MOOD publications

@article{10.1093/aje/kwab252,

title = {Pressure on the Health-Care System and Intensive Care Utilization During the COVID-19 Outbreak in the Lombardy Region of Italy: A Retrospective Observational Study in 43,538 Hospitalized Patients},

author = {Filippo Trentini and Valentina Marziano and Giorgio Guzzetta and Marcello Tirani and Danilo Cereda and Piero Poletti and Raffaella Piccarreta and Antonio Barone and Giuseppe Preziosi and Fabio Arduini and Petra Giulia Della Valle and Alberto Zanella and Francesca Grosso and Gabriele Castillo and Ambra Castrofino and Giacomo Grasselli and Alessia Melegaro and Alessandra Piatti and Aida Andreassi and Maria Gramegna and Marco Ajelli and Stefano Merler},

url = {https://doi.org/10.1093/aje/kwab252},

doi = {10.1093/aje/kwab252},

issn = {0002-9262},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {American Journal of Epidemiology},

abstract = {During the spring of 2020, the COVID-19 epidemic caused an unprecedented demand for intensive care resources in Lombardy, Italy. Using data on 43,538 hospitalized patients admitted between February 21 and July 12, 2020, we evaluated variations in intensive care unit (ICU) admissions and mortality over three periods: the early phase (February 20-March 13), the period of highest pressure on healthcare (March 14-April 25, when COVID-19 patients exceeded the ICU pre-pandemic bed capacity), and the declining phase (April 26-July 12).Compared to the early phase, patients above 70 years of age were admitted less often to an ICU during highest pressure on healthcare (odds ratio OR 0.47, 95%CI: 0.41-0.54) with longer delays (incidence rate ratio IRR 1.82, 95%CI: 1.52-2.18), and lower chances of death in ICU (OR 0.47, 95%CI: 0.34-0.64). Patients under 56 years of age reported more limited changes in the probability (OR 0.65, 95%CI: 0.56-0.76) and delay to ICU admission (IRR 1.16, 95%CI: 0.95-1.42) and an increased mortality (OR 1.43, 95%CI: 1.00-2.07). In the declining phase, all quantities decreased for all age groups.These patterns may suggest that limited healthcare resources during the peak epidemic phase in Lombardy forced a shift in ICU admission criteria to prioritize patients with higher chances of survival.},

note = {kwab252},

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

pubstate = {published},

tppubtype = {article}

}

@article{ingelbeen2021reducing,

title = {Reducing contacts to stop SARS-CoV-2 transmission during the second pandemic wave in Brussels, Belgium, August to November 2020},

author = {Brecht Ingelbeen and Laur`ene Peckeu and Marie Laga and Ilona Hendrix and Inge Neven and Marianne AB Sande and Esther Kleef},

doi = {https://doi.org/10.1371/journal.pbio.3001115},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Eurosurveillance},

volume = {26},

number = {7},

pages = {2100065},

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

abstract = {To evaluate the effect of physical distancing and school reopening in Brussels between August and November 2020, we monitored changes in the number of reported contacts per SARS-CoV-2 case and associated SARS-CoV-2 transmission. The second COVID-19 pandemic wave in Brussels was the result of increased social contact across all ages following school reopening. Physical distancing measures including closure of bars and restaurants, and limiting close contacts, while primary and secondary schools remained open, reduced social mixing and controlled SARS-CoV-2 transmission.},

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

pubstate = {published},

tppubtype = {article}

}

@article{MATEOURDIALES2021102642,

title = {Socioeconomic patterns and COVID-19 outcomes before, during and after the lockdown in Italy (2020)},

author = {Alberto Mateo-Urdiales and Massimo Fabiani and Aldo Rosano and Maria Fenicia Vescio and Martina Del Manso and Antonino Bella and Flavia Riccardo and Patrizio Pezzotti and Enrique Regidor and Xanthi Andrianou},

url = {https://www.sciencedirect.com/science/article/pii/S1353829221001386},

doi = {https://doi.org/10.1016/j.healthplace.2021.102642},

issn = {1353-8292},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Health & Place},

