Pandemic Management Research Articles

Crisis risk communication and public behavior: Analysis of the COVID-19 pandemic in Pakistan

This study underpins the crisis risk communication (CRC) theory, investigating the integral role of social media in Pakistan towards COVID-19, its influence on cognitive priming processes, and behavior regulation. Empirical findings reveal predominantly positive public sentiment toward preventive behaviors, with mask-wearing and sanitizer use widely embraced. However, the inclination to avoid gatherings with risk groups is comparatively lower. This collective adherence to preventive measures suggests a shared effort to curb COVID-19, influenced by factors like the perceived helpfulness of social media. The populace exhibits a predominantly favorable attitude towards COVID-19 vaccination, signifying widespread acceptance of vaccines within the country. Additionally, the study assesses public satisfaction with the government’s pandemic management, revealing an overall positive sentiment. Nonetheless, concerns emerge regarding essential needs, including food, shelter, and treatment. Enhancing our insight, this study sheds light on the intricate interplay between CRC, social media, and public behavior amidst a health crisis. The insights gleaned from this study provide valuable guidance for policymakers and public health practitioners to refine future communication strategies and address societal concerns effectively during pandemics.

Mental Health Disorders in Healthcare Workers Exposed to COVID-19, Mashhad, Iran: A Cross-sectional Study

Background: Healthcare workers (HCWs) who are directly involved in the management of patients with SARS-CoV-2 infection are at higher risk of psychological disorders. Objective: In this study, we evaluated the risk factors related to mental health disorders in HCWs active in the management of the COVID-19 pandemic in Mashhad, Iran. Methods: This was a cross-sectional investigation performed between April 2020 and May 2020 at five referral centers in Mashhad, Iran. Patient Health Questionnaire (PHQ-9), 7-item Generalized Anxiety Disorder scale (GAD-7), Insomnia Severity Index (ISI), Impact of Event Scale–Revised (IESR), and VAS (for fear from COVID-19) to assess depression, anxiety, insomnia, distress and fear related to COVID-19 were filled. Risk factors were evaluated by the regression model. Results: Mean ± SD age of participants was 33.84 ± 7.03 years. Most were females (64.2%). Of 360 participants, 252 had some degree of an anxiety disorder (70%), 63.8% had depression, 55.8% had insomnia, and 72.8% had distress. Work experiences (OR: 0.94, 95% CI = 0.901- 0.983, p = 0.007), exposure to COVID-19 patients (OR: 2.54, 95% CI = 1.37-4.69, p < 0.001), and job status (OR: 1.40, 95% CI = 1.19 -1.54, p < 0.001) were significant predictors of anxiety. Age (OR: 1.15, 95% CI = 1.05-1.27, p = 0.002), exposure to COVID-19 patients (OR: 1.95, 95% CI = 1.04-3.64, p = 0.037), work experience (OR: 0.86, 95% CI = 0.78-0.93, p < 0.001), having children (OR: 0.58, 95% CI = 0.39- 0.87, p = 0.008), and being infected with COVID-19 (OR: 9.95, 95% CI = 1.92-51.64, p < 0.001) were the significant independent factors for depression in participants. Conclusion: COVID-19 could significantly affect HCWs mental health status, specifically depression. By targeting these predictors, health policymakers can reduce the burden of psychological disorders in HCWs.

Enhancing CNN‐LSTM neural networks using jellyfish search algorithm for pandemic modeling

SummaryThis paper presents a comprehensive three‐step approach (CNN‐JSO‐LSTM) for predictive modeling using a pandemic such as COVID‐19 as a test case. Initially, a Convolutional Neural Network (CNN) is employed to extract crucial features pertinent to the pandemic. Subsequently, the Jellyfish Search Optimizer (JSO) algorithm is applied for feature selection, identifying the most relevant factors. These chosen features are then inputted into a Long Short‐Term Memory (LSTM) network, responsible for classifying samples into “healthy” and “diseased” categories. Our method enhances LSTM performance using the Jellyfish Search optimizer, resulting in exceptional prediction accuracy. Our experiments achieved remarkable metrics, with an accuracy of 95.32%, high sensitivity (94.87%), and precision (94.28%), surpassing alternative methods. In conclusion, our study presents a promising and highly accurate approach for pandemic prediction, harnessing deep learning and swarm intelligence techniques. These findings suggest a potential for more effective pandemic management and intervention strategies.

