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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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Li H, Zhang H. Cost-effectiveness analysis of COVID-19 screening strategy under China's dynamic zero-case policy. Front Public Health 2023; 11:1099116. [PMID: 37228729 PMCID: PMC10203195 DOI: 10.3389/fpubh.2023.1099116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 04/17/2023] [Indexed: 05/27/2023] Open
Abstract
This study aims to optimize the COVID-19 screening strategies under China's dynamic zero-case policy through cost-effectiveness analysis. A total of 9 screening strategies with different screening frequencies and combinations of detection methods were designed. A stochastic agent-based model was used to simulate the progress of the COVID-19 outbreak in scenario I (close contacts were promptly quarantined) and scenario II (close contacts were not promptly quarantined). The primary outcomes included the number of infections, number of close contacts, number of deaths, the duration of the epidemic, and duration of movement restriction. Net monetary benefit (NMB) and the incremental cost-benefit ratio were used to compare the cost-effectiveness of different screening strategies. The results indicated that under China's COVID-19 dynamic zero-case policy, high-frequency screening can help contain the spread of the epidemic, reduce the size and burden of the epidemic, and is cost-effective. Mass antigen testing is not cost-effective compared with mass nucleic acid testing in the same screening frequency. It would be more cost-effective to use AT as a supplemental screening tool when NAT capacity is insufficient or when outbreaks are spreading very rapidly.
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Affiliation(s)
- Haonan Li
- School of Medical Business, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
- Guangdong Health Economics and Health Promotion Research Center, Guangzhou, Guangdong, China
| | - Hui Zhang
- School of Medical Business, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
- Guangdong Health Economics and Health Promotion Research Center, Guangzhou, Guangdong, China
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3
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Sulis E, Mariani S, Montagna S. A survey on agents applications in healthcare: Opportunities, challenges and trends. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107525. [PMID: 37084529 DOI: 10.1016/j.cmpb.2023.107525] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/31/2023] [Accepted: 04/01/2023] [Indexed: 05/03/2023]
Abstract
BACKGROUND AND OBJECTIVE The agent abstraction is a powerful one, developed decades ago to represent crucial aspects of artificial intelligence research. The meaning has transformed over the years and now there are different nuances across research communities. At its core, an agent is an autonomous computational entity capable of sensing, acting, and capturing interactions with other agents and its environment. This review examines how agent-based techniques have been implemented and evaluated in a specific and very important domain, i.e. healthcare research. METHODS We survey key areas of agent-based research in healthcare, e.g. individual and collective behaviours, communicable and non-communicable diseases, and social epidemiology. We propose a systematic search and critical review of relevant recent works, introduced by an exploratory network analysis. RESULTS Network analysis enables to devise out 5 main research clusters, the most active authors, and 4 main research topics. CONCLUSIONS Our findings support discussion of some future directions for increasing the value of agent-based approaches in healthcare.
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Affiliation(s)
- Emilio Sulis
- Computer Science Department, University of Torino, Via Pessinetto 12, Turin, 10149, Italy.
| | - Stefano Mariani
- Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Viale A. Allegri 9, Reggio Emilia, 42121, Italy
| | - Sara Montagna
- Department of Pure and Applied Sciences, University of Urbino, Piazza della Repubblica, 13, Urbino, 61029, Italy
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4
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Rafiei H, Salehi A, Baghbani F, Parsa P, Akbarzadeh-T MR. Interval type-2 Fuzzy control and stochastic modeling of COVID-19 spread based on vaccination and social distancing rates. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 232:107443. [PMID: 36889249 PMCID: PMC9951621 DOI: 10.1016/j.cmpb.2023.107443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Besides efforts on vaccine discovery, robust and intuitive government policies could also significantly influence the pandemic state. However, such policies require realistic virus spread models, and the major works on COVID-19 to date have been only case-specific and use deterministic models. Additionally, when a disease affects large portions of the population, countries develop extensive infrastructures to contain the condition that should adapt continuously and extend the healthcare system's capabilities. An accurate mathematical model that reasonably addresses these complex treatment/population dynamics and their corresponding environmental uncertainties is necessary for making appropriate and robust strategic decisions. METHODS Here, we propose an interval type-2 fuzzy stochastic modeling and control strategy to deal with the realistic uncertainties of pandemics and manage the size of the infected population. For this purpose, we first modify a previously established COVID-19 model with definite parameters to a Stochastic SEIAR (S2EIAR) approach with uncertain parameters and variables. Next, we propose to use normalized inputs, rather than the usual parameter settings in the previous case-specific studies, hence offering a more generalized control structure. Furthermore, we examine the proposed genetic algorithm-optimized fuzzy system in two scenarios. The first scenario aims to keep infected cases below a certain threshold, while the second addresses the changing healthcare capacities. Finally, we examine the proposed controller on stochasticity and disturbance in parameters, population sizes, social distance, and vaccination rate. RESULTS The results show the robustness and efficiency of the proposed method in the presence of up to 1% noise and 50% disturbance in tracking the desired size of the infected population. The proposed method is compared to Proportional Derivative (PD), Proportional Integral Derivative (PID), and type-1 fuzzy controllers. In the first scenario, both fuzzy controllers perform more smoothly despite PD and PID controllers reaching a lower mean squared error (MSE). Meanwhile, the proposed controller outperforms PD, PID, and the type-1 fuzzy controller for the MSE and decision policies for the second scenario. CONCLUSIONS The proposed approach explains how we should decide on social distancing and vaccination rate policies during pandemics against the prevalent uncertainties in disease detection and reporting.
