Spread Of Disease Research Articles

Diagnostic Techniques for Infantile Subglottic Hemangiomas: A Scoping Review.

Infantile subglottic hemangioma (SGH) poses a risk of airway compromise if untreated. Traditionally, operative endoscopy (OH) diagnoses SGH, but since the discovery of beta-blockers' efficacy in treating infantile hemangiomas (IHs) in 2008, and advances in endoscopic technology, nonoperative methods have emerged. This review identifies endoscopic practices for diagnosing and monitoring infantile SGH during the oral beta-blocker treatment era. A comprehensive literature search in October 2022 and August 2023 covered PubMed, Embase, Cochrane Library, SCOPUS, and Web of Science. The search was limited to English-language studies published since 2008, considering this when propranolol was demonstrated as an effective treatment option for IH. The articles were screened for relevance based on predefined inclusion and exclusion criteria. After inclusion and exclusion criteria, sixty final studies were identified, describing 240 cases of infantile SGH. Most children were diagnosed using OE alone (73.3%; n = 176/240), 23.3% (n = 56/240) using office-based laryngoscopy procedures (OBPs) followed by OE, 3.3% using OBP alone (n = 8/240). There were no reported diagnostic endoscopy-related complications. Twenty-nine studies described using endoscopy plus diagnostic imaging to either confirm an SGH lesion, characterize the extent of disease spread, or rule out other causes of presenting symptoms. The proportion of infants diagnosed with OE alone decreased from 2008 to 2023. Operative endoscopy remains the SGH diagnostic standard, but OBP adoption is increasing. Further research is needed to determine the optimal SGH diagnosis and management approach. NA Laryngoscope, 2024.

Rebound in epidemic control: how misaligned vaccination timing amplifies infection peaks

In this study, we explore the dynamic interplay between the timing of vaccination campaigns and the trajectory of disease spread in a population. Through modeling and comprehensive data analysis of model output, we have uncovered a counter-intuitive phenomenon: initiating a vaccination process at an inopportune moment can paradoxically result in a more pronounced second peak of infections. This “rebound” phenomenon challenges the conventional understanding of vaccination impacts on epidemic dynamics. We provide a detailed examination of how improperly timed vaccination efforts can inadvertently reduce the overall immunity level in a population, considering both natural and vaccine-induced immunity. Our findings reveal that such a decrease in population-wide immunity can lead to a delayed, yet more severe, resurgence of cases. This study not only adds a critical dimension to our understanding of vaccination strategies in controlling pandemics but also underscores the necessity for strategically timed interventions to optimize public health outcomes. Furthermore, we compute which vaccination strategies are optimal for a COVID-19 tailored mathematical model, and find that there are two types of optimal strategies. The first type prioritizes vaccinating early and rapidly to reduce the number of deaths, while the second type acts later and more slowly to reduce the number of cases; both of them target primarily the elderly population. Our results hold significant implications for the formulation of vaccination policies, particularly in the context of rapidly evolving infectious diseases.

Mathematical Exploration of Malaria Transmission Dynamics: Insights from Fractional Models and Numerical Simulation

AbstractThis study presents an innovative mathematical model denoted as the fractional SIP(H)–SI(M) model, which aims to analyze and understand the dynamics of malaria transmission and spread. This model is distinguished by incorporating memory effects through fractional differential equations, allowing for a more accurate and realistic analysis of disease spread compared to traditional models. The proposed model is applied to Algeria by estimating its parameters using recent health data (from 2000). The results revealed that the disease‐free equilibrium is stable only when the basic reproduction number is less than one, indicating that controlling the spread of malaria and possibly eradicating it can be achieved by implementing appropriate preventive measures. Simulations also demonstrated a direct correlation between the rate of infection transmission and an increase in the number of infected individuals, highlighting the need for swift action when signs of an outbreak emerge. Based on these findings, a set of preventive measures is recommended, including insecticide spraying programs, widespread distribution of insecticide‐treated bed nets, and implementation of effective treatment protocols for infected individuals. This study also emphasizes the importance of continuous monitoring of health data and updating model parameters to ensure the effectiveness and sustainability of preventive measures.

