Pandemic Response Strategies Research Articles

Implementation of COVID-19 measures for Dutch nursing home staff and their insights

Abstract Background Measures against COVID-19 in nursing homes impacted both clients and staff, yet the importance of measures from the staff's perspective remain underexplored. This study examined measures related to staff during the COVID-19 pandemic, staff perspectives of their importance and the involvement of staff in deciding these measures. Methods In this qualitative study, the minutes of 41 nursing home outbreak teams in the period March to November 2020 were analyzed and 4 group meetings were conducted between June-November 2020. Qualitative content analysis was used for the minutes data, whereas reflexive thematic analysis was used for the group meeting data. Results Measures implemented for staff focused on prevention of COVID-19 transmission, (suspension of) educational activities, testing, additional tasks and staffing capacity, promoting well-being, and other means of support. The results showed overlap between the implemented measures and the measures considered important by staff, except for measures on decision-making support and communication, which were considered important by staff but not listed among the implemented measures. Measures were prioritized by staff because they affected their well-being, workforce scheduling, decision making, or preventing infections. Furthermore, the group meetings revealed that decision making shifted from mainly implementing national guidelines to more context -adjusted decision making in the staff's or clients’ situation. Conclusions Although there was overlap regarding measures taken, our findings suggest that organizations should be encouraged to explore ways of timely involvement of staff in decision making when implementing measures in future pandemics. During the COVID-19 pandemic, information about the pandemic response strategies and policies were shared with policymakers and nursing home organizations to learn from each other and make more informed decisions about how to respond to the pandemic. Key messages • The information gained through the group meetings can lead to an improved fit between implemented measures and staffs’ working procedures during future pandemics. • Decision making support and communication should receive more attention according to staff during future pandemics.

Political institutions and their role during health emergencies of global scale (COVID-19)

This research consistently investigates the pivotal role and significance of political institutions during global emergencies, with a specific focus on pandemics. The analysis centers on the case of the COVID-19 pandemic, which has underscored the paramount importance of political institutions in orchestrating and formulating pandemic response strategies. Notably, health authorities, alongside the broader healthcare system, anchor their actions in alignment with guidelines and directives promulgated by political institutions spanning various tiers of government. In such exigent circumstances, the executive branch of political power plays a particularly vital role. The efficacy and seamless collaboration among these institutions become instrumental in executing successful crisis management policies. The COVID-19 pandemic serves as a vivid illustration of the situation, where executive branches at various governmental levels wielded substantial influence over decision-making processes and resource allocation. This paper contends that the harmonious interplay and synchronized efforts of political institutions are indispensable for effectively navigating and surmounting crises of global magnitude, such as pandemics.

Enhanced Diagnosis of Influenza and COVID-19 Using Machine Learning

The Coronavirus Disease 2019 (COVID-19) has rapidly spread globally, causing a significant impact on public health. This study proposes a predictive model employing machine learning techniques to distinguish between influenza-like illness and COVID-19 based on clinical symptoms and diagnostic parameters. Leveraging a dataset sourced from BMC Med Inform Decis Mak, comprising cases of influenza and COVID-19, we explore a diverse set of features, including clinical symptoms and blood assay parameters. Two prominent machine learning algorithms, XGBoost and Random Forest, are employed and compared for their predictive capabilities. The XGBoost model, in particular, demonstrates superior accuracy with an AUC under the ROC curve of 98.8%, showcasing its potential for clinical diagnosis, especially in settings with limited specialized testing equipment. Our model's practical applicability in community-based testing positions it as a valuable tool for efficient COVID-19 detection. This study advances the field of predictive modeling for disease detection, offering promising prospects for improved public health outcomes and pandemic response strategies. The model's reliability and effectiveness make it a valuable asset in the ongoing fight against the COVID-19 pandemic.