volume = {71},

pages = {102642},

abstract = {The objective was to investigate the association between deprivation and COVID-19 outcomes in Italy during pre-lockdown, lockdown and post-lockdown periods using a retrospective cohort study with 38,534,169 citizens and 222,875 COVID-19 cases. Multilevel negative binomial regression models, adjusting for age, sex, population-density and region of residence were conducted to evaluate the association between area-level deprivation and COVID-19 incidence, case-hospitalisation rate and case-fatality. During lockdown and post-lockdown, but not during pre-lockdown, higher incidence of cases was observed in the most deprived municipalities compared with the least deprived ones. No differences in case-hospitalisation and case-fatality according to deprivation were observed in any period under study.},

keywords = {Covid-19 (Coronavirus)},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1126/science.abj0113,

title = {Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence},

author = {MUG Kraemer and V Hill and C Ruis and S Dellicour and S Bajaj and JT McCrone and G Baele and KV Parag and AL Battle and B Gutierrez and B Jackson and R Colquhoun and A O'Toole and B Klein and A Vespignani and COVID-19 Genomics UK (COG-UK) Consortium and E Volz and NR Faria and DM Aanensen and NJ Loman NJ and L du Plessis and S Cauchemez and A Rambaut A and SV Scarpino SV and OG Pybus },

url = {https://www.science.org/doi/abs/10.1126/science.abj0113},

doi = {10.1126/science.abj0113},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Science},

volume = {373},

number = {6557},

pages = {889-895},

abstract = {The B.1.1.7 lineage of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused fast-spreading outbreaks globally. Intrinsically, this variant has greater transmissibility than its predecessors, but this capacity has been amplified in some circumstances to tragic effect by a combination of human behavior and local immunity. What are the extrinsic factors that help or hinder the rapid dissemination of variants? Kraemer et al. explored the invasion dynamics of B.1.1.7. in fine detail, from its location of origin in Kent, UK, to its heterogenous spread around the country. A combination of mobile phone and virus data including more than 17,000 genomes shows how distinct phases of dispersal were related to intensity of mobility and the timing of lockdowns. As the local outbreaks grew, importation from the London source area became less important. Had B.1.1.7. emerged at a slightly different time of year, its impact might have been different. Disentangling the factors that contribute to the rapid spread of virus variants is essential for understanding their epidemiological consequences. Understanding the causes and consequences of the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is crucial to pandemic control yet difficult to achieve because they arise in the context of variable human behavior and immunity. We investigated the spatial invasion dynamics of lineage B.1.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based polymerase chain reaction data. We identified a multistage spatial invasion process in which early B.1.1.7 growth rates were associated with mobility and asymmetric lineage export from a dominant source location, enhancing the effects of B.1.1.7's increased intrinsic transmissibility. We further explored how B.1.1.7 spread was shaped by nonpharmaceutical interventions and spatial variation in previous attack rates. Our findings show that careful accounting of the behavioral and epidemiological context within which variants of concern emerge is necessary to interpret correctly their observed relative growth rates.},

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

pubstate = {published},

tppubtype = {article}

}

@article{pullano2021underdetection,

title = {Underdetection of cases of COVID-19 in France threatens epidemic control},

author = {Giulia Pullano and Laura Di Domenico and Chiara E Sabbatini and Eugenio Valdano and Clément Turbelin and Marion Debin and Caroline Guerrisi and Charly Kengne-Kuetche and Cécile Souty and Thomas Hanslik and others},

doi = { https://doi.org/10.1038/s41586-020-03095-6},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Nature},

volume = {590},

number = {7844},

pages = {134--139},

publisher = {Nature Publishing Group},

abstract = {As countries in Europe gradually relaxed lockdown restrictions after the first wave, test–trace–isolate strategies became critical to maintain the incidence of coronavirus disease 2019 (COVID-19) at low levels1,2. Reviewing their shortcomings can provide elements to consider in light of the second wave that is currently underway in Europe. Here we estimate the rate of detection of symptomatic cases of COVID-19 in France after lockdown through the use of virological3 and participatory syndromic4 surveillance data coupled with mathematical transmission models calibrated to regional hospitalizations2. Our findings indicate that around 90,000 symptomatic infections, corresponding to 9 out 10 cases, were not ascertained by the surveillance system in the first 7 weeks after lockdown from 11 May to 28 June 2020, although the test positivity rate did not exceed the 5% recommendation of the World Health Organization (WHO)5. The median detection rate increased from 7% (95% confidence interval, 6–8%) to 38% (35–44%) over time, with large regional variations, owing to a strengthening of the system as well as a decrease in epidemic activity. According to participatory surveillance data, only 31% of individuals with COVID-19-like symptoms consulted a doctor in the study period. This suggests that large numbers of symptomatic cases of COVID-19 did not seek medical advice despite recommendations, as confirmed by serological studies6,7. Encouraging awareness and same-day healthcare-seeking behaviour of suspected cases of COVID-19 is critical to improve detection. However, the capacity of the system remained insufficient even at the low epidemic activity achieved after lockdown, and was predicted to deteriorate rapidly with increasing incidence of COVID-19 cases. Substantially more aggressive, targeted and efficient testing with easier access is required to act as a tool to control the COVID-19 pandemic. The testing strategy will be critical to enable partial lifting of the current restrictive measures in Europe and to avoid a third wave.},

