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Wang K, Wang P, Jiang Z, Wang L, Zhou L, Qi D, Yin W, Meng P. Data-driven assessment of immune evasion and dynamic Zero-COVID policy on fast-spreading Omicron in Changchun. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21692-21716. [PMID: 38124616 DOI: 10.3934/mbe.2023960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Due to its immune evasion capability, the SARS-CoV-2 Omicron variant was declared a variant of concern by the World Health Organization. The spread of Omicron in Changchun (i.e., the capital of Jilin province in northeast of China) during the spring of 2022 was successfully curbed under the strategy of a dynamic Zero-COVID policy. To evaluate the impact of immune evasion on vaccination and other measures, and to understand how the dynamic Zero-COVID measure stopped the epidemics in Changchun, we establish a compartmental model over different stages and parameterized the model with actual reported data. The model simulation firstly shows a reasonably good fit between our model prediction and the data. Second, we estimate the testing rate in the early stage of the outbreak to reveal the real infection size. Third, numerical simulations show that the coverage of vaccine immunization in Changchun and the regular nucleic acid testing could not stop the epidemic, while the 'non-pharmaceutical' intervention measures utilized in the dynamic Zero-COVID policy could play significant roles in the containment of Omicron. Based on the parameterized model, numerical analysis demonstrates that if one wants to achieve epidemic control by fully utilizing the effect of 'dynamic Zero-COVID' measures, therefore social activities are restricted to the minimum level, and then the economic development may come to a halt. The insight analysis in this work could provide reference for infectious disease prevention and control measures in the future.
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Affiliation(s)
- Kun Wang
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Peng Wang
- Jilin Provincial Joint Key Labortory of Big Data Science and Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Zhengang Jiang
- Jilin Provincial Joint Key Labortory of Big Data Science and Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Lu Wang
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Linhua Zhou
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Dequan Qi
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Weishi Yin
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Pinchao Meng
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
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Ruksakulpiwat S, Zhou W, Niyomyart A, Wang T, Kudlowitz A. How does the COVID-19 pandemic impact medication adherence of patients with chronic disease?: A systematic review. Chronic Illn 2023; 19:495-513. [PMID: 35971949 PMCID: PMC9382573 DOI: 10.1177/17423953221110151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/02/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To determine how the COVID-19 pandemic impacts patients with chronic disease medication adherence. METHODS Four electronic databases, PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text, were searched for literature between 2019 and 2021. Abstracts and later full texts were independently screened by the authors of this review using inclusion and exclusion criteria to determine relevance to our study. Joanna Briggs Institute (JBI) critical appraisal tools were used to assess the quality of included texts. Relevant information and data from the included texts were extracted into tables for data synthesis and analysis. RESULTS Ten studies met the study criteria, the most popular study design was cross-sectional design (n = 9, 90.0%), others were case series (n = 1, 10.0%). Barriers to medication adherence and facilitators of medication adherence were the major two themes that participants reported regarding the impact of COVID-19 on medication adherence. Moreover, these two main themes have been organized in sub-themes that are dealt with in-depth. DISCUSSION Our results could heighten healthcare providers, stakeholders, and policy leaders' awareness of providing appropriate support for chronic disease patients, especially regarding medication adherence. Future research incorporating programs that support patients' needs is recommended.
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Affiliation(s)
- Suebsarn Ruksakulpiwat
- Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand
| | - Wendie Zhou
- School of Nursing, Peking University, Beijing, China
| | - Atsadaporn Niyomyart
- Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Tongyao Wang
- School of Nursing, The University of Hong Kong, Hong Kong, China
| | - Aaron Kudlowitz
- The College of Arts and Sciences, Case Western Reserve University, Cleveland, USA
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Preston LE, Rey A, Dumas S, Rodriguez A, Gertz AM, Delea KC, Alvarado-Ramy F, Christensen DL, Brown C, Chen TH. SARS-CoV-2 Cases Reported on International Arriving and Domestic Flights: United States, January 2020-December 2021. Am J Public Health 2023; 113:904-908. [PMID: 37319391 PMCID: PMC10323842 DOI: 10.2105/ajph.2023.307325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Objectives. To describe trends in the number of air travelers categorized as infectious with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2; the virus that causes COVID-19) in the context of total US COVID-19 vaccinations administered, and overall case counts of SARS-CoV-2 in the United States. Methods. We searched the Quarantine Activity Reporting System (QARS) database for travelers with inbound international or domestic air travel, a positive SARS-CoV-2 lab result, and a surveillance categorization of SARS-CoV-2 infection reported during January 2020 to December 2021. Travelers were categorized as infectious during travel if they had arrival dates from 2 days before to 10 days after symptom onset or a positive viral test. Results. We identified 80 715 persons meeting our inclusion criteria; 67 445 persons (83.6%) had at least 1 symptom reported. Of 67 445 symptomatic passengers, 43 884 (65.1%) reported an initial symptom onset date after their flight arrival date. The number of infectious travelers mirrored the overall number of US SARS-CoV-2 cases. Conclusions. Most travelers in the study were asymptomatic during travel, and therefore unknowingly traveled while infectious. During periods of high community transmission, it is important for travelers to stay up to date with COVID-19 vaccinations and consider wearing a high-quality mask to decrease the risk of transmission. (Am J Public Health. 2023;113(8):904-908. https://doi.org/10.2105/AJPH.2023.307325).
