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Yang J, Ma S, Ma J, Ran J, Bai X. Stochastic Analysis for the Dual Virus Parallel Transmission Model with Immunity Delay. J Comput Biol 2024; 31:1291-1304. [PMID: 39422568 DOI: 10.1089/cmb.2024.0662] [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] [Indexed: 10/19/2024] Open
Abstract
In this article, the qualitative properties of a stochastic dual virus parallel transmission model with immunity delay are analyzed. First, we use Lyapunov theory to study the existence and uniqueness of the global positive solution of the proposed model. Second, the threshold values of the persistence and extinction of two viruses were obtained. Finally, the numerical simulation verifies the theoretical results. The results show that the immunity delay and the intensity of noise have important effects on the two diseases spreading in parallel.
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Affiliation(s)
- Jing Yang
- School of Mathematics and Information Science, North Minzu University, Yinchuan, China
| | - Shaojuan Ma
- School of Mathematics and Information Science, North Minzu University, Yinchuan, China
- Ningxia Key Laboratory of Intelligent Information and Big Data Processing, Yinchuan, China
- Ningxia Basic Science Research Center of Mathematics, Yinchuan, China
| | - Juan Ma
- School of Mathematics and Information Science, North Minzu University, Yinchuan, China
| | - Jinhua Ran
- School of Mathematics and Information Science, North Minzu University, Yinchuan, China
- Ningxia Key Laboratory of Intelligent Information and Big Data Processing, Yinchuan, China
| | - Xinyu Bai
- School of Mathematics and Information Science, North Minzu University, Yinchuan, China
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2
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Munaf S, Swingler K, Brülisauer F, O'Hare A, Gunn G, Reeves A. Spatio-temporal evaluation of social media as a tool for livestock disease surveillance. One Health 2023; 17:100657. [PMID: 38116453 PMCID: PMC10728316 DOI: 10.1016/j.onehlt.2023.100657] [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: 05/27/2023] [Revised: 11/22/2023] [Accepted: 11/22/2023] [Indexed: 12/21/2023] Open
Abstract
Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.
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Affiliation(s)
- Samuel Munaf
- Division of Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom
- Centre for Epidemiology and Planetary Health, Department of Veterinary and Animal Sciences, Northern Faculty, Scotland's Rural College (SRUC), Inverness, United Kingdom
| | - Kevin Swingler
- Division of Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom
| | - Franz Brülisauer
- SRUC Veterinary Services, Scotland's Rural College (SRUC), Inverness, United Kingdom
| | - Anthony O'Hare
- Division of Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom
| | - George Gunn
- Centre for Epidemiology and Planetary Health, Department of Veterinary and Animal Sciences, Northern Faculty, Scotland's Rural College (SRUC), Inverness, United Kingdom
| | - Aaron Reeves
- Centre for Applied public health research, RTI international, Raleigh, NC, USA
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3
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Sun HC, Pei S, Wang L, Sun YY, Xu XK. The Impact of Spring Festival Travel on Epidemic Spreading in China. Viruses 2023; 15:1527. [PMID: 37515214 PMCID: PMC10384880 DOI: 10.3390/v15071527] [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: 05/20/2023] [Revised: 07/03/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
The large population movement during the Spring Festival travel in China can considerably accelerate the spread of epidemics, especially after the relaxation of strict control measures against COVID-19. This study aims to assess the impact of population migration in Spring Festival holiday on epidemic spread under different scenarios. Using inter-city population movement data, we construct the population flow network during the non-holiday time as well as the Spring Festival holiday. We build a large-scale metapopulation model to simulate the epidemic spread among 371 Chinese cities. We analyze the impact of Spring Festival travel on the peak timing and peak magnitude nationally and in each city. Assuming an R0 (basic reproduction number) of 15 and the initial conditions as the reported COVID-19 infections on 17 December 2022, model simulations indicate that the Spring Festival travel can substantially increase the national peak magnitude of infection. The infection peaks arrive at most cities 1-4 days earlier as compared to those of the non-holiday time. While peak infections in certain large cities, such as Beijing and Shanghai, are decreased due to the massive migration of people to smaller cities during the pre-Spring Festival period, peak infections increase significantly in small- or medium-sized cities. For a less transmissible disease (R0 = 5), infection peaks in large cities are delayed until after the Spring Festival. Small- or medium-sized cities may experience a larger infection due to the large-scale population migration from metropolitan areas. The increased disease burden may impose considerable strain on the healthcare systems in these resource-limited areas. For a less transmissible disease, particular attention needs to be paid to outbreaks in large cities when people resume work after holidays.
