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1.
Applied Sciences ; 13(3):1469, 2023.
Article in English | ProQuest Central | ID: covidwho-2276127

ABSTRACT

Provisioning of health services such as care, monitoring, and remote surgery is being improved thanks to fifth-generation cellular technology (5G). As 5G expands globally, more smart healthcare applications have been developed due to its extensive eMBB (Enhanced Mobile Broadband) and URLLC (Ultra-Reliable Low Latency Communications) features that can be used to generate healthcare systems that allow minimizing the face-to-face assistance of patients at hospital centers. This powerful network provides high transmission speeds, ultra-low latency, and a network capacity greater than that of 4G. Fifth-generation cellular technology is expected to be a means to provide excellent quality of medical care, through its technological provision to the use of IoMT (Internet of Medical Things) devices. Due to the numerous contributions in research on this topic, it is necessary to develop a review that provides an orderly perspective on research trends and niches for researchers to use as a starting point for their work. In this context, this article presents a systematic review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), with article selection based on inclusion and exclusion criteria that avoid bias. This research was based on research questions that were answered from the included works. These questions focus on technical characteristics, health benefits, and security protocols necessary for the development of smart healthcare applications. We have identified that a high percentage of existing works in the literature are proposals (56.81%, n = 25) and theoretical studies (22.73%, n = 10);few implementations (15.91%, n = 7) and prototypes (4.55%, n = 2) exist, due to the limited global deployment of 5G. However, the panorama looks promising based on proposals and future work that these technological systems allow, all based on improving healthcare for people.

2.
PLoS One ; 18(2): e0280245, 2023.
Article in English | MEDLINE | ID: covidwho-2229043

ABSTRACT

BACKGROUND: Throughout US history, chronic and infectious diseases have severely impacted minority communities due to a lack of accessibility to quality healthcare and accurate information, as well as underlying racism. These fault lines in the care of minority communities in the US have been further exacerbated by the rise of the COVID-19 pandemic. This study examined the factors associated with COVID-19 vaccine hesitancy by race and ethnicity, particularly among African American and Latinx communities in Eastern Pennsylvania (PA). METHODS: Survey data was collected in July 2021 in Philadelphia, Scranton, Wilkes-Barre, and Hazleton, PA. The 203 participants (38.7% Black, 27.5% Latinx) completed the 28-question survey of COVID-19 vaccination attitudes in either English or Spanish. RESULT: Out of the 203 participants, 181 participants met all the inclusion criteria, including completed surveys; of these participants, over three-fifths (63.5%) were acceptant of the COVID-19 vaccine whereas the remainder (36.5%) were hesitant. Binary logistic regression results showed that age, concern for vaccine efficacy, race, knowledge on the vaccine, and belief that the COVID-19 virus is serious significantly influenced COVID-19 vaccine hesitancy. Minorities were more likely to be hesitant toward vaccination (OR: 2.8, 95% CI: 1.1, 6.8) than non-Hispanic whites. Those who believed the COVID-19 vaccine was ineffective (OR: 8.3, 95% CI: 3.8, 18.2), and that the virus is not serious (OR: 8.3, 95% CI: 1.1, 61.8) showed the greatest odds of hesitancy. CONCLUSIONS: Minority status, age less than 45 years, misinformation about seriousness of COVID-19 illness, and concern about vaccine efficacy were contributing factors of COVID-19 vaccine hesitancy. Therefore, understanding and addressing the barriers to COVID-19 vaccination in minority groups is essential to decreasing transmission and controlling this pandemic, and will provide lessons on how to implement public health measures in future pandemics.


Subject(s)
COVID-19 , Ethnicity , Humans , Middle Aged , COVID-19 Vaccines/therapeutic use , COVID-19/epidemiology , COVID-19/prevention & control , Pandemics , Philadelphia , Vaccination
4.
Rev. peru. med. exp. salud publica ; 37(2):195-202, 2020.
Article in Spanish | LILACS (Americas) | ID: grc-741511

