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Yin ZJ, Xiao H, McDonald S, Brusic V, Qiu TY. Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID-19. J Med Virol 2023; 95:e29301. [PMID: 38087460 DOI: 10.1002/jmv.29301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/16/2023] [Accepted: 11/24/2023] [Indexed: 12/18/2023]
Abstract
The COVID-19 pandemic was characterized by multiple subsequent, overlapping outbreaks, as well as extremely rapid changes in viral genomes. The information about local epidemics spread and the epidemic control measures was shared on a daily basis (number of cases and deaths) via centralized repositories. The vaccines were developed within the first year of the pandemic. New modes of monitoring and sharing of epidemic data were implemented using Internet resources. We modified the basic SEIR compartmental model to include public health measures, multiwave scenarios, and the variation of viral infectivity and transmissibility reflected by the basic reproduction number R0 of emerging viral variants. SVEIR(MH) model considers the capacity of the medical system, lockdowns, vaccination, and changes in viral reproduction rate on the epidemic spread. The developed model uses daily infection reports for assessing the epidemic dynamics, and daily changes of mobility data from mobile phone networks to assess the lockdown effectiveness. This model was deployed to six European regions Baden-Württemberg (Germany), Belgium, Czechia, Lombardy (Italy), Sweden, and Switzerland for the first 2 years of the pandemic. The correlation coefficients between observed and reported infection data showed good concordance for both years of the pandemic (ρ = 0.84-0.94 for the raw data and ρ = 0.91-0.98 for smoothed 7-day averages). The results show stability across the regions and the different epidemic waves. Optimal control of epidemic waves can be achieved by dynamically adjusting epidemic control measures in real-time. SVEIR(MH) model can simulate different scenarios and inform adjustments to the public health policies to achieve the target outcomes. Because this model is highly representative of actual epidemic situations, it can be used to assess both the public health and socioeconomic effects of the public health measures within the first 7 days of the outbreak.
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Affiliation(s)
- Zuo-Jing Yin
- Institute of Clinical Science, Zhongshan Hospital; Shanghai Institute of Infectious Disease and Biosecurity; Intelligent Medicine Institute, Fudan University, Shanghai, China
| | - Han Xiao
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Stuart McDonald
- Smart Medicine Laboratory, School of Economics, University of Nottingham Ningbo China, Ningbo, China
| | - Vladimir Brusic
- Smart Medicine Laboratory, School of Economics, University of Nottingham Ningbo China, Ningbo, China
| | - Tian-Yi Qiu
- Institute of Clinical Science, Zhongshan Hospital; Shanghai Institute of Infectious Disease and Biosecurity; Intelligent Medicine Institute, Fudan University, Shanghai, China
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Li P, Chen X, Ma C, Zhu C, Lu W. Risk assessment of COVID-19 infection for subway commuters integrating dynamic changes in passenger numbers. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:74715-74724. [PMID: 35639325 PMCID: PMC9153871 DOI: 10.1007/s11356-022-20920-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/13/2022] [Indexed: 06/15/2023]
Abstract
The COVID-19 global pandemic has had a significant impact on mass travel. We examined the risk of transmission of COVID-19 infection between subway commuters using the Susceptible Exposed Infected Recovered (SEIR) model. The model considered factors that may influence virus transmission, namely subway disinfection, ventilation capacity, average commuter spacing, single subway journey time, COVID-19 transmission capacity, and dynamic changes in passenger numbers. Based on these parameters, above a certain threshold (25 min), the risk of infection for susceptible people increased significantly as journey time increased. Average distance between commuters and levels of ventilation and disinfection were also important influencing factors. Meanwhile, the model also indicated that the risk of infection varied at different times of the day. Therefore, this paper recommends strengthening ventilation and disinfection in the carriages and limiting the time of single journeys, with an average distance of at least 1 m between passengers. In this light, subway commuters need to take proactive precautions to reduce their risk of COVID-19 infection. Also, the results show the importance of managing subway stations efficiently during epidemic and post-epidemic eras.