Comparative analysis of the machine learning models determining COVID-19 patient risk levels

The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, emphasizing the need for predictive tools for resource allocation and patient care. This study delves into the potential of machine learning models to predict the risk levels of COVID-19 patients using a comprehensive dataset. This study aimed to evaluate and compare the efficacy of three distinct machine learning methodologies – Bayesian Criterion, Logistic Regression, and Gradient Boosting – in predicting the risk associated with COVID-19 patients based on their symptoms, status, and medical history. This research is targeted at the process of patient state determination. The research subjects are machine learning methods for patient state determination. To achieve the aim of the research, the following tasks have been formulated: methods and models of the COVID-19 patients state determination should be analyzed; classification model of the patient state determination based on Bayes criterion should be developed; classification model of the patient state determination based on logistic regression should be developed; classification model of the patient state determination based on gradient boosting should be developed; the information system should be developed; the experimental study based on machine learning methods should be provided; and the results of the experimental study should be analyzed. Methods: using a dataset provided by the Mexican government, which encompasses over a million unique patients with 21 distinct features, we developed an information system in C# programming language. This system allows users to select their preferred method for risk calculation, offering a real-time decision-making tool for healthcare professionals. Results: All models demonstrated commendable accuracy levels. However, subtle differences in their performance metrics, such as sensitivity, precision, and the F1-score, were observed. The Gradient Boosting method slightly outperformed the other models in terms of overall accuracy. Conclusions: While each model showcased its merits, the choice of method should be based on the specific needs and constraints of the healthcare system. The Gradient Boosting method emerged as marginally superior in this study. This research underscores the potential of machine learning in enhancing pandemic response strategies, offering both scientific insights and practical tools for healthcare professionals.

An Integrated Research-Clinical BSL-2 Platform for a Live SARS-CoV-2 Neutralization Assay.

A reliable and efficient serological test is crucial for monitoring neutralizing antibodies against SARS-CoV-2 and its variants of concern (VOCs). Here, we present an integrated research-clinical platform for a live SARS-CoV-2 neutralization assay, utilizing highly attenuated SARS-CoV-2 (Δ3678_WA1-spike). This strain contains mutations in viral transcription regulation sequences and deletion in the open-reading-frames 3, 6, 7, and 8, allowing for safe handling in biosafety level 2 (BSL-2) laboratories. Building on this backbone, we constructed a genetically stable reporter virus (mGFP Δ3678_WA1-spike) by incorporating a modified green fluorescent protein sequence (mGFP). We also constructed mGFP Δ3678_BA.5-spike and mGFP Δ3678_XBB.1.5-spike by substituting the WA1 spike with variants BA.5 and XBB.1.5 spike, respectively. All three viruses exhibit robust fluorescent signals in infected cells and neutralization titers in an optimized fluorescence reduction neutralization assay that highly correlates with a conventional plaque reduction assay. Furthermore, we established that a streamlined robot-aided Bench-to-Clinics COVID-19 Neutralization Test workflow demonstrated remarkably sensitive, specific, reproducible, and accurate characteristics, allowing the assessment of neutralization titers against SARS-CoV-2 variants within 24 h after sample receiving. Overall, our innovative approach provides a valuable avenue for large-scale testing of clinical samples against SARS-CoV-2 and VOCs at BSL-2, supporting pandemic preparedness and response strategies.

Role of hospital leadership in pandemic preparedness: experience at a tertiary hospital in Kenya during the COVID-19 pandemic

IntroductionPandemic preparedness refers to being ready for, responding to and recovering from public health crises, and is integral for health security. Hospital leadership is a critical building block of an...

"Our interventions are still here to support communities during the pandemic": Resuming mass drug administration for neglected tropical diseases after COVID-19 implementation delays.