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

pubstate = {published},

tppubtype = {article}

}

@article{lemey2021untangling,

title = {Untangling introductions and persistence in COVID-19 resurgence in Europe},

author = {Philippe Lemey and Nick Ruktanonchai and Samuel L Hong and Vittoria Colizza and Chiara Poletto and Frederik Van den Broeck and Mandev S Gill and Xiang Ji and Anthony Levasseur and Bas B Oude Munnink and others},

doi = {https://doi.org/10.1038/s41586-021-03754-2},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Nature},

volume = {595},

number = {7869},

pages = {713--717},

publisher = {Nature Publishing Group},

abstract = {After the first wave of SARS-CoV-2 infections in spring 2020, Europe experienced a resurgence of the virus starting in late summer 2020 that was deadlier and more difficult to contain1. Relaxed intervention measures and summer travel have been implicated as drivers of the second wave2. Here we build a phylogeographical model to evaluate how newly introduced lineages, as opposed to the rekindling of persistent lineages, contributed to the resurgence of COVID-19 in Europe. We inform this model using genomic, mobility and epidemiological data from 10 European countries and estimate that in many countries more than half of the lineages circulating in late summer resulted from new introductions since 15 June 2020. The success in onward transmission of newly introduced lineages was negatively associated with the local incidence of COVID-19 during this period. The pervasive spread of variants in summer 2020 highlights the threat of viral dissemination when restrictions are lifted, and this needs to be carefully considered in strategies to control the current spread of variants that are more transmissible and/or evade immunity. Our findings indicate that more effective and coordinated measures are required to contain the spread through cross-border travel even as vaccination is reducing disease burden.},

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

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2},

author = {Philippe Lemey and Samuel L Hong and Verity Hill and Guy Baele and Chiara Poletto and Vittoria Colizza and Áine O’Toole and John T. McCrone and Kristian G. Andersen and Michael Worobey and Martha I. Nelson and Andrew Rambaut and Marc A. Suchard },

doi = {https://doi.org/10.1038/s41467-020-18877-9},

year  = {2020},

date = {2020-10-09},

urldate = {2020-10-09},

journal = {Nature Communications},

volume = {11},

number = {5110},

abstract = {Spatiotemporal bias in genome sampling can severely confound discrete trait phylogeographic inference. This has impeded our ability to accurately track the spread of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, despite the availability of unprecedented numbers of SARS-CoV-2 genomes. Here, we present an approach to integrate individual travel history data in Bayesian phylogeographic inference and apply it to the early spread of SARS-CoV-2. We demonstrate that including travel history data yields i) more realistic hypotheses of virus spread and ii) higher posterior predictive accuracy compared to including only sampling location. We further explore methods to ameliorate the impact of sampling bias by augmenting the phylogeographic analysis with lineages from undersampled locations. Our reconstructions reinforce specific transmission hypotheses suggested by the inclusion of travel history data, but also suggest alternative routes of virus migration that are plausible within the epidemiological context but are not apparent with current sampling efforts.},

