MOOD project is at the forefront of European research of infectious disease surveillance and modelling from a data science perspective, investigating the impact of global warming on disease outbreaks, and proposing innovations for building of One Health systems across Europe and the world.
In the table below are listed all MOOD publications. Use the filter to select the most relevant articles.
10.
Decoupes, Rémy; Rodrique, Kafando; Roche, Mathieu; Teisseire, Maguelonne
H-TFIDF: What makes areas specific over time in the massive flow of tweets related to the covid pandemic? Journal Article
In: AGILE: GIScience Series, vol. 2, no. 4, pp. 1-8, 2021.
Abstract | Links | BibTeX | Tags: COVID-19, EBS, media extraction, Model, population dynamics, Public Health, Twitter
@article{,
title = {H-TFIDF: What makes areas specific over time in the massive flow of tweets related to the covid pandemic?},
author = {Rémy Decoupes and Kafando Rodrique and Mathieu Roche and Maguelonne Teisseire },
doi = {10.5194/agile-giss-2-2-2021},
year = {2021},
date = {2021-06-01},
urldate = {2020-01-01},
journal = {AGILE: GIScience Series},
volume = {2},
number = {4},
pages = {1-8},
publisher = {European Centre for Disease Prevention and Control},
abstract = {Data produced by social networks may contain weak signals of possible epidemic outbreaks. In this paper, we focus on Twitter data during the waiting period before the appearance of COVID-19 first cases outside China. Among the huge flow of tweets that reflects a global growing concern in all countries, we propose to analyze such data with an adaptation of the TF-IDF measure. It allows the users to extract the discriminant vocabularies used across time and space. The results are then discussed to show how the specific spatio-temporal anchoring of the extracted terms make it possible to follow the crisis dynamics on different scales of time and space.},
keywords = {COVID-19, EBS, media extraction, Model, population dynamics, Public Health, Twitter},
pubstate = {published},
tppubtype = {article}
}
Data produced by social networks may contain weak signals of possible epidemic outbreaks. In this paper, we focus on Twitter data during the waiting period before the appearance of COVID-19 first cases outside China. Among the huge flow of tweets that reflects a global growing concern in all countries, we propose to analyze such data with an adaptation of the TF-IDF measure. It allows the users to extract the discriminant vocabularies used across time and space. The results are then discussed to show how the specific spatio-temporal anchoring of the extracted terms make it possible to follow the crisis dynamics on different scales of time and space.
9.
Domenico, Laura Di; Pullano, Giulia; Sabbatini, Chiara E.; Bo""elle, Pierre-Yves; Colizza, Vittoria
Modelling safe protocols for reopening schools during the COVID-19 pandemic in France Journal Article
In: medRxiv, 2021.
Abstract | Links | BibTeX | Tags: COVID-19, France, Model, school
@article{DiDomenico2020.05.08.20095521,
title = {Modelling safe protocols for reopening schools during the COVID-19 pandemic in France},
author = {Laura Di Domenico and Giulia Pullano and Chiara E. Sabbatini and Pierre-Yves Bo""elle and Vittoria Colizza},
url = {https://www.medrxiv.org/content/early/2021/01/14/2020.05.08.20095521},
doi = {10.1101/2020.05.08.20095521},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {medRxiv},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {As countries in Europe implement strategies to control COVID-19 pandemic, different options are chosen regarding schools. Through a stochastic age-structured transmission model calibrated to the observed epidemic in Ile-de-France in the first wave, we explored scenarios of partial, progressive, or full school reopening. Given the uncertainty on children role, we found that reopening schools after lockdown may increase COVID-19 cases, yet protocols exist that maintain the epidemic controlled. Under a scenario with stable epidemic activity if schools were closed, reopening pre-schools and primary schools would lead up to 76% [67, 84]% occupation of ICU beds if no other school level reopened, or if middle and high schools reopened later. Immediately reopening all school levels may overwhelm the ICU system. Priority should be given to pre- and primary schools allowing younger children to resume learning and development, whereas full attendance in middle and high schools is not recommended for stable or increasing epidemic activity. Large-scale tests and trace are required to maintain the epidemic under control. Ex-post assessment shows that progressive reopening of schools, limited attendance, and strong adoption of preventive measures contributed to a decreasing epidemic after lifting the first lockdown.},
keywords = {COVID-19, France, Model, school},
pubstate = {published},
tppubtype = {article}
}
As countries in Europe implement strategies to control COVID-19 pandemic, different options are chosen regarding schools. Through a stochastic age-structured transmission model calibrated to the observed epidemic in Ile-de-France in the first wave, we explored scenarios of partial, progressive, or full school reopening. Given the uncertainty on children role, we found that reopening schools after lockdown may increase COVID-19 cases, yet protocols exist that maintain the epidemic controlled. Under a scenario with stable epidemic activity if schools were closed, reopening pre-schools and primary schools would lead up to 76% [67, 84]% occupation of ICU beds if no other school level reopened, or if middle and high schools reopened later. Immediately reopening all school levels may overwhelm the ICU system. Priority should be given to pre- and primary schools allowing younger children to resume learning and development, whereas full attendance in middle and high schools is not recommended for stable or increasing epidemic activity. Large-scale tests and trace are required to maintain the epidemic under control. Ex-post assessment shows that progressive reopening of schools, limited attendance, and strong adoption of preventive measures contributed to a decreasing epidemic after lifting the first lockdown.
8.
