National COVID-19 vaccination plan: using artificial spatial intelligence to overcome challenges in Brazil. / Plano nacional de vacinação contra a COVID-19: uso de inteligência artificial espacial para superação de desafios.

This article explores the use of spatial artificial intelligence to estimate the resources needed to implement Brazil's COVID-19 immu nization campaign. Using secondary data, we conducted a cross-sectional ecological study adop ting a time-series design. The unit of analysis was Brazil's primary care centers (PCCs). A four-step analysis was performed to estimate the popula tion in PCC catchment areas using artificial in telligence algorithms and satellite imagery. We also assessed internet access in each PCC and con ducted a space-time cluster analysis of trends in cases of SARS linked to COVID-19 at municipal level. Around 18% of Brazil's elderly population live more than 4 kilometer from a vaccination point. A total of 4,790 municipalities showed an upward trend in SARS cases. The number of PCCs located more than 5 kilometer from cell towers was largest in the North and Northeast regions. Innovative stra tegies are needed to address the challenges posed by the implementation of the country's National COVID-19 Vaccination Plan. The use of spatial artificial intelligence-based methodologies can help improve the country's COVID-19 response.

O objetivo deste artigo é analisar o uso da inteligência artificial espacial no contexto da imunização contra COVID-19 para a seleção adequada dos recursos necessários. Trata-se de estudo ecológico de caráter transversal baseado em uma abordagem espaço-temporal utilizando dados secundários, em Unidades Básicas de Saúde do Brasil. Foram adotados quatro passos analíticos para atribuir um volume de população por unidade básica, aplicando algoritmos de inteligência artificial a imagens de satélite. Em paralelo, as condições de acesso à internet móvel e o mapeamento de tendências espaço-temporais de casos graves de COVID-19 foram utilizados para caracterizar cada município do país. Cerca de 18% da população idosa brasileira está a mais de 4 quilômetros de distância de uma sala de vacina. No total, 4.790 municípios apresentaram tendência de agudização de casos de Síndrome Respiratória Aguda Grave. As regiões Norte e Nordeste apresentaram o maior número de Unidades Básicas de Saúde com mais de 5 quilômetros de distância de antenas de celular. O Plano nacional de vacinação requer o uso de estratégias inovadoras para contornar os desafios do país. O uso de metodologias baseadas em inteligência artificial espacial pode contribuir para melhoria do planejamento das ações de resposta à COVID-19.

A Survey on Handover and Mobility Management in 5G HetNets: Current State, Challenges, and Future Directions.

Fifth-generation (5G) networks offer high-speed data transmission with low latency, increased base station volume, improved quality of service (QoS), and massive multiple-input-multiple-output (M-MIMO) channels compared to 4G long-term evolution (LTE) networks. However, the COVID-19 pandemic has disrupted the achievement of mobility and handover (HO) in 5G networks due to significant changes in intelligent devices and high-definition (HD) multimedia applications. Consequently, the current cellular network faces challenges in propagating high-capacity data with improved speed, QoS, latency, and efficient HO and mobility management. This comprehensive survey paper specifically focuses on HO and mobility management issues within 5G heterogeneous networks (HetNets). The paper thoroughly examines the existing literature and investigates key performance indicators (KPIs) and solutions for HO and mobility-related challenges while considering applied standards. Additionally, it evaluates the performance of current models in addressing HO and mobility management issues, taking into account factors such as energy efficiency, reliability, latency, and scalability. Finally, this paper identifies significant challenges associated with HO and mobility management in existing research models and provides detailed evaluations of their solutions along with recommendations for future research.

A call for implementing augmented intelligence in pediatric dermatology.

