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, epidemiology, ICU, Model, platform, Switzerland},

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{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 = {Chikungunya, Spatiotemporal spread, Transmission chain, Transmission distance},

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, measures, mobility, Model, population dynamics},

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},

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, epidemiology, Italy, lock-down, reproduction number, transmission, zoonoses},

pubstate = {published},

tppubtype = {article}

}

@article{10.1371/journal.pmed.1003193,

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},

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

doi = {10.1371/journal.pmed.1003193},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {PLOS Medicine},

volume = {17},

number = {7},

pages = {1-13},

publisher = {Public Library of Science},

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, epidemiology, reverse transcription-polymerase chain reaction, RT-PCR, STROBE},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Is clinical primary care surveillance for tularaemia a useful addition to laboratory surveillance? An analysis of notification data for Finland, 2013 to 2019},

author = {Hammer, Charlotte C and Dub, Timothee and Luomala, Oskari and Sane, Jussi},

url = {https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2022.27.4.2100098},

doi = {https://doi.org/10.2807/1560-7917.ES.2022.27.4.2100098},

journal = {Eurosurveillance},

volume = {27},

number = {4},

abstract = {Background

In Finland, surveillance of tularaemia relies on laboratory-confirmed case notifications to the National infectious Diseases Register (NIDR).



Aim

The aim of the study was to assess the suitability and usefulness of clinical surveillance as an addition to laboratory notification to improve tularaemia surveillance in Finland.



Methods

We retrieved NIDR tularaemia surveillance and primary healthcare data on clinically diagnosed tularaemia cases in Finland between 2013 and 2019. We compared incidences, demographic distributions and seasonal trends between the two data sources.



Results

The median annual incidence was 0.6 (range: 0.1–12.7) and 0.8 (range: 0.6–7.2) per 100,000 for NIDR notifications and primary healthcare notifications, respectively. Cases reported to NIDR were slightly older than cases reported to primary healthcare (median: 53 years vs 50 years, p = 0.04), but had similar sex distribution. Seasonal peaks differed between systems, both in magnitude and in timing. On average, primary healthcare notifications peaked 3 weeks before NIDR. However, peaks in NIDR were more pronounced, for example in 2017, monthly incidence per 100,000 of NIDR notifications peaked at 12.7 cases in September, while primary healthcare notifications peaked at 7.2 (1.8 ratio) in August.



Conclusions

Clinically diagnosed cases provide a valuable additional data source for surveillance of tularaemia in Finland. A primary healthcare-based system would allow for earlier detection of increasing incidences and thereby for early warning of outbreaks. This is crucial in order to implement targeted control and prevention measures as early as possible.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{@article{VALENTIN2021100357,

title = {PADI-web 3.0: A new framework for extracting and disseminating fine-grained information from the news for animal disease surveillance},

author = {Sarah Valentin and Elena Arsevska and Julien Rabatel and Sylvain Falala and Alizé Mercier and Renaud Lancelot and Mathieu Roche},

url = {https://www.sciencedirect.com/science/article/pii/S2352771421001476},

doi = {https://doi.org/10.1016/j.onehlt.2021.100357},

issn = {2352-7714},

journal = {One Health},

volume = {13},

pages = {100357},

keywords = {Animal disease surveillance, Software, Text mining},

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 = {},

pubstate = {published},

tppubtype = {article}

}