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Affiliation(s)
- H Rafiei
- Departments of Electrical and Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran
| | - A Salehi
- Departments of Electrical and Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran
| | - F Baghbani
- Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran
| | - P Parsa
- Departments of Electrical and Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran
| | - M-R Akbarzadeh-T
- Departments of Electrical and Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran.
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Wong SC, Au AKW, Lo JYC, Ho PL, Hung IFN, To KKW, Yuen KY, Cheng VCC. Evolution and Control of COVID-19 Epidemic in Hong Kong. Viruses 2022; 14:2519. [PMID: 36423128 PMCID: PMC9698160 DOI: 10.3390/v14112519] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/10/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022] Open
Abstract
Hong Kong SAR has adopted universal masking, social distancing, testing of all symptomatic and high-risk groups for isolation of confirmed cases in healthcare facilities, and quarantine of contacts as epidemiological control measures without city lockdown or border closure. These measures successfully suppressed the community transmission of pre-Omicron SARS-CoV-2 variants or lineages during the first to the fourth wave. No nosocomial SARS-CoV-2 infection was documented among healthcare workers in the first 300 days. The strategy of COVID-19 containment was adopted to provide additional time to achieve population immunity by vaccination. The near-zero COVID-19 situation for about 8 months in 2021 did not enable adequate immunization of the eligible population. A combination of factors was identified, especially population complacency associated with the low local COVID-19 activity, together with vaccine hesitancy. The importation of the highly transmissible Omicron variant kickstarted the fifth wave of COVID-19, which could no longer be controlled by our initial measures. The explosive fifth wave, which was partially contributed by vertical airborne transmission in high-rise residential buildings, resulted in over one million cases of infection. In this review, we summarize the epidemiology of COVID-19 and the infection control and public health measures against the importation and dissemination of SARS-CoV-2 until day 1000.
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Affiliation(s)
- Shuk-Ching Wong
- Infection Control Team, Queen Mary Hospital, Hong Kong West Cluster, Hong Kong SAR, China
| | - Albert Ka-Wing Au
- Centre for Health Protection, Department of Health, Hong Kong SAR, China
| | - Janice Yee-Chi Lo
- Centre for Health Protection, Department of Health, Hong Kong SAR, China
| | - Pak-Leung Ho
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Carol Yu Center for Infection, The University of Hong Kong, Hong Kong SAR, China
| | - Ivan Fan-Ngai Hung
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Kelvin Kai-Wang To
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Kwok-Yung Yuen
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Vincent Chi-Chung Cheng
- Infection Control Team, Queen Mary Hospital, Hong Kong West Cluster, Hong Kong SAR, China
- Department of Microbiology, Queen Mary Hospital, Hong Kong SAR, China
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Gupta A, Katarya R. Possibility of the COVID-19 third wave in India: mapping from second wave to third wave. INDIAN JOURNAL OF PHYSICS AND PROCEEDINGS OF THE INDIAN ASSOCIATION FOR THE CULTIVATION OF SCIENCE (2004) 2022; 97:389-399. [PMID: 35855730 PMCID: PMC9281261 DOI: 10.1007/s12648-022-02425-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
Abstract
After a consistent drop in daily new coronavirus cases during the second wave of COVID-19 in India, there is speculation about the possibility of a future third wave of the virus. The pandemic is returning in different waves; therefore, it is necessary to determine the factors or conditions at the initial stage under which a severe third wave could occur. Therefore, first, we examine the effect of related multi-source data, including social mobility patterns, meteorological indicators, and air pollutants, on the COVID-19 cases during the initial phase of the second wave so as to predict the plausibility of the third wave. Next, based on the multi-source data, we proposed a simple short-term fixed-effect multiple regression model to predict daily confirmed cases. The study area findings suggest that the coronavirus dissemination can be well explained by social mobility. Furthermore, compared with benchmark models, the proposed model improves prediction R 2 by 33.6%, 10.8%, 27.4%, and 19.8% for Maharashtra, Kerala, Karnataka, and Tamil Nadu, respectively. Thus, the simplicity and interpretability of the model are a meaningful contribution to determining the possibility of upcoming waves and direct pandemic prevention and control decisions at a local level in India.