Asymmetric Fiscal Federalism and Public Provision of Healthcare: New Insights from India

In this article, we show undesirable consequences of a market-based system and argue for a public provision of healthcare. We examine the healthcare provision from the fiscal federalism perspective. With the asymmetric fiscal federal structure characterised by vertical and horizontal imbalances, states are dependent on transfers from union to provide public goods. Our analysis shows a widespread disparity in the public provision of healthcare in India due to fiscal imbalances. The performance of states in the management of the COVID-19 pandemic reveals that a robust public healthcare system is a prerequisite in managing a health emergency. We identify the withdrawal of governments from healthcare provision and concentration of resources with the union government as critical impediments to achieving universal healthcare access. In light of our analysis, we emphasise the need for resource devolution and cooperative federalism to ensure public provision for a dignified life in a functioning democracy.

The maze of interpretation: abortion laws and legal indeterminacy in Indian courts

ABSTRACT This article outlines the structural barriers to abortions in India, exacerbated by the COVID-19 pandemic and ensuing lockdowns. It discusses the limits to access inherent within India’s abortion law, strict state-imposed pandemic management measures, the unequal public healthcare system, and resultant systemic coercion of pregnant persons to approach courts to seek abortions. It reviews abortion-related cases to argue that the judiciary’s neglect of its responsibilities during the pandemic has been mirrored in its lack of willingness to realize the reproductive rights of pregnant persons requiring abortions during and after the pandemic. Cases are examined using the legal indeterminacy framework to understand how abortion laws overlook pregnant persons’ autonomy. These laws have relied on stereotypes, betraying the underlying paternalistic and protectionist rationale of the judiciary. The judiciary has assumed the role of ultimate arbiter in determining what constitutes a “good” or “bad” abortion and who is deemed deserving of abortion access.

Risk Mapping of COVID-19 to Create a Common Operating Picture Using Data from Wastewater Monitoring

Monitoring wastewater quality at the inlet of a wastewater treatment plant provides an overview of the health of the community connected to the respective wastewater treatment plant. Wastewater monitoring has been increasingly recognized as a valid tool in pandemic risk management, especially during the coronavirus disease 2019 (COVID-19) crisis. However, the results of wastewater monitoring are not yet fully utilized in the decision-making process of public governance across most of the Asia-Pacific region. This study explored the potential of geographic information systems to map the results of wastewater monitoring and provide a common operating picture for swift and informed decision-making, especially in times of disasters. By creating an online dashboard tool, we found that an additional merit of mapping wastewater monitoring is that it allows overlaying various other information, such as evacuation shelters, onto the same map. As the service areas of wastewater treatment plants, or sewersheds, are not necessarily aligned with geopolitical boundaries, mapping provides opportunities for re-calculating wastewater monitoring results at the city or district levels. The potential for recalculation is also applicable to overlayed information, indicating that information from wastewater monitoring may be used to assess disaster preparedness or multi-hazard risks. The dashboard prototype was discussed with government officials and feedback was obtained. We aim to further develop the findings so that the information from wastewater monitoring can be shared on a platform, such as the shared information platform for disaster management, in the future for effective risk governance.

