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Duval D, Evans B, Sanders A, Hill J, Simbo A, Kavoi T, Lyell I, Simmons Z, Qureshi M, Pearce-Smith N, Arevalo CR, Beck CR, Bindra R, Oliver I. Non-pharmaceutical interventions to reduce COVID-19 transmission in the UK: a rapid mapping review and interactive evidence gap map. J Public Health (Oxf) 2024; 46:e279-e293. [PMID: 38426578 PMCID: PMC11141784 DOI: 10.1093/pubmed/fdae025] [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: 10/16/2023] [Revised: 01/15/2024] [Accepted: 01/23/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Non-pharmaceutical interventions (NPIs) were crucial in the response to the COVID-19 pandemic, although uncertainties about their effectiveness remain. This work aimed to better understand the evidence generated during the pandemic on the effectiveness of NPIs implemented in the UK. METHODS We conducted a rapid mapping review (search date: 1 March 2023) to identify primary studies reporting on the effectiveness of NPIs to reduce COVID-19 transmission. Included studies were displayed in an interactive evidence gap map. RESULTS After removal of duplicates, 11 752 records were screened. Of these, 151 were included, including 100 modelling studies but only 2 randomized controlled trials and 10 longitudinal observational studies.Most studies reported on NPIs to identify and isolate those who are or may become infectious, and on NPIs to reduce the number of contacts. There was an evidence gap for hand and respiratory hygiene, ventilation and cleaning. CONCLUSIONS Our findings show that despite the large number of studies published, there is still a lack of robust evaluations of the NPIs implemented in the UK. There is a need to build evaluation into the design and implementation of public health interventions and policies from the start of any future pandemic or other public health emergency.
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Affiliation(s)
- D Duval
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - B Evans
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - A Sanders
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - J Hill
- Clinical and Public Health Response Division, UKHSA, London E14 5EA, UK
| | - A Simbo
- Evaluation and Epidemiological Science Division, UKHSA, Colindale NW9 5EQ, UK
| | - T Kavoi
- Cheshire and Merseyside Health Protection Team, UKHSA, Liverpool L3 1DS, UK
| | - I Lyell
- Greater Manchester Health Protection Team, UKHSA, Manchester M1 3BN, UK
| | - Z Simmons
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - M Qureshi
- Clinical and Public Health Response Division, UKHSA, London E14 5EA, UK
| | - N Pearce-Smith
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - C R Arevalo
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - C R Beck
- Evaluation and Epidemiological Science Division, UKHSA, Salisbury SP4 0JG, UK
| | - R Bindra
- Clinical and Public Health Response Division, UKHSA, London E14 5EA, UK
| | - I Oliver
- Director General Science and Research and Chief Scientific Officer, UKHSA, London E14 5EA, UK
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Jiang X, Jia R, Yang L. Assessing the economic ripple effect of flood disasters in light of the recovery process: Insights from an agent-based model. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024; 44:203-228. [PMID: 37121578 DOI: 10.1111/risa.14147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/08/2023] [Accepted: 03/20/2023] [Indexed: 06/19/2023]
Abstract
To assess the economic ripple effect, this study integrates agent-based modeling (ABM) with a multiregional input-output (MRIO) table to develop an assessment model that considers capacity recovery process. The intermediate and final demands in the MRIO table are used to describe the agents' interdependence. Survival analysis is used to construct capacity rate curves. By defining the first- and second-order ripple effects, ABM is used to capture the ripple process in days. To conduct a case study, the service and retail sectors in Enshi in Hubei, China, are selected as disaster-affected sectors (they were severely affected by the July 17, 2020 flood disaster). The main findings are as follows: (1) With the first-order ripple effect, the losses caused by service and retail are concentrated within Enshi. Enshi's final demand, construction, and raw materials manufacturing sectors as well as Wuhan's construction sector are seriously affected. (2) With the second-order ripple effect, the losses caused by the service and retail sectors expand, forming a prominent industrial ripple chain: "service (retail)-raw materials manufacturing-construction." (3) The direct and indirect losses caused by the service sector are more significant than those caused by the retail sector. However, the loss ratio of the service sector is smaller than that of the retail sector because of its sound industrial structure and strong resilience. Hence, the indirect losses caused by different sectors are not entirely determined by their direct losses; instead, they are also related to the degree of perfection of the structures of different sectors.