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Affiliation(s)
- Peikun Li
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing, 100044, China
| | - Xumei Chen
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing, 100044, China.
| | - Chaoqun Ma
- College of Transportation Engineering, Chang'an University, Xi'an, 710064, Shaanxi, China
| | - Caihua Zhu
- College of Transportation Engineering, Chang'an University, Xi'an, 710064, Shaanxi, China
| | - Wenbo Lu
- School of Transportation, Southeast University, Nanjing, 214135, Jiangsu, China
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Cui Z, Cai M, Xiao Y, Zhu Z, Yang M, Chen G. Forecasting the transmission trends of respiratory infectious diseases with an exposure-risk-based model at the microscopic level. ENVIRONMENTAL RESEARCH 2022; 212:113428. [PMID: 35568232 PMCID: PMC9095069 DOI: 10.1016/j.envres.2022.113428] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/30/2022] [Accepted: 05/02/2022] [Indexed: 05/03/2023]
Abstract
Respiratory infectious diseases (e.g., COVID-19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies focus on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of transmission trends. Firstly, the front two modules reproduce the movements of individuals and the droplets of infectors' expiratory activities, respectively. Then, the outputs are fed to the third module to estimate the personal exposure risk. Finally, the number of new cases is predicted in the final module. By predicting the new COVID- 19 cases in the United States, the performances of our model and 4 other existing macroscopic or microscopic models are compared. Specifically, the mean absolute error, root mean square error, and mean absolute percentage error provided by the proposed model are respectively 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models. The quantitative results reveal that our model can accurately predict the transmission trends from a microscopic perspective, and it can benefit the further investigation of many microscopic disease transmission factors (e.g., non-walkable areas and facility layouts).
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Affiliation(s)
- Ziwei Cui
- School of Intelligent System Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
| | - Ming Cai
- School of Intelligent System Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
| | - Yao Xiao
- School of Intelligent System Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
| | - Zheng Zhu
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Mofeng Yang
- Maryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland at College Park, Maryland, USA.
| | - Gongbo Chen
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China.
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Musse SR, Cassol VJ, Thalmann D. A history of crowd simulation: the past, evolution, and new perspectives. THE VISUAL COMPUTER 2021; 37:3077-3092. [PMID: 34376881 PMCID: PMC8339167 DOI: 10.1007/s00371-021-02252-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 06/13/2023]
Abstract
This paper aims to discuss the past, evolution, and new perspectives in crowd simulation. Many work have been produced and published in this area that was launched approximately 30 years ago. In this paper, we re-visited the main aspects of the area, presenting the periods and evolution we had in the past. In addition, we also discuss the present and possible trends for the future.
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Du B, Zhao Z, Zhao J, Yu L, Sun L, Lv W. Modelling the epidemic dynamics of COVID-19 with consideration of human mobility. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2021; 12:369-382. [PMID: 34189256 PMCID: PMC8221990 DOI: 10.1007/s41060-021-00271-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 06/03/2021] [Indexed: 01/24/2023]
Abstract
So far COVID-19 has resulted in mass deaths and huge economic losses across the world. Various measures such as quarantine and social distancing have been taken to prevent the spread of this disease. These prevention measures have changed the transmission dynamics of COVID-19 and introduced new challenges for epidemic modelling and prediction. In this paper, we study a novel disease spreading model with two important aspects. First, the proposed model takes the quarantine effect of confirmed cases on transmission dynamics into account, which can better resemble the real-world scenario. Second, our model incorporates two types of human mobility, where the intra-region human mobility is related to the internal transmission speed of the disease in the focal area and the inter-region human mobility reflects the scale of external infectious sources to a focal area. With the proposed model, we use the human mobility data from 24 cities in China and 8 states in the USA to analyse the disease spreading patterns. The results show that our model could well fit/predict the reported cases in both countries. The predictions and findings shed light on how to effectively control COVID-19 by managing human mobility behaviours.
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Affiliation(s)
- Bowen Du
- State Key Laboratory of Software Development Environment, Beihang University, Haidian District, Beijing, 100191 China
| | - Zirong Zhao
- State Key Laboratory of Software Development Environment, Beihang University, Haidian District, Beijing, 100191 China
| | - Jiejie Zhao
- State Key Laboratory of Software Development Environment, Beihang University, Haidian District, Beijing, 100191 China
| | - Le Yu
- State Key Laboratory of Software Development Environment, Beihang University, Haidian District, Beijing, 100191 China
| | - Leilei Sun
- State Key Laboratory of Software Development Environment, Beihang University, Haidian District, Beijing, 100191 China
| | - Weifeng Lv
- State Key Laboratory of Software Development Environment, Beihang University, Haidian District, Beijing, 100191 China
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