The COVID-19 pandemic disrupted essential health services, including those provided by national neglected tropical disease (NTD) programs. Most mass drug administration (MDA) programs were postponed for 6-12 months following World Health Organization guidance released in April 2020 to temporarily halt NTD programs and launch necessary COVID-19 precautions. While NTD-endemic countries have since resumed MDA activities, it is critical to understand implementers' perspectives on the key challenges and opportunities for program relaunch, as these insights are critical for maximizing gains towards disease control and elimination during public health emergencies. Using data from using online surveys and focus group discussions, this mixed-methods study sought perspectives from Ministry of Health NTD Program Managers and implementing partners from non-governmental organizations working in sub-Saharan Africa. Data analysis revealed that findings converged around several main themes: disruptions for MDA programs included resource shortages due to prioritization of pandemic response, challenges adhering to COVID-19 safety protocols, and community hesitancy due to coronavirus transmission fears. Identified solutions for restarting MDA programs focused on adapting intervention delivery and packaging to minimize disease transmission, embracing technology to optimize intervention planning and delivery, and identifying opportunities to promote program integration between pandemic response strategies and NTD campaign delivery. Findings identifies key challenges due to disruptions to NTD program delivery and provide strategic recommendations for endemic countries to build resilient programs that can continue to perform during and beyond global pandemics.

Contact-tracing of the COVID-19 spreading using digital technologies with artificial intelligence (literary review)

The emergence of COVID-19 almost coincided with the beginning of an active phase of the digitalization process in all areas, including the healthcare system. Moreover, COVID-19 unwittingly became the impetus that accelerated the adoption of digital technologies, and initiated new, often innovative solutions to combat both the virus and its devastating social and economic consequences.
 The purpose of the study: the current study conducts a literature review of existing scientific reports in the field of digital contact-tracing COVID-19 using artificial intelligence (AI) technologies, to discuss issues related to the security of personal data when using official mobile applications, to draw conclusions and make recommendations in the field of effective and ethical management of digital contact-tracing as one of the main tools for preventing the spread of the pandemic.
 Scientific reports contained in the scientific research databases of Ebsco Publishing and SpringerLink for the period March 2020 — April 2021 were analyzed.
 Digital contact-tracing solutions have found their important place among other anti-epidemic measures in many countries around the world. However, the same solutions, but already using AI, are still gaining popularity. National governments in numerous developed and developing countries understand the importance of national contact-tracing systems, which in turn has introduced such approaches in national pandemic response strategies.
 Conclusion: Digital contact-tracing technologies using AI can be an effective tool in the fight against COVID-19 and similar pandemics. However, such digital systems are still at a preliminary stage of their development and implementation, and it will take time before the results will be visible. Very few of the considered examples and models of digital tracing solutions using AI technologies have operational maturity at this stage.

Pandemic response strategies and threshold phenomena

This paper critically evaluates the Suppression Threshold Strategy (STS) for controlling Covid-19 (C-19). STS asserts a “fundamental distinction” between suppression and mitigation strategies, reflected in very different outcomes in eventual mortality depending on whether reproductive number R is caused to fall below 1. We show that there is no real distinction based on any value of R which falls in any case from early on in an epidemic wave. We show that actual mortality outcomes lay on a continuum, correlating with suppression levels, but not exhibiting any step changes or threshold effects. We argue that an excessive focus on achieving suppression at all costs, driven by the erroneous notion that suppression is a threshold, led to a lack of information on how to trade off the effects of different specific interventions. This led many countries to continue with inappropriate intervention-packages even after it became clear that their initial goal was not going to be attained. Future pandemic planning must support the design of “Plan B", which may be quite different from “Plan A".

Optimal non-pharmaceutical pandemic response strategies depend critically on time horizons and costs

The COVID-19 pandemic has called for swift action from local governments, which have instated non-pharmaceutical interventions (NPIs) to curb the spread of the disease. The swift implementation of social distancing policies has raised questions about the costs and benefits of strategies that either aim to keep cases as low as possible (suppression) or aim to reach herd immunity quickly (mitigation) to tackle the COVID-19 pandemic. While curbing COVID-19 required blunt instruments, it is unclear whether a less-transmissible and less-deadly emerging pathogen would justify the same response. This paper illuminates this question using a parsimonious transmission model by formulating the social distancing lives vs. livelihoods dilemma as a boundary value problem using calculus of variations. In this setup, society balances the costs and benefits of social distancing contingent on the costs of reducing transmission relative to the burden imposed by the disease. We consider both single-objective and multi-objective formulations of the problem. To the best of our knowledge, our approach is distinct in the sense that strategies emerge from the problem structure rather than being imposed a priori. We find that the relative time-horizon of the pandemic (i.e., the time it takes to develop effective vaccines and treatments) and the relative cost of social distancing influence the choice of the optimal policy. Unsurprisingly, we find that the appropriate policy response depends on these two factors. We discuss the conditions under which each policy archetype (suppression vs. mitigation) appears to be the most appropriate.