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

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {The emergence of SARS-CoV-2 in Europe and North America},

url = {https://www.science.org/doi/10.1126/science.abc8169},

doi = {https://doi.org/10.1126/science.abc8169},

year  = {2020},

date = {2020-09-10},

urldate = {2020-09-10},

journal = {Science},

volume = {370},

number = {6516},

pages = {564-570},

abstract = {Accurate understanding of the global spread of emerging viruses is critical for public health responses and for anticipating and preventing future outbreaks. Here we elucidate when, where, and how the earliest sustained severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission networks became established in Europe and North America. Our results suggest that rapid early interventions successfully prevented early introductions of the virus from taking hold in Germany and the United States. Other, later introductions of the virus from China to both Italy and Washington state, United States, founded the earliest sustained European and North America transmission networks. Our analyses demonstrate the effectiveness of public health measures in preventing onward transmission and show that intensive testing and contact tracing could have prevented SARS-CoV-2 outbreaks from becoming established in these regions.},

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

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies},

author = {Laura Di Domenico et al.},

url = {https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01698-4#article-info},

doi = {10.1186/s12916-020-01698-4},

year  = {2020},

date = {2020-07-30},

urldate = {2020-07-30},

abstract = {Background

More than half of the global population is under strict forms of social distancing. Estimating the expected impact of lockdown and exit strategies is critical to inform decision makers on the management of the COVID-19 health crisis.



Methods

We use a stochastic age-structured transmission model integrating data on age profile and social contacts in Île-de-France to (i) assess the epidemic in the region, (ii) evaluate the impact of lockdown, and (iii) propose possible exit strategies and estimate their effectiveness. The model is calibrated to hospital admission data before lockdown. Interventions are modeled by reconstructing the associated changes in the contact matrices and informed by mobility reductions during lockdown evaluated from mobile phone data. Different types and durations of social distancing are simulated, including progressive and targeted strategies, with large-scale testing.



Results

We estimate the reproductive number at 3.18 [3.09, 3.24] (95% confidence interval) prior to lockdown and at 0.68 [0.66, 0.69] during lockdown, thanks to an 81% reduction of the average number of contacts. Model predictions capture the disease dynamics during lockdown, showing the epidemic curve reaching ICU system capacity, largely strengthened during the emergency, and slowly decreasing. Results suggest that physical contacts outside households were largely avoided during lockdown. Lifting the lockdown with no exit strategy would lead to a second wave overwhelming the healthcare system, if conditions return to normal. Extensive case finding and isolation are required for social distancing strategies to gradually relax lockdown constraints.



Conclusions

As France experiences the first wave of COVID-19 pandemic in lockdown, intensive forms of social distancing are required in the upcoming months due to the currently low population immunity. Extensive case finding and isolation would allow the partial release of the socio-economic pressure caused by extreme measures, while avoiding healthcare demand exceeding capacity. Response planning needs to urgently prioritize the logistics and capacity for these interventions.},

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

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Tracing and analysis of 288 early SARS-CoV-2 infections outside China: A modeling study},

author = {Francesco Pinotti and Laura Di Domenico and Ernesto Ortega and Marco Mancastroppa and Giulia Pullano and Eugenio Valdano and Pierre-Yves Boëlle and Chiara Poletto and Vittoria Colizza },

doi = {https://doi.org/10.1371/journal.pmed.1003193},

year  = {2020},

date = {2020-07-01},

urldate = {2020-07-01},

journal = {PLOS Medicine},

volume = {17},

number = {7},

pages = {1-13},

abstract = {Background In the early months of 2020, a novel coronavirus disease (COVID-19) spread rapidly from China across multiple countries worldwide. As of March 17, 2020, COVID-19 was officially declared a pandemic by the World Health Organization. We collected data on COVID-19 cases outside China during the early phase of the pandemic and used them to predict trends in importations and quantify the proportion of undetected imported cases.   Methods and findings Two hundred and eighty-eight cases have been confirmed out of China from January 3 to February 13, 2020. We collected and synthesized all available information on these cases from official sources and media. We analyzed importations that were successfully isolated and those leading to onward transmission. We modeled their number over time, in relation to the origin of travel (Hubei province, other Chinese provinces, other countries) and interventions. We characterized the importation timeline to assess the rapidity of isolation and epidemiologically linked clusters to estimate the rate of detection. We found a rapid exponential growth of importations from Hubei, corresponding to a doubling time of 2.8 days, combined with a slower growth from the other areas. We predicted a rebound of importations from South East Asia in the successive weeks. Time from travel to detection has considerably decreased since first importation, from 14.5 ± 5.5 days on January 5, 2020, to 6 ± 3.5 days on February 1, 2020. However, we estimated 36% of detection of imported cases. This study is restricted to the early phase of the pandemic, when China was the only large epicenter and foreign countries had not discovered extensive local transmission yet. Missing information in case history was accounted for through modeling and imputation.   Conclusions Our findings indicate that travel bans and containment strategies adopted in China were effective in reducing the exportation growth rate. However, the risk of importation was estimated to increase again from other sources in South East Asia. Surveillance and management of traveling cases represented a priority in the early phase of the epidemic. With the majority of imported cases going undetected (6 out of 10), countries experienced several undetected clusters of chains of local transmissions, fueling silent epidemics in the community. These findings become again critical to prevent second waves, now that countries have reduced their epidemic activity and progressively phase out lockdown},