Augmented intelligence (AI), the combination of artificial based intelligence with human intelligence from a practitioner, has become an increased focus of clinical interest in the field of dermatology. Technological advancements have led to the development of deep-learning based models to accurately diagnose complex dermatological diseases such as melanoma in adult datasets. Models for pediatric dermatology remain scarce, but recent studies have shown applications in the diagnoses of facial infantile hemangiomas and X-linked hypohidrotic ectodermal dysplasia; however, we see unmet needs in other complex clinical scenarios and rare diseases, such as diagnosing squamous cell carcinoma in patients with epidermolysis bullosa. Given the still limited number of pediatric dermatologists, especially in rural areas, AI has the potential to help overcome health disparities by helping primary care physicians treat or triage patients.

Horizon scanning, rapid reviews and living evidence to support decision-making: lessons from the work of the Critical Intelligence Unit in New South Wales, Australia during the COVID-19 pandemic.

The COVID-19 pandemic has seen an increase in rapidly disseminated scientific evidence and highlighted that traditional evidence synthesis methods, such as time and resource intensive systematic reviews, may not be successful in responding to rapidly evolving policy and practice needs. In New South Wales (NSW) Australia, the Critical Intelligence Unit (CIU) was established early in the pandemic and acted as an intermediary organisation. It brought together clinical, analytical, research, organisational and policy experts to provide timely and considered advice to decision-makers. This paper provides an overview of the functions, challenges and future implications of the CIU, particularly the Evidence Integration Team. Outputs from the Evidence Integration Team included a daily evidence digest, rapid evidence checks and living evidence tables. These products have been widely disseminated and used to inform policy decisions in NSW, making valuable impacts. Changes and innovations to evidence generation, synthesis and dissemination in response to the COVID-19 pandemic provide an opportunity to shift the way evidence is used in future. The experience and methods of the CIU have potential to be adapted and applied to the broader health system nationally and internationally.

Compliance with COVID-19 Preventive Measures: The Role of Intelligence, the Dark Triad and Dysfunctional Impulsivity.

BACKGROUND: Although measures to prevent COVID-19 infection have been greatly relaxed in many countries, they are still quite stringent in others. However, not all citizens comply with them to the same extent. Many studies show the importance of personality traits in predicting compliance with these measures, but it is not so clear what the role of intelligence is. Therefore, we aimed to assess whether intelligence is related to compliance with these measures, and what its predictive role is when considered together with the dark triad and dysfunctional impulsivity. METHOD: A total of 786 participants answered four questionnaires. We performed correlations, multiple regression analysis, and structural equation analysis. RESULTS: Multiple regression analysis showed that psychopathy and dysfunctional impulsivity were the variables that contributed most to compliance, while intelligence contributed very little. The results of the structural equation modelling suggested that intelligence had only an indirect relationship with compliance, through its relationship with the negative personality traits dysfunctional impulsivity and the dark triad. CONCLUSIONS: Intelligence seems to modulate the relationship between negative personality traits and compliance. Therefore, more intelligent people with negative personality traits would not tend to have such low levels of compliance.

Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants.

Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.

Epidemic intelligence studies: A research agenda for political scientists.

This research letter introduces readers to health intelligence by conceptualizing critical components and providing a primer for research within political science broadly considered. Accordingly, a brief review of the literature is provided, concluding with possible future research agendas. The aim is to elaborate on the importance of public health intelligence to national security studies, and to political science more generally.

Using Location Intelligence to Evaluate the COVID-19 Vaccination Campaign in the United States: Spatiotemporal Big Data Analysis.