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Affiliation(s)
- Leigh Ellyn Preston
- Leigh Ellyn Preston, Araceli Rey, Simone Dumas, Andrea Rodriguez, Alida Gertz, Kristin C. Delea, Francisco Alvarado-Ramy, Deborah L. Christensen, Clive Brown, and Tai-Ho Chen are with the CDC COVID-19 Ports of Entry Team, Atlanta, GA
| | - Araceli Rey
- Leigh Ellyn Preston, Araceli Rey, Simone Dumas, Andrea Rodriguez, Alida Gertz, Kristin C. Delea, Francisco Alvarado-Ramy, Deborah L. Christensen, Clive Brown, and Tai-Ho Chen are with the CDC COVID-19 Ports of Entry Team, Atlanta, GA
| | - Simone Dumas
- Leigh Ellyn Preston, Araceli Rey, Simone Dumas, Andrea Rodriguez, Alida Gertz, Kristin C. Delea, Francisco Alvarado-Ramy, Deborah L. Christensen, Clive Brown, and Tai-Ho Chen are with the CDC COVID-19 Ports of Entry Team, Atlanta, GA
| | - Andrea Rodriguez
- Leigh Ellyn Preston, Araceli Rey, Simone Dumas, Andrea Rodriguez, Alida Gertz, Kristin C. Delea, Francisco Alvarado-Ramy, Deborah L. Christensen, Clive Brown, and Tai-Ho Chen are with the CDC COVID-19 Ports of Entry Team, Atlanta, GA
| | - Alida M Gertz
- Leigh Ellyn Preston, Araceli Rey, Simone Dumas, Andrea Rodriguez, Alida Gertz, Kristin C. Delea, Francisco Alvarado-Ramy, Deborah L. Christensen, Clive Brown, and Tai-Ho Chen are with the CDC COVID-19 Ports of Entry Team, Atlanta, GA
| | - Kristin C Delea
- Leigh Ellyn Preston, Araceli Rey, Simone Dumas, Andrea Rodriguez, Alida Gertz, Kristin C. Delea, Francisco Alvarado-Ramy, Deborah L. Christensen, Clive Brown, and Tai-Ho Chen are with the CDC COVID-19 Ports of Entry Team, Atlanta, GA
| | - Francisco Alvarado-Ramy
- Leigh Ellyn Preston, Araceli Rey, Simone Dumas, Andrea Rodriguez, Alida Gertz, Kristin C. Delea, Francisco Alvarado-Ramy, Deborah L. Christensen, Clive Brown, and Tai-Ho Chen are with the CDC COVID-19 Ports of Entry Team, Atlanta, GA
| | - Deborah L Christensen
- Leigh Ellyn Preston, Araceli Rey, Simone Dumas, Andrea Rodriguez, Alida Gertz, Kristin C. Delea, Francisco Alvarado-Ramy, Deborah L. Christensen, Clive Brown, and Tai-Ho Chen are with the CDC COVID-19 Ports of Entry Team, Atlanta, GA
| | - Clive Brown
- Leigh Ellyn Preston, Araceli Rey, Simone Dumas, Andrea Rodriguez, Alida Gertz, Kristin C. Delea, Francisco Alvarado-Ramy, Deborah L. Christensen, Clive Brown, and Tai-Ho Chen are with the CDC COVID-19 Ports of Entry Team, Atlanta, GA
| | - Tai-Ho Chen
- Leigh Ellyn Preston, Araceli Rey, Simone Dumas, Andrea Rodriguez, Alida Gertz, Kristin C. Delea, Francisco Alvarado-Ramy, Deborah L. Christensen, Clive Brown, and Tai-Ho Chen are with the CDC COVID-19 Ports of Entry Team, Atlanta, GA
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Jiang S, You C, Zhang S, Chen F, Peng G, Liu J, Xie D, Li Y, Guo X. Using search trends to analyze web-based users' behavior profiles connected with COVID-19 in mainland China: infodemiology study based on hot words and Baidu Index. PeerJ 2022; 10:e14343. [PMID: 36389414 PMCID: PMC9653070 DOI: 10.7717/peerj.14343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/14/2022] [Indexed: 11/10/2022] Open
Abstract
Background Mainland China, the world's most populous region, experienced a large-scale coronavirus disease 2019 (COVID-19) outbreak in 2020 and 2021, respectively. Existing infodemiology studies have primarily concentrated on the prospective surveillance of confirmed cases or symptoms which met the criterion for investigators; nevertheless, the actual impact regarding COVID-19 on the public and subsequent attitudes of different groups towards the COVID-19 epidemic were neglected. Methods This study aimed to examine the public web-based search trends and behavior patterns related to COVID-19 outbreaks in mainland China by using hot words and Baidu Index (BI). The initial hot words (the high-frequency words on the Internet) and the epidemic data (2019/12/01-2021/11/30) were mined from infodemiology platforms. The final hot words table was established by two-rounds of hot words screening and double-level hot words classification. Temporal distribution and demographic portraits of COVID-19 were queried by search trends service supplied from BI to perform the correlation analysis. Further, we used the parameter estimation to quantitatively forecast the geographical distribution of COVID-19 in the future. Results The final English-Chinese bilingual table was