A framework on interplay of knowledge types and dimensions in pandemics – Example of COVID‐19

AbstractRecent literature has documented numerous knowledge management (KM) policy frameworks and models utilised for managing pandemics such as COVID‐19. Perhaps the major focus of existing scholarly work is on the role and relevance of healthcare KM in the COVID‐19 crisis compared to the understanding of healthcare knowledge dimensions and knowledge types in pandemic management and control. Considering the complexities of COVID‐19, we attempt to address a literature gap in the management knowledge of pandemics as the study problem in this paper. The paper proposes a framework to analyse the interplay between the dimensions and knowledge types and its application benefits using the example of COVID‐19 management in the state of Kerala. Considering the disparities in how the pandemic was managed globally, we use the Kerala case to illuminate the different interactions between the three types and four dimensions of knowledge for pandemic management. Future studies may explore the extension and testing of the proposed interplay framework in other contexts.

Crisis Management, Resilience, and Organizational Learning in the Event Management Sector: An Insight From Malaysia

The management of major crises, resilience, and organizational learning are important issues that have received scant attention in the event management industry, and limited information can be found in mainstream literature. The current research focuses on crisis management practices, resilience, and organizational learning of event planners and management companies during the health pandemic crisis. A total of 15 in-depth interviews were conducted with event planners and management companies in Malaysia. The results showed that there were five stages of encountering the pandemic: a) panic and anxiety, b) crumbling motivation and thwarting career moves, c) paralyzing decision-making processes, d) bleeding inwards and creating an uncertain and complex environment in the organization, and e) reconsidering business operation. Moreover, three resilience adaptation strategies (e.g., psychological, technological, and organizational resilience) were found. Event organizational learning in the form of knowledge management was another important practice during the pandemic crisis management. The study significantly contributes to literature, exploring the implication of organizational learning and resilience in event crisis management. It further offers practical implications to event management stakeholders.

Correlating Quantitative and Genomic SARS-CoV-2 Wastewater Data with Clinical Metrics in Metropolitan Perth, Western Australia

Wastewater-based epidemiology (WBE) has emerged as a key method for the continuous monitoring of COVID-19 prevalence including circulating SARS-CoV-2 lineages. WBE addresses the limitations of traditional clinical COVID-19 surveillance such as clinical test availability, fluctuating testing rates, and increased reliance on rapid antigen tests. Our study in Perth, Western Australia found a significant positive correlation between SARS-CoV-2 concentrations in wastewater and clinical PCR positivity rates (rs = 0.772; p < 0.001) over an 18-month period that included four successive COVID-19 waves. A strong positive correlation was apparent between the proportions of SARS-CoV-2 lineages in wastewater and clinical cases within the same region (rs = 0.728, p < 0.001), including earlier detection of Omicron and recombinant lineages in wastewater before clinical case confirmation. The successful integration of WBE with healthcare data underscores its critical role in enhancing public health decision-making and pandemic management. This approach not only demonstrates the value of WBE in current global health surveillance efforts but also highlights the potential of WBE to address future public health challenges, as a comprehensive disease monitoring and response approach.

PANDEM-Source, a tool to collect or generate surveillance indicators for pandemic management: a use case with COVID-19 data.

PANDEM-Source (PS) is a tool to collect and integrate openly available public health-related data from heterogeneous data sources to support the surveillance of infectious diseases for pandemic management. The tool may also be used for pandemic preparedness by generating surveillance data for training purposes. It was developed as part of the EU-funded Horizon 2020 PANDEM-2 project during the COVID-19 pandemic as a result of close collaboration in a consortium of 19 partners, including six European public health agencies, one hospital, and three first responder organizations. This manuscript describes PS's features and design to disseminate its characteristics and capabilities to strengthen pandemic preparedness and response. A requirement-gathering process with EU pandemic managers in the consortium was performed to identify and prioritize a list of variables and indicators useful for surveillance and pandemic management. Using the COVID-19 pandemic as a use case, we developed PS with the purpose of feeding all necessary data to be displayed in the PANDEM-2 dashboard. PS routinely monitors, collects, and standardizes data from open or restricted heterogeneous data sources (users can upload their own data). It supports indicators and health resources related data from traditional data sources reported by national and international agencies, and indicators from non-traditional data sources such as those captured in social and mass media, participatory surveillance, and seroprevalence studies. The tool can also calculate indicators and be used to produce data for training purposes by generating synthetic data from a minimal set of indicators to simulate pandemic scenarios. PS is currently set up for COVID-19 surveillance at the European level but can be adapted to other diseases or threats and regions. With the lessons learnt during the COVID-19 pandemic, it is important to keep building capacity to monitor potential threats and develop tools that can facilitate training in all the necessary aspects to manage future pandemics. PS is open source and its design provides flexibility to collect heterogeneous data from open data sources or to upload end users's own data and customize surveillance indicators. PS is easily adaptable to future threats or different training scenarios. All these features make PS a unique and valuable tool for pandemic management.