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Affiliation(s)
- Hao-Chen Sun
- College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China; (H.-C.S.); (Y.-Y.S.)
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK;
| | - Yuan-Yuan Sun
- College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China; (H.-C.S.); (Y.-Y.S.)
| | - Xiao-Ke Xu
- Computational Communication Research Center, Beijing Normal University, Zhuhai 519087, China
- School of Journalism and Communication, Beijing Normal University, Beijing 100875, China
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4
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Al Qundus J, Gupta S, Abusaimeh H, Peikert S, Paschke A. Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak. GLOBAL JOURNAL OF FLEXIBLE SYSTEMS MANAGEMENT 2023; 24:235-246. [PMID: 37101929 PMCID: PMC10020765 DOI: 10.1007/s40171-023-00337-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 02/18/2023] [Indexed: 03/18/2023]
Abstract
Predicting the outbreak of a pandemic is an important measure in order to help saving people lives threatened by Covid-19. Having information about the possible spread of the pandemic, authorities and people can make better decisions. For example, such analyses help developing better strategies for distributing vaccines and medicines. This paper has modified the original Susceptible-Infectious-Recovered (SIR) model to Susceptible-Immune-Infected-Recovered (SIRM) which includes the Immunity ratio as a parameter to enhance the prediction of the pandemic. SIR is a widely used model to predict the spread of a pandemic. Many types of pandemics imply many variants of the SIR models which make it very difficult to find out the best model that matches the running pandemic. The simulation of this paper used the published data about the spread of the pandemic in order to examine our new SIRM. The results showed clearly that our new SIRM covering the aspects of vaccine and medicine is an appropriate model to predict the behavior of the pandemic.
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Affiliation(s)
- Jamal Al Qundus
- Faculty of Information Technology, Middle East University, Amman, 11831 Jordan
| | - Shivam Gupta
- Department of Information Systems, Supply Chain Management & Decision Support, NEOMA Business School, 51100 Reims, France
| | - Hesham Abusaimeh
- Faculty of Information Technology, Middle East University, Amman, 11831 Jordan
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5
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Huberts NFD, Thijssen JJJ. Optimal timing of non-pharmaceutical interventions during an epidemic. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023; 305:1366-1389. [PMID: 35765314 PMCID: PMC9221090 DOI: 10.1016/j.ejor.2022.06.034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/15/2022] [Indexed: 05/10/2023]
Abstract
In response to the recent outbreak of the SARS-CoV-2 virus governments have aimed to reduce the virus's spread through, inter alia, non-pharmaceutical intervention. We address the question when such measures should be implemented and, once implemented, when to remove them. These issues are viewed through a real-options lens and we develop an SIRD-like continuous-time Markov chain model to analyze a sequence of options: the option to intervene and introduce measures and, after intervention has started, the option to remove these. Measures can be imposed multiple times. We implement our model using estimates from empirical studies and, under fairly general assumptions, our main conclusions are that: (1) measures should be put in place not long after the first infections occur; (2) if the epidemic is discovered when there are many infected individuals already, then it is optimal never to introduce measures; (3) once the decision to introduce measures has been taken, these should stay in place until the number of susceptible or infected members of the population is close to zero; (4) it is never optimal to introduce a tier system to phase-in measures but it is optimal to use a tier system to phase-out measures; (5) a more infectious variant may reduce the duration of measures being in place; (6) the risk of infections being brought in by travelers should be curbed even when no other measures are in place. These results are robust to several variations of our base-case model.
