public health decisions and scientific research.  Method. We analyzed a dataset collected during contact tracing activities in the province of Reggio Emilia, Italy, throughout 2021. We determined the distributions of the incubation period using information on negative PCR tests and the date of last exposure from 282 symptomatic cases. We estimated the distributions of the intrinsic generation time (the time between the infection dates of an infector and its secondary cases under a fully susceptible population) using a Bayesian inference approach applied to 4,435 SARS-CoV-2 cases clustered in 1,430 households where at least one secondary case was recorded. Results. We estimated a mean incubation period of 4.9 days (95% credible intervals, CrI, 4.4-5.4; 95 percentile of the mean distribution: 1-12) for Alpha and 4.5 days (95%CrI 4.0-5.0; 95 percentile: 1-10) for Delta. The intrinsic generation time was estimated to have a mean of 6.0 days (95% CrI 5.6-6.4; 95 percentile: 1-15) for Alpha and of 6.6 days (95%CrI 6.0-7.3; 95 percentile: 1-18) for Delta. The household serial interval was 2.6 days (95%CrI 2.4-2.7) for Alpha and 2.4 days (95%CrI 2.2-2.6) for Delta, and the estimated proportion of pre-symptomatic transmission was 54-55% for both variants. Conclusions. These results indicate limited differences in the incubation period and intrinsic generation time of SARS-CoV-2 variants Alpha and Delta compared to ancestral lineages. Funding. EU grant 874850 MOOD },

keywords = {Covid-19 (Coronavirus)},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {West Nile Virus in Africa: current epidemiological situation and knowledge gaps},

author = {Giulia Mencattelli et al.},

url = {https://www.sciencedirect.com/science/article/pii/S1201971221011838},

doi = {10.1016/j.ijid.2021.12.291},

year  = {2022},

date = {2022-03-08},

urldate = {2022-03-08},

abstract = {West Nile virus (WNV) is an arthropod-borne zoonotic pathogen which represents a continuous source of concern for public health worldwide due to its expansion and invasion into new regions. Its distribution and circulation intensity in African countries is only partially known. The aim of the present study is to provide an updated overview on the current knowledge of WNV epidemiology in Africa, providing available data on incidence in humans and animals, the circulating lineages and clades, other than an updated list of the principal arthropod vectors identified and the availability of vector competence studies.



},

keywords = {WNV (West Nile Virus)},

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 = {Co-circulation of SARS-CoV-2 Alpha and Gamma variants in Italy, February and March 2021},

author = {Paola Stefanelli et al. and COVID-19 National Microbiology Surveillance Study Group},

url = {https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2022.27.5.2100429},

doi = {10.2807/1560-7917.ES.2022.27.5.2100429},

year  = {2022},

date = {2022-02-03},

urldate = {2022-02-03},

journal = {Eurosurveillance},

volume = {27},

number = {5},

pages = {2100429},

abstract = {Background

Several SARS-CoV-2 variants of concern (VOC) have emerged through 2020 and 2021. There is need for tools to estimate the relative transmissibility of emerging variants of SARS-CoV-2 with respect to circulating strains.



Aim

We aimed to assess the prevalence of co-circulating VOC in Italy and estimate their relative transmissibility.



Methods

We conducted two genomic surveillance surveys on 18 February and 18 March 2021 across the whole Italian territory covering 3,243 clinical samples and developed a mathematical model that describes the dynamics of co-circulating strains.



Results

The Alpha variant was already dominant on 18 February in a majority of regions/autonomous provinces (national prevalence: 54%) and almost completely replaced historical lineages by 18 March (dominant across Italy, national prevalence: 86%). We found a substantial proportion of the Gamma variant on 18 February, almost exclusively in central Italy (prevalence: 19%), which remained similar on 18 March. Nationally, the mean relative transmissibility of Alpha ranged at 1.55–1.57 times the level of historical lineages (95% CrI: 1.45–1.66). The relative transmissibility of Gamma varied according to the assumed degree of cross-protection from infection with other lineages and ranged from 1.12 (95% CrI: 1.03–1.23) with complete immune evasion to 1.39 (95% CrI: 1.26–1.56) for complete cross-protection.



Conclusion

We assessed the relative advantage of competing viral strains, using a mathematical model assuming different degrees of cross-protection. We found substantial co-circulation of Alpha and Gamma in Italy. Gamma was not able to outcompete Alpha, probably because of its lower transmissibility.},

keywords = {Covid-19 (Coronavirus)},

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}

}

@article{nokey,

title = {Considerable escape of SARS-CoV-2 Omicron to antibody neutralization},

author = {Delphine Planas and Nell Saunders and Piet Maes and Florence Guivel-Benhassine and Cyril Planchais and Julian Buchrieser and William-Henry Bolland and Françoise Porrot and Isabelle Staropoli and Frederic Lemoine and Hélène Péré and David Veyer and Julien Puech and Julien Rodary and Guy Baele and Simon Dellicour and Joren Raymenants and Sarah Gorissen and Caspar Geenen and Bert Vanmechelen and Tony Wawina-Bokalanga and Joan Martí-Carreras and Lize Cuypers and Aymeric Sève and Laurent Hocqueloux and Thierry Prazuck and Félix A. Rey and Etienne Simon-Loriere and Timothée Bruel and Hugo Mouquet and Emmanuel André and Olivier Schwart },

url = {https://www.nature.com/articles/s41586-021-04389-z#citeas},

doi = {https://doi.org/10.1038/s41586-021-04389-z},

year  = {2021},

date = {2021-12-23},

urldate = {2021-12-23},

journal = {Nature},

volume = {602},

pages = {671–675},

abstract = {The SARS-CoV-2 Omicron variant was first identified in November 2021 in Botswana and South Africa1,2,3. It has since spread to many countries and is expected to rapidly become dominant worldwide. The lineage is characterized by the presence of around 32 mutations in spike—located mostly in the N-terminal domain and the receptor-binding domain—that may enhance viral fitness and enable antibody evasion. Here we isolated an infectious Omicron virus in Belgium from a traveller returning from Egypt. We examined its sensitivity to nine monoclonal antibodies that have been clinically approved or are in development4, and to antibodies present in 115 serum samples from COVID-19 vaccine recipients or individuals who have recovered from COVID-19. Omicron was completely or partially resistant to neutralization by all monoclonal antibodies tested. Sera from recipients of the Pfizer or AstraZeneca vaccine, sampled five months after complete vaccination, barely inhibited Omicron. Sera from COVID-19-convalescent patients collected 6 or 12 months after symptoms displayed low or no neutralizing activity against Omicron. Administration of a booster Pfizer dose as well as vaccination of previously infected individuals generated an anti-Omicron neutralizing response, with titres 6-fold to 23-fold lower against Omicron compared with those against Delta. Thus, Omicron escapes most therapeutic monoclonal antibodies and, to a large extent, vaccine-elicited antibodies. However, Omicron is neutralized by antibodies generated by a booster vaccine dose.},

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

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

author = {Valentina Marziano and al.},

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},

journal = {Nature Communications},

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 (Coronavirus), OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

author = {Laura Di Domenico et al.},

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

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 (Coronavirus)},

pubstate = {published},

tppubtype = {article}

}