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Krishnamurthy P, Mulvey MS, Gowda K, Singh M, Venkatesan NK, Syam SB, Shah P, Kumar S, Chaudhuri A, Narayanan R, Perne AL, Pangaria A. Drivers of vaccine hesitancy among vulnerable populations in India: a cross-sectional multi-state study. Front Public Health 2023; 11:1177634. [PMID: 37900017 PMCID: PMC10600374 DOI: 10.3389/fpubh.2023.1177634] [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: 03/04/2023] [Accepted: 08/28/2023] [Indexed: 10/31/2023] Open
Abstract
Objectives India's Covid-19 vaccination campaign engaged frontline workers (FLWs) to encourage vaccination among vulnerable segments of society. The FLWs report encountering a variety of barriers to vaccination and are often unsuccessful despite multiple visits to the same person. This cross-sectional study aims to pinpoint which of these barriers drive vaccine hesitancy among these segments, to help streamline vaccine communication, including FLW training, to better safeguard the population. Methods Trained field enumerators contacted 893 individuals from five states across India and collected self-reported assessments of fifteen vaccination barriers (identified through discussions with FLWs), current vaccination status and future vaccination intentions, and covariates (demographics/comorbidities). Factor analysis of the fifteen barriers yielded two factors, one relating to fear of vaccine adverse effects and a second focused on peripheral concerns regarding the vaccine. The covariates significantly associated with current vaccination status were combined under a latent class regime to yield three cluster types (health access, financial strength, and demographics). The primary analysis examined the effect of the two barrier factors, the covariate clusters, and comorbidity, on current vaccination status and future vaccine intentions. Results Fear of vaccine adverse effects was the primary driver of vaccine hesitancy; peripheral concerns frequently mentioned by the FLWs had no impact. Although cluster membership and the presence of comorbidities predicted vaccine uptake, neither of them materially altered the effect of fear of vaccine adverse effects with the following exception: fear of adverse effects was not associated with vaccination status among young Muslim men. Conclusion Subject to limitations, these results indicate that interventions to decrease vaccine hesitancy should focus primarily on fear associated with vaccines rather than spend resources trying to address peripheral concerns.
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Affiliation(s)
- Parthasarathy Krishnamurthy
- Department of Marketing and Entrepreneurship, C. T. Bauer College of Business, University of Houston, Houston, TX, United States
| | - Michael S. Mulvey
- Telfer School of Management, LIFE Research Institute, University of Ottawa, Ottawa, ON, Canada
| | | | | | | | | | - Prerak Shah
- Catalyst Management Services, Bengaluru, India
| | - Shiv Kumar
- Catalyst Management Services, Bengaluru, India
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Chen C, Xing Z, Xi Y, Tiong R. Ensuring sufficient cabin hospital beds for curbing the spread of COVID-19 - Findings from petri net analysis. Heliyon 2022; 8:e11202. [PMID: 36284770 PMCID: PMC9584841 DOI: 10.1016/j.heliyon.2022.e11202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/28/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
Due to the complexity of the virus and its rapid rate of spread, many countries face the same challenges of providing adequate medical resources. This paper provides an analytical approach for evaluating the possibility of the regional construction industry constructing a large number of cabin hospitals within a short time. The key idea is to compare the demand and supply of patient beds using a Petri net-based approach that incorporates a neural network for the prediction of demand, fuzzy logic for decision-making, and a linear model for predicting supply. The data reported in the Shanghai Omicron battle is used to validate the developed model. Our results show that the fastest conversion speed and the least manpower requirement are obtained from high-rise buildings. Then, preparing some high-rises for easy conversion into cabin hospitals seems a possible solution for future citywide preparedness toward pandemic resilience. A Petri net analytical tool for studying cabin hospital demand and supply. The case of the Shanghai Omicron outbreak. Sensitivity analysis for the impact of manpower and impact of venue size. Vertical cabin hospitals are recommended to build post-pandemic resilience.