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

pubstate = {published},

tppubtype = {article}

}

@article{ruktanonchai2020assessing,

title = {Assessing the impact of coordinated COVID-19 exit strategies across Europe},

author = {Nick Warren Ruktanonchai and JR Floyd and Shengjie Lai and Corrine Warren Ruktanonchai and Adam Sadilek and Pedro Rente-Lourenco and Xue Ben and Alessandra Carioli and Joshua Gwinn and JE Steele and others},

doi = {10.1126/science.abc5096},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Science},

volume = {369},

number = {6510},

pages = {1465--1470},

publisher = {American Association for the Advancement of Science},

abstract = {As rates of new COVID-19 cases decline across Europe due to non-pharmaceutical interventions such as social distancing policies and lockdown measures, countries require guidance on how to ease restrictions while minimizing the risk of resurgent outbreaks. Here, we use mobility and case data to quantify how coordinated exit strategies could delay continental resurgence and limit community transmission of COVID-19. We find that a resurgent continental epidemic could occur as many as 5 weeks earlier when well-connected countries with stringent existing interventions end their interventions prematurely. Further, we found that appropriate coordination can greatly improve the likelihood of eliminating community transmission throughout Europe. In particular, synchronizing intermittent lockdowns across Europe meant half as many lockdown periods were required to end community transmission continent-wide.},

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

pubstate = {published},

tppubtype = {article}

}

@article{zhang2020changes,

title = {Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China},

author = {Juanjuan Zhang and Maria Litvinova and Yuxia Liang and Yan Wang and Wei Wang and Shanlu Zhao and Qianhui Wu and Stefano Merler and Cécile Viboud and Alessandro Vespignani and others},

doi = {https://doi.org/10.1038/s41467-021-25695-0},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Science},

volume = {368},

number = {6498},

pages = {1481--1486},

publisher = {American Association for the Advancement of Science},

abstract = {Intense nonpharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease 2019 (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact survey data for Wuhan and Shanghai before and during the outbreak and contact-tracing information from Hunan province. Daily contacts were reduced seven- to eightfold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0 to 14 years of age are less susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than adults 15 to 64 years of age (odds ratio 0.34, 95% confidence interval 0.24 to 0.49), whereas individuals more than 65 years of age are more susceptible to infection (odds ratio 1.47, 95% confidence interval 1.12 to 1.92). Based on these data, we built a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. Although proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40 to 60% and delay the epidemic.},

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

pubstate = {published},

tppubtype = {article}

}

@article{roche2020covid,

title = {COVID-19 and Media datasets: Period-and location-specific textual data mining},

author = {Mathieu Roche},

doi = {10.1016/j.dib.2020.106356},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Data in brief},

volume = {33},

pages = {106356},

publisher = {Elsevier},

abstract = {The vocabulary used in news on a disease such as COVID-19 changes according the period [4]. This aspect is discussed on the basis of MEDISYS-sourced media datasets via two studies. The first focuses on terminology extraction and the second on period prediction according to the textual content using machine learning approaches.},