ANÁLISE DE RISCOS NA PRODUÇÃO DE FRIGORÍFICOS: ESTRATÉGIAS PARA PREVENÇÃO E CONTROLE

This paper is a scientific article whose theme is the analysis of risks in the production of meatpacking plants: strategies for prevention and control. In this environment, workers face physical risks, biological risks and risks of accidents. This is because, in meatpacking plants, we find places where the temperature is extremely low (usually below 0º). Therefore, the function of the PPE provided is to provide safety to avoid humidity, low temperatures, noise, injuries caused by sharp instruments and biological contamination. What strategies should be adopted to prevent and control the occurrence of accidents and/or the spread of diseases in the production line of meatpacking plants, based on risk analysis? The general objective is: to describe what risk management is as well as the best way to apply it in a meatpacking plant to prevent accidents and control occupational diseases. The specific objectives are: to characterize what risk analysis and management are in a meatpacking plant; to present the main characteristics of the production environment as well as the main precautions that should be taken to ensure the physical integrity of the worker; describe the guidelines set out in NR-32 and its determinations for occupational health and safety, especially in companies that work with the slaughter and processing of meat and meat products. The methodology used will be a descriptive and exploratory literature review

The association between adherence to unhealthy plant-based diet and risk of COVID-19: a cross-sectional study

BackgroundThe fast spread of the coronavirus disease 2019 (COVID-19) epidemic and its high mortality were quickly considered by the health community. Dietary patterns play an important role in strengthening or weakening the immune system and thus incidence of diseases.AimThe present study can provide a comprehensive picture of the association between adherence to unhealthy plant-based diet (uPDI) and COVID-19 incidence.MethodsThis study was undertaken on 8157 adults’ participants of the Yazd Health Study (YaHS) and Taghzieh Mardom-e-Yazd (TAMIZ) study aged 20 to 70 years. Data on dietary intakes were obtained using a validated food frequency questionnaire (FFQ). Multivariable logistic regression analysis was used to assess the association between uPDI and COVID-19.ResultsWe found a significant association between uPDI and the risk of COVID-19 (OR: 1.36; 95% CI: 1.05–1.75) in the crude model. After adjusting potential confounders, a significant increasing trend in the odds of COVID-19 across increasing quintiles of uPDI (OR: 1.58;95% CI: 1.05–2.37; P-value: 0.027) was observed. Stratified analysis based on sex indicated that uPDI significantly increased the risk of COVID-19 only in males (OR: 1.73;95% CI: 1.12–2.67; P-value: 0.012) and had no effect on females.ConclusionsParticipants in the highest quintiles of the uPDI had 58% higher odds of COVID-19 compared to subjects in the lowest quintile of uPDI. Although our study has promising results, stronger clinical studies are needed.

Insect immunity in the Anthropocene.

Anthropogenic activities result in global change, including climate change, landscape degradation and pollution, that can alter insect physiology and immune defences. These changes may have contributed to global insect decline and the dynamics of insect-transmitted diseases. The ability of insects to mount immune responses upon infection is crucial for defence against pathogens and parasites. Suppressed immune defences reduce fitness by causing disease-driven mortality and elevated immune responses reduce energy available to invest in other fitness traits such as reproduction. Understanding the impact of anthropogenic factors on insect-pathogen interactions is therefore key to determining the contribution of anthropogenic global change to pathogen-driven global insect decline and the emergence and transmission of insect-borne diseases. Here, we synthesise evidence of the impact of anthropogenic factors on insect immunity. We found evidence that anthropogenic factors, such as insecticides and heavy metals, directly impacting insect immune responses by inhibiting immune activation pathways. Alternatively, factors such as global warming, heatwaves, elevated CO2 and landscape degradation can indirectly reduce insect immune responses via reducing the energy available for immune function. We further review how anthropogenic factors impact pathogen clearance and contribute to an increase in vector-borne diseases. We discuss the fitness cost of anthropogenic factors via pathogen-driven mortality and reduced reproductive output and how this can contribute to species extinction. We found that most research has determined the impact of a single anthropogenic factor on insect immune responses or pathogen resistance. We recommend studying the combined impact of multiple stressors on immune response and pathogen resistance to understand better how anthropogenic factors affect insect immunity. We conclude by highlighting the importance of initiatives to mitigate the impact of anthropogenic factors on insect immunity, to reduce the spread of vector-borne diseases, and to protect vulnerable ecosystems from emerging diseases.

Dengue and chikungunya virus dynamics, identification, and monitoring in wastewater.

