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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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2
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Yu J, Azzam EI, Jadhav AB, Wang Y. COVID-19: The Disease, the Immunological Challenges, the Treatment with Pharmaceuticals and Low-Dose Ionizing Radiation. Cells 2021; 10:2212. [PMID: 34571861 PMCID: PMC8470324 DOI: 10.3390/cells10092212] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 08/20/2021] [Accepted: 08/23/2021] [Indexed: 02/07/2023] Open
Abstract
The year 2020 will be carved in the history books-with the proliferation of COVID-19 over the globe and with frontline health workers and basic scientists worldwide diligently fighting to alleviate life-threatening symptoms and curb the spread of the disease. Behind the shocking prevalence of death are countless families who lost loved ones. To these families and to humanity as a whole, the tallies are not irrelevant digits, but a motivation to develop effective strategies to save lives. However, at the onset of the pandemic, not many therapeutic choices were available besides supportive oxygen, anti-inflammatory dexamethasone, and antiviral remdesivir. Low-dose radiation (LDR), at a much lower dosage than applied in cancer treatment, re-emerged after a 75-year silence in its use in unresolved pneumonia, as a scientific interest with surprising effects in soothing the cytokine storm and other symptoms in severe COVID-19 patients. Here, we review the epidemiology, symptoms, immunological alterations, mutations, pharmaceuticals, and vaccine development of COVID-19, summarizing the history of X-ray irradiation in non-COVID diseases (especially pneumonia) and the currently registered clinical trials that apply LDR in treating COVID-19 patients. We discuss concerns, advantages, and disadvantages of LDR treatment and potential avenues that may provide empirical evidence supporting its potential use in defending against the pandemic.
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Affiliation(s)
- Jihang Yu
- Radiobiology and Health, Isotopes, Radiobiology & Environment Directorate (IRED), Canadian Nuclear Laboratories (CNL), Chalk River, ON K0J 1J0, Canada; (J.Y.); (E.I.A.); (A.B.J.)
| | - Edouard I. Azzam
- Radiobiology and Health, Isotopes, Radiobiology & Environment Directorate (IRED), Canadian Nuclear Laboratories (CNL), Chalk River, ON K0J 1J0, Canada; (J.Y.); (E.I.A.); (A.B.J.)
| | - Ashok B. Jadhav
- Radiobiology and Health, Isotopes, Radiobiology & Environment Directorate (IRED), Canadian Nuclear Laboratories (CNL), Chalk River, ON K0J 1J0, Canada; (J.Y.); (E.I.A.); (A.B.J.)
| | - Yi Wang
- Radiobiology and Health, Isotopes, Radiobiology & Environment Directorate (IRED), Canadian Nuclear Laboratories (CNL), Chalk River, ON K0J 1J0, Canada; (J.Y.); (E.I.A.); (A.B.J.)
- Department of Biochemistry Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
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Lu S, Wang W, Cheng Y, Yang C, Jiao Y, Xu M, Bai Y, Yang J, Song H, Wang L, Wang J, Rong B, Xu J. Food-trade-associated COVID-19 outbreak from a contaminated wholesale food supermarket in Beijing. JOURNAL OF BIOSAFETY AND BIOSECURITY 2021; 3:58-65. [PMID: 34222840 PMCID: PMC8233866 DOI: 10.1016/j.jobb.2021.04.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 04/06/2021] [Accepted: 04/14/2021] [Indexed: 11/19/2022] Open
Abstract
The re-emerging outbreak of COVID-19 in Beijing, China, in the summer of 2020 originated from a SARS-CoV-2-infested wholesale food supermarket. We postulated that the Xinfadi market outbreak has links with food-trade activities. Our Susceptible to the disease, Infectious, and Recovered coupled Agent Based Modelling (SIR-ABM) analysis for studying the diffusion of SARS-CoV-2 particles suggested that the trade-distancing strategy effectively reduces the reproduction number (R0). The retail shop closure strategy reduced the number of visitors to the market by nearly half. In addition, the buy-local policy option reduced the infection by more than 70% in total. Therefore, retail closures and buy-local policies could serve as significantly effective strategies that have the potential to reduce the size of the outbreak and prevent probable outbreaks in the future.
