Using Artificial Intelligence in Infection Prevention

Healthcare as a driver, reservoir and amplifier of antimicrobial resistance: opportunities for interventions

Antimicrobial resistance (AMR) is a global health challenge that threatens humans, animals and the environment. Evidence is emerging for a role of healthcare infrastructure, environments and patient pathways in promoting and maintaining AMR via direct and indirect mechanisms. Advances in vaccination and monoclonal antibody therapies together with integrated surveillance, rapid diagnostics, targeted antimicrobial therapy and infection control measures offer opportunities to address healthcare-associated AMR risks more effectively. Additionally, innovations in artificial intelligence, data linkage and intelligent systems can be used to better predict and reduce AMR and improve healthcare resilience. In this Review, we examine the mechanisms by which healthcare functions as a driver, reservoir and amplifier of AMR, contextualized within a One Health framework. We also explore the opportunities and innovative solutions that can be used to combat AMR throughout the patient journey. We provide a perspective on the current evidence for the effectiveness of interventions designed to mitigate healthcare-associated AMR and promote healthcare resilience within high-income and resource-limited settings, as well as the challenges associated with their implementation.

Utilizing natural language processing and large language models in the diagnosis and prediction of infectious diseases: A systematic review

BACKGROUND

Natural Language Processing (NLP) and Large Language Models (LLMs) hold largely untapped potential in infectious disease management. This review explores their current use and uncovers areas needing more attention.

METHODS

This analysis followed systematic review procedures, registered with the Prospective Register of Systematic Reviews. We conducted a search across major databases including PubMed, Embase, Web of Science, and Scopus, up to December 2023, using keywords related to NLP, LLM, and infectious diseases. We also employed the Quality Assessment of Diagnostic Accuracy Studies-2 tool for evaluating the quality and robustness of the included studies.

RESULTS

Our review identified 15 studies with diverse applications of NLP in infectious disease management. Notable examples include GPT-4's application in detecting urinary tract infections and BERTweet's use in Lyme Disease surveillance through social media analysis. These models demonstrated effective disease monitoring and public health tracking capabilities. However, the effectiveness varied across studies. For instance, while some NLP tools showed high accuracy in pneumonia detection and high sensitivity in identifying invasive mold diseases from medical reports, others fell short in areas like bloodstream infection management.

CONCLUSIONS

This review highlights the yet-to-be-fully-realized promise of NLP and LLMs in infectious disease management. It calls for more exploration to fully harness AI's capabilities, particularly in the areas of diagnosis, surveillance, predicting disease courses, and tracking epidemiological trends.

Infection Prevention and Control Strategies According to the Type of Multidrug-Resistant Bacteria and Candida auris in Intensive Care Units: A Pragmatic Resume including Pathogens R0 and a Cost-Effectiveness Analysis

Multidrug-resistant organism (MDRO) outbreaks have been steadily increasing in intensive care units (ICUs). Still, healthcare institutions and workers (HCWs) have not reached unanimity on how and when to implement infection prevention and control (IPC) strategies. We aimed to provide a pragmatic physician practice-oriented resume of strategies towards different MDRO outbreaks in ICUs. We performed a narrative review on IPC in ICUs, investigating patient-to-staff ratios; education, isolation, decolonization, screening, and hygiene practices; outbreak reporting; cost-effectiveness; reproduction numbers (R0); and future perspectives. The most effective IPC strategy remains unknown. Most studies focus on a specific pathogen or disease, making the clinician lose sight of the big picture. IPC strategies have proven their cost-effectiveness regardless of typology, country, and pathogen. A standardized, universal, pragmatic protocol for HCW education should be elaborated. Likewise, the elaboration of a rapid outbreak recognition tool (i.e., an easy-to-use mathematical model) would improve early diagnosis and prevent spreading. Further studies are needed to express views in favor or against MDRO decolonization. New promising strategies are emerging and need to be tested in the field. The lack of IPC strategy application has made and still makes ICUs major MDRO reservoirs in the community. In a not-too-distant future, genetic engineering and phage therapies could represent a plot twist in MDRO IPC strategies.

Infection prevention and control in 2030: a first qualitative survey by the Crystal Ball Initiative

BACKGROUND

Healthcare delivery is undergoing radical changes that influence effective infection prevention and control (IPC). Futures research (short: Futures), the science of deliberating on multiple potential future states, is increasingly employed in many core societal fields. Futures might also be helpful in IPC to facilitate current education and organisational decisions. Hence, we conducted an initial survey as part of the IPC Crystal Ball Initiative.