Monitoring wastewater is an effective strategy for supporting clinical surveillance for viral infections. Wastewater monitoring, also known as wastewater-based epidemiology (WBE), uses existing wastewater collection networks to obtain a composite sample of a population that can be used to predict disease dynamics in a specific area. Viruses such as dengue and chikungunya are primarily transmitted through the bites of infected Aedes mosquito species. The prevalence of the Aedes mosquito in tropical and subtropical regions makes these diseases a serious threat to public health. Employing wastewater surveillance, monitoring, and regulating the spread of diseases like dengue and chikungunya-notably caused by mosquitoes-has been recommended. However, understanding the dynamics of viral release and its persistence in wastewater is critical for monitoring purposes. Although methods for recovering RNA for some viruses from wastewater have been developed, the same approach does not work equally well for viruses such as dengue and chikungunya due to low levels of viral RNA and susceptibility to degradation. As a result, a tailored approach to recovering these viruses from wastewater is required. This review summarizes viral release from infected hosts, its dynamics, and approaches for dengue and chikungunya wastewater surveillance. The review also identifies existing knowledge gaps in viral persistence in wastewater and recovery.

Validating a cassava production spatial disaggregation model in sub-Saharan Africa.

Cassava is a staple in the diet of millions of people in sub-Saharan Africa, as it can grow in poor soils with limited inputs and can withstand a wide range of environmental conditions, including drought. Previous studies have shown that the distribution of rural populations is an important predictor of cassava density in sub-Saharan Africa's landscape. Our aim is to explore relationships between the distribution of cassava from the cassava production disaggregation models (CassavaMap and MapSPAM) and rural population density, looking at potential differences between countries and regions. We analysed various properties of cassava cultivations collected from surveys at 69 locations in Côte d'Ivoire and 87 locations in Uganda conducted between February and March 2018. The relationships between the proportion of surveyed land under cassava cultivation and rural population and settlement data were examined using a set of generalized additive models within each country. Information on rural settlements was aggregated around the survey locations at 2, 5 and 10 km circular buffers. The analysis of the original survey data showed no significant correlation between rural population and cassava production in both MapSPAM and CassavaMap. However, as we aggregate settlement buffers around the survey locations using CassavaMap, we find that at a large scale this model does capture large-scale variations in cassava production. Moreover, through our analyses, we discovered country-specific spatial trends linked to areas of higher cassava production. These analyses are useful for validating disaggregation models of cassava production. As the certainty that existing cassava production maps increases, analyses that rely on the disaggregation maps, such as models of disease spread, nutrient availability from cassava with respect to population in a region, etc. can be performed with increased confidence. These benefit social and natural scientists, policymakers and the population in general by ensuring that cassava production estimates are increasingly reliable.

Analysis of stochastic epidemic model with awareness decay and heterogeneous individuals on multi-weighted networks.

In the current study, we present a stochastic SAIS (unaware susceptible-aware susceptible-infectious-unaware susceptible) epidemic dynamic model on complex networks with multi-weights. The disease dynamic is influenced by random perturbations to the force of the infection rates, as well as awareness rates. To analyze the problem of extinction, we discuss both the stochastic asymptotic stability in the large and almost surely exponential stability of the trivial solution. Then, we get some sufficient conditions, which guarantee the stochastic persistence of infectious disease. Based on the Erdös-Réyni random graph, the numerical simulations are given. These not only validate our conclusions but also obtain else significative results. Both theoretical results and numerical simulations further reflect that improvement of risk awareness and reduction of decay in awareness are highly effective in preventing disease spread. And then, environmental noises play a significant role in disease transmission.

A Reviewed Literature on the Effect of Global Warming on Infectious Diseases' Dissemination

Climate change presents an urgent and impending threat to virtually all global biological systems. In recent years, there has been a surge in research endeavors aimed at investigating the potential relationship between changes in climate patterns and the spread of infectious diseases. This scoping review aims to assess the influence of climate change and global warming on the spread and occurrence of numerous infectious diseases worldwide. It is recommended that there is an urgent need to carry out some educative programs in all countries to combat global warming and then reduce infectious diseases’ dissemination. This review followed the PRISMA flowchart that included studies on global warming and infectious diseases from 2019 to 2023, The studies were selected by a systematic search using different electronic databases from Science Direct, EBSCO, PUBMED, BIOMED CENTRAL, CINAHL, and Google Scholar, which were filtered to include only studies that were published after January 2019 using MeSH terms to find from total 980 research articles were found in different databases, only 10 studies were eligible for the review scope and aims, they found that climate change has a remarkable impact on the dissemination and incidence of many infectious diseases, there was a significant correlation between precipitation, temperature, and the transmission of pathogens and arthropods, the potential economic devastation caused by a bacterial infection in rice could escalate significantly in the coming decades, also, the highest temperature was shown to have a strong relationship with the incidence of dengue, leptospirosis, food poisoning, TB, and hand, foot, and mouth infections in addition to several viral infections such as COVID-19. The governmental efforts to educate and counsel people to reduce their habits and some wring manners such as fuel burning to decrease global warming as one of the main factors causing infections dissemination.