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Affiliation(s)
- Aakansha Gupta
- Big Data Analytics and Web Intelligence Laboratory, Department of Computer Science and Engineering, Delhi Technological University, New Delhi, India
| | - Rahul Katarya
- Big Data Analytics and Web Intelligence Laboratory, Department of Computer Science and Engineering, Delhi Technological University, New Delhi, India
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7
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Valles TE, Shoenhard H, Zinski J, Trick S, Porter MA, Lindstrom MR. Networks of necessity: Simulating COVID-19 mitigation strategies for disabled people and their caregivers. PLoS Comput Biol 2022; 18:e1010042. [PMID: 35584133 PMCID: PMC9232173 DOI: 10.1371/journal.pcbi.1010042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 06/24/2022] [Accepted: 03/21/2022] [Indexed: 01/08/2023] Open
Abstract
A major strategy to prevent the spread of COVID-19 is the limiting of in-person contacts. However, limiting contacts is impractical or impossible for the many disabled people who do not live in care facilities but still require caregivers to assist them with activities of daily living. We seek to determine which interventions can best prevent infections of disabled people and their caregivers. To accomplish this, we simulate COVID-19 transmission with a compartmental model that includes susceptible, exposed, asymptomatic, symptomatically ill, hospitalized, and removed/recovered individuals. The networks on which we simulate disease spread incorporate heterogeneity in the risk levels of different types of interactions, time-dependent lockdown and reopening measures, and interaction distributions for four different groups (caregivers, disabled people, essential workers, and the general population). Of these groups, we find that the probability of becoming infected is largest for caregivers and second largest for disabled people. Consistent with this finding, our analysis of network structure illustrates that caregivers have the largest modal eigenvector centrality of the four groups. We find that two interventions-contact-limiting by all groups and mask-wearing by disabled people and caregivers-most reduce the number of infections in disabled and caregiver populations. We also test which group of people spreads COVID-19 most readily by seeding infections in a subset of each group and comparing the total number of infections as the disease spreads. We find that caregivers are the most potent spreaders of COVID-19, particularly to other caregivers and to disabled people. We test where to use limited infection-blocking vaccine doses most effectively and find that (1) vaccinating caregivers better protects disabled people from infection than vaccinating the general population or essential workers and that (2) vaccinating caregivers protects disabled people from infection about as effectively as vaccinating disabled people themselves. Our results highlight the potential effectiveness of mask-wearing, contact-limiting throughout society, and strategic vaccination for limiting the exposure of disabled people and their caregivers to COVID-19.
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Affiliation(s)
- Thomas E Valles
- Department of Mathematics, University of California, San Diego, San Diego, California, United States of America
- Department of Mathematics, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Hannah Shoenhard
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Joseph Zinski
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Sarah Trick
- Assistant Editor at tvo.org (TVOntario), Toronto, Ontario, Canada
| | - Mason A Porter
- Department of Mathematics, University of California, Los Angeles, Los Angeles, California, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Michael R Lindstrom
- Department of Mathematics, University of California, Los Angeles, Los Angeles, California, United States of America
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8
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Krishna A, Rodrigues J, Mitschke V, Eder AB. Self-reported mask-related worrying reduces relative avoidance bias toward unmasked faces in individuals with low Covid19 anxiety syndrome. Cogn Res Princ Implic 2021; 6:75. [PMID: 34806154 PMCID: PMC8606225 DOI: 10.1186/s41235-021-00344-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/04/2021] [Indexed: 11/23/2022] Open
Abstract
Facial masks have become and may remain ubiquitous. Though important for preventing infection, they may also serve as a reminder of the risks of disease. Thus, they may either act as cues for threat, priming avoidance-related behavior, or as cues for a safe interaction, priming social approach. To distinguish between these possibilities, we assessed implicit and explicit evaluations of masked individuals as well as avoidance bias toward relatively unsafe interactions with unmasked individuals in an approach-avoidance task in an online study. We further assessed Covid19 anxiety and specific attitudes toward mask-wearing, including mask effectiveness and desirability, hindrance of communication from masks, aesthetic appeal of masks, and mask-related worrying. Across one sample of younger (18-35 years, N = 147) and one of older adults (60+ years, N = 150), we found neither an average approach nor avoidance bias toward mask-wearing compared to unmasked individuals in the indirect behavior measurement task. However, across the combined sample, self-reported mask-related worrying correlated with reduced avoidance tendencies toward unmasked individuals when Covid19 anxiety was low, but not when it was high. This relationship was specific to avoidance tendencies and was not observed in respect to explicit or implicit preference for mask-wearing individuals. We conclude that unsafe interaction styles may be reduced by targeting mask-related worrying with public interventions, in particular for populations that otherwise have low generalized Covid19 anxiety.
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Affiliation(s)
- Anand Krishna
- Lehrstuhl Für Psychologie II, Julius-Maximilians-Universität Würzburg, Röntgenring 10, 97070, Würzburg, Germany.
| | - Johannes Rodrigues
- Lehrstuhl Für Psychologie II, Julius-Maximilians-Universität Würzburg, Röntgenring 10, 97070, Würzburg, Germany
| | - Vanessa Mitschke
- Lehrstuhl Für Psychologie II, Julius-Maximilians-Universität Würzburg, Röntgenring 10, 97070, Würzburg, Germany
| | - Andreas B Eder
- Lehrstuhl Für Psychologie II, Julius-Maximilians-Universität Würzburg, Röntgenring 10, 97070, Würzburg, Germany
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