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Affiliation(s)
- Chen Chen
- Department of Structural Engineering, College of Civil Engineering, Tongji University, 1239 Siping Road, Yangpu District, Shanghai, 200092, China,School of Civil & Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore,Corresponding author.
| | - Zijie Xing
- Department of Structural Engineering, College of Civil Engineering, Tongji University, 1239 Siping Road, Yangpu District, Shanghai, 200092, China
| | - Yonghui Xi
- Department of Structural Engineering, College of Civil Engineering, Tongji University, 1239 Siping Road, Yangpu District, Shanghai, 200092, China
| | - Robert Tiong
- School of Civil & Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore
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Carballosa A, Balsa-Barreiro J, Boullosa P, Garea A, Mira J, Miramontes Á, Muñuzuri AP. Assessing the risk of pandemic outbreaks across municipalities with mathematical descriptors based on age and mobility restrictions. CHAOS, SOLITONS, AND FRACTALS 2022; 160:112156. [PMID: 35637663 PMCID: PMC9132613 DOI: 10.1016/j.chaos.2022.112156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 04/24/2022] [Indexed: 06/15/2023]
Abstract
By March 14th 2022, Spain is suffering the sixth wave of the COVID-19 pandemic. All the previous waves have been intimately related to the degree of imposed mobility restrictions and its consequent release. Certain factors explain the incidence of the virus across regions revealing the weak locations that probably require some medical reinforcements. The most relevant ones relate with mobility restrictions by age and administrative competence, i.e., spatial constrains. In this work, we aim to find a mathematical descriptor that could identify the critical communities that are more likely to suffer pandemic outbreaks and, at the same time, to estimate the impact of different mobility restrictions. We analyze the incidence of the virus in combination with mobility flows during the so-called second wave (roughly from August 1st to November 30th, 2020) using a SEIR compartmental model. After that, we derive a mathematical descriptor based on linear stability theory that quantifies the potential impact of becoming a hotspot. Once the model is validated, we consider different confinement scenarios and containment protocols aimed to control the virus spreading. The main findings from our simulations suggest that the confinement of the economically non-active individuals may result in a significant reduction of risk, whose effects are equivalent to the confinement of the total population. This study is conducted across the totality of municipalities in Spain.
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Affiliation(s)
- Alejandro Carballosa
- Group of Nonlinear Physics, Fac. Physics, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Galician Center for Mathematical Research and Technology (CITMAga), 15782 Santiago de Compostela, Spain
| | - José Balsa-Barreiro
- Institute IDEGA, Department of Geography, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- MIT Media Lab, Massachusetts Institute of Technology, 75 Amherst St, Cambridge, MA 02139, USA
| | - Pablo Boullosa
- Group of Nonlinear Physics, Fac. Physics, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Departamento de Física Aplicada, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Adrián Garea
- Group of Nonlinear Physics, Fac. Physics, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Departamento de Física Aplicada, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Jorge Mira
- Departamento de Física Aplicada, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Instituto de Materiais (iMATUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Ángel Miramontes
- Institute IDEGA, Department of Geography, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Alberto P Muñuzuri
- Group of Nonlinear Physics, Fac. Physics, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Galician Center for Mathematical Research and Technology (CITMAga), 15782 Santiago de Compostela, Spain
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Pei Y, Li J, Xu S, Xu Y. Adaptive Multi-Factor Quantitative Analysis and Prediction Models: Vaccination, Virus Mutation and Social Isolation on COVID-19. Front Med (Lausanne) 2022; 9:828691. [PMID: 35372421 PMCID: PMC8965859 DOI: 10.3389/fmed.2022.828691] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/14/2022] [Indexed: 12/14/2022] Open
Abstract
Different countries have adopted various control measures for the COVID-19 pandemic in different periods, and as the virus continues to mutate, the progression of the pandemic and preventive measures adopted have varied dynamically over time. Thus, quantitative analysis of the dynamic impact of different factors such as vaccination, mutant virus, social isolation, etc., on transmission and predicting pandemic progress has become a difficult task. To overcome the challenges above and enable governments to formulate reasonable countermeasures against the ongoing COVID-19 pandemic, we integrate several mathematical methods and propose a new adaptive multifactorial and geographically diverse epidemiological model based on a modified version of the classical susceptible-exposed-infectious-recovered (SEIR) model. Based on public datasets, a multi-center study was carried out considering 21 regions. First, a retrospective study was conducted to predict the number of infections over the next 30 days in 13 representative pandemic areas worldwide with an accuracy of 87.53%, confirming the robustness of the proposed model. Second, the impact of three scenarios on COVID-19 was quantified based on the scalability of the model: two different vaccination regimens were analyzed, and it was found that the number of infections would progressively decrease over time after vaccination; variant virus caused a 301.55% increase in infections in the United Kingdom; and 3-tier social lockdown in the United Kingdom reduced the infections by 47.01%. Third, we made short-term prospective predictions for the next 15 and 30 days for six countries with severe COVID-19 transmission and the predicted trend is accurate. This study is expected to inform public health responses. Code and data are publicly available at https://github.com/yuanyuanpei7/covid-19.