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Affiliation(s)
- Shan Lu
- State Key Laboratory of Infectious Disease Prevention and Control, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, PR China
- Shanghai Public Health Clinical Center, Shanghai Institute for Emerging and Re-emerging Infectious Diseases, Shanghai, PR China
- Research Units of Discovery of Unknown Bacteria and Function, Chinese Academy of Medical Sciences, Beijing, PR China
| | | | - Yanpeng Cheng
- Scholl of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, PR China
| | - Caixin Yang
- Scholl of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, PR China
| | - Yifan Jiao
- Scholl of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, PR China
| | - Mingchao Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, PR China
| | - Yibo Bai
- Scholl of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, PR China
| | - Jing Yang
- State Key Laboratory of Infectious Disease Prevention and Control, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, PR China
- Shanghai Public Health Clinical Center, Shanghai Institute for Emerging and Re-emerging Infectious Diseases, Shanghai, PR China
- Research Units of Discovery of Unknown Bacteria and Function, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Hongbin Song
- Center for Disease Control and Prevention of Chinese People's Liberation Army, Fengtai District, Beijing, PR China
| | - Ligui Wang
- Center for Disease Control and Prevention of Chinese People's Liberation Army, Fengtai District, Beijing, PR China
| | - Jiaojiao Wang
- The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, PR China
| | - Bing Rong
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, PR China
| | - Jianguo Xu
- State Key Laboratory of Infectious Disease Prevention and Control, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, PR China
- Shanghai Public Health Clinical Center, Shanghai Institute for Emerging and Re-emerging Infectious Diseases, Shanghai, PR China
- Research Units of Discovery of Unknown Bacteria and Function, Chinese Academy of Medical Sciences, Beijing, PR China
- Institute of Public Health, Nankai University, Tianjing, PR China
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Khan IM, Haque U, Zhang W, Zafar S, Wang Y, He J, Sun H, Lubinda J, Rahman MS. COVID-19 in China: Risk Factors and R 0 Revisited. Acta Trop 2021; 213:105731. [PMID: 33164890 PMCID: PMC7581355 DOI: 10.1016/j.actatropica.2020.105731] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 09/25/2020] [Accepted: 10/14/2020] [Indexed: 01/06/2023]
Abstract
The COVID-19 epidemic spread rapidly through China and subsequently proliferated globally leading to a pandemic situation around the globe. Human-to-human transmission, as well as asymptomatic transmission of the infection, have been confirmed. As of April 03, 2020, public health crisis in China due to COVID-19 was potentially under control. We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020. Understanding the characteristics of spatial clustering of the COVID-19 epidemic and R0 is critical in effectively preventing and controlling the ongoing global pandemic. Considering this, the prefectures were grouped based on several relevant features using unsupervised machine learning techniques. Subsequently, we performed a computational analysis utilizing the reported cases in China to estimate the revised R0 among different regions. Finally, our overall research indicates that the impact of temperature and demographic factors on virus transmission may be characterized using a stochastic transmission model. Such predictions will help in prevention planning in an ongoing global pandemic, prioritizing segments of a given community/region for action and providing a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.
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Affiliation(s)
- Irtesam Mahmud Khan
- Department of Computer Science & Engineering, Bangladesh University of Engineering & Technology, West Palasi, Dhaka 1205, Bangladesh.
| | - Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, USA.
| | - Wenyi Zhang
- Center for Disease Surveillance and Research, Center for Disease Control and Prevention of PLA, Beijing, People's Republic of China.
| | | | - Yong Wang
- Center for Disease Surveillance and Research, Center for Disease Control and Prevention of PLA, Beijing, People's Republic of China.
| | - Junyu He
- Ocean College, Zhejiang University, Zhoushan, People's Republic of China; Ocean Academy, Zhejiang University, Zhoushan, People's Republic of China.
| | - Hailong Sun
- Center for Disease Surveillance and Research, Center for Disease Control and Prevention of PLA, Beijing, People's Republic of China.
| | - Jailos Lubinda
- School of Geography and Environmental Sciences, Ulster University, Coleraine, UK.
| | - M Sohel Rahman
- Department of Computer Science & Engineering, Bangladesh University of Engineering & Technology, West Palasi, Dhaka 1205, Bangladesh.
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