Comparative Performance of Serological (IgM/IgG) and Molecular Testing (RT-PCR) of COVID-19 in Three Private Universities in Cameroon during the Pandemic.

COVID-19 remains a rapidly evolving and deadly pandemic worldwide. This necessitates the continuous assessment of existing diagnostic tools for a robust, up-to-date, and cost-effective pandemic response strategy. We sought to determine the infection rate (PCR-positivity) and degree of spread (IgM/IgG) of SARS-CoV-2 in three university settings in Cameroon Method: Study volunteers were recruited from November 2020 to July 2021 among COVID-19 non-vaccinated students in three Universities from two regions of Cameroon (West and Centre). Molecular testing was performed by RT-qPCR on nasopharyngeal swabs, and IgM/IgG antibodies in plasma were detected using the Abbott Panbio IgM/IgG rapid diagnostic test (RDT) at the Virology Laboratory of CREMER/IMPM/MINRESI. The molecular and serological profiles were compared, and p < 0.05 was considered statistically significant. Amongst the 291 participants enrolled (mean age 22.59 ± 10.43 years), 19.59% (57/291) were symptomatic and 80.41% (234/291) were asymptomatic. The overall COVID-19 PCR-positivity rate was 21.31% (62/291), distributed as follows: 25.25% from UdM-Bangangte, 27.27% from ISSBA-Yaounde, and 5% from IUEs/INSAM-Yaounde. Women were more affected than men (28.76% [44/153] vs. 13.04% [18/138], p < 0.0007), and had higher seropositivity rates to IgM+/IgG+ (15.69% [24/153] vs. 7.25% [10/138], p < 0.01). Participants from Bangangté, the nomadic, and the "non-contact cases" primarily presented an active infection compared to those from Yaoundé (p= 0.05, p = 0.05, and p = 0.01, respectively). Overall IgG seropositivity (IgM-/IgG+ and IgM+/IgG+) was 24.4% (71/291). A proportion of 26.92% (7/26) presenting COVID-19 IgM+/IgG- had negative PCR vs. 73.08% (19/26) with positive PCR, p < 0.0001. Furthermore, 17.65% (6/34) with COVID-19 IgM+/IgG+ had a negative PCR as compared to 82.35% with a positive PCR (28/34), p < 0.0001. Lastly, 7.22% (14/194) with IgM-/IgG- had a positive PCR. This study calls for a rapid preparedness and response strategy in higher institutes in the case of any future pathogen with pandemic or epidemic potential. The observed disparity between IgG/IgM and the viral profile supports prioritizing assays targeting the virus (nucleic acid or antigen) for diagnosis and antibody screening for sero-surveys.

Cell-Free Dot Blot: an Ultra-Low-Cost and Practical Immunoassay Platform for Detection of Anti-SARS-CoV-2 Antibodies in Human and Animal Sera.