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Affiliation(s)
- Yuanyuan Pei
- Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Juan Li
- Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Songhua Xu
- Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Yi Xu
- Department of Infectious Diseases, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
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Oshinubi K, Buhamra SS, Al-Kandari NM, Waku J, Rachdi M, Demongeot J. Age Dependent Epidemic Modeling of COVID-19 Outbreak in Kuwait, France, and Cameroon. Healthcare (Basel) 2022; 10:healthcare10030482. [PMID: 35326960 PMCID: PMC8954002 DOI: 10.3390/healthcare10030482] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/21/2022] [Accepted: 02/28/2022] [Indexed: 02/01/2023] Open
Abstract
Revisiting the classical model by Ross and Kermack-McKendrick, the Susceptible−Infectious−Recovered (SIR) model used to formalize the COVID-19 epidemic, requires improvements which will be the subject of this article. The heterogeneity in the age of the populations concerned leads to considering models in age groups with specific susceptibilities, which makes the prediction problem more difficult. Basically, there are three age groups of interest which are, respectively, 0−19 years, 20−64 years, and >64 years, but in this article, we only consider two (20−64 years and >64 years) age groups because the group 0−19 years is widely seen as being less infected by the virus since this age group had a low infection rate throughout the pandemic era of this study, especially the countries under consideration. In this article, we proposed a new mathematical age-dependent (Susceptible−Infectious−Goneanewsusceptible−Recovered (SIGR)) model for the COVID-19 outbreak and performed some mathematical analyses by showing the positivity, boundedness, stability, existence, and uniqueness of the solution. We performed numerical simulations of the model with parameters from Kuwait, France, and Cameroon. We discuss the role of these different parameters used in the model; namely, vaccination on the epidemic dynamics. We open a new perspective of improving an age-dependent model and its application to observed data and parameters.
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Affiliation(s)
- Kayode Oshinubi
- Laboratory AGEIS EA 7407, Team Tools for e-Gnosis Medical, Faculty of Medicine, University Grenoble Alpes (UGA), 38700 La Tronche, France; (K.O.); (M.R.); (J.D.)
| | - Sana S. Buhamra
- Department of Information Science, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait
- Correspondence:
| | - Noriah M. Al-Kandari
- Department of Statistics and Operations Research, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait;
| | - Jules Waku
- UMMISCO UMI IRD 209 & LIRIMA, University of Yaoundé I, Yaoundé P.O. Box 337, Cameroon;
| | - Mustapha Rachdi
- Laboratory AGEIS EA 7407, Team Tools for e-Gnosis Medical, Faculty of Medicine, University Grenoble Alpes (UGA), 38700 La Tronche, France; (K.O.); (M.R.); (J.D.)
| | - Jacques Demongeot
- Laboratory AGEIS EA 7407, Team Tools for e-Gnosis Medical, Faculty of Medicine, University Grenoble Alpes (UGA), 38700 La Tronche, France; (K.O.); (M.R.); (J.D.)
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