1
|
Zhang W, Liu S, Osgood N, Zhu H, Qian Y, Jia P. Using simulation modelling and systems science to help contain COVID-19: A systematic review. SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE 2022; 40:SRES2897. [PMID: 36245570 PMCID: PMC9538520 DOI: 10.1002/sres.2897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 05/23/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.
Collapse
Affiliation(s)
- Weiwei Zhang
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Shiyong Liu
- Institute of Advanced Studies in Humanities and Social SciencesBeijing Normal University at ZhuhaiZhuhaiChina
| | - Nathaniel Osgood
- Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
- Department of Community Health and EpidemiologyUniversity of SaskatchewanSaskatoonCanada
| | - Hongli Zhu
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Ying Qian
- Business SchoolUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Peng Jia
- School of Resource and Environmental SciencesWuhan UniversityWuhanHubeiChina
- International Institute of Spatial Lifecourse HealthWuhan UniversityWuhanHubeiChina
| |
Collapse
|
2
|
Kühn MJ, Abele D, Binder S, Rack K, Klitz M, Kleinert J, Gilg J, Spataro L, Koslow W, Siggel M, Meyer-Hermann M, Basermann A. Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany. BMC Infect Dis 2022; 22:333. [PMID: 35379190 PMCID: PMC8978163 DOI: 10.1186/s12879-022-07302-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 03/21/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. METHODS We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. RESULTS We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics. CONCLUSIONS Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent.
Collapse
Affiliation(s)
- Martin J Kühn
- Institute for Software Technology, German Aerospace Center, Cologne, Germany.
| | - Daniel Abele
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Sebastian Binder
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
| | - Kathrin Rack
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Margrit Klitz
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Jan Kleinert
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Jonas Gilg
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Luca Spataro
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Wadim Koslow
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Martin Siggel
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Michael Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
| | - Achim Basermann
- Institute for Software Technology, German Aerospace Center, Cologne, Germany.
| |
Collapse
|
4
|
Asada K, Komatsu M, Shimoyama R, Takasawa K, Shinkai N, Sakai A, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Kaneko S, Hamamoto R. Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics. J Pers Med 2021; 11:886. [PMID: 34575663 PMCID: PMC8471764 DOI: 10.3390/jpm11090886] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 12/12/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.
Collapse
Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryo Shimoyama
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Akira Sakai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masayoshi Yamada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of Endoscopy, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Kazuma Kobayashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| |
Collapse
|