MOOD publications

@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{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 = {Belgium, COVID-19, epidemiology, Model, school, transmission},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/cid/ciab836,

title = {Risk of SARS-CoV-2 Transmission among Air Passengers in China},

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

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

doi = {10.1093/cid/ciab836},

issn = {1058-4838},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Clinical Infectious Diseases},

abstract = {Modern transportation plays a key role in the spread of 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 COVID-19 cases and close contacts on domestic airplanes departing from Wuhan city in China before the lockdown on January 23, 2020, we estimated the upper and lower bounds of overall transmission risk of COVID-19 among travellers.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%CI 0.43%-0.84%) and 0.33% (18/5400, 95%CI 0.21%-0.53%), 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 0.4%-1.2%). The upper-bound AR increased from 0.7% (95%CI 0.5%-1.0%) to 1.2% (95%CI 0.4%-3.3%) when the co-travel time increased from 2.0 hours to 3.3 hours.The ARs among travellers 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.},

note = {ciab836},

keywords = {airplanes, COVID-19, mobility, transmission},

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, Europe, lock-down, measures, transmission},

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 = {China, COVID-19, mobility, outbreak, transmission},

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 = {big data, contac tracing, COVID-19, epidemiology, France, measures, mobility, transmission},

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 = {big data, Brazil, Contact tracing, COVID-19, epidemiology, measures, mobility, transmission},

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

pubstate = {published},

tppubtype = {article}

}

@article{guzzetta2020potential,

title = {Potential short-term outcome of an uncontrolled COVID-19 epidemic in Lombardy, Italy, February to March 2020},

author = {Giorgio Guzzetta and Piero Poletti and Marco Ajelli and Filippo Trentini and Valentina Marziano and Danilo Cereda and Marcello Tirani and Giulio Diurno and Annalisa Bodina and Antonio Barone and others},

doi = {https://doi.org/10.2807/1560-7917.ES.2020.25.12.2000293},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Eurosurveillance},

volume = {25},

number = {12},

pages = {2000293},

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

abstract = {Sustained coronavirus disease (COVID-19) transmission is ongoing in Italy, with 7,375 reported cases and 366 deaths by 8 March 2020. We provide a model-based evaluation of patient records from Lombardy, predicting the impact of an uncontrolled epidemic on the healthcare system. It has the potential to cause more than 250,039 (95% credible interval (CrI): 147,717–459,890) cases within 3 weeks, including 37,194 (95% CrI: 22,250–67,632) patients requiring intensive care. Aggressive containment strategies are required.},

keywords = {COVID-19, Italy, Model, Public Health, transmission},

pubstate = {published},

tppubtype = {article}

}

@article{gilbert2020preparedness,

title = {Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study},

author = {Marius Gilbert and Giulia Pullano and Francesco Pinotti and Eugenio Valdano and Chiara Poletto and Pierre-Yves Bo""elle and Eric dÓrtenzio and Yazdan Yazdanpanah and Serge Paul Eholie and Mathias Altmann and others},

doi = {https://doi.org/10.1016/S0140-6736(20)30411-6},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {The Lancet},

volume = {395},

number = {10227},

pages = {871--877},

publisher = {Elsevier},

abstract = {Background

The novel coronavirus disease 2019 (COVID-19) epidemic has spread from China to 25 countries. Local cycles of transmission have already occurred in 12 countries after case importation. In Africa, Egypt has so far confirmed one case. The management and control of COVID-19 importations heavily rely on a country's health capacity. Here we evaluate the preparedness and vulnerability of African countries against their risk of importation of COVID-19.

Methods

We used data on the volume of air travel departing from airports in the infected provinces in China and directed to Africa to estimate the risk of importation per country. We determined the country's capacity to detect and respond to cases with two indicators: preparedness, using the WHO International Health Regulations Monitoring and Evaluation Framework; and vulnerability, using the Infectious Disease Vulnerability Index. Countries were clustered according to the Chinese regions contributing most to their risk.

Findings

Countries with the highest importation risk (ie, Egypt, Algeria, and South Africa) have moderate to high capacity to respond to outbreaks. Countries at moderate risk (ie, Nigeria, Ethiopia, Sudan, Angola, Tanzania, Ghana, and Kenya) have variable capacity and high vulnerability. We identified three clusters of countries that share the same exposure to the risk originating from the provinces of Guangdong, Fujian, and the city of Beijing, respectively.

Interpretation

Many countries in Africa are stepping up their preparedness to detect and cope with COVID-19 importations. Resources, intensified surveillance, and capacity building should be urgently prioritised in countries with moderate risk that might be ill-prepared to detect imported cases and to limit onward transmission.},

keywords = {Africa, COVID-19, transmission, vulnerability},

pubstate = {published},

tppubtype = {article}

}

@article{guzzetta2020impact,

title = {The impact of a nation-wide lockdown on COVID-19 transmissibility in Italy},

author = {Giorgio Guzzetta and Flavia Riccardo and Valentina Marziano and Piero Poletti and Filippo Trentini and Antonino Bella and Xanthi Andrianou and Martina Del Manso and Massimo Fabiani and Stefania Bellino and others},

doi = {10.3201/eid2701.202114},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {arXiv preprint arXiv:2004.12338},

abstract = {On March 11, 2020, Italy imposed a national lockdown to curtail the spread of severe acute respiratory syndrome coronavirus 2. We estimate that, 14 days after lockdown, the net reproduction number had dropped below 1 and remained stable at »0.76 (95% CI 0.67-0.85) in all regions for >3 of the following weeks.},

keywords = {COVID-19, epidemiology, Italy, lock-down, reproduction number, transmission, zoonoses},

pubstate = {published},

tppubtype = {article}

}