BACKGROUND: Highly effective COVID-19 vaccines are available and free of charge in the United States. With adequate coverage, their use may help return life back to normal and reduce COVID-19-related hospitalization and death. Many barriers to widespread inoculation have prevented herd immunity, including vaccine hesitancy, lack of vaccine knowledge, and misinformation. The Ad Council and COVID Collaborative have been conducting one of the largest nationwide targeted campaigns ("It's Up to You") to communicate vaccine information and encourage timely vaccination across the United States. More than 300 major brands, digital and print media companies, and community-based organizations support the campaigns to reach distinct audiences. OBJECTIVE: The goal of this study was to use aggregated mobility data to assess the effectiveness of the campaign on COVID-19 vaccine uptake. METHODS: Campaign exposure data were collected from the Cuebiq advertising impact measurement platform consisting of about 17 million opted-in and deidentified mobile devices across the country. A Bayesian spatiotemporal hierarchical model was developed to assess campaign effectiveness through estimating the association between county-level campaign exposure and vaccination rates reported by the Centers for Disease Control and Prevention. To minimize potential bias in exposure to the campaign, the model included several control variables (eg, age, race or ethnicity, income, and political affiliation). We also incorporated conditional autoregressive residual models to account for apparent spatiotemporal autocorrelation. RESULTS: The data set covers a panel of 3104 counties from 48 states and the District of Columbia during a period of 22 weeks (March 29 to August 29, 2021). Officially launched in February 2021, the campaign reached about 3% of the anonymous devices on the Cuebiq platform by the end of March, which was the start of the study period. That exposure rate gradually declined to slightly above 1% in August 2021, effectively ending the study period. Results from the Bayesian hierarchical model indicate a statistically significant positive association between campaign exposure and vaccine uptake at the county level. A campaign that reaches everyone would boost the vaccination rate by 2.2% (95% uncertainty interval: 2.0%-2.4%) on a weekly basis, compared to the baseline case of no campaign. CONCLUSIONS: The "It's Up to You" campaign is effective in promoting COVID-19 vaccine uptake, suggesting that a nationwide targeted mass media campaign with multisectoral collaborations could be an impactful health communication strategy to improve progress against this and future pandemics. Methodologically, the results also show that location intelligence and mobile phone-based monitoring platforms can be effective in measuring impact of large-scale digital campaigns in near real time.

Next Generation Infectious Diseases Monitoring Gages via Incremental Federated Learning: Current Trends and Future Possibilities.

Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.

How much (evil) intelligence can be encoded by 30 kb?

Genomes of most RNA viruses are rarely larger than the size of an average human gene (10-15 kb) and still code for a number of biologically active polypeptides that modify the immune system and metabolism of the host organism in an amazingly complex way. Prolonged coevolution developed tricks by which viruses can dodge many protective mechanisms of the host and lead to the formation of molecular mimicry patterns. Some viruses inhibit the interferon response, interfere with the membrane destroying effects of the activated complement cascade. They can replicate in cellular compartments formed by inner membranes of the cell hiding their characteristic features from diverse pattern recognition receptors. In many cases-and in this respect, the new coronavirus is a champion-they can exploit our own defensive mechanisms to cause serious harm, severe symptoms and frequently deadly disease.

The World Health Organization's public health intelligence activities during the COVID-19 pandemic response, December 2019 to December 2021.

The coronavirus disease (COVID-19) presented a unique opportunity for the World Health Organization (WHO) to utilise public health intelligence (PHI) for pandemic response. WHO systematically captured mainly unstructured information (e.g. media articles, listservs, community-based reporting) for public health intelligence purposes. WHO used the Epidemic Intelligence from Open Sources (EIOS) system as one of the information sources for PHI. The processes and scope for PHI were adapted as the pandemic evolved and tailored to regional response needs. During the early months of the pandemic, media monitoring complemented official case and death reporting through the International Health Regulations mechanism and triggered alerts. As the pandemic evolved, PHI activities prioritised identifying epidemiological trends to supplement the information available through indicator-based surveillance reported to WHO. The PHI scope evolved over time to include vaccine introduction, emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, unusual clinical manifestations and upsurges in cases, hospitalisation and death incidences at subnational levels. Triaging the unprecedented high volume of information challenged surveillance activities but was managed by collaborative information sharing. The evolution of PHI activities using multiple sources in WHO's response to the COVID-19 pandemic illustrates the future directions in which PHI methodologies could be developed and used.

Social Responsibility and Spiritual Intelligence: University Students' Attitudes during COVID-19.