Since its emergence in late 2019, the coronavirus disease 2019 (COVID-19) pandemic has caused severe disruption to key aspects of human life globally and highlighted the need for timely, adaptive, and accessible pandemic response strategies. Here, we introduce the cell-free dot blot (CFDB) method, a practical and ultra-low-cost immune diagnostic platform capable of rapid response and mass immunity screening for the current and future pandemics. Similar in mechanism to the widely used enzyme-linked immunosorbent assays (ELISAs), our method is novel and advantageous in that (i) it uses linear DNA to produce the target viral antigen fused to a SpyTag peptide in a cell-free expression system without the need for traditional cloning and antigen purification, (ii) it uses SpyCatcher2-Apex2, an Escherichia coli-produced peroxidase conjugate as a universal secondary detection reagent, obviating the need for commercial or sophisticated enzyme conjugates, and (iii) sera are spotted directly on a nitrocellulose membrane, enabling a simple "dipping" mechanism for downstream incubation and washing steps, as opposed to individual processing of wells in a multiwell plate. To demonstrate the utility of our method, we performed CFDB to detect anti-severe acute respiratory syndrome coronavirus 2 nucleocapsid protein antibodies in precharacterized human sera (23 negative and 36 positive for COVID-19) and hamster sera (16 negative and 36 positive for COVID-19), including independent testing at a collaborating laboratory, and we show assay performance comparable to that of conventional ELISAs. At a similar capacity to 96-well plate ELISA kits, one CFDB assay costs only ~$3 USD. We believe that CFDB can become a valuable pandemic response tool for adaptive and accessible sero-surveillance in human and animal populations. IMPORTANCE The recent COVID-19 pandemic has highlighted the need for diagnostic platforms that are rapidly adaptable, affordable, and accessible globally, especially for low-resource settings. To address this need, we describe the development and functional validation of a novel immunoassay technique termed the cell-free dot blot (CFDB) method. Based on the principles of cell-free synthetic biology and alternative dot blotting procedures, our CFDB immunoassay is designed to provide for timely, practical, and low-cost responses to existing and emerging public health threats, such as the COVID-19 pandemic, at a similar throughput and comparable performance as conventional ELISAs. Notably, the molecular detection reagents used in CFDB can be produced rapidly in-house, using established protocols and basic laboratory infrastructure, minimizing reliance on strained commercial reagents. In addition, the materials and imaging instruments required for CFDB are the same as those used for common Western blotting experiments, further expanding the reach of CFDB in decentralized facilities.

Socioeconomic Drivers and Risk Factors of Covid-19 Pandemic in Nigeria

Relevance. The Covid-19 pandemic has prompted the need for a comprehensive understanding of its drivers and risk factors, particularly in the socioeconomic dimension. While previous research has primarily focused on biological vectors and mortality rates, less is known about the influence of socioeconomic factors on the spread of the virus. Understanding these factors is crucial for effective policy responses and addressing state-specific peculiarities. Research Objectives. This paper aims to assess the socioeconomic drivers and risk factors of the Covid-19 pandemic in Nigeria. Specifically, it examines the impact of socioeconomic forces on infection and mortality rates. The study seeks to shed light on the role of geographic distance to epicenters, the business environment, and income inequality in shaping the spread and impact of the virus. Data and Methods. The analysis employs two pooled multivariate regression models using data from 37 sub-national entities (States) in Nigeria. The first model explores the effect of socioeconomic forces on Covid-19 infection rates, while the second model examines their influence on fatality rates. The models are based on comprehensive observations and utilize state-specific data to account for variations across regions. Results. We found that proximity to epicenters is associated with higher infection rates, while areas with weaker business environments and higher inequality are more vulnerable. Income inequality emerges as the sole significant driver of mortality, possibly due to limited access to testing, vaccination, and treatment centers among income-constrained populations. Conclusions. The study emphasizes the importance of considering socioeconomic factors in pandemic response strategies, particularly in the context of Covid-19 in Nigeria. We reveal that geographic proximity to epicenters, business environment strength, and income inequality significantly influence infection rates. Addressing these factors, along with recognizing the impact of income inequality on mortality, can inform targeted policies and interventions for effective pandemic management. Policymakers should consider sub-national characteristics and state-specific peculiarities to tailor responses and mitigate the spread and impact of Covid-19.

