MOOD publications

@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{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}

}

@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 = {OpenDataSet, Text mining},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Early Genomic Surveillance and Phylogeographic Analysis of Getah Virus, a Reemerging Arbovirus, in Livestock in China},

author = {Jin Zhao and Simon Dellicour and Ziqing Yan and Michael Veit and Mandev S. Gill and Wan-Ting He and Xiaofeng Zhai and Xiang Ji and Marc A. Suchard and Philippe Lemey and Shuo Su},

url = {https://journals.asm.org/doi/10.1128/jvi.01091-22},

doi = {10.1128/jvi.01091-22},

journal = {Journal of Virology},

abstract = {Getah virus (GETV) mainly causes disease in livestock and may pose an epidemic risk due to its expanding host range and the potential of long-distance dispersal through animal trade. Here, we used metagenomic next-generation sequencing (mNGS) to identify GETV as the pathogen responsible for reemerging swine disease in China and subsequently estimated key epidemiological parameters using phylodynamic and spatially-explicit phylogeographic approaches. The GETV isolates were able to replicate in a variety of cell lines, including human cells, and showed high pathogenicity in a mouse model, suggesting the potential for more mammal hosts. We obtained 16 complete genomes and 79 E2 gene sequences from viral strains collected in China from 2016 to 2021 through large-scale surveillance among livestock, pets, and mosquitoes. Our phylogenetic analysis revealed that three major GETV lineages are responsible for the current epidemic in livestock in China. We identified three potential positively selected sites and mutations of interest in E2, which may impact the transmissibility and pathogenicity of the virus. Phylodynamic inference of the GETV demographic dynamics identified an association between livestock meat consumption and the evolution of viral genetic diversity. Finally, phylogeographic reconstruction of GETV dispersal indicated that the sampled lineages have preferentially circulated within areas associated with relatively higher mean annual temperature and pig population density. Our results highlight the importance of continuous surveillance of GETV among livestock in southern Chinese regions associated with relatively high temperatures.},

keywords = {OpenDataSet},

pubstate = {published},

tppubtype = {article}

}