MOOD publications

@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{:/content/10.2807/1560-7917.ES.2020.25.4.2000057,

title = {Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020},

author = {Giulia Pullano and Francesco Pinotti and Eugenio Valdano and Pierre-Yves Boëlle and Chiara Poletto and Vittoria Colizza},

url = {https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.4.2000057},

doi = {https://doi.org/10.2807/1560-7917.ES.2020.25.4.2000057},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Eurosurveillance},

volume = {25},

number = {4},

abstract = {As at 27 January 2020, 42 novel coronavirus (2019-nCoV) cases were confirmed outside China. We estimate the risk of case importation to Europe from affected areas in China via air travel. We consider travel restrictions in place, three reported cases in France, one in Germany. Estimated risk in Europe remains high. The United Kingdom, Germany and France are at highest risk. Importation from Beijing and Shanghai would lead to higher and widespread risk for Europe.},

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

pubstate = {published},

tppubtype = {article}

}

@article{marini2020influence,

title = {Influence of temperature on the life-cycle dynamics of Aedes albopictus population established at temperate latitudes: A laboratory experiment},

author = {Giovanni Marini and Mattia Manica and Daniele Arnoldi and Enrico Inama and Roberto Ros`a and Annapaola Rizzoli},

doi = {https://doi.org/10.3390/insects11110808},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Insects},

volume = {11},

number = {11},

pages = {808},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {The mosquito species Aedes albopictus has successfully colonized many areas at temperate latitudes, representing a major public health concern. As mosquito bionomics is critically affected by temperature, we experimentally investigated the influence of different constant rearing temperatures (10, 15, 25, and 30 °C) on the survival rates, fecundity, and developmental times of different life stages of Ae. albopictus using a laboratory colony established from specimens collected in northern Italy. We compared our results with previously published data obtained with subtropical populations. We found that temperate Ae. albopictus immature stages are better adapted to colder temperatures: temperate larvae were able to develop even at 10 °C and at 15 °C, larval survivorship was comparable to the one observed at warmer conditions. Nonetheless, at these lower temperatures, we did not observe any blood-feeding activity. Adult longevity and fecundity were substantially greater at 25 °C with respect to the other tested temperatures. Our findings highlight the ability of Ae. albopictus to quickly adapt to colder environments and provide new important insights on the bionomics of this species at temperate latitudes.},

keywords = {},

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{dellicour2020epidemiological,

title = {Epidemiological hypothesis testing using a phylogeographic and phylodynamic framework},

author = {Simon Dellicour and Sebastian Lequime and Bram Vrancken and Mandev S Gill and Paul Bastide and Karthik Gangavarapu and Nathaniel L Matteson and Yi Tan and Louis Du Plessis and Alexander A Fisher and others},

doi = { https://doi.org/10.1038/s41467-020-19122-z},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Nature communications},

volume = {11},

number = {1},

pages = {1--11},

publisher = {Nature Publishing Group},

abstract = {Computational analyses of pathogen genomes are increasingly used to unravel the dispersal history and transmission dynamics of epidemics. Here, we show how to go beyond historical reconstructions and use spatially-explicit phylogeographic and phylodynamic approaches to formally test epidemiological hypotheses. We illustrate our approach by focusing on the West Nile virus (WNV) spread in North America that has substantially impacted public, veterinary, and wildlife health. We apply an analytical workflow to a comprehensive WNV genome collection to test the impact of environmental factors on the dispersal of viral lineages and on viral population genetic diversity through time. We find that WNV lineages tend to disperse faster in areas with higher temperatures and we identify temporal variation in temperature as a main predictor of viral genetic diversity through time. By contrasting inference with simulation, we find no evidence for viral lineages to preferentially circulate within the same migratory bird flyway, suggesting a substantial role for non-migratory birds or mosquito dispersal along the longitudinal gradient.},

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

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}

}

@article{antibiotics9120918,

title = {Global Trends in Antimicrobial Use in Food Animals from 2017 to 2030},

author = {Katie Tiseo and Laura Huber and Marius Gilbert and Timothy P. Robinson and Thomas P. Van Boeckel},

url = {https://www.mdpi.com/2079-6382/9/12/918},

doi = {10.3390/antibiotics9120918},

issn = {2079-6382},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Antibiotics},