Human behavior during COVID-19 has led to the study of attitude and preferences among the population in different circumstances. In this sense, studying human behavior can contribute to creating policies for integral education, which should consider the convergence between social responsibility and spiritual intelligence. This can lead to the sensitization of practices and attitude modification within society. The purpose of our research was to explore the spiritual intelligence attitudes of university students from the perspective of social responsibility, considering the sociodemographic characteristics of the research subjects during the COVID-19 pandemic. Our research design is quantitative and sectional, due to the use of two quantitative scales. The participants were university students from a city located in south-central Chile. A total of 415 participations were collected, of which 362 applications were valid. Statistically significant differences were found according to gender and age. Women and the student cohort between 18 and 24 years of age placed more importance on spiritual necessities. We thus highlight the necessity to have adequate spaces for spiritual intelligence training given its links with socially responsible behavior and, finally, the development of explanatory studies to determine its causalities. In practice, these results contribute to designing an educational policy on the formation of integral spiritual intelligence for future professionals.

Patent intelligence of RNA viruses: Implications for combating emerging and re-emerging RNA virus based infectious diseases.

The recent outbreak of one of the RNA viruses (2019-nCoV) has affected most of the population and the fatalities reported may label it as a modern-day scourge. Active research on RNA virus infections and vaccine development had more commercial impact which leads to an increase in patent filings. Patents are a goldmine of information whose mining yields crucial technological inputs for further research. In this research article, we have investigated both the patent applications and granted patents, to identify the technological trends and their impact on 2019-nCoV infection using biotechnology-related keywords such as genes, proteins, nucleic acid etc. related to the RNA virus infection. In our research, patent analysis was majorly focused on prospecting for patent data related to the RNA virus infections. Our patent analysis specifically identified spike protein (S protein) and nucleocapsid proteins (N proteins) as the most actively researched macromolecules for vaccine and/or drug development for diagnosis and treatment of RNA virus based infectious diseases. The outcomes of this patent intelligence study will be useful for the researchers who are actively working in the area of vaccine or drug development for RNA virus-based infections including 2019-nCoV and other emerging and re-emerging viral infections in the near future.

Applying Cultural Intelligence to Improve Vaccine Hesitancy Among Black, Indigenous, and People of Color.

Despite the overwhelming evidence to support the benefits of vaccines for preventable diseases and improving health outcomes throughout the world, vaccine hesitancy and resistance continues to be a concern during the COVID-19 pandemic. Although Black, Indigenous, and People of Color (BIPOC) experience the highest rates of morbidity and mortality from COVID-19, mistrust and historical unethical research and medical practices continue to preclude this population from getting the vaccine. This article urges clinicians to subscribe to development and application of cultural intelligence to understand the impact of structural racism and cultural considerations of BIPOC to partner in strategy development.

Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR Scans.

Chest X-ray (CXR) scans are emerging as an important diagnostic tool for the early spotting of COVID and other significant lung diseases. The recognition of visual symptoms is difficult and can take longer time by radiologists as CXR provides various signs of viral infection. Therefore, artificial intelligence-based method for automated identification of COVID by utilizing X-ray images has been found to be very promising. In the era of deep learning, effective utilization of existing pretrained generalized models is playing a decisive role in terms of time and accuracy. In this paper, the benefits of weights of existing pretrained model VGG16 and InceptionV3 have been taken. Base model has been created using pretrained models (VGG16 and InceptionV3). The last fully connected (FC) layer has been added as per the number of classes for classification of CXR in binary and multi-class classification by appropriately using transfer learning. Finally, combination of layers is made by integrating the FC layer weights of both the models (VGG16 and InceptionV3). The image dataset used for experimentation consists of healthy, COVID, pneumonia viral, and pneumonia bacterial. The proposed weight fusion method has outperformed the existing models in terms of accuracy, achieved 99.5% accuracy in binary classification over 20 epochs, and 98.2% accuracy in three-class classification over 100 epochs.