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Affiliation(s)
- Xinyu Jiang
- School of Management, Wuhan University of Technology, Wuhan, Hubei, China
- Research Institute of Digital Governance and Management Decision Innovation, Wuhan University of Technology, Wuhan, Hubei, China
| | - Ruiying Jia
- School of Management, Wuhan University of Technology, Wuhan, Hubei, China
| | - Lijiao Yang
- School of Management, Harbin Institute of Technology, Harbin, Heilongjiang, China
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Sun Z, Bai R, Bai Z. The application of simulation methods during the COVID-19 pandemic: A scoping review. J Biomed Inform 2023; 148:104543. [PMID: 37956729 DOI: 10.1016/j.jbi.2023.104543] [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: 02/03/2023] [Revised: 10/19/2023] [Accepted: 11/09/2023] [Indexed: 11/15/2023]
Abstract
With the outbreak of COVID-19 pandemic, simulation modelling approaches have become effective tools to simulate the potential effects of different intervention measures and predict the dynamic COVID-19 trends. In this scoping review, Studies published between February 2020 and May 2022 that investigated the spread of COVID-19 using four common simulation modeling methods were systematically reported and summarized. Publication trend, characteristics, software, and code availability of included articles were analyzed. Among the included 340 studies, most articles used agent-based model (ABM; n = 258; 75.9 %), followed by the models of system dynamics (n = 42; 12.4 %), discrete event simulation (n = 25; 7.4 %), and hybrid simulation (n = 15; 4.4 %). Furthermore, our review emphasized the purposes and sample time period of included articles. We classified the purpose of the 340 included studies into five categories, most studies mainly analyzed the spread of COVID-19 under policy interventions. For the sample time period analysis, most included studies analyzed the COVID-19 spread in the second wave. Our findings play a crucial role for policymakers to make evidence-based decisions in preventing the spread of COVID-19 pandemic and help in providing scientific decision-makings resilient to similar events and infectious diseases in the future.
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Affiliation(s)
- Zhuanlan Sun
- High-Quality Development Evaluation Institute, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Ruhai Bai
- Evidence-Based Research Center of Social Science and Health, School of Public Affairs, Nanjing University of Science and Technology, Nanjing, China
| | - Zhenggang Bai
- Evidence-Based Research Center of Social Science and Health, School of Public Affairs, Nanjing University of Science and Technology, Nanjing, China.
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Al-Bazi A, Madi F, Monshar AA, Eliya Y, Adediran T, Khudir KA. Modelling the impact of non-pharmaceutical interventions on COVID-19 exposure in closed-environments using agent-based modelling. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2023. [DOI: 10.1080/20479700.2023.2189555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
Affiliation(s)
- Ammar Al-Bazi
- Aston Business School, Aston University, Birmingham, UK
| | - Faris Madi
- Faculty of Engineering, Environment and Computing, Coventry University, Coventry, UK
| | | | - Yousif Eliya
- Department of Health Research Methods, Evidence & Impact, Health Sciences Centre, McMaster University, Hamilton, Canada
| | - Tunde Adediran
- Faculty of Engineering, Environment and Computing, Coventry University, Coventry, UK
| | - Khaled Al Khudir
- Faculty of Engineering, Environment and Computing, Coventry University, Coventry, UK
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Hezam IM, Almshnanah A, Mubarak AA, Das A, Foul A, Alrasheedi AF. COVID-19 and Rumors: A Dynamic Nested Optimal Control Model. PATTERN RECOGNITION 2023; 135:109186. [PMID: 36405882 PMCID: PMC9663144 DOI: 10.1016/j.patcog.2022.109186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 11/11/2022] [Accepted: 11/13/2022] [Indexed: 06/16/2023]
Abstract
Unfortunately, the COVID-19 outbreak has been accompanied by the spread of rumors and depressing news. Herein, we develop a dynamic nested optimal control model of COVID-19 and its rumor outbreaks. The model aims to curb the epidemics by reducing the number of individuals infected with COVID-19 and reducing the number of rumor-spreaders while minimizing the cost associated with the control interventions. We use the modified approximation Karush-Kuhn-Tucker conditions with the Hamiltonian function to simplify the model before solving it using a genetic algorithm. The present model highlights three prevention measures that affect COVID-19 and its rumor outbreaks. One represents the interventions to curb the COVID-19 pandemic. The other two represent interventions to increase awareness, disseminate the correct information, and impose penalties on the spreaders of false rumors. The results emphasize the importance of interventions in curbing the spread of the COVID-19 pandemic and its associated rumor problems alike.