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Affiliation(s)
- Nick F D Huberts
- Management School, University of York, Heslington, York YO10 5ZF, United Kingdom
| | - Jacco J J Thijssen
- Management School, University of York, Heslington, York YO10 5ZF, United Kingdom
- Department of Mathematics, University of York, Heslington, York YO10 5ZF, United Kingdom
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6
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Silveira SCT, Ferreira Filho HR, Pontes AN, Lopes HDS, Manfrini GC. The COVID-19 pandemic through the lens of humanitarian logistics. CIENCIA & SAUDE COLETIVA 2023; 28:749-759. [PMID: 36888859 DOI: 10.1590/1413-81232023283.11762022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/28/2022] [Indexed: 03/08/2023] Open
Abstract
An integrative literature review (ILR) was conducted to identify the relationship between humanitarian logistics and the development of the COVID-19 pandemic based on research in the SCOPUS, MEDLINE and ENEGEP databases in April and May 2022. In all, 61 articles were evaluated according to the following criteria: original article or review of literature published in a scientific journal; abstract and full text available; article on humanitarian logistics in relation to the COVID-19 pandemic. The resulting sample comprised eleven publications organized and analyzed through a synthesis matrix, where 72% were published in international journals and mostly in 2021 (56%). The presence of the supply chain defines the course of action of economic and social sectors, which in turn determine, by means of an interdisciplinary approach, humanitarian operations in the face of the COVID-19 pandemic. The lack of studies narrows down humanitarian logistics to mitigate the impacts caused by these disasters, both in the context of the current pandemic and in future events of the same nature. However, as a global emergency, it suggests the need to increase scientific knowledge on the subject of humanitarian logistics related to disaster events.
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Affiliation(s)
| | | | - Altem Nascimento Pontes
- Universidade do Estado do Pará. R. Marechal Deodoro 813, Ianetama. 68745-690 Castanhal PA Brasil.
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7
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Silveira SCT, Ferreira Filho HR, Pontes AN, Lopes HDS, Manfrini GC. The COVID-19 pandemic through the lens of humanitarian logistics. CIENCIA & SAUDE COLETIVA 2023. [DOI: 10.1590/1413-81232023283.11762022en] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
Abstract An integrative literature review (ILR) was conducted to identify the relationship between humanitarian logistics and the development of the COVID-19 pandemic based on research in the SCOPUS, MEDLINE and ENEGEP databases in April and May 2022. In all, 61 articles were evaluated according to the following criteria: original article or review of literature published in a scientific journal; abstract and full text available; article on humanitarian logistics in relation to the COVID-19 pandemic. The resulting sample comprised eleven publications organized and analyzed through a synthesis matrix, where 72% were published in international journals and mostly in 2021 (56%). The presence of the supply chain defines the course of action of economic and social sectors, which in turn determine, by means of an interdisciplinary approach, humanitarian operations in the face of the COVID-19 pandemic. The lack of studies narrows down humanitarian logistics to mitigate the impacts caused by these disasters, both in the context of the current pandemic and in future events of the same nature. However, as a global emergency, it suggests the need to increase scientific knowledge on the subject of humanitarian logistics related to disaster events.
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8
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Koutou O, Diabaté AB, Sangaré B. Mathematical analysis of the impact of the media coverage in mitigating the outbreak of COVID-19. MATHEMATICS AND COMPUTERS IN SIMULATION 2023; 205:600-618. [PMID: 36312512 PMCID: PMC9596178 DOI: 10.1016/j.matcom.2022.10.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 08/25/2022] [Accepted: 10/15/2022] [Indexed: 05/25/2023]
Abstract
In this paper, a mathematical model with a standard incidence rate is proposed to assess the role of media such as facebook, television, radio and tweeter in the mitigation of the outbreak of COVID-19. The basic reproduction numberR 0 which is the threshold dynamics parameter between the disappearance and the persistence of the disease has been calculated. And, it is obvious to see that it varies directly to the number of hospitalized people, asymptomatic, symptomatic carriers and the impact of media coverage. The local and the global stabilities of the model have also been investigated by using the Routh-Hurwitz criterion and the Lyapunov's functional technique, respectively. Furthermore, we have performed a local sensitivity analysis to assess the impact of any variation in each one of the model parameter on the thresholdR 0 and the course of the disease accordingly. We have also computed the approximative rate at which herd immunity will occur when any control measure is implemented. To finish, we have presented some numerical simulation results by using some available data from the literature to corroborate our theoretical findings.
