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 all publications to which the MOOD project contributed are listed. Use the filter to select the most relevant articles.
181.
Candido, Darlan S; Claro, Ingra M; Jesus, Jaqueline G De; Souza, William M; Moreira, Filipe RR; Dellicour, Simon; Mellan, Thomas A; Plessis, Louis Du; Pereira, Rafael HM; Sales, Flavia CS; others,
Evolution and epidemic spread of SARS-CoV-2 in Brazil Journal Article
In: Science, vol. 369, no. 6508, pp. 1255–1260, 2020.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus), OpenDataSet
@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}
}
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.
182.
Zhang, Juanjuan; Litvinova, Maria; Wang, Wei; Wang, Yan; Deng, Xiaowei; Chen, Xinghui; Li, Mei; Zheng, Wen; Yi, Lan; Chen, Xinhua; others,
Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study Journal Article
In: The Lancet Infectious Diseases, vol. 20, no. 7, pp. 793–802, 2020.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus), OpenDataSet
@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}
}
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.
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.
183.
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 (Antimicrobial Resistance), OpenDataSet
@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}
}
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.
184.
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 (Coronavirus)
@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}
}
185.
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 (Coronavirus), OpenDataSet
@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}
}
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.
186.
Gilbert, Marius; Pullano, Giulia; Pinotti, Francesco; Valdano, Eugenio; Poletto, Chiara; Bo""elle, Pierre-Yves; dÓrtenzio, Eric; Yazdanpanah, Yazdan; Eholie, Serge Paul; Altmann, Mathias; others,
Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study Journal Article
In: The Lancet, vol. 395, no. 10227, pp. 871–877, 2020.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus), OpenDataSet
@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}
}
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.
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.
187.
Guzzetta, Giorgio; Vairo, Francesco; Mammone, Alessia; Lanini, Simone; Poletti, Piero; Manica, Mattia; Rosa, Roberto; Caputo, Beniamino; Solimini, Angelo; Torre, Alessandra Della; others,
Spatial modes for transmission of chikungunya virus during a large chikungunya outbreak in Italy: a modeling analysis Journal Article
In: BMC medicine, vol. 18, no. 1, pp. 1–10, 2020.
Abstract | Links | BibTeX | Tags: CHIK (Chikungunya), OpenDataSet
@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}
}
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.
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.
188.
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 | Links | BibTeX | Tags: Covid-19 (Coronavirus), OpenDataSet
@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}
}
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.
189.
Guzzetta, Giorgio; Riccardo, Flavia; Marziano, Valentina; Poletti, Piero; Trentini, Filippo; Bella, Antonino; Andrianou, Xanthi; Manso, Martina Del; Fabiani, Massimo; Bellino, Stefania; others,
Impact of a nationwide lockdown on SARS-COV-2 transmissibility, Italy Journal Article
In: Centers for Disease Control and Prevention, vol. 27, no. 1, pp. 267-270, 2020.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus)
@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}
}
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.
190.
Mitra B. Drakulovic Giovanni Marini, Verica Jovanovic
Drivers and epidemiological patterns of West Nile virus in Serbia Journal Article
In: Frontiers in Public Health, vol. 12, 2014.
Abstract | Links | BibTeX | Tags: WNV (West Nile Virus)
@article{Marini2014,
title = {Drivers and epidemiological patterns of West Nile virus in Serbia},
author = {Giovanni Marini, Mitra B. Drakulovic, Verica Jovanovic, Francesca Dagostin, Willy Wint, Valentina Tagliapietra, Annapaola Rizzoli},
doi = {https://doi.org/10.3389/fpubh.2024.1429583},
year = {2014},
date = {2014-07-17},
journal = {Frontiers in Public Health},
volume = {12},
abstract = {West Nile virus (WNV) is a mosquito-borne virus, part of the genus Flavivirus which is rapidly becoming one of the most widespread emerging pathogens in Europe (1). It is maintained in an enzootic cycle between avian hosts and mosquito vectors, especially those belonging to the Culex genus (2). Mosquitoes acquire the infection after biting an infected bird and, after an incubation period, can then transmit the virus through subsequent blood meals. Mammals, including humans and equines, act as incidental dead end hosts in the natural transmission cycle, i.e., they cannot transmit the virus to mosquitoes (3). However, human-to-human transmission may occur through blood transfusions or organ transplantation (3). Although most of the human infections are asymptomatic, about 25% present symptoms such as fever and headache, and less than 1% develop severe neurological complications which can have a fatal outcome (3).
