MOOD publications

@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{guzzetta2020impact,

title = {Impact of a nationwide lockdown on SARS-COV-2 transmissibility, 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},

url = {https://wwwnc.cdc.gov/eid/article/27/1/20-2114_article},

doi = {10.3201/eid2701.202114},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Centers for Disease Control and Prevention},

volume = {27},

number = {1},

pages = {267-270},

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 (Coronavirus)},

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{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{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}

}

@article{nokey_41,

title = {A population-based cohort approach  to assess excess mortality due  to the spread of COVID-19 in Italy,  January-May 2020},

author = {Maria Dorrucci and Giada Minelli and Stefano Boros and Valerio Manno and Sabrina Prati and 

Marco Battaglini and Gianni Corsetti and Xanthi Andrianou and Flavia Riccardo and 

Massimo Fabiani and Maria Fenicia Vescio and Matteo Spuri and Alberto Mateo-Urdiales and 

Martina Del Manso and Patrizio Pezzotti and Antonino Bella and the Italian Integrated 

Surveillance COVID-19 Group},

url = {https://doi.org/10.4415/ann_22_01_04},

doi = {10.4415/ANN_22_01_04},

volume = {58},

number = {1},

pages = {25-33},

abstract = {Aims. To assess the impact of the COVID-19 pandemic on all-cause mortality in Italy 

during the first wave of the epidemic, taking into consideration the geographical heterogeneity of the spread of COVID-19.

Methods. This study is a retrospective, population-based cohort study using national statistics throughout Italy. Survival analysis was applied to data aggregated by day of death, 

age groups, sex, and Italian administrative units (107 provinces). We applied Cox models 

to estimate the relative hazards (RH) of excess mortality, comparing all-cause deaths in 

2020 with the expected deaths from all causes in the same time period. The RH of excess 

deaths was estimated in areas with a high, moderate, and low spread of COVID-19. We 

reported the estimate also restricting the analysis to the period of March-April 2020 (first 

peak of the epidemic).

Results. The study population consisted of 57,204,501 individuals living in Italy as of 

January 1, 2020. The number of excess deaths was 36,445, which accounts for 13.4% 

of excess mortalities from all causes during January-May 2020 (i.e., RH = 1.134; 95% 

confidence interval (CI): 1.129-1.140). In the macro-area with a relatively higher spread 

of COVID-19 (i.e., incidence rate, IR): 450-1,610 cases per 100,000 residents), the RH 

of excess deaths was 1.375 (95% CI: 1.364-1.386). In the area with a relatively moderate 

spread of COVID-19 (i.e., IR: 150-449 cases) it was 1.049 (95% CI: 1.038-1.060). In 

the area with a relatively lower spread of COVID-19 (i.e., IR: 30-149 cases), it was 0.967 

(95% CI: 0.959-0.976). Between March and April (peak months of the first wave of the 

epidemic in Italy), we estimated an excess mortality from all causes of 43.5%. The RH of 

all-cause mortality for increments of 500 cases per 100,000 residents was 1.352 (95% CI: 

1.346-1.359), corresponding to an increase of about 35%.

Conclusions. Our analysis, making use of a population-based cohort model, estimated 

all-cause excess mortality in Italy taking account of both time period and of COVID-19 

geographical spread. The study highlights the importance of a temporal/geographic 

framework in analyzing the risk of COVID-19-epidemy related mortality.},

keywords = {Covid-19 (Coronavirus)},

pubstate = {published},

tppubtype = {article}

}