103. The Impact of COVID-19 restrictions on Open Fracture Emergency Fixation Procedures in eight institutions around South and Southeast Asia

Introduction: Around the time COVID-19 was declared a global pandemic, various restrictions and measures were implemented at all levels to limit disease spread and preserve healthcare capacity. Data is lacking in many low and middle-income countries on how these measures impact healthcare utilization and outcomes. This study aims to determine the impact of COVID-19 restrictions on volumes of the open fracture emergency fixation procedures in South and Southeast Asia. Methods: This was a retrospective cohort study of patients who underwent open fracture fixation from 1 January 2018 to 31 December 2021 at one of 8 institutions in Singapore, Malaysia, Indonesia, India and Vietnam. We compared the weekly volumes, baseline characteristics and outcomes of each procedure across different time periods in each institution defined by different level of COVID-19 restrictions using selected indicators from a city-level and hospital-level response index. Results: A total of 74 (AMH) to 299 (SGH) open fracture fixations were performed over the 4-year period. Weekly volumes decreased significantly at GNRC (p = 1.33 x 10-5), AMH (p = 1.68 x 10-6) and UGH. At UGH there was a drop in the proportion of males during HR1 (66.7%) and HR3 (46.7%) compared to pre-COVID (83.0%) and HR2 (82.4%) (p = 2.02 x 10-4). None of the outcomes were significantly different in any of the institutions. Conclusions: COVID-19 restrictions on the city and hospital level were associated with a decrease in surgical volumes of the open fracture fixation emergency procedures in some of the institutions. There was some suggestion of surgical capacity limitations in UGH and MH175 during periods of more stringent COVID-19 restrictions.

Impedimetric immunosensor versus qPCR for Huanglongbing detection

Citriculture is facing challenges due to the spread of various diseases, among which the most threatening, with worldwide occurrence, is Huanglongbing (HLB), caused by the bacteria Candidatus Liberibacter spp., vectored by psyllids. In orchards, HLB-infected plants are identified by visual observation of symptoms. For laboratory diagnosis, the gold standard is the polymerase chain reaction (PCR), requiring expertise and specific equipment, and with high financial cost. In this work, a selective impedimetric immunosensor was developed for the detection and determination of Ca. L. asiaticus (CLas) in citrus samples. An anti-HLB antibody against the outer membrane protein (OMP) sequence of CLas was obtained and immobilized on previously synthesized and characterized magnetic nanoparticles. The immobilized antibody was presented to various citrus leaf sample extracts. After the affinity reaction with the antigen, a washing step was performed to minimize matrix effects. The affinity reaction was monitored by electrochemical impedance spectroscopy, using a glassy carbon working electrode containing a neodymium magnet. The developed device was able to distinguish HLB-positive samples from HLB-negative samples and those with other infections. The results obtained with the proposed methodology were in good agreement with quantitative PCR (qPCR).

Modeling the interplay between disease spread, behaviors, and disease perception with a data-driven approach

Individuals’ perceptions of disease influence their adherence to preventive measures, shaping the dynamics of disease spread. Despite extensive research on the interaction between disease spread, human behaviors, and interventions, few models have incorporated real-world behavioral data on disease perception, limiting their applicability. In this study, we propose an approach to integrate survey data on contact patterns and disease perception into a data-driven compartmental model, by hypothesizing that perceived severity is a determinant of behavioral change. We explore scenarios involving a competition between a COVID-19 wave and a vaccination campaign, where individuals’ behaviors vary based on their perceived severity of the disease. Results indicate that behavioral heterogeneities influenced by perceived severity affect epidemic dynamics, in a way depending on the interplay between two contrasting effects. On the one hand, longer adherence to protective measures by groups with high perceived severity provides greater protection to vulnerable individuals, while premature relaxation of behaviors by low perceived severity groups facilitates virus spread. Differences in behavior across different population groups may impact strongly the epidemiological curves, with a transition from a scenario with two successive epidemic peaks to one with only one (higher) peak and overall more numerous severe outcomes and deaths. The specific modeling choices for how perceived severity modulates behavior parameters do not strongly impact the model’s outcomes. Moreover, the study of several simplified models indicate that the observed phenomenology depends on the combination of data describing age-stratified contact patterns and of the feedback loop between disease perception and behavior, while it is robust with respect to the lack of precise information on the distribution of perceived severity in the population. Sensitivity analyses confirm the robustness of our findings, emphasizing the consistent impact of behavioral heterogeneities across various scenarios. Our study underscores the importance of integrating risk perception into infectious disease transmission models and gives hints on the type of data that further extensive data collection should target to enhance model accuracy and relevance.