volume = {9},

number = {12},

abstract = {Demand for animal protein is rising globally and has been facilitated by the expansion of intensive farming. However, intensive animal production relies on the regular use of antimicrobials to maintain health and productivity on farms. The routine use of antimicrobials fuels the development of antimicrobial resistance, a growing threat for the health of humans and animals. Monitoring global trends in antimicrobial use is essential to track progress associated with antimicrobial stewardship efforts across regions. We collected antimicrobial sales data for chicken, cattle, and pig systems in 41 countries in 2017 and projected global antimicrobial consumption from 2017 to 2030. We used multivariate regression models and estimated global antimicrobial sales in 2017 at 93,309 tonnes (95% CI: 64,443, 149,886). Globally, sales are expected to rise by 11.5% in 2030 to 104,079 tonnes (95% CI: 69,062, 172,711). All continents are expected to increase their antimicrobial use. Our results show lower global antimicrobial sales in 2030 compared to previous estimates, owing to recent reports of decrease in antimicrobial use, in particular in China, the world's largest consumer. Countries exporting a large proportion of their production are more likely to report their antimicrobial sales data than countries with small export markets.},

keywords = {AMR (Antimicrobial Resistance), OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{Zhao2020icumonitoringchAP,

title = {icumonitoring.ch: a platform for short-term forecasting of intensive care unit occupancy during the COVID-19 epidemic in Switzerland.},

author = {Cheng Zhao and Burcu Tepekule and Nicola G. Criscuolo and Pedro David Wendel Garcia and Matthias Peter Hilty and Risc-Icu Consortium Investigators In Switzerland and Thierry Fumeaux and Thomas P. Van Boeckel},

doi = {10.4414/smw.2020.20277},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Swiss medical weekly},

volume = {150},

pages = {w20277},

keywords = {Covid-19 (Coronavirus)},

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 (Coronavirus), OpenDataSet},

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

pubstate = {published},

tppubtype = {article}

}

@article{guzzetta2020spatial,

title = {Spatial modes for transmission of chikungunya virus during a large chikungunya outbreak in Italy: a modeling analysis},

author = {Giorgio Guzzetta and Francesco Vairo and Alessia Mammone and Simone Lanini and Piero Poletti and Mattia Manica and Roberto Rosa and Beniamino Caputo and Angelo Solimini and Alessandra Della Torre and others},

doi = {https://doi.org/10.1186/s12916-020-01674-y},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {BMC medicine},

volume = {18},

number = {1},

pages = {1--10},

publisher = {BioMed Central},

abstract = {Background

The spatial spread of many mosquito-borne diseases occurs by focal spread at the scale of a few hundred meters and over longer distances due to human mobility. The relative contributions of different spatial scales for transmission of chikungunya virus require definition to improve outbreak vector control recommendations.



Methods

We analyzed data from a large chikungunya outbreak mediated by the mosquito Aedes albopictus in the Lazio region, Italy, consisting of 414 reported human cases between June and November 2017. Using dates of symptom onset, geographic coordinates of residence, and information from epidemiological questionnaires, we reconstructed transmission chains related to that outbreak.



Results

Focal spread (within 1 km) accounted for 54.9% of all cases, 15.8% were transmitted at a local scale (1–15 km) and the remaining 29.3% were exported from the main areas of chikungunya circulation in Lazio to longer distances such as Rome and other geographical areas. Seventy percent of focal infections (corresponding to 38% of the total 414 cases) were transmitted within a distance of 200 m (the buffer distance adopted by the national guidelines for insecticide spraying). Two main epidemic clusters were identified, with a radius expanding at a rate of 300–600 m per month. The majority of exported cases resulted in either sporadic or no further transmission in the region.



Conclusions

Evidence suggest that human mobility contributes to seeding a relevant number of secondary cases and new foci of transmission over several kilometers. Reactive vector control based on current guidelines might allow a significant number of secondary clusters in untreated areas, especially if the outbreak is not detected early. Existing policies and guidelines for control during outbreaks should recommend the prioritization of preventive measures in neighboring territories with known mobility flows to the main areas of transmission.},

keywords = {CHIK (Chikungunya), OpenDataSet},

pubstate = {published},

tppubtype = {article}

}

@article{chinazzi2020effect,

title = {The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak},

author = {Matteo Chinazzi and Jessica T Davis and Marco Ajelli and Corrado Gioannini and Maria Litvinova and Stefano Merler and Ana Pastore Piontti and Kunpeng Mu and Luca Rossi and Kaiyuan Sun and others},

url = {https://www.science.org/doi/10.1126/science.aba9757},

doi = {10.1126/science.aba9757},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Science},

volume = {368},

number = {6489},

pages = {395--400},

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

abstract = {Motivated by the rapid spread of coronavirus disease 2019 (COVID-19) in mainland China, we use a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic. The model is calibrated on the basis of internationally reported cases and shows that, at the start of the travel ban from Wuhan on 23 January 2020, most Chinese cities had already received many infected travelers. The travel quarantine of Wuhan delayed the overall epidemic progression by only 3 to 5 days in mainland China but had a more marked effect on the international scale, where case importations were reduced by nearly 80% until mid-February. Modeling results also indicate that sustained 90% travel restrictions to and from mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.},

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

pubstate = {published},

tppubtype = {article}

}