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Lao X, Luo L, Lei Z, Fang T, Chen Y, Liu Y, Ding K, Zhang D, Wang R, Zhao Z, Rui J, Zhu Y, Xu J, Wang Y, Yang M, Yi B, Chen T. The epidemiological characteristics and effectiveness of countermeasures to contain coronavirus disease 2019 in Ningbo City, Zhejiang Province, China. Sci Rep 2021; 11:9545. [PMID: 33953243 PMCID: PMC8099873 DOI: 10.1038/s41598-021-88473-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 04/05/2021] [Indexed: 12/15/2022] Open
Abstract
A novel coronavirus (SARS-CoV-2) has spread worldwide and led to high disease burden around the world. This study aimed to explore the key parameters of SARS-CoV-2 infection and to assess the effectiveness of interventions to control the coronavirus disease 2019 (COVID-19). A susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) model was developed for the assessment. The information of each confirmed case and asymptomatic infection was collected from Ningbo Center for Disease Control and Prevention (CDC) to calculate the key parameters of the model in Ningbo City, China. A total of 157 confirmed COVID-19 cases (including 51 imported cases and 106 secondary cases) and 30 asymptomatic infections were reported in Ningbo City. The proportion of asymptomatic infections had an increasing trend. The proportion of elder people in the asymptomatic infections was lower than younger people, and the difference was statistically significant (Fisher's Exact Test, P = 0.034). There were 22 clusters associated with 167 SARS-CoV-2 infections, among which 29 cases were asymptomatic infections, accounting for 17.37%. We found that the secondary attack rate (SAR) of asymptomatic infections was almost the same as that of symptomatic cases, and no statistical significance was observed (χ2 = 0.052, P = 0.819) by Kruskal-Wallis test. The effective reproduction number (Reff) was 1.43, which revealed that the transmissibility of SARS-CoV-2 was moderate. If the interventions had not been strengthened, the duration of the outbreak would have lasted about 16 months with a simulated attack rate of 44.15%. The total attack rate (TAR) and duration of the outbreak would increase along with the increasing delay of intervention. SARS-CoV-2 had moderate transmissibility in Ningbo City, China. The proportion of asymptomatic infections had an increase trend. Asymptomatic infections had the same transmissibility as symptomatic infections. The integrated interventions were implemented at different stages during the outbreak, which turned out to be exceedingly effective in China.
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Affiliation(s)
- Xuying Lao
- Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Haishu District, Ningbo City, Zhejiang Province, People's Republic of China
| | - Li Luo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Zhao Lei
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Ting Fang
- Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Haishu District, Ningbo City, Zhejiang Province, People's Republic of China
| | - Yi Chen
- Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Haishu District, Ningbo City, Zhejiang Province, People's Republic of China
| | - Yuhui Liu
- Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Haishu District, Ningbo City, Zhejiang Province, People's Republic of China
| | - Keqin Ding
- Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Haishu District, Ningbo City, Zhejiang Province, People's Republic of China
| | - Dongliang Zhang
- Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Haishu District, Ningbo City, Zhejiang Province, People's Republic of China
| | - Rong Wang
- Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Haishu District, Ningbo City, Zhejiang Province, People's Republic of China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Yuanzhao Zhu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Jingwen Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Meng Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Bo Yi
- Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Haishu District, Ningbo City, Zhejiang Province, People's Republic of China.
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China.
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Akoko JM, Pelle R, Lukambagire AS, Machuka EM, Nthiwa D, Mathew C, Fèvre EM, Bett B, Cook EAJ, Othero D, Bonfoh B, Kazwala RR, Shirima G, Schelling E, Halliday JEB, Ouma C. Molecular epidemiology of Brucella species in mixed livestock-human ecosystems in Kenya. Sci Rep 2021; 11:8881. [PMID: 33893352 PMCID: PMC8065124 DOI: 10.1038/s41598-021-88327-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 04/09/2021] [Indexed: 12/12/2022] Open
Abstract
Brucellosis, caused by several species of the genus Brucella, is a zoonotic disease that affects humans and animal species worldwide. Information on the Brucella species circulating in different hosts in Kenya is largely unknown, thus limiting the adoption of targeted control strategies. This study was conducted in multi-host livestock populations in Kenya to detect the circulating Brucella species and assess evidence of host-pathogen associations. Serum samples were collected from 228 cattle, 162 goats, 158 sheep, 49 camels, and 257 humans from Narok and Marsabit counties in Kenya. Information on age, location and history of abortion or retained placenta were obtained for sampled livestock. Data on age, gender and location of residence were also collected for human participants. All samples were tested using genus level real-time PCR assays with primers specific for IS711 and bcsp31 targets for the detection of Brucella. All genus positive samples (positive for both targets) were further tested with a speciation assay for AlkB and BMEI1162 targets, specific for B. abortus and B. melitensis, respectively. Samples with adequate quantities aggregating to 577 were also tested with the Rose Bengal Test (RBT). A total of 199 (33.3%) livestock and 99 (38.5%) human samples tested positive for genus Brucella. Animal Brucella PCR positive status was positively predicted by RBT positive results (OR = 8.3, 95% CI 4.0-17.1). Humans aged 21-40 years had higher odds (OR = 2.8, 95% CI 1.2-6.6) of being Brucella PCR positive compared to the other age categories. The data on detection of different Brucella species indicates that B. abortus was detected more often in cattle (OR = 2.3, 95% CI 1.1-4.6) and camels (OR = 2.9, 95% CI 1.3-6.3), while B. melitensis was detected more in sheep (OR = 3.6, 95% CI 2.0-6.7) and goats (OR = 1.7, 95% CI 1.0-3.1). Both B. abortus and B. melitensis DNA were detected in humans and in multiple livestock host species, suggesting cross-transmission of these species among the different hosts. The detection of these two zoonotic Brucella species in humans further underpins the importance of One Health prevention strategies that target multiple host species, especially in the multi-host livestock populations.