METHODS

In 2019, international IPC experts were invited to answer a 10-item online questionnaire, including demographics, housekeeping, and open-ended core questions (Q) on the "status of IPC in 2030" (Q1), "people in charge of IPC" (Q2), "necessary skills in IPC" (Q3), and "burning research questions" (Q4). The four core questions were submitted to a three-step inductive and deductive qualitative content analysis. A subsequent cross-case matrix produced overarching leitmotifs. Q1 statements were additionally coded for sentiment analysis (positive, neutral, or negative).

RESULTS

Overall, 18 of 44 (41%) invited experts responded (from 11 countries; 12 physicians, four nurses, one manager, one microbiologist; all of them in senior positions). The emerging leitmotifs were "System integration", "Beyond the hospital", "Behaviour change and implementation", "Automation and digitalisation", and "Anticipated scientific progress and innovation". The statements reflected an optimistic outlook in 66% of all codes of Q1.

CONCLUSIONS

The first exercise of the IPC Crystal Ball Initiative reflected an optimistic outlook on IPC in 2030, and participants envisioned leveraging technological and medical progress to increase IPC effectiveness, freeing IPC personnel from administrative tasks to be more present at the point of care and increasing IPC integration and expansion through the application of a broad range of skills. Enhancing participant immersion in future Crystal Ball Initiative exercises through simulation would likely further increase the authenticity and comprehensiveness of the envisioned futures.

Mobile Applications for Learning Hand Hygiene: A Comparative Analysis

Infection control is crucial for high-quality patient care. One of the most effective and commonly used infection control procedures is hand hygiene which, it is known, requires repeated refresher training. There are many ways to educate healthcare professionals about hand hygiene, including the use of mobile applications (apps). Our aim is to review such hand hygiene apps, and to identify which have been available since 2021 and to assess their quality. We conducted a review using the PRISMA diagram to document our app selection process in the Google Play Store and Apple store in March 2024. For the evaluation of apps, we used the user version of the Mobile Application Rating Scale questionnaire (uMARS). Of 16 apps only five adhere to WHO hand hygiene guidelines. Timers were included in 12 of the 16 apps and reminders were included in 10 of 16 apps. The highest overall uMARS scoring app was Give Me 5-Hand Hygiene (4.31 ± 0.28), while Wash your hands! (1.17 ± 0.14) had the lowest score. We found that more than half of the apps were unavailable from the 2021 review. We believe that app-based education could effectively sustain hand hygiene knowledge in healthcare settings.

Confronting the Disruption of the Infectious Diseases Workforce by Artificial Intelligence: What This Means for Us and What We Can Do About It

With the rapid advancement of artificial intelligence (AI), the field of infectious diseases (ID) faces both innovation and disruption. AI and its subfields including machine learning, deep learning, and large language models can support ID clinicians' decision making and streamline their workflow. AI models may help ensure earlier detection of disease, more personalized empiric treatment recommendations, and allocation of human resources to support higher-yield antimicrobial stewardship and infection prevention strategies. AI is unlikely to replace the role of ID experts, but could instead augment it. However, its limitations will need to be carefully addressed and mitigated to ensure safe and effective implementation. ID experts can be engaged in AI implementation by participating in training and education, identifying use cases for AI to help improve patient care, designing, validating and evaluating algorithms, and continuing to advocate for their vital role in patient care.

Is Zero Ventilator-Associated Pneumonia Achievable? Updated Practical Approaches to Ventilator-Associated Pneumonia Prevention

Ventilator-associated pneumonia (VAP) remains a significant clinical entity with reported incidence rates of 7% to 15%. Given the considerable adverse consequences associated with this infection, VAP prevention became a core measure required in most US hospitals. Many institutions took pride in implementing effective VAP prevention bundles that combined at least head of bed elevation, hand hygiene, chlorhexidine oral care, and subglottic drainage. Spontaneous breathing and awakening trials have also consistently been shown to shorten the duration of mechanical ventilation and secondarily reduce the occurrence of VAP.

Embryo Selection in the Context of In Vitro Fertilization

From the Catholic perspective, in vitro fertilization (IVF) is morally problematic because it artificially separates the procreative and unitive aspects of the conjugal act. Embryo selection (ES) in the context of IVF is an injustice against the resulting embryos because it treats them as commodities and works against their right to life by determining their implantation potential in light of their features. The Church opposes the eugenics mentality underlying ES. Meanwhile, the IVF industry increasingly uses artificial intelligence (AI) for ES. However, doing so could worsen the injustice by deepening the disrespect of human lives under the technocratic paradigm. As such, Catholic bioethicists are encouraged to advocate for the Church's teachings with renewed vigor. In this commentary, we will examine (1) ES in the context of IVF, (2) using AI for ES, (3) the moral implications of using AI for ES, and (4) points for further consideration. Summary: Using AI for Embryo selection in the context of IVF deepens the disrespect of human lives under the technocratic paradigm.