},
keywords = {WNV (West Nile Virus)},
pubstate = {published},
tppubtype = {article}
}
West Nile virus (WNV) is a mosquito-borne virus, part of the genus Flavivirus which is rapidly becoming one of the most widespread emerging pathogens in Europe (1). It is maintained in an enzootic cycle between avian hosts and mosquito vectors, especially those belonging to the Culex genus (2). Mosquitoes acquire the infection after biting an infected bird and, after an incubation period, can then transmit the virus through subsequent blood meals. Mammals, including humans and equines, act as incidental dead end hosts in the natural transmission cycle, i.e., they cannot transmit the virus to mosquitoes (3). However, human-to-human transmission may occur through blood transfusions or organ transplantation (3). Although most of the human infections are asymptomatic, about 25% present symptoms such as fever and headache, and less than 1% develop severe neurological complications which can have a fatal outcome (3).
191.
Hammer, Charlotte C; Dub, Timothee; Luomala, Oskari; Sane, Jussi
In: Eurosurveillance, vol. 27, no. 4, 0000.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
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.
192.
Valentin, Sarah; Arsevska, Elena; Rabatel, Julien; Falala, Sylvain; Mercier, Alizé; Lancelot, Renaud; Roche, Mathieu
PADI-web 3.0: A new framework for extracting and disseminating fine-grained information from the news for animal disease surveillance Journal Article
In: One Health, vol. 13, pp. 100357, 0000, ISSN: 2352-7714.
Links | BibTeX | Tags: OpenDataSet, Text mining
@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}
}
193.
Syed, Mehtab Alam; Arsevska, Elena; Roche, Mathieu; Teisseire, Maguelonne
Feature Selection for Sentiment Classification of COVID-19 Tweets: H-TFIDF Featuring BERT Conference
Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF, INSTICC SciTePress, 0000, ISBN: 978-989-758-552-4.
Links | BibTeX | Tags: Covid-19 (Coronavirus), OpenDataSet, Text mining
@conference{@conference{healthinf22,
title = {Feature Selection for Sentiment Classification of COVID-19 Tweets: H-TFIDF Featuring BERT},
author = {Syed, Mehtab Alam and Arsevska, Elena and Roche, Mathieu and Teisseire, Maguelonne},
url = {https://www.scitepress.org/Link.aspx?doi=10.5220/0010887800003123},
doi = {10.5220/0010887800003123},
isbn = {978-989-758-552-4},
booktitle = {Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF},
pages = {648-656},
publisher = {SciTePress},
organization = {INSTICC},
keywords = {Covid-19 (Coronavirus), OpenDataSet, Text mining},
pubstate = {published},
tppubtype = {conference}
}
194.
Dorrucci, Maria; Minelli, Giada; Boros, Stefano; Manno, Valerio; Prati, Sabrina; Battaglini, Marco; Corsetti, Gianni; Andrianou, Xanthi; Riccardo, Flavia; Fabiani, Massimo; Vescio, Maria Fenicia; Spuri, Matteo; Mateo-Urdiales, Alberto; Manso, Martina Del; Pezzotti, Patrizio; Bella, Antonino; the Italian Integrated Surveillance COVID-19 Group,
A population-based cohort approach to assess excess mortality due to the spread of COVID-19 in Italy, January-May 2020 Journal Article
In: vol. 58, no. 1, pp. 25-33, 0000.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus)
@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}
}
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.
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.
195.
Zhao, Jin; Dellicour, Simon; Yan, Ziqing; Veit, Michael; Gill, Mandev S.; He, Wan-Ting; Zhai, Xiaofeng; Ji, Xiang; Suchard, Marc A.; Lemey, Philippe; Su, Shuo
Early Genomic Surveillance and Phylogeographic Analysis of Getah Virus, a Reemerging Arbovirus, in Livestock in China Journal Article
In: Journal of Virology, 0000.
Abstract | Links | BibTeX | Tags: OpenDataSet
@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}
}
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.