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Affiliation(s)
- Ousmane Koutou
- CUP-Kaya/Université Joseph KI-ZERBO, 01 BP 7021 Ouagadougou 01, Burkina Faso, Burkina Faso
| | - Abou Bakari Diabaté
- Département de mathématiques/Université Nazi BONI, 01 BP 1091 Bobo-Dioulasso 01, Burkina Faso
| | - Boureima Sangaré
- Département de mathématiques/Université Nazi BONI, 01 BP 1091 Bobo-Dioulasso 01, Burkina Faso
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9
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Gülmez B. A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images. ANNALS OF OPERATIONS RESEARCH 2022; 328:1-25. [PMID: 36591406 PMCID: PMC9790088 DOI: 10.1007/s10479-022-05151-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively.
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Affiliation(s)
- Burak Gülmez
- Department of Industrial Engineering, Erciyes University, Kayseri, Türkiye
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10
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Saito T, Gupta S. Big data applications with theoretical models and social media in financial management. ANNALS OF OPERATIONS RESEARCH 2022:1-23. [PMID: 36533273 PMCID: PMC9749645 DOI: 10.1007/s10479-022-05136-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
This study presents big data applications with quantitative theoretical models in financial management and investigates possible incorporation of social media factors into the models. Specifically, we examine three models, a revenue management model, an interest rate model with market sentiments, and a high-frequency trading equity market model, and consider possible extensions of those models to include social media. Since social media plays a substantial role in promoting products and services, engaging with customers, and sharing sentiments among market participants, it is important to include social media factors in the stochastic optimization models for financial management. Moreover, we compare the three models from a qualitative and quantitative point of view and provide managerial implications on how these models are synthetically used along with social media in financial management with a concrete case of a hotel REIT. The contribution of this research is that we investigate the possible incorporation of social media factors into the three models whose objectives are revenue management and debt and equity financing, essential areas in financial management, which helps to estimate the effect and the impact of social media quantitatively if internal data necessary for parameter estimation are available, and provide managerial implications for the synthetic use of the three models from a higher viewpoint. The numerical experiment along with the proposition indicates that the model can be used in the revenue management of hotels, and by improving the social media factor, the hotel can work on maximizing its sales.
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Affiliation(s)
- Taiga Saito
- Graduate School of Economics, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 1130033 Japan
| | - Shivam Gupta
- Department of Information Systems, Supply Chain Management & Decision Support, NEOMA Business School, 59 Rue Pierre Taittinger, 51100 Reims, France
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11
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Ovezmyradov B. Product availability and stockpiling in times of pandemic: causes of supply chain disruptions and preventive measures in retailing. ANNALS OF OPERATIONS RESEARCH 2022:1-33. [PMID: 36467007 PMCID: PMC9709757 DOI: 10.1007/s10479-022-05091-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 11/17/2022] [Indexed: 06/17/2023]
Abstract
The coronavirus pandemic in 2020 brought global supply chain disruptions for retailers responding to the increased demand of consumers for popular merchandise. There is a need to adapt the existing supply chain models to describe the disruptions and offer the potential measures that businesses and governments can take to minimize adverse effects from a retail logistics perspective. This research analyses the possible reasons for supply and demand disruptions using a mathematical model of a retail supply chain with uncertain lead times and stochastic demand of strategic consumers. The established concepts of supply chain management are applied for the model analysis: multi-period inventory policies, bullwhip effect, and strategic consumers. The impact of the pandemic outbreaks in the model is two-fold: increased lead-time uncertainty affects supply, while consumer stockpiling affects demand. Consumers' rational hoarding and irrational panic buying significantly increase retailers' costs due to higher safety stock and demand variability. The bullwhip effect further exacerbates the disruption. The research contributes to the recent literature on business response to supply chain disruptions by developing a model where both retailers and consumers decide on the order quantity and reorder point during a pandemic outbreak. Buying limits, continuous inventory review, government rationing, substitutability, and omnichannel fulfillment are the measures that can limit the damage of supply chain disruptions from stockpiling during the pandemic. Effective communication and price and availability guarantees can mitigate the negative impact of panic buying.