established including six domains and 32 subordinate hot words. According to the temporal distribution of domains and subordinate hot words in 2020 and 2021, the peaks of searching subordinate hot words and COVID-19 outbreak periods had significant temporal correlation and the subordinate hot words in COVID-19 Related and Territory domains were reliable for COVID-19 surveillance. Gender distribution results showed that Territory domain (the male proportion: 67.69%; standard deviation (SD): 5.88%) and Symptoms/Symptom and Public Health (the female proportion: 57.95%, 56.61%; SD: 0, 9.06%) domains were searched more by male and female groups respectively. The results of age distribution of hot words showed that people aged 20-50 (middle-aged people) had a higher online search intensity, and the group of 20-29, 30-39 years old focused more on Media and Symptoms/Symptom (proportion: 45.43%, 51.66%; SD: 15.37%, 16.59%) domains respectively. Finally, based on frequency rankings of searching hot words and confirmed cases in Mainland China, the epidemic situation of provinces and Chinese administrative divisions were divided into 5 levels of early-warning regions. Central, East and South China regions would be impacted again by the COVID-19 in the future.
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Affiliation(s)
- Shuai Jiang
- College of Biology, Hunan University, Changsha, Hunan Province, China
| | - Changqiao You
- NanHua Bio-medicine Co.,Ltd., Changsha, Hunan, China
| | - Sheng Zhang
- College of Biology, Hunan University, Changsha, Hunan Province, China
| | - Fenglin Chen
- College of Biology, Hunan University, Changsha, Hunan Province, China
| | - Guo Peng
- College of Biology, Hunan University, Changsha, Hunan Province, China
| | - Jiajie Liu
- College of Biology, Hunan University, Changsha, Hunan Province, China
| | - Daolong Xie
- College of Biology, Hunan University, Changsha, Hunan Province, China
| | - Yongliang Li
- College of Biology, Hunan University, Changsha, Hunan Province, China
| | - Xinhong Guo
- College of Biology, Hunan University, Changsha, Hunan Province, China
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Guan J, Zhao Y, Wei Y, Shen S, You D, Zhang R, Lange T, Chen F. Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges. MEDICAL REVIEW (BERLIN, GERMANY) 2022; 2:89-109. [PMID: 35658113 PMCID: PMC9047651 DOI: 10.1515/mr-2021-0022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 01/03/2022] [Indexed: 12/20/2022]
Abstract
Since late 2019, the beginning of coronavirus disease 2019 (COVID-19) pandemic, transmission dynamics models have achieved great development and were widely used in predicting and policy making. Here, we provided an introduction to the history of disease transmission, summarized transmission dynamics models into three main types: compartment extension, parameter extension and population-stratified extension models, highlight the key contribution of transmission dynamics models in COVID-19 pandemic: estimating epidemiological parameters, predicting the future trend, evaluating the effectiveness of control measures and exploring different possibilities/scenarios. Finally, we pointed out the limitations and challenges lie ahead of transmission dynamics models.
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Affiliation(s)
- Jinxing Guan
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yang Zhao
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,China International Cooperation Center for Environment and Human Health, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China.,Center of Biomedical BigData, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yongyue Wei
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,China International Cooperation Center for Environment and Human Health, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Sipeng Shen
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Dongfang You
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ruyang Zhang
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Theis Lange
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Feng Chen
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,China International Cooperation Center for Environment and Human Health, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China
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