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

pubstate = {published},

tppubtype = {article}

}

@article{rader2020crowding,

title = {Crowding and the shape of COVID-19 epidemics},

author = {Benjamin Rader and Samuel V Scarpino and Anjalika Nande and Alison L Hill and Ben Adlam and Robert C Reiner and David M Pigott and Bernardo Gutierrez and Alexander E Zarebski and Munik Shrestha and others},

doi = {https://doi.org/10.1038/s41591-020-1104-0},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Nature medicine},

volume = {26},

number = {12},

pages = {1829--1834},

publisher = {Nature Publishing Group},

abstract = {The coronavirus disease 2019 (COVID-19) pandemic is straining public health systems worldwide, and major non-pharmaceutical interventions have been implemented to slow its spread. During the initial phase of the outbreak, dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was primarily determined by human mobility from Wuhan, China. Yet empirical evidence on the effect of key geographic factors on local epidemic transmission is lacking. In this study, we analyzed highly resolved spatial variables in cities, together with case count data, to investigate the role of climate, urbanization and variation in interventions. We show that the degree to which cases of COVID-19 are compressed into a short period of time (peakedness of the epidemic) is strongly shaped by population aggregation and heterogeneity, such that epidemics in crowded cities are more spread over time, and crowded cities have larger total attack rates than less populated cities. Observed differences in the peakedness of epidemics are consistent with a meta-population model of COVID-19 that explicitly accounts for spatial hierarchies. We paired our estimates with globally comprehensive data on human mobility and predict that crowded cities worldwide could experience more prolonged epidemics.},

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

pubstate = {published},

tppubtype = {article}

}

@article{lai2020effect,

title = {Effect of non-pharmaceutical interventions to contain COVID-19 in China},

author = {Shengjie Lai and Nick W Ruktanonchai and Liangcai Zhou and Olivia Prosper and Wei Luo and Jessica R Floyd and Amy Wesolowski and Mauricio Santillana and Chi Zhang and Xiangjun Du and others},

doi = {https://doi.org/10.1038/s41586-020-2293-x},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {nature},

volume = {585},

number = {7825},

pages = {410--413},

publisher = {Nature Publishing Group},

abstract = {On 11 March 2020, the World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) a pandemic1. The strategies based on non-pharmaceutical interventions that were used to contain the outbreak in China appear to be effective2, but quantitative research is still needed to assess the efficacy of non-pharmaceutical interventions and their timings3. Here, using epidemiological data on COVID-19 and anonymized data on human movement, we develop a modelling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We estimate that there were a total of 114,325 cases of COVID-19 (interquartile range 76,776–164,576) in mainland China as of 29 February 2020. Without non-pharmaceutical interventions, we predict that the number of cases would have been 67-fold higher (interquartile range 44–94-fold) by 29 February 2020, and we find that the effectiveness of different interventions varied. We estimate that early detection and isolation of cases prevented more infections than did travel restrictions and contact reductions, but that a combination of non-pharmaceutical interventions achieved the strongest and most rapid effect. According to our model, the lifting of travel restrictions from 17 February 2020 does not lead to an increase in cases across China if social distancing interventions can be maintained, even at a limited level of an on average 25% reduction in contact between individuals that continues until late April. These findings improve our understanding of the effects of non-pharmaceutical interventions on COVID-19, and will inform response efforts across the world.},

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

pubstate = {published},

tppubtype = {article}

}

@article{pullano2020evaluating,

title = {Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study},

author = {Giulia Pullano and Eugenio Valdano and Nicola Scarpa and Stefania Rubrichi and Vittoria Colizza},

doi = {10.1016/S2589-7500(20)30243-0 },

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {The Lancet Digital Health},

volume = {2},

number = {12},

pages = {e638--e649},

publisher = {Elsevier},

abstract = {Background: On March 17, 2020, French authorities implemented a nationwide lockdown to respond to the COVID-19 epidemic and curb the surge of patients requiring critical care. Assessing the effect of lockdown on individual displacements is essential to quantify achievable mobility reductions and identify the factors driving the changes in social dynamics that affected viral diffusion. We aimed to use mobile phone data to study how mobility in France changed before and during the lockdown, breaking down our findings by trip distance, user age and residency, and time of day, and analysing regional data and spatial heterogeneities. For this population-based study, we used temporally resolved travel flows among 1436 administrative areas of mainland France reconstructed from mobile phone trajectories. Data were stratified by age class (younger than 18 years, 18-64 years, and 65 years or older). We distinguished between residents and non-residents and used population data and regional socio-economic indicators from the French National Statistical Institute. We measured mobility changes before and during lockdown at both local and country scales using a case-crossover framework. We analysed all trips combined and trips longer than 100 km (termed long trips), and separated trips by daytime or night-time, weekdays or weekends, and rush hours.