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Affiliation(s)
- Berdymyrat Ovezmyradov
- Department of Transportation and Logistics, Transport and Telecommunication Institute, Lomonosova Iela 1, Riga, 1019 Latvia
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Kumar A, Joshi S, Sharma M, Vishvakarma N. Digital humanitarianism and crisis management: an empirical study of antecedents and consequences. JOURNAL OF HUMANITARIAN LOGISTICS AND SUPPLY CHAIN MANAGEMENT 2022. [DOI: 10.1108/jhlscm-02-2022-0020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThis study proposes a digital humanitarianism dynamic capability (DHDC) paradigm that explores the direct effects of DHDC on disaster risk reduction (DRR) and the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between DHDC and DRR.Design/methodology/approachTo validate the proposed model, the authors used an offline survey to gather data from 260 district magistrates in India managing the COVID-19 pandemic.FindingsThe results affirm the importance of the DHDC system for DRR. The findings depict that the impact of PODC on DRR in the DHDC system is negligible. This study can help policymakers in planning during emergencies.Research limitations/implicationsTechnological innovation has reshaped the way humanitarian organizations (HOs) respond to humanitarian crises. These organizations are able to provide immediate aid to affected communities through digital humanitarianism (DH), which involves significant innovations to match the specific needs of people in real-time through online platforms. Despite the growing need for DH, there is still limited know-how regarding how to leverage such technological concepts into disaster management. Moreover, the impact of DH on DRR is rarely examined.Originality/valueThe present study examines the impact of the dynamic capabilities of HOs on DRR by applying the resource-based view (RBV) and dynamic capability theory (DCT).
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13
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Sooknanan J, Seemungal TAR. FOMO (fate of online media only) in infectious disease modeling: a review of compartmental models. INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2022; 11:892-899. [PMID: 35855912 PMCID: PMC9281210 DOI: 10.1007/s40435-022-00994-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 06/05/2022] [Accepted: 06/17/2022] [Indexed: 10/24/2022]
Abstract
Mathematical models played in a major role in guiding policy decisions during the COVID-19 pandemic. These models while focusing on the spread and containment of the disease, largely ignored the impact of media on the disease transmission. Media plays a major role in shaping opinions, attitudes and perspectives and as the number of people online increases, online media are fast becoming a major source for news and health related information and advice. Consequently, they may influence behavior and in due course disease dynamics. Unlike traditional media, online media are themselves driven and influenced by their users and thus have unique features. The main techniques used to incorporate online media mathematically into compartmental models, with particular reference to the ongoing COVID-19 pandemic are reviewed. In doing so, features specific to online media that have yet to be fully integrated into compartmental models such as misinformation, different time scales with regards to disease transmission and information, time delays, information super spreaders, the predatory nature of online media and other factors are identified together with recommendations for their incorporation.
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Affiliation(s)
- Joanna Sooknanan
- The University of the West Indies Open Campus, Bridgetown, Barbados
| | - Terence A. R. Seemungal
- Faculty of Medical Sciences, The University of the West Indies, St. Augustine Campus, St. Augustine, Trinidad and Tobago
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14
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Chou SM, Hu YN, Lee CH, Chen YT, Peng DX, Hsiao SH. Effectiveness of Social Media Use for Digital Marketing Planning During the COVID-19 Pandemic in Taiwan. Asia Pac J Public Health 2022; 34:712-715. [DOI: 10.1177/10105395221109526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Shih-Min Chou
- Division of Multi-Media Marketing, Public Affairs Office, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yi-Ning Hu
- Public Affairs Office, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chia-Hua Lee
- Public Affairs Office, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yun-Tung Chen
- Public Affairs Office, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ding-Xuan Peng
- Public Affairs Office, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Shih-Huai Hsiao
- Department of Public Health, Kaohsiung Medical University, Kaohsiung, Taiwan
- Taiwan College of Healthcare Executives, Taipei, Taiwan
- International Medical Service Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
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15
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Nyawa S, Tchuente D, Fosso-Wamba S. COVID-19 vaccine hesitancy: a social media analysis using deep learning. ANNALS OF OPERATIONS RESEARCH 2022:1-39. [PMID: 35729983 PMCID: PMC9202977 DOI: 10.1007/s10479-022-04792-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
Hesitant attitudes have been a significant issue since the development of the first vaccines-the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population's decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%.