ABSTRACT

RESUMEN Objetivos: Determinar la probabilidad de control del brote de la COVID-19 en el Perú, en un escenario pre y poscuarentena en modelos de simulaciones matemáticas. Materiales y métodos: Se realizan simu laciones de brotes para la pandemia de COVID-19, usando ecuaciones estocásticas bajo los siguientes supuestos: un R0 poblacional precuarentena de 2,7 o 3,5, y un R0 poscuarentena de 1,5, 2 o 2,7, positivos asintomáticos del 18% o 40%, y una capacidad resolutiva máxima de 50 o 150 pacientes en las unidades de cuidados intensivos. Se evalúa el éxito del aislamiento y rastreo de contactos, no se incluyen otras medidas de mitigación. Resultados: En la etapa precuarentena, el éxito en el control de más del 80% de las simulaciones se daba solo si el aislamiento de casos positivos se implantaba desde el primer caso, luego se tenía menos de un 40% de probabilidad de éxito. En la poscuarentena, con 60 casos positivos es necesario aislarlos precozmente, rastrear al 100% sus contactos y disminuir el R0 a 1,5 para que el control del brote tenga éxito en más del 80% de los casos. Otros escenarios tienen baja probabilidad de éxito. Conclusiones: El control del brote en el Perú en la etapa precuarentena demandaba requisitos de difícil cumplimiento, por ello la cuarentena era necesaria;para suspenderla con éxito se requeriría una impor tante reducción de la dinámica de propagación de la enfermedad, el aislamiento precoz de los positivos y el seguimiento de todos los contactos. ABSTRACT Objectives: To determine the probability of controlling the outbreak of COVID-19 in Peru, in a pre- and post-quarantine scenario using mathematical simulation models. Materials and methods: Outbreak si mulations for the COVID-19 pandemic are performed, using stochastic equations under the following assumptions: a pre-quarantine population R0 of 2.7 or 3.5, a post-quarantine R0 of 1.5, 2 or 2.7, 18% or 40%, of asymptomatic positives and a maximum response capacity of 50 or 150 patients in the intensive care units. The success of isolation and contact tracing is evaluated, no other mitigation measures are included. Results: In the pre-quarantine stage, success in controlling more than 80% of the simulations occurred only if the isolation of positive cases was implemented from the first case, after which there was less than 40% probability of success. In post-quarantine, with 60 positive cases it is necessary to isolate them early, track all of their contacts and decrease the R0 to 1.5 for outbreak control to be successful in more than 80% of cases. Other scenarios have a low probability of success. Conclusions: The control of the outbreak in Peru during pre-quarantine stage demanded requirements that were difficult to comply with, therefore quarantine was necessary;to successfully suspend it would require a significant reduction in the spread of the disease, early isolation of positives and follow-up of all contacts of positive patients.

5.
Rev Peru Med Exp Salud Publica ; 37(2): 195-202, 2020.
Article in Spanish, English | MEDLINE | ID: covidwho-740608

ABSTRACT

OBJECTIVES: To determine the probability of controlling the outbreak of COVID-19 in Peru, in a pre- and post-quarantine scenario using mathematical simulation models. MATERIALS AND METHODS: Outbreak si mulations for the COVID-19 pandemic are performed, using stochastic equations under the following assumptions: a pre-quarantine population R0 of 2.7 or 3.5, a post-quarantine R0 of 1.5, 2 or 2.7, 18% or 40%, of asymptomatic positives and a maximum response capacity of 50 or 150 patients in the intensive care units. The success of isolation and contact tracing is evaluated, no other mitigation measures are included. RESULTS: In the pre-quarantine stage, success in controlling more than 80% of the simulations occurred only if the isolation of positive cases was implemented from the first case, after which there was less than 40% probability of success. In post-quarantine, with 60 positive cases it is necessary to isolate them early, track all of their contacts and decrease the R0 to 1.5 for outbreak control to be successful in more than 80% of cases. Other scenarios have a low probability of success. CONCLUSIONS: The control of the outbreak in Peru during pre-quarantine stage demanded requirements that were difficult to comply with, therefore quarantine was necessary; to successfully suspend it would require a significant reduction in the spread of the disease, early isolation of positives and follow-up of all contacts of positive patients.


OBJETIVOS: Determinar la probabilidad de control del brote de la COVID-19 en el Perú, en un escenario pre y poscuarentena en modelos de simulaciones matemáticas. MATERIALES Y MÉTODOS: Se realizan simu laciones de brotes para la pandemia de COVID-19, usando ecuaciones estocásticas bajo los siguientes supuestos: un R0 poblacional precuarentena de 2,7 o 3,5, y un R0 poscuarentena de 1,5, 2 o 2,7, positivos asintomáticos del 18% o 40%, y una capacidad resolutiva máxima de 50 o 150 pacientes en las unidades de cuidados intensivos. Se evalúa el éxito del aislamiento y rastreo de contactos, no se incluyen otras medidas de mitigación. RESULTADOS: En la etapa precuarentena, el éxito en el control de más del 80% de las simulaciones se daba solo si el aislamiento de casos positivos se implantaba desde el primer caso, luego se tenía menos de un 40% de probabilidad de éxito. En la poscuarentena, con 60 casos positivos es necesario aislarlos precozmente, rastrear al 100% sus contactos y disminuir el R0 a 1,5 para que el control del brote tenga éxito en más del 80% de los casos. Otros escenarios tienen baja probabilidad de éxito. CONCLUSIONES: El control del brote en el Perú en la etapa precuarentena demandaba requisitos de difícil cumplimiento, por ello la cuarentena era necesaria; para suspenderla con éxito se requeriría una impor tante reducción de la dinámica de propagación de la enfermedad, el aislamiento precoz de los positivos y el seguimiento de todos los contactos.


Subject(s)
Computer Simulation , Coronavirus Infections/epidemiology , Disease Outbreaks/prevention & control , Pneumonia, Viral/epidemiology , COVID-19 , Contact Tracing/methods , Coronavirus Infections/prevention & control , Humans , Intensive Care Units/statistics & numerical data , Models, Theoretical , Pandemics/prevention & control , Peru/epidemiology , Pneumonia, Viral/prevention & control , Probability , Quarantine
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