Estimating actual SARS-CoV-2 infections from secondary data

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation—rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

Avian Influenza A Viruses Modulate the Cellular Cytoskeleton during Infection of Mammalian Hosts.

Influenza is one of the most prevalent causes of death worldwide. Influenza A viruses (IAVs) naturally infect various avian and mammalian hosts, causing seasonal epidemics and periodic pandemics with high morbidity and mortality. The recent SARS-CoV-2 pandemic showed how an animal virus strain could unpredictably acquire the ability to infect humans with high infection transmissibility. Importantly, highly pathogenic avian influenza A viruses (AIVs) may cause human infections with exceptionally high mortality. Because these latter infections pose a pandemic potential, analyzing the ecology and evolution features of host expansion helps to identify new broad-range therapeutic strategies. Although IAVs are the prototypic example of molecular strategies that capitalize on their coding potential, the outcome of infection depends strictly on the complex interactions between viral and host cell factors. Most of the studies have focused on the influenza virus, while the contribution of host factors remains largely unknown. Therefore, a comprehensive understanding of mammals' host response to AIV infection is crucial. This review sheds light on the involvement of the cellular cytoskeleton during the highly pathogenic AIV infection of mammalian hosts, allowing a better understanding of its modulatory role, which may be relevant to therapeutic interventions for fatal disease prevention and pandemic management.

Social support and mental well-being among people with and without chronic illness during the Covid-19 pandemic: evidence from the longitudinal UCL covid survey

PurposeAn immediate research priority recovering from the COVID-19 pandemic is well-being among some of our most vulnerable—people with chronic illness. We studied how mental health changed among people with and without chronic illness throughout the pandemic and the mediating role of social support.MethodsWe used the 3-waves of COVID-19 survey within the Millennium Cohort Study (MCS, age 19, N = 5522) and MCS Parent (MCSP, age > > 19, N = 7479) samples, with additional pre-pandemic measures of some outcomes and exposure. Using Structural Equation Panel Models with Full Information Maximum Likelihood estimation to address missing data, we studied differences between respondents with a chronic illness and without, regarding depressive symptoms and mental well-being, with social provision, social support, and loneliness as potential mediators.ResultsMental well-being (SWEMWBS) and psychological distress (Kessler-6) worsened significantly during the pandemic relative to baseline for people with and without chronic illness, while the latter group had substantially better well-being at all waves and the baseline regarding both outcomes. When the lockdown was lifted during wave-2, mental well-being temporarily rebounded, and distress waned among people without chronic illness but continued to worsen among people with chronic illness. Social support partially mediated the link between chronic illness and mental well-being.ConclusionsThe large mental well-being gap between people with and without chronic illness persisted during the pandemic. However, social support and provision can partially narrow this gap, hence should be employed in future pandemic management.

How to increase acceptance of the COVID-19 vaccine among poor people in Africa?

This study aims to analyze whether good government management of the COVID-19 pandemic can increase the likelihood of vaccine uptake among poor people in Africa. The analysis is based on a sample of 18,010 people living in 34 African countries, drawn from data collected by Afrobarometer (Merged Round 8 data (34 countries), database, 2022). The econometric results, obtained using a bivariate probit regression, show that poverty significantly reduces the odds of accepting the said COVID-19 vaccine. However, acceptance of the vaccine increases among poor individuals when there is (i) trust in the government's published statistics on COVID-19, (ii) control of corruption by the government in managing the pandemic, (iii) individual confidence in the government's ability to ensure the safety of the COVID-19 vaccine, and (iv) belief that the Covid 19 vaccine will be more effective than religious prayer in the fight against this pandemic.