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Affiliation(s)
- Serge Nyawa
- Department of Information, Operations and Management Sciences, TBS Business School, 1 Place Alphonse Jourdain, 31068 Toulouse, France
| | - Dieudonné Tchuente
- Department of Information, Operations and Management Sciences, TBS Business School, 1 Place Alphonse Jourdain, 31068 Toulouse, France
| | - Samuel Fosso-Wamba
- Department of Information, Operations and Management Sciences, TBS Business School, 1 Place Alphonse Jourdain, 31068 Toulouse, France
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16
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Kumar P, Singh RK, Shahgholian A. Learnings from COVID-19 for managing humanitarian supply chains: systematic literature review and future research directions. ANNALS OF OPERATIONS RESEARCH 2022; 335:1-37. [PMID: 35694371 PMCID: PMC9175170 DOI: 10.1007/s10479-022-04753-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/29/2022] [Indexed: 05/03/2023]
Abstract
The COVID-19 pandemic has been experienced as the most significant global disaster after the Spanish flue in 1918. Millions of people lost their life due to a lack of preparedness and ineffective strategies for managing humanitarian supply chains (HSC). Based on the learnings from this pandemic outbreak, different strategies for managing the effective HSC have been explored in the present context of pandemics through a systematic literature review. The findings highlight some of the major challenges faced during the COVID-19 pandemic, such as lack of planning and preparedness, extended shortages of essential lifesaving items, inadequate lab capacity, lack of transparency and visibility, inefficient distribution network, high response time, dependencies on single sourcing for the medical equipment and medicines, lack of the right information on time, and lack of awareness about the protocol for the treatment of the viral disease. Some of the significant learnings observed from this analysis are the use of multiple sourcing of essential items, joint procurement, improving collaboration among all stakeholders, applications of IoT and blockchain technologies for improving tracking and traceability of essential commodities, application of data analytics tools for accurate prediction of next possible COVID wave/disruptions and optimization of distribution network. Limited studies are focused on finding solutions to these problems in managing HSC. Therefore, as a future scope, researchers could find solutions to optimizing the distribution network in context to pandemics, improving tracing and tracking of items during sudden demand, improving trust and collaborations among different agencies involved in HSC.
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Affiliation(s)
- Pravin Kumar
- Department of Mechanical Engineering, Delhi Technological University, Delhi, India
| | | | - Azar Shahgholian
- Liverpool Business School, Liverpool John Moores University, Liverpool, UK
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Li P, Chen B, Devaux G, Tao W, Luo Y, Wen J, Zheng Y. Do Chinese netizens cross-verify the accuracy of unofficial social media information before changing health behaviors during COVID-19? A Web-based study in China. JMIR Public Health Surveill 2022; 8:e33577. [PMID: 35486529 PMCID: PMC9198829 DOI: 10.2196/33577] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 03/14/2022] [Accepted: 04/27/2022] [Indexed: 02/05/2023] Open
Abstract
Background As social media platforms have become significant sources of information during the pandemic, a significant volume of both factual and inaccurate information related to the prevention of COVID-19 has been disseminated through social media. Thus, disparities in COVID-19 information verification across populations have the potential to promote the dissemination of misinformation among clustered groups of people with similar characteristics. Objective This study aimed to identify the characteristics of social media users who obtained COVID-19 information through unofficial social media accounts and were (1) most likely to change their health behaviors according to web-based information and (2) least likely to actively verify the accuracy of COVID-19 information, as these individuals may be susceptible to inaccurate prevention measures and may exacerbate transmission. Methods An online questionnaire consisting of 17 questions was disseminated by West China Hospital via its official online platforms, between May 18, 2020, and May 31, 2020. The questionnaire collected the sociodemographic information of 14,509 adults, and included questions surveying Chinese netizens’ knowledge about COVID-19, personal social media use, health behavioral change tendencies, and cross-verification behaviors for web-based information during the pandemic. Multiple stepwise regression models were used to examine the relationships between social media use, behavior changes, and information cross-verification. Results Respondents who were most likely to change their health behaviors after obtaining web-based COVID-19 information from celebrity sources had the following characteristics: female sex (P=.004), age ≥50 years (P=.009), higher COVID-19 knowledge and health literacy (P=.045 and P=.03, respectively), non–health care professional (P=.02), higher frequency of searching on social media (P<.001), better health conditions (P<.001), and a trust rating score of more than 3 for information released by celebrities on social media (P=.005). Furthermore, among participants who were most likely to change their health behaviors according to social media information released by celebrities, female sex (P<.001), living in a rural residence rather than first-tier city (P<.001), self-reported medium health status and lower health care literacy (P=.007 and P<.001, respectively), less frequent search for COVID-19 information on social media (P<.001), and greater level of trust toward celebrities’ social media accounts with a trust rating score greater than 1 (P≤.04) were associated with a lack of cross-verification of information. Conclusions The findings suggest that governments, health care agencies, celebrities, and technicians should combine their efforts to decrease the risk in vulnerable groups that are inclined to change health behaviors according to web-based information but do not perform any fact-check verification of the accuracy of the unofficial information. Specifically, it is necessary to correct the false information related to COVID-19 on social media, appropriately apply celebrities’ star power, and increase Chinese netizens’ awareness of information cross-verification and eHealth literacy for evaluating the veracity of web-based information.
