Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic

Stochastic Analysis for the Dual Virus Parallel Transmission Model with Immunity Delay

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.

Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

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.

The Impact of Spring Festival Travel on Epidemic Spreading in China

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.

Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak

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.

Optimal timing of non-pharmaceutical interventions during an epidemic

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.

The COVID-19 pandemic through the lens of humanitarian logistics

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.

The COVID-19 pandemic through the lens of humanitarian logistics

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.

Mathematical analysis of the impact of the media coverage in mitigating the outbreak of COVID-19

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.

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images

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.

Big data applications with theoretical models and social media in financial management

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.

Product availability and stockpiling in times of pandemic: causes of supply chain disruptions and preventive measures in retailing

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.

Digital humanitarianism and crisis management: an empirical study of antecedents and consequences

Purpose

This 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/approach

To validate the proposed model, the authors used an offline survey to gather data from 260 district magistrates in India managing the COVID-19 pandemic.

Findings

The 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/implications

Technological 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/value

The 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).

FOMO (fate of online media only) in infectious disease modeling: a review of compartmental models

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.

COVID-19 vaccine hesitancy: a social media analysis using deep learning

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%.

Learnings from COVID-19 for managing humanitarian supply chains: systematic literature review and future research directions

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.

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

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.

An extended robust mathematical model to project the course of COVID-19 epidemic in Iran

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.

How is COVID-19 altering the manufacturing landscape? A literature review of imminent challenges and management interventions

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.

A structured literature review on the interplay between emerging technologies and COVID-19 - insights and directions to operations fields

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.

Harnessing Social Media in the Modelling of Pandemics-Challenges and Opportunities

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.