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Affiliation(s)
- Ibrahim M Hezam
- Statistics & Operations Research Department, College of Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Abdulkarem Almshnanah
- Computer & Information Technology, Jordan University of Science and Technology, Irbid, Jorden
| | - Ahmed A Mubarak
- School of Computer and Science- Shaanxi Normal University-Xian- China, 710119
| | - Amrit Das
- School of Advanced Sciences, Vellore Institute of Technology, Chennai, India
- Department of Industrial Engineering, Pusan National University, Busan 46241, Korea
| | - Abdelaziz Foul
- Statistics & Operations Research Department, College of Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Adel Fahad Alrasheedi
- Statistics & Operations Research Department, College of Sciences, King Saud University, Riyadh, Saudi Arabia
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Meintrup D, Nowak-Machen M, Borgmann S. A Comparison of Germany and the United Kingdom Indicates That More SARS-CoV-2 Circulation and Less Restrictions in the Warm Season Might Reduce Overall COVID-19 Burden. LIFE (BASEL, SWITZERLAND) 2022; 12:life12070953. [PMID: 35888043 PMCID: PMC9322333 DOI: 10.3390/life12070953] [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: 05/19/2022] [Revised: 06/17/2022] [Accepted: 06/22/2022] [Indexed: 12/03/2022]
Abstract
(1) Background: Between March 2020 and January 2022 severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) caused five infection waves in Europe. The first and the second wave was caused by wildtype SARS-CoV-2, while the following waves were caused by the variants of concern Alpha, Delta, and Omicron respectively. (2) Methods: In the present analysis, the first four waves were compared in Germany and the UK, in order to examine the COVID-19 epidemiology and its modulation by non-pharmaceutical interventions (NPI). (3) Results: The number of COVID-19 patients on intensive care units and the case fatality rate were used to estimate disease burden, the excess mortality to assess the net effect of NPI and other measures on the population. The UK was more severely affected by the first and the third wave while Germany was more affected by the second wave. The UK had a higher excess mortality during the first wave, afterwards the excess mortality in both countries was nearly identical. While most NPI were lifted in the UK in July 2021, the measures were kept and even aggravated in Germany. Nevertheless, in autumn 2021 Germany was much more affected, nearly resulting in a balanced sum of infections and deaths compared to the UK. Within the whole observation period, in Germany the number of COVID-19 patients on ICUs was up to four times higher than in the UK. Our results show that NPI have a limited effect on COVID-19 burden, seasonality plays a crucial role, and a higher virus circulation in a pre-wave situation could be beneficial. (4) Conclusions: Although Germany put much more effort and resources to fight the pandemic, the net balance of both countries was nearly identical, questioning the benefit of excessive ICU treatments and of the implementation of NPI, especially during the warm season.
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Affiliation(s)
- David Meintrup
- Faculty of Engineering and Management, University of Applied Sciences Ingolstadt, 85049 Ingolstadt, Germany
- Correspondence:
| | - Martina Nowak-Machen
- Department of Anaesthesia and Intensive Care Medicine, Ingolstadt Hospital, 85049 Ingolstadt, Germany;
- Department of Anesthesiology and Intensive Care Medicine, Teaching Faculty, University Hospital Tuebingen, Eberhard-Karls-University, 72076 Tuebingen, Germany
| | - Stefan Borgmann
- Department of Infectious Diseases and Infection Control, Ingolstadt Hospital, 85049 Ingolstadt, Germany;
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