Survey of state animal health officials reveals lack of personnel and resources to manage equine infectious disease outbreaks.

The purpose of the study was to assess the challenges faced by state animal health officials (SAHOs) when managing equine disease outbreaks. A survey was conducted to assess SAHOs' collaboration with horse owners and practicing veterinarians to provide biosecurity and infection disease control. It was sent to 180 email addresses listed in the Equine Disease Communication Center website database. The responses in each category for each question were tabulated and reported as a percentage. There were 72 survey responses from 45 states. The number of states responding suggested that the results represent SAHOs' current opinions. Fifty percent of responders identified the lack of resources and training for equine health management and equine reportable disease prevention and control. There is a lack of owner compliance in managing disease outbreaks, and boarding stables, horse shows, and racing facilities require the most resources for outbreak management. The survey indicated on-site communication as the most effective for managing an outbreak, and the Equine Disease Communication Center is to be a valued resource for alerts and disease information. At the state level, there is a lack of needed personnel and resources to effectively manage an equine infectious disease outbreak. Furthermore, failure of veterinary and owner compliance in reporting and managing equine infectious diseases allows disease spread. Lack of sufficient resources for management of reportable diseases will potentially result in disease spread with a negative impact on horse health and the equine industry.

Covid-19, the Eucharist and spreading disease through Christianity’s holiest rite

During the Covid-19 pandemic in the USA, churches were faced with a question of medical and metaphysical importance: can Christianity’s rite of Holy Communion spread Covid-19? This article examines Christian responses to Covid-19 through a case study of five of Christianity’s multifaceted Covid eucharistic policies, arguing that the responses of churches in times of crisis reflect a nuanced understanding of the metaphysical theories surrounding the Eucharist. The goal of this article is to serve as a primer on the relationships between beliefs of the Eucharist and Covid-19 in American churches, seeking to provide an overview and facilitate further inquiry into modern concerns of Christianity’s holiest rite and the spread of disease.

Global dynamics of a periodically forced SI disease model of Lotka–Volterra type

In this paper, we investigate the dynamics of an SI disease model of Lotka–Volterra type in the presence of a periodically fluctuating environment. We give a global analysis of the dynamical behavior of the model. Interestingly, our results show that the permanence guarantees the existence of a unique positive harmonic time-periodic solution which is globally attracting when the horizontal disease transmission has a weaker impact than the intraspecific competition. While for the case when the horizontal disease transmission has a stronger impact than the intraspecific competition, we numerically show that complex dynamics such as chaos can occur in a permanent system. Nonetheless, we provide sufficient conditions for the existence and uniqueness of the positive harmonic time-periodic solution for the latter case. The impact of the environment on the spread of disease is studied by using a bifurcation analysis. We show that in each of the qualitatively different cases of the associated autonomous SI model in a constant environment, an alternative possibility can appear in the periodic model.

Multiclass Classification of Tomato Leaf Diseases Using Convolutional Neural Networks and Transfer Learning

ABSTRACTTomato (biological name: Solanum lycopersicum) is an important food crop worldwide. However, due to climatic changes and various diseases, the yield of tomatoes decreased significantly, being detrimental from an economic point of view. Various diseases infect the tomato leaves, such as bacterial and septorial leaf spots, early blight and mosaic virus, to name a few. If uncared, these tomato leaf diseases (TLDs) can spread to other leaves and the fruit. Hence it is vital to detect these diseases as early as possible. Leaf examination is one of the standard techniques to identify and control the spread of diseases. Big Data has made substantial progress, and with the help of computer vision and deep learning techniques to analyse data, we can identify the diseased leaves and help control the disease's spread further. This study used three lightweight midgeneration convolutional neural networks (CNNs) classification network architectures which has the scope to be deployed in IoT devices to help the agricultural community tackle TLDs. It also shows the efficacy of the models with and without geometric data augmentation. The model was trained on a Kaggle data set containing a more significant number of samples to make a robust model aware of broader data distribution and validated on the Plant Village dataset to test its efficacy. The results show that applying transfer learning using ImageNet weights to the MobileNet Architecture using geometrically augmented sample images yields a train and test accuracy of 99.71% and 99.49%, respectively.