Ingelbeen, Brecht; Peckeu, Laur`ene; Laga, Marie; Hendrix, Ilona; Neven, Inge; Sande, Marianne AB; Kleef, Esther
Reducing contacts to stop SARS-CoV-2 transmission during the second pandemic wave in Brussels, Belgium, August to November 2020 Journal Article
In: Eurosurveillance, vol. 26, no. 7, pp. 2100065, 2021.
Abstract | Links | BibTeX | Tags: Belgium, COVID-19, epidemiology, Model, school, transmission
@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}
}
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.
7.
Oidtman, Rachel J; Omodei, Elisa; Kraemer, Moritz UG; Casteneda-Orjuela, Carlos A; Cruz-Rivera, Erica; Misnaza-Castrillon, Sandra; Cifuentes, Myriam Patricia; Rincon, Luz Emilse; Canon, Viviana; Alarcon, Pedro; others,
Trade-offs between individual and ensemble forecasts of an emerging infectious disease Journal Article
In: medRxiv, 2021.
Abstract | Links | BibTeX | Tags: epidemiology, infectious diseases, Model, Zika
@article{oidtman2021trade,
title = {Trade-offs between individual and ensemble forecasts of an emerging infectious disease},
author = {Rachel J Oidtman and Elisa Omodei and Moritz UG Kraemer and Carlos A Casteneda-Orjuela and Erica Cruz-Rivera and Sandra Misnaza-Castrillon and Myriam Patricia Cifuentes and Luz Emilse Rincon and Viviana Canon and Pedro Alarcon and others},
doi = {https://doi.org/10.1038/s41467-021-25695-0},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {medRxiv},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.},
keywords = {epidemiology, infectious diseases, Model, Zika},
pubstate = {published},
tppubtype = {article}
}
Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.
6.
Pinotti, Francesco; Domenico, Laura Di; Ortega, Ernesto; Mancastroppa, Marco; Pullano, Giulia; Valdano, Eugenio; Boëlle, Pierre-Yves; Poletto, Chiara; Colizza, Vittoria
Tracing and analysis of 288 early SARS-CoV-2 infections outside China: A modeling study Journal Article
In: PLOS Medicine, vol. 17, no. 7, pp. 1-13, 2020.
Abstract | Links | BibTeX | Tags: China, Contact tracing, COVID-19, Model
@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},
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 = {China, Contact tracing, COVID-19, Model},
pubstate = {published},
tppubtype = {article}
}
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
5.
Lai, Shengjie; Ruktanonchai, Nick W; Zhou, Liangcai; Prosper, Olivia; Luo, Wei; Floyd, Jessica R; Wesolowski, Amy; Santillana, Mauricio; Zhang, Chi; Du, Xiangjun; others,
Effect of non-pharmaceutical interventions to contain COVID-19 in China Journal Article
In: nature, vol. 585, no. 7825, pp. 410–413, 2020.
Abstract | Links | BibTeX | Tags: COVID-19, epidemiology, health policy, Model
@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, epidemiology, health policy, Model},
pubstate = {published},
tppubtype = {article}
}
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.
4.
Tiseo, Katie; Huber, Laura; Gilbert, Marius; Robinson, Timothy P.; Boeckel, Thomas P. Van
Global Trends in Antimicrobial Use in Food Animals from 2017 to 2030 Journal Article
In: Antibiotics, vol. 9, no. 12, 2020, ISSN: 2079-6382.
Abstract | Links | BibTeX | Tags: AMR, Animal disease surveillance, Anitmicrobial Resistance, Human health, Model
@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, Animal disease surveillance, Anitmicrobial Resistance, Human health, Model},
pubstate = {published},
tppubtype = {article}
}
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.
3.
Zhao, Cheng; Tepekule, Burcu; Criscuolo, Nicola G.; Garcia, Pedro David Wendel; Hilty, Matthias Peter; Switzerland, Risc-Icu Consortium Investigators In; Fumeaux, Thierry; Boeckel, Thomas P. Van
icumonitoring.ch: a platform for short-term forecasting of intensive care unit occupancy during the COVID-19 epidemic in Switzerland. Journal Article
In: Swiss medical weekly, vol. 150, pp. w20277, 2020.
Links | BibTeX | Tags: COVID-19, epidemiology, ICU, Model, platform, Switzerland
@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, epidemiology, ICU, Model, platform, Switzerland},
pubstate = {published},
tppubtype = {article}
}
2.
Guzzetta, Giorgio; Poletti, Piero; Ajelli, Marco; Trentini, Filippo; Marziano, Valentina; Cereda, Danilo; Tirani, Marcello; Diurno, Giulio; Bodina, Annalisa; Barone, Antonio; others,
Potential short-term outcome of an uncontrolled COVID-19 epidemic in Lombardy, Italy, February to March 2020 Journal Article
In: Eurosurveillance, vol. 25, no. 12, pp. 2000293, 2020.
Abstract | Links | BibTeX | Tags: COVID-19, Italy, Model, Public Health, transmission
@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}
}
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.
1.
Chinazzi, Matteo; Davis, Jessica T; Ajelli, Marco; Gioannini, Corrado; Litvinova, Maria; Merler, Stefano; Piontti, Ana Pastore; Mu, Kunpeng; Rossi, Luca; Sun, Kaiyuan; others,
The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak Journal Article
In: Science, vol. 368, no. 6489, pp. 395–400, 2020.
Abstract | BibTeX | Tags: COVID-19, measures, mobility, Model, population dynamics
@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},
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, measures, mobility, Model, population dynamics},
pubstate = {published},
tppubtype = {article}
}
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.