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Affiliation(s)
- Peiyi Li
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China, Guo Xue Xiang 37, Chengdu, CN
| | - Bo Chen
- Institute of Hospital Management, West China Hospital of Sichuan University, Chengdu, CN
| | - Genevieve Devaux
- Milken Institute School of Public Health, George Washington University, Washington, US
| | - Wenjuan Tao
- Institute of Hospital Management, West China Hospital of Sichuan University, Chengdu, CN
| | - Yunmei Luo
- Institute of Hospital Management, West China Hospital of Sichuan University, Chengdu, CN
| | - Jin Wen
- Institute of Hospital Management, West China Hospital of Sichuan University, Chengdu, CN
| | - Yuan Zheng
- Publicity Department, West China Hospital, Sichuan University, Chengdu, CN
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Lotfi R, Kheiri K, Sadeghi A, Babaee Tirkolaee E. An extended robust mathematical model to project the course of COVID-19 epidemic in Iran. ANNALS OF OPERATIONS RESEARCH 2022:1-25. [PMID: 35013634 PMCID: PMC8732964 DOI: 10.1007/s10479-021-04490-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/07/2021] [Indexed: 05/08/2023]
Abstract
This research develops a regression-based Robust Optimization (RO) approach to efficiently predict the number of patients with confirmed infection caused by the recent Coronavirus Disease (COVID-19). The main idea is to study the dynamics of the COVID-19 outbreak at the first stage and then provide efficient insights to estimate the necessary resources accordingly. The convex RO with Mean Absolute Deviation (MAD) objective function is utilized to project the course of COVID-19 epidemic in Iran. To validate the performance of the suggested model, a real-case study is investigated and compared to several well-known forecasting models including Simple Moving Average, Exponential Moving Average, Weighted Moving Average and Exponential Smoothing with Trend Adjustment models. Furthermore, the effect of parameter uncertainties is examined using a set of sensitivity analyses. The results demonstrate that by increasing the degree (coefficient) of regression up to 8, MAD value decreases to 1378.12, and consequently, the corresponding equation becomes more accurate. On the other hand, from the 8th degree onwards, MAD value follows an upward trend. Furthermore, by increasing the level of regression uncertainty, MAD value follows a downward trend to reach 1309.28 and the estimation accuracy of the model increases accordingly. Finally, our proposed model achieves the least MAD and the greatest correlation coefficient against the other models.
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Affiliation(s)
- Reza Lotfi
- Department of Industrial Engineering, Yazd University, Yazd, Iran
- Behineh Gostar Sanaye Arman, Tehran, Iran
| | - Kiana Kheiri
- Department of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Ali Sadeghi
- Department of Industrial Engineering, Yazd University, Yazd, Iran
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Kapoor K, Bigdeli AZ, Dwivedi YK, Raman R. How is COVID-19 altering the manufacturing landscape? A literature review of imminent challenges and management interventions. ANNALS OF OPERATIONS RESEARCH 2021; 335:1-33. [PMID: 34803204 PMCID: PMC8596861 DOI: 10.1007/s10479-021-04397-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/29/2021] [Indexed: 05/08/2023]
Abstract
Disruption from the COVID-19 pandemic has caused major upheavals for manufacturing, and has severe implications for production networks, and the demand and supply chains underpinning manufacturing operations. This paper is the first of its kind to pull together research on both-the pandemic-related challenges and the management interventions in a manufacturing context. This systematic literature review reveals the frailty of supply chains and production networks in withstanding the pressures of lockdowns and other safety protocols, including product and workforce shortages. These, altogether, have led to closed facilities, reduced capacities, increased costs, and severe economic uncertainty for manufacturing businesses. In managing these challenges and stabilising their operations, manufacturers are urgently intervening by-investing in digital technologies, undertaking resource redistribution and repurposing, regionalizing and localizing, servitizing, and targeting policies that can help them survive in this altered economy. Based on holistic analysis of these challenges and interventions, this review proposes an extensive research agenda for future studies to pursue.