Evolutionary dynamics of voluntary vaccination for imperfect multi-efficacy vaccines

Voluntary vaccination is crucial for public health. The likelihood that the vaccinated get fully immune is typically referred to as the vaccine efficacy. In addition to this, the efficacy of vaccines also depends on inhibiting the transmission rates and protecting patients from critical symptoms. It has yet to be answered how the multi-efficacy of vaccines affects the vaccination behaviour. Here, we propose a game theoretical model of vaccination behaviour with a heterogeneous-transmission epidemiological process. Intuitively, individuals are likely to take vaccination for either high-efficient vaccines or epidemics with a large basic reproductive number. We find, however, a low economic discount rate can promote the uptake level for low-effectiveness vaccines and epidemics with small basic reproductive numbers. This result suggests that the perceived cost is more important than the risk of infection and the effectiveness of vaccines. In addition, we develop an analytical approximation to address the challenge arising from the coupled dynamics of vaccination and disease spread. This study shows how the interplay among three types of vaccine efficacies (transmission blocking, symptom mitigating and full immunity protecting) affects individual vaccination decisions and epidemic outcomes, and the model can be insightful for epidemic control.

Maximizing vertex rescue: investigating the firefighter problem with edge subdivision

The firefighter problem is a game played on a connected graph where a fire breaks out at a vertex. In each round, a firefighter chooses a vertex to protect, and the fire then spreads to all unprotected neighbors of the burning vertex. This sequence continues until the fire can no longer spread. The objective is to maximize the number of saved vertices, necessitating a strategic approach by the firefighter. This problem serves as a simplistic model for scenarios such as disease spread in a network or people’s movement under specific circumstances. To simulate a scenario with limited resources, we introduce a new firefighter method, involving the protection of one vertex and subdividing one edge. This method, achieved by adding a new vertex to the edge, effectively delays the burning of the vertex connected to the edge, providing additional time for the firefighter to protect it. Our investigation delves into the firefighter problem with subdividing edges, focusing on specific graphs. Notably, we present an optimal strategy for a graph with a maximum degree at most three.

Assessing Barriers to COVID-19 Diagnostic Testing in Community Pharmacies: Insights from Alabama.

The COVID-19 pandemic stressed the critical role of diagnostic testing in controlling the spread of infectious diseases. Due to their accessibility and presence in underserved areas, community pharmacies have become vital in decentralizing COVID-19 testing. Despite their potential, pharmacies face significant barriers in integrating testing services. This study aimed to assess the provision of COVID-19 diagnostic testing across community pharmacies in Alabama and investigate how various pharmacist and pharmacy characteristics, along with perceived barriers, influence the likelihood of offering COVID-19 testing services. A cross-sectional study was conducted targeting 1,172 community pharmacies in Alabama. Data were collected via a mixed-mode survey from February to April 2023, achieving a response rate of 20.5% (n = 240). The primary measure was the new Pharmacist Perceived COVID-19 Diagnostic Testing Barriers (PDT-B), a nine-item Likert scale. Bivariate and multivariable logistic regression analyses were conducted to identify factors associated with testing provision. Out of 240 pharmacies, 28.3% provided COVID-19 testing. Providers were significantly more likely to be corporately-owned (61.8%, p < 0.001); they also had higher prescription volumes (median 300, p < 0.001) compared to independent pharmacies. Lower perceived barriers in Organizational Logistics significantly increased the likelihood of offering testing (OR = 0.55, p = 0.028). In corporately-owned pharmacies, lower perceived personal exposure risk was a significant factor (OR = 0.65, p = 0.047), whereas independent pharmacies faced challenges related to regulatory compliance (OR = 0.35, p = 0.025). The study highlights distinct barriers faced by corporately-owned and independent pharmacies. Corporately-owned pharmacies benefited from higher operational capacities but faced notable exposure concerns. Independent pharmacies, however, struggled with regulatory compliance, which impacted their testing service provision. Addressing the unique barriers different pharmacy settings face with tailored strategies is crucial for enhancing COVID-19 testing accessibility and strengthening public health efforts and pandemic preparedness.