Overlooked voices under strict lockdown: mapping humanitarian needs in 2022 Shanghai COVID-19 outbreak

ABSTRACT The COVID-19 pandemic that emerged in 2020 has caused significant health crises worldwide. Countries and regions around the globe have practiced different policies to contain the pandemic. One commonly adopted strategy is the Zero-COVID policy, comprising two phases: an initial suppression phase, typically enforced through lockdowns, followed by a sustained containment phase. However, sudden and strict lockdown policies in the suppression phase would inevitably bring unexpected challenges to vulnerable populations. There is a need to identify the emergent needs of underprepared, vulnerable populations under lockdowns to inform future pandemic management. In this study, the messages posted on an online mutual help-requesting platform during the 2022 Shanghai lockdown were leveraged as the near real-time agent to represent the side effects of the lockdown policy. This work explores the spatiotemporal correlation of the posts to their potential influencing factors and mines knowledge from the textual content of the posts. The results indicate that the help requests were clustered in downtown Shanghai. The access to profuse medical resources and public services may be hindered in districts with more stringent lockdown policies. The help requests’ content unveils a need for medication and groceries under lockdown. It also underscores that the elderly population was affected most by the lockdown. Based on these insights, policymakers can better anticipate and address the urgent residential needs that may arise in future lockdowns. This knowledge can contribute to more effective planning and preparedness efforts to mitigate the impact on vulnerable populations and ensure their well-being during similar crisis situations.

The COVID-19 quandemic

BackgroundThe terms syndemic and infodemic have both been applied to the COVID-19 pandemic, and emphasize concurrent socio-cultural dynamics that are distinct from the epidemiological outbreak itself. We argue that the COVID-19 pandemic has exposed yet another important socio-political dynamic that can best be captured by the concept of a quandemic – a portmanteau of “quantification” and “pandemic”.Main textThe use of quantifiable metrics in policymaking and evaluation has increased throughout the last decades, and is driven by a synergetic relationship between increases in supply and advances in demand for data. In most regards this is a welcome development. However, a quandemic, refers to a situation where a small subset of quantifiable metrics dominate policymaking and the public debate, at the expense of more nuanced and multi-disciplinary discourse. We therefore pose that a quandemic reduces a complex pandemic to a few metrics that present an overly simplified picture. During COVID-19, these metrics were different iterations of case numbers, deaths, hospitalizations, diagnostic tests, bed occupancy rates, the R-number and vaccination coverage. These limited metrics came to constitute the internationally recognized benchmarks for effective pandemic management.Based on experience from the Nordic region, we propose four distinct dynamics that characterize a quandemic: 1) A limited number of metrics tend to dominate both political, expert, and public spheres and exhibit a great deal of rigidity over time. 2) These few metrics crowd-out other forms of evidence relevant to pandemic response. 3) The metrics tend to favour certain outcomes of pandemic management, such as reducing hospitalization rates, while not capturing potential adverse effects such as social isolation and loneliness. 4) Finally, the metrics are easily standardized across countries, and give rise to competitive dynamics based on international comparisons and benchmarking.ConclusionA quandemic is not inevitable. While metrics are an indispensable part of evidence-informed policymaking, being attentive to quandemic dynamics also means identifying relevant evidence that might not be captured by these few but dominant metrics. Pandemic responses need to account for and consider multilayered vulnerabilities and risks, including socioeconomic inequities and comorbidities.

The Pandemics of Mass Destruction: A Comparative Analysis of HIV/AIDS and Coronavirus (COVID-19).