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Affiliation(s)
- James M Akoko
- Department of Biomedical Sciences and Technology, Maseno University, Kisumu, Kenya.
- Biosciences Eastern and Central Africa-International Livestock Research Institute (BecA-ILRI) Hub KE, Nairobi, Kenya.
- International Livestock Research Institute, Nairobi, Kenya.
| | - Roger Pelle
- Biosciences Eastern and Central Africa-International Livestock Research Institute (BecA-ILRI) Hub KE, Nairobi, Kenya
| | | | - Eunice M Machuka
- Biosciences Eastern and Central Africa-International Livestock Research Institute (BecA-ILRI) Hub KE, Nairobi, Kenya
| | - Daniel Nthiwa
- Department of Biological Sciences, University of Embu, Embu, Kenya
| | | | - Eric M Fèvre
- International Livestock Research Institute, Nairobi, Kenya
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Bernard Bett
- International Livestock Research Institute, Nairobi, Kenya
| | - Elizabeth A J Cook
- International Livestock Research Institute, Nairobi, Kenya
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Doreen Othero
- Department of Public Health, Maseno University, Kisumu, Kenya
| | - Bassirou Bonfoh
- Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, Abidjan, Côte d'Ivoire
| | | | - Gabriel Shirima
- Nelson Mandela African Institute of Science and Technology, Arusha, Tanzania
| | | | - Jo E B Halliday
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Collins Ouma
- Department of Biomedical Sciences and Technology, Maseno University, Kisumu, Kenya
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Bosetti C, Traini E, Alam T, Allen CA, Carreras G, Compton K, Fitzmaurice C, Force LM, Gallus S, Gorini G, Harvey JD, Kocarnik JM, La Vecchia C, Lugo A, Naghavi M, Pennini A, Piccinelli C, Ronfani L, Xu R, Monasta L. National burden of cancer in Italy, 1990-2017: a systematic analysis for the global burden of disease study 2017. Sci Rep 2020; 10:22099. [PMID: 33328623 PMCID: PMC7744506 DOI: 10.1038/s41598-020-79176-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 11/25/2020] [Indexed: 12/20/2022] Open
Abstract
We monitored the burden of cancer in Italy and its trends over the last three decades, providing estimates of cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs), for cancer overall and 30 cancer sites using data from the Global Burden of Disease study 2017. An overview of mortality trends between 1990 and 2017 was also provided. In 2017, there were 254,336 new cancer cases in men and 214,994 in women, corresponding to an age-standardized incidence rate (ASIR) of 438 and 330/100,000, respectively. Between 1990 and 2017, incident cancer cases, and, to a lesser extent, ASIRs significantly increased overall and for almost all cancer sites, but ASIRs significantly declined for lung and other tobacco-related neoplasms. In 2017, there were 101,659 cancer deaths in men (age-standardized death rate, ASDR, 158.5/100,000) and 78,918 in women (ASDR 93.9/100,000). Cancer deaths significantly increased between 1990 and 2017 (+ 18%), but ASDR significantly decreased (- 28%). Deaths significantly increased for many cancer sites, but decreased for stomach, esophageal, laryngeal, Hodgkin lymphoma, and testicular cancer. ASDRs significantly decreased for most neoplasms, with the main exceptions of cancer of the pancreas and uterus, and multiple myeloma. In 2017, cancer caused 3,204,000 DALYs. Between 1990 and 2017, DALYs and age-standardized DALY rates significantly declined (-3.4% and -33%, respectively). Age-standardized mortality rates in Italy showed favorable patterns over the last few decades. However, the absolute number of cancer cases and, to a lower extent, of cancer deaths increased likely due to the progressive ageing of the population, this calling for a continuous effort in cancer prevention, early diagnosis, and treatment.
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Affiliation(s)
- Cristina Bosetti
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milan, Italy.
| | - Eugenio Traini
- Clinical Epidemiology and Public Health Research Unit, Institute for Maternal and Child Health IRCCS Burlo Garofolo, Trieste, Italy
| | - Tahiya Alam
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Christine A Allen
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Giulia Carreras
- Oncologic Network, Prevention, and Research Institute, Florence, Italy
| | - Kelly Compton
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Christina Fitzmaurice
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Division of Hematology, University of Washington, Seattle, WA, USA
| | - Lisa M Force
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Global Pediatric Medicine, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Silvano Gallus
- Department of Environmental Health Sciences, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Giuseppe Gorini
- Oncologic Network, Prevention, and Research Institute, Florence, Italy
| | - James D Harvey
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Jonathan M Kocarnik
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Carlo La Vecchia
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Alessandra Lugo
- Department of Environmental Health Sciences, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Mohsen Naghavi
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Alyssa Pennini
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Luca Ronfani
- Clinical Epidemiology and Public Health Research Unit, Institute for Maternal and Child Health IRCCS Burlo Garofolo, Trieste, Italy
| | - Rixing Xu
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Lorenzo Monasta
- Clinical Epidemiology and Public Health Research Unit, Institute for Maternal and Child Health IRCCS Burlo Garofolo, Trieste, Italy
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