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Andrup L, Krogfelt KA, Stephansen L, Hansen KS, Graversen BK, Wolkoff P, Madsen AM. Reduction of acute respiratory infections in day-care by non-pharmaceutical interventions: a narrative review. Front Public Health 2024; 12:1332078. [PMID: 38420031 PMCID: PMC10899481 DOI: 10.3389/fpubh.2024.1332078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/02/2024] [Indexed: 03/02/2024] Open
Abstract
Objective Children who start in day-care have 2-4 times as many respiratory infections compared to children who are cared for at home, and day-care staff are among the employees with the highest absenteeism. The extensive new knowledge that has been generated in the COVID-19 era should be used in the prevention measures we prioritize. The purpose of this narrative review is to answer the questions: Which respiratory viruses are the most significant in day-care centers and similar indoor environments? What do we know about the transmission route of these viruses? What evidence is there for the effectiveness of different non-pharmaceutical prevention measures? Design Literature searches with different terms related to respiratory infections in humans, mitigation strategies, viral transmission mechanisms, and with special focus on day-care, kindergarten or child nurseries, were conducted in PubMed database and Web of Science. Searches with each of the main viruses in combination with transmission, infectivity, and infectious spread were conducted separately supplemented through the references of articles that were retrieved. Results Five viruses were found to be responsible for ≈95% of respiratory infections: rhinovirus, (RV), influenza virus (IV), respiratory syncytial virus (RSV), coronavirus (CoV), and adenovirus (AdV). Novel research, emerged during the COVID-19 pandemic, suggests that most respiratory viruses are primarily transmitted in an airborne manner carried by aerosols (microdroplets). Conclusion Since airborne transmission is dominant for the most common respiratory viruses, the most important preventive measures consist of better indoor air quality that reduces viral concentrations and viability by appropriate ventilation strategies. Furthermore, control of the relative humidity and temperature, which ensures optimal respiratory functionality and, together with low resident density (or mask use) and increased time outdoors, can reduce the occurrence of respiratory infections.
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Affiliation(s)
- Lars Andrup
- The National Research Centre for the Working Environment, Copenhagen, Denmark
| | - Karen A Krogfelt
- Department of Science and Environment, Molecular and Medical Biology, PandemiX Center, Roskilde University, Roskilde, Denmark
| | - Lene Stephansen
- Gladsaxe Municipality, Social and Health Department, Gladsaxe, Denmark
| | | | | | - Peder Wolkoff
- The National Research Centre for the Working Environment, Copenhagen, Denmark
| | - Anne Mette Madsen
- The National Research Centre for the Working Environment, Copenhagen, Denmark
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Deng Q, Xiao X, Zhu L, Cao X, Liu K, Zhang H, Huang L, Yu F, Jiang H, Liu Y. A national risk analysis model (NRAM) for the assessment of COVID-19 epidemic. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2023; 43:1946-1961. [PMID: 36617495 DOI: 10.1111/risa.14087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 11/18/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
COVID-19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision-making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision-making in the context of COVID-19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID-19 at the national level.
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Affiliation(s)
- Qing Deng
- School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China
| | - Xingyu Xiao
- School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China
| | - Lin Zhu
- School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China
| | - Xue Cao
- School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China
| | - Kai Liu
- School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China
| | - Hui Zhang
- Deparment of Engineering Physics, Tsinghua University, Beijing, China
| | - Lida Huang
- Deparment of Engineering Physics, Tsinghua University, Beijing, China
| | - Feng Yu
- School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China
| | - Huiling Jiang
- School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China
| | - Yi Liu
- School of Public Security and Traffic Management, People's Public Security University of China, Beijing, China
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Chen K, Jiang X, Li Y, Zhou R. A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility. NONLINEAR DYNAMICS 2023; 111:1-17. [PMID: 37361002 PMCID: PMC10148626 DOI: 10.1007/s11071-023-08489-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 03/20/2023] [Indexed: 06/28/2023]
Abstract
The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. A major challenge in forecasting the transmission of COVID-19 is the accurate assessment of the multiscale human mobility and how it impacts infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of geographical places, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behavior and individual health conditions on the disease outbreak and the probability of zero-COVID in the population. Specifically, individuals perform power law-type local movements within a container and global transport between different-level containers. It is revealed that frequent long-distance movements inside a small-level container (e.g., a road or a county) and a small population size reduce both the local crowdedness and disease transmission. It takes only half of the time to induce global disease outbreaks when the population increases from 150 to 500 (normalized unit). When the exponent c 1 of the long-tail distribution of distance k moved in the same-level container, p ( k ) ∼ k - c 1 · level , increases, the outbreak time decreases rapidly from 75 to 25 (normalized unit). In contrast, travel between large-level containers (e.g., cities and nations) facilitates global spread of the disease and outbreak. When the mean traveling distance across containers 1 d increases from 0.5 to 1 (normalized unit), the outbreak occurs almost twice as fast. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or to a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing population size and restricting global travel help achieve zero-COVID-19. Specifically, when c 1 is smaller than 0.2, the ratio of people with low levels of mobility is larger than 80% and the population size is smaller than 400, zero-COVID can be achieved within fewer than 1000 time steps. In summary, the Mob-Cov model considers more realistic human mobility at a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to apply when investigating pandemic dynamics and when planning actions against disease. Supplementary Information The online version contains supplementary material available at 10.1007/s11071-023-08489-5.