Historically, humanity has suffered and endured deadly pandemics of varying mortality rates. Irrefutably, research shows that the outbreak of pandemics is flooded by mythical and fallacious information among the public, hence stifling the prevention, treatment, and management of diseases. This paper focused on comparing selected aspects of the two pandemics, that is, HIV/AIDS and Coronavirus. This comparative analysis is important for drawing informative lessons for effective response and management of pandemics in the future. Through a literature review analysis, the paper established that both pandemics have more similarities than distinctions. The etiology and epidemiology of the diseases assume a similar cascading trajectory; the public health information about the diseases is characterized by myths, conspiracy theories, illusions, and delusions from the public. The myths associated with pandemics prevail around causation, disease transmission, and cure. The pandemics present economic paradoxes, though arguably the negatives outdo the positives. There is a need for the governments and international health custodians to be richly prepared for the pandemics in the future. This implies having special budgetary allocations for possible pandemic outbreaks, investing in vaccine development and disease surveillance, and training and skilling personnel in all social-health-related sectors.

[Development and progress of the National Autopsy Network (NATON)].

Autopsies have long been considered the gold standard for quality assurance in medicine, yet their significance in basic research has been relatively overlooked. The COVID-19 pandemic underscored the potential of autopsies in understanding pathophysiology, therapy, and disease management. In response, the German Registry for COVID-19 Autopsies (DeRegCOVID) was established in April 2020, followed by the DEFEAT PANDEMIcs consortium (2020-2021), which evolved into the National Autopsy Network (NATON). DeRegCOVID collected and analyzed autopsy data from COVID-19 deceased in Germany over three years, serving as the largest national multicenter autopsy study. Results identified crucial factors in severe/fatal cases, such as pulmonary vascular thromboemboli and the intricate virus-immune interplay. DeRegCOVID served as acentral hub for data analysis, research inquiries, and public communication, playing avital role in informing policy changes and responding to health authorities. Initiated by the Network University Medicine (NUM), NATON emerged as asustainable infrastructure for autopsy-based research. NATON aims to provide adata and method platform, fostering collaboration across pathology, neuropathology, and legal medicine. Its structure supports aswift feedback loop between research, patient care, and pandemic management. DeRegCOVID has significantly contributed to understanding COVID-19 pathophysiology, leading to the establishment of NATON. The National Autopsy Registry (NAREG), as its successor, embodies amodular and adaptable approach, aiming to enhance autopsy-based research collaboration nationally and, potentially, internationally.

Examining the impact of COVID-19 on Māori:non-Māori health inequities in Aotearoa, New Zealand: an observational study protocol

IntroductionThe COVID-19 pandemic has had both direct and indirect impacts on the health of populations worldwide. While racial/ethnic health inequities in COVID-19 infection are now well known (and ongoing), knowledge...

Cinnamomum impressinervium meisn.: antioxidant and antimicrobial activities

Background: Medical interns develop apprehensions about the vulnerability of their exposure to infection while treating COVID-19 patients, which may further affect their work patterns and efficiency. Studies have shown that the outbreak of infectious diseases would result in mental health issues. Because of this, our study aims to assess psychosocial factors such as anxiety, depression, stress, relationships with peers, and changes in personal roles among medical interns at a private medical college. Methodology: This cross-sectional was conducted among 248 medical interns using a simple random sampling method. Depression, anxiety, and stress were assessed using a standardized 21-item depression, anxiety, and stress questionnaire (DASS– 21). A pretested semi-structured questionnaire was used to acquire information regarding the study participants' socio-demographic data and social elements. Results: The overall prevalence of Depression [58%], Anxiety [70%], and Stress [44%] were found among the 248 study participants. Around [70.6%] of the study participants reported that their social life had been affected. The prevalence of various factors associated with depression, anxiety, and stress were assessed. Conclusion: This study reports higher levels of psychosocial distress among the study participants. Adequate knowledge about the pandemic and stress management measures will be the top priority among these budding medicos during such unfavorable pandemic situations.