Data Analytics for Pandemic Management using MapReduce and Apriori Algorithm

Amidst the profound impact of the COVID-19 pandemic on global economies and healthcare systems, effective data analysis has become paramount. Our research paper, titled ”Data Analytics for Pandemic Management Using MapReduce and Apriori Algo- rithm,” presents a comprehensive framework to analyze pandemic data. We harness the power of the MapReduce and Apriori algorithms, with a parallel processing model achieving an average speedup of 50%. This approach involves data collection, pre- processing, and algorithmic application to extract valuable insights from pandemic-related data. Notably, our findings reveal a substantial 22.29% support rate for ”n95 masks,” indicating high demand. Additionally, we identify strong co-occurrence patterns, exemplified by the perfect support rate of 1.00 between ”n95 masks” and ”chloroquine,” highlighting their interconnectedness. Our framework goes beyond data analysis, enhancing personalized marketing, and optimizing inventory management for efficient resource allocation during crises. We also uncover robust associations, such as the 0.220 confidence in joint purchases of ”butyl rubber gloves” and ”surgical masks”.Hence, the research aligns seamlessly with the pressing need for effective pandemic data management and response strategies. By validating our approach through numerical insights, we aim to contribute to mitigating pandemic-related challenges and support global efforts to better prepare for and manage future health emergencies.

Data concerning Covid-19 cases in Poland at the end of the pandemic

Purpose: The main purpose of this article is to analyze and understand trends in COVID-19 cases in Poland during what is considered the end of the pandemic. Design/methodology/approach: The article uses data visualization methods to assess trends and identify key factors influencing the dynamics of the pandemic. The analysis included data on the pandemic in the period from 1 January, 2023 to 30 June, 2023. On 1 July, 2023, the state of epidemic threat in Poland was canceled. Findings: Analysis of epidemiological data showed that the number of new COVID-19 cases in Poland showed a gradual decline during the period considered to be the end of the pandemic. This reduction in case numbers has been consistent across regions of the country, although some areas have seen periodic increases. The data clearly demonstrate the significant impact of vaccinations in reducing the number of new infections and severe disease outcomes. Regions with higher vaccination rates saw lower COVID-19-related hospitalizations and deaths. Research limitations/implications: The surveys discussed in this article may contribute to further empirical studies, including but not limited to initiating works to improve the pandemic analysis. Practical implications: The study provides important information that can be used by public health decision-makers to shape effective pandemic response strategies. The results highlight the importance of maintaining and adapting intervention measures, such as vaccination, to control the spread of the virus and reduce the number of severe cases. Originality/value: This study stands out from other works on the COVID-19 pandemic by its unique focus on the final stage of the pandemic in Poland. While most studies focus on the early stages of the pandemic or its global impact, our study offers detailed insight into local aspects of pandemic management and its evolution in a specific country. We present a comprehensive analysis that combines epidemiological data, the impact of health policy, social behavior and vaccination effectiveness. Keywords: pandemic, COVID-19, data analysis, Poland. Category of the paper: General review, Research paper.

The relationship of social vulnerability and travel behavior with COVID-19 in Metro Manila, Philippines

The COVID-19 pandemic has disproportionately affected vulnerable populations and permanently changed travel behavior. This study aims to explore the relationship of social vulnerability and travel behavior with COVID-19 in Metro Manila, Philippines. Using data from an online questionnaire survey, and applying ordinal logistic regression analysis, results showed that gender, household crowding, lack of vehicle access and type of occupation can contribute to virus exposure. In addition, multiple linear regression analysis was applied to mobility data from Google and Apple. Results from the analysis support the hypothesis that traveling to groceries or retail stores can increase the risk of exposure to the virus, while staying at home and walking as transport mode, lessens the risk. Findings from this research provide a perspective of COVID-19 impacts in a developing country; moreover, results can be used by policymakers as a guide in formulating pandemic response strategies which considers the most vulnerable groups.