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Affiliation(s)
- Kejie Chen
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
| | - Xiaomo Jiang
- Provincial Key Lab of Digital Twin for Industrial Equipment, Dalian, 116024 China
- School of Energy and Power Engineering, Dalian, 116024 China
| | - Yanqing Li
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
| | - Rongxin Zhou
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
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Bai Z, Ma Z, Jing L, Li Y, Wang S, Wang BG, Wu Y, Han X. Estimation and sensitivity analysis of a COVID-19 model considering the use of face mask and vaccination. Sci Rep 2023; 13:6434. [PMID: 37081069 PMCID: PMC10116124 DOI: 10.1038/s41598-023-33499-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 04/13/2023] [Indexed: 04/22/2023] Open
Abstract
To model the COVID-19 infection and develop effective control measures, this paper proposes an SEIR-type epidemic model considering the impact of face-mask wearing and vaccination. Firstly, the effective reproduction number and the threshold conditions are obtained. Secondly, based on the data of South Korea from January 20, 2022 to March 21, 2022, the model parameters are estimated. Finally, a sensitivity analysis and the numerical study are conducted. The results show that the face-mask wearing is associated with [Formula: see text] and [Formula: see text] reductions in the numbers of cumulative cases and newly confirmed cases, respectively, after a period of 60 days, when the face mask wearing rate increases by [Formula: see text]. Furthermore, the vaccination rate is associated with [Formula: see text] and [Formula: see text] reductions in the numbers of cumulative cases and the newly confirmed cases, respectively, after the same period of 60 days when the vaccination rate is increased by [Formula: see text]. A combined measure involving face-mask wearing and vaccination may be more effective and reasonable in preventing and controlling this infection. It is also suggested that disease control departments should strongly recommended the wearing of face masks s as well as vaccination to prevent the unvaccinated people from becoming infected.
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Affiliation(s)
- Zhongtian Bai
- The Second Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou University, Lanzhou, 730000, Gansu, People's Republic of China
- Gansu Province Key Laboratory Biotherapy and Regenerative Medicine, Lanzhou, 730000, Gansu, People's Republic of China
| | - Zhihui Ma
- School of Mathematics and Statistics, Lanzhou University, Lanzhou, 730000, Gansu, People's Republic of China.
| | - Libaihe Jing
- School of Life Sciences, Lanzhou University, Lanzhou, 730000, Gansu, People's Republic of China
| | - Yonghong Li
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, People's Republic of China
| | - Shufan Wang
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, 730000, Gansu, People's Republic of China
| | - Bin-Guo Wang
- School of Mathematics and Statistics, Lanzhou University, Lanzhou, 730000, Gansu, People's Republic of China
| | - Yan Wu
- School of Mathematics and Statistics, Lanzhou University, Lanzhou, 730000, Gansu, People's Republic of China
| | - Xiaotao Han
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, 730000, Gansu, People's Republic of China
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Social distancing as a public-good dilemma for socio-economic cost: an evolutionary game approach. Heliyon 2022; 8:e11497. [DOI: 10.1016/j.heliyon.2022.e11497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 09/11/2022] [Accepted: 11/04/2022] [Indexed: 11/16/2022] Open
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Jin W, Dong S, Yu C, Luo Q. A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning. Comput Biol Med 2022; 146:105560. [PMID: 35551008 PMCID: PMC9042415 DOI: 10.1016/j.compbiomed.2022.105560] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 03/18/2022] [Accepted: 04/06/2022] [Indexed: 01/31/2023]
Abstract
The COVID-19 outbreak poses a huge challenge to international public health. Reliable forecast of the number of cases is of great significance to the planning of health resources and the investigation and evaluation of the epidemic situation. The data-driven machine learning models can adapt to complex changes in the epidemic situation without relying on correct physical dynamics modeling, which are sensitive and accurate in predicting the development of the epidemic. In this paper, an ensemble hybrid model based on Temporal Convolutional Networks (TCN), Gated Recurrent Unit (GRU), Deep Belief Networks (DBN), Q-learning, and Support Vector Machine (SVM) models, namely TCN-GRU-DBN-Q-SVM model, is proposed to achieve the forecasting of COVID-19 infections. Three widely-used predictors, TCN, GRU, and DBN are used as elements of the hybrid model ensembled by the weights provided by reinforcement learning method. Furthermore, an error predictor built by SVM, is trained with validation set, and the final prediction result could be obtained by combining the TCN-GRU-DBN-Q model with the SVM error predictor. In order to investigate the forecasting performance of the proposed hybrid model, several comparison models (TCN-GRU-DBN-Q, LSTM, N-BEATS, ANFIS, VMD-BP, WT-RVFL, and ARIMA models) are selected. The experimental results show that: (1) the prediction effect of the TCN-GRU-DBN-Q-SVM model on COVID-19 infection is satisfactory, which has been verified in three national infection data from the UK, India, and the US, and the proposed model has good generalization ability; (2) in the proposed hybrid model, SVM can efficiently predict the possible error of the predicted series given by TCN-GRU-DBN-Q components; (3) the integrated weights based on Q-learning can be adaptively adjusted according to the characteristics of the data in the forecasting tasks in different countries and multiple situations, which ensures the accuracy, robustness and generalization of the proposed model.
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Affiliation(s)
- Weiqiu Jin
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, PR China,School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, PR China
| | - Shuqing Dong
- School of Traffic and Transportation Engineering, Central South University, Hunan, 410075, PR China
| | - Chengqing Yu
- School of Traffic and Transportation Engineering, Central South University, Hunan, 410075, PR China
| | - Qingquan Luo
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, PR China,School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, PR China,Corresponding author. Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, PR China
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