Findings: Lockdown caused a 65% reduction in the countrywide number of displacements (from about 57 million to about 20 million trips per day) and was particularly effective in reducing work-related short-range mobility, especially during rush hour, and long trips. Geographical heterogeneities showed anomalous increases in long-range movements even before lockdown announcement that were tightly localised in space. During lockdown, mobility drops were unevenly distributed across regions (eg, Île-de-France, the region of Paris, went from 585 000 to 117 000 outgoing trips per day). They were strongly associated with active populations, workers employed in sectors highly affected by lockdown, and number of hospitalisations per region, and moderately associated with the socioeconomic level of the regions. Major cities largely shrank their pattern of connectivity, reducing it mainly to short-range commuting (95% of traffic leaving Paris was contained in a 201 km radius before lockdown, which was reduced to 29 km during lockdown).},

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

pubstate = {published},

tppubtype = {article}

}

@article{candido2020evolution,

title = {Evolution and epidemic spread of SARS-CoV-2 in Brazil},

author = {Darlan S Candido and Ingra M Claro and Jaqueline G De Jesus and William M Souza and Filipe RR Moreira and Simon Dellicour and Thomas A Mellan and Louis Du Plessis and Rafael HM Pereira and Flavia CS Sales and others},

doi = {10.1126/science.abd2161},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Science},

volume = {369},

number = {6508},

pages = {1255--1260},

publisher = {American Association for the Advancement of Science},

abstract = {Brazil currently has one of the fastest-growing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics in the world. Because of limited available data, assessments of the impact of nonpharmaceutical interventions (NPIs) on this virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1 to 1.6 in São Paulo and Rio de Janeiro. Sequencing of 427 new genomes and analysis of a geographically representative genomic dataset identified >100 international virus introductions in Brazil. We estimate that most (76%) of the Brazilian strains fell in three clades that were introduced from Europe between 22 February and 11 March 2020. During the early epidemic phase, we found that SARS-CoV-2 spread mostly locally and within state borders. After this period, despite sharp decreases in air travel, we estimated multiple exportations from large urban centers that coincided with a 25% increase in average traveled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil and provides evidence that current interventions remain insufficient to keep virus transmission under control in this country.},

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

pubstate = {published},

tppubtype = {article}

}

@article{zhang2020evolving,

title = {Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study},

author = {Juanjuan Zhang and Maria Litvinova and Wei Wang and Yan Wang and Xiaowei Deng and Xinghui Chen and Mei Li and Wen Zheng and Lan Yi and Xinhua Chen and others},

doi = {https://doi.org/10.1016/S1473-3099(20)30230-9},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {The Lancet Infectious Diseases},

volume = {20},

number = {7},

pages = {793--802},

publisher = {Elsevier},

abstract = {The coronavirus disease 2019 (COVID-19) epidemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), began in Wuhan city, Hubei province, in December, 2019, and has spread throughout China. Understanding the evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy.

Methods

We collected individual information from official public sources on laboratory-confirmed cases reported outside Hubei in mainland China for the period of Jan 19 to Feb 17, 2020. We used the date of the fourth revision of the case definition (Jan 27) to divide the epidemic into two time periods (Dec 24 to Jan 27, and Jan 28 to Feb 17) as the date of symptom onset. We estimated trends in the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (Rt) at the provincial level.

Findings

We collected data on 8579 cases from 30 provinces. The median age of cases was 44 years (33–56), with an increasing proportion of cases in younger age groups and in elderly people (ie, aged >64 years) as the epidemic progressed. The mean time from symptom onset to hospital admission decreased from 4·4 days (95% CI 0·0–14·0) for the period of Dec 24 to Jan 27, to 2·6 days (0·0–9·0) for the period of Jan 28 to Feb 17. The mean incubation period for the entire period was estimated at 5·2 days (1·8–12·4) and the mean serial interval at 5·1 days (1·3–11·6). The epidemic dynamics in provinces outside Hubei were highly variable but consistently included a mixture of case importations and local transmission. We estimated that the epidemic was self-sustained for less than 3 weeks, with mean Rt reaching peaks between 1·08 (95% CI 0·74–1·54) in Shenzhen city of Guangdong province and 1·71 (1·32–2·17) in Shandong province. In all the locations for which we had sufficient data coverage of Rt, Rt was estimated to be below the epidemic threshold (ie, <1) after Jan 30.

},

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

pubstate = {published},

tppubtype = {article}

}