Navigating the dual dilemma between lives, rights and livelihoods: COVID-19 responses in China, Singapore, and South Korea

The COVID-19 pandemic has created a dual dilemma for governments worldwide: between the protection of lives and of individual rights, and more long-term between safeguarding lives and preserving livelihoods. Taking a dynamic approach, this paper asks how different regime types have navigated this dual dilemma by adjusting their pandemic-response strategies over the course of time. Three case studies from East Asia are selected to represent different regime types—autocratic China, hybrid Singapore, and democratic South Korea—that share experience with previous coronavirus episodes. Comparing the three cases between late 2019 to mid-2022, remarkable differences are found in the adaptability of response strategies. China’s authoritarian regime appeared to be at a clear strategic advantage due to its indifference toward individual rights during the first COVID-19 wave. In the longer run, however, the picture has changed substantially. While China has exclusively prioritized the protection of lives, fixating on its “Zero-COVID” strategy, Singapore has attached at least equal weight to sustaining livelihoods, experiencing a drawn-out zigzagging before pivoting to a “Living with COVID” strategy. Among the three cases, only South Korea has made consistent efforts to protecting individual rights while gradually recalibrating lives and livelihoods. Over time, the high degree of responsiveness of South Korea’s democratic regime has allowed for a relatively smooth transition to coexisting with the virus. The paper concludes with some lessons that European democracies might learn from pandemic responses in East Asia in a longitudinal perspective.

Retaining talented employees during COVID-19 pandemic: The leverage of hotel pandemic response strategies

This study aims to examine the impact of hotels' pandemic response strategies (service automation, downsizing, restructuring, health protection, and training) on talent retention intentions with the mediation of talent satisfaction and moderation of job insecurity in 4- and 5-star hotels. The sample was composed of 357 talented hotel employees. Findings reveal that automation services, health and safety, and training support were found to positively affect talents' satisfaction with response strategies and favourably enhance talents' retention intention through the mediating role of talents' satisfaction. The findings also suggest that high job insecurity would undermine the positive impact of talent satisfaction on retention intentions. The study contributes to the existing literature by providing theoretical and practical implications in the hotel context and directions for future research.

Trust Takes Two….

Research throughout the COVID-19 pandemic, including investigations of resulting pandemic response strategies, evolving public health recommendations, and vaccine development, has highlighted the role of trust between physicians and patients. The focus, however, has largely been on patient trust in physicians. Although the importance of patient trust in physicians has long been recognized, physician trust in patients remains underappreciated. Physician trust in patients is an important factor in the physician-patient relationship. When physicians trust patients, patients can communicate freely, their experiences are validated, and trust may be engendered through reciprocal trust. Thus, a bidirectional approach to trust is necessary that acknowledges the role of physician trust in patients. We posit that shared trust is the dyadic factor that influences positive patient outcomes and is the foundation of shared decision making. Recognizing shared trust as an important outcome of the physician-patient relationship is a necessary step in evaluating how our practice, research, and education can influence or sow distrust of patients. In this commentary, we discuss the importance of attending to shared trust and physician trust in patients, particularly in family medicine.

Exploring the Antiviral Properties of Dietary Plant Extracts Against SARS-CoV-2: A Comprehensive Review

The ongoing COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has catalyzed a global surge in research aimed at identifying effective antiviral agents. Amidst the search for therapeutic solutions, dietary plant extracts have emerged as a promising area of study due to their rich bioactive compounds and established antiviral properties against various pathogens. This comprehensive review explores the potential of dietary plant extracts as antiviral agents against SARS-CoV-2. It examines the mechanisms by which these extracts inhibit viral entry, replication, and spread, highlighting key compounds such as flavonoids, polyphenols, and terpenoids. The review synthesizes current research findings from in vitro and in vivo studies, as well as clinical trials, to provide a holistic understanding of the efficacy and safety of these natural products. Additionally, it addresses the challenges and limitations associated with their use, including variability in bioactive compound concentrations and bioavailability issues. By consolidating existing knowledge and identifying gaps in research, this review aims to inform future studies and guide the development of plant-based antiviral therapies for COVID-19. Through a nuanced analysis of dietary plant extracts, this review underscores their potential as complementary agents in the fight against SARS-CoV-2, contributing to the broader landscape of pandemic response strategies. Keywords: SARS-CoV-2, COVID-19, dietary plants, plant extracts, antiviral properties, prevention, therapeutic strategies.