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Affiliation(s)
| | | | - Yogesh K. Dwivedi
- Emerging Markets Research Centre (EMaRC), School of Management, Swansea University, Room #323, Bay Campus, Fabian Bay, Swansea, SA1 8EN Wales, UK
- Symbiosis Institute of Business Management, Pune & Symbiosis International (Deemed University), Pune, India
| | - Ramakrishnan Raman
- Symbiosis Institute of Business Management, Pune & Symbiosis International (Deemed University), Pune, India
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Queiroz MM, Fosso Wamba S. A structured literature review on the interplay between emerging technologies and COVID-19 - insights and directions to operations fields. ANNALS OF OPERATIONS RESEARCH 2021; 335:1-27. [PMID: 34226781 PMCID: PMC8243624 DOI: 10.1007/s10479-021-04107-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/06/2021] [Indexed: 05/11/2023]
Abstract
In recent years, emerging technologies have gained popularity and being implemented in different fields. Thus, critical leading-edge technologies such as artificial intelligence and other related technologies (blockchain, simulation, 3d printing, etc.) are transforming the operations and other traditional fields and proving their value in fighting against unprecedented COVID-19 pandemic outbreaks. However, due to this relation's novelty, little is known about the interplay between emerging technologies and COVID-19 and its implications to operations-related fields. In this vein, we mapped the extant literature on this integration by a structured literature review approach and found essential outcomes. In addition to the literature mapping, this paper's main contributions were identifying literature scarcity on this hot topic by operations-related fields; consequently, our paper emphasizes an urgent call to action. Also, we present a novel framework considering the primary emerging technologies and the operations processes concerning this pandemic outbreak. Also, we provided an exciting research agenda and four propositions derived from the framework, which are collated to operations processes angle. Thus, scholars and practitioners have the opportunity to adapt and advance the framework and empirically investigate and validate the propositions for this and other highly disruptive crisis.
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Affiliation(s)
- Maciel M. Queiroz
- Postgraduate Program in Business Administration, Paulista University–UNIP, Dr. Bacelar Street 1212, Sao Paulo, 04026-002 Brazil
- School of Engineering, Mackenzie Presbyterian University, Consolação Street 930, Sao Paulo, 01302-000 Brazil
| | - Samuel Fosso Wamba
- Information, Operations and Management Sciences, TBS Business School, 1 Place Alphonse Jourdain, 31068 Toulouse, France
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Harnessing Social Media in the Modelling of Pandemics-Challenges and Opportunities. Bull Math Biol 2021; 83:57. [PMID: 33835296 PMCID: PMC8033284 DOI: 10.1007/s11538-021-00895-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 03/25/2021] [Indexed: 02/07/2023]
Abstract
As COVID-19 spreads throughout the world without a straightforward treatment or widespread vaccine coverage in the near future, mathematical models of disease spread and of the potential impact of mitigation measures have been thrust into the limelight. With their popularity and ability to disseminate information relatively freely and rapidly, information from social media platforms offers a user-generated, spontaneous insight into users' minds that may capture beliefs, opinions, attitudes, intentions and behaviour towards outbreaks of infectious disease not obtainable elsewhere. The interactive, immersive nature of social media may reveal emergent behaviour that does not occur in engagement with traditional mass media or conventional surveys. In recognition of the dramatic shift to life online during the COVID-19 pandemic to mitigate disease spread and the increasing threat of further pandemics, we examine the challenges and opportunities inherent in the use of social media data in infectious disease modelling with particular focus on their inclusion in compartmental models.
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