Genomic surveillance for antimicrobial resistance - a One Health perspective

Antimicrobial resistance (AMR) - the ability of microorganisms to adapt and survive under diverse chemical selection pressures - is influenced by complex interactions between humans, companion and food-producing animals, wildlife, insects and the environment. To understand and manage the threat posed to health (human, animal, plant and environmental) and security (food and water security and biosecurity), a multifaceted 'One Health' approach to AMR surveillance is required. Genomic technologies have enabled monitoring of the mobilization, persistence and abundance of AMR genes and mutations within and between microbial populations. Their adoption has also allowed source-tracing of AMR pathogens and modelling of AMR evolution and transmission. Here, we highlight recent advances in genomic AMR surveillance and the relative strengths of different technologies for AMR surveillance and research. We showcase recent insights derived from One Health genomic surveillance and consider the challenges to broader adoption both in developed and in lower- and middle-income countries.

The leading global health challenges in the artificial intelligence era

Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.

Greater agreement is required to harness the potential of health intelligence: a critical interpretive synthesis

OBJECTIVES

To synthesize existing knowledge on the features of, and approaches to, health intelligence, including definitions, key concepts, frameworks, methods and tools, types of evidence used, and research gaps.

STUDY DESIGN AND SETTING

We applied a critical interpretive synthesis methodology, combining systematic searching, purposive sampling, and inductive analysis to explore the topic. We conducted electronic and supplementary searches to identify records (papers, books, websites) based on their potential relevance to health intelligence. The key themes identified in the literature were combined under each of the compass subquestions and circulated among the research team for discussion and interpretation.

RESULTS

Of the 290 records screened, 40 were included in the synthesis. There is no clear definition of health intelligence in the literature. Some records describe it in similar terms as public health surveillance. Some focus on the use of artificial intelligence, while others refer to health intelligence in a military or security sense. And some authors have suggested a broader definition of health intelligence that explicitly includes the concepts of synthesis of research evidence for informed decision making.

CONCLUSION

Rather than developing a new or all-encompassing definition, we suggest incorporating the concept and scope of health intelligence within the evidence ecosystem.

A state-of-the-art analysis of pharmacological delivery and artificial intelligence techniques for inner ear disease treatment

Over the last several decades, both the academic and therapeutic fields have seen significant progress in the delivery of drugs to the inner ear due to recent delivery methods established for the systemic administration of drugs in inner ear treatment. Novel technologies such as nanoparticles and hydrogels are being investigated, in addition to the traditional treatment methods. Intracochlear devices, which utilize current developments in microsystems technology, are on the horizon of inner ear drug delivery methods and are designed to provide medicine directly into the inner ear. These devices are used for stem cell treatment, RNA interference, and the delivery of neurotrophic factors and steroids during cochlear implantation. An in-depth analysis of artificial neural networks (ANNs) in pharmaceutical research may be found in ANNs for Drug Delivery, Design, and Disposition. This prediction tool has a great deal of promise to assist researchers in more successfully designing, developing, and delivering successful medications because of its capacity to learn and self-correct in a very complicated environment. ANN achieved a high level of accuracy exceeding 0.90, along with a sensitivity of 95% and a specificity of 100%, in accurately distinguishing illness. Additionally, the ANN model provided nearly perfect measures of 0.99%. Nanoparticles exhibit potential as a viable therapeutic approach for bacterial infections that are challenging to manage, such as otitis media. The utilization of ANNs has the potential to enhance the effectiveness of nanoparticle therapy, particularly in the realm of automated identification of otitis media. Polymeric nanoparticles have demonstrated effectiveness in the treatment of prevalent bacterial infections in pediatric patients, suggesting significant potential for forthcoming therapeutic interventions. Finally, this study is based on a research of how inner ear diseases have been treated in the last ten years (2012-2022) using machine learning.

Using artificial intelligence to improve public health: a narrative review

Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, in public health, the widespread employment of AI only began recently, with the advent of COVID-19. This review examines the advances of AI in public health and the potential challenges that lie ahead. Some of the ways AI has aided public health delivery are via spatial modeling, risk prediction, misinformation control, public health surveillance, disease forecasting, pandemic/epidemic modeling, and health diagnosis. However, the implementation of AI in public health is not universal due to factors including limited infrastructure, lack of technical understanding, data paucity, and ethical/privacy issues.

Advancing tuberculosis management: the role of predictive, preventive, and personalized medicine

Tuberculosis is a major global health issue, with approximately 10 million people falling ill and 1.4 million dying yearly. One of the most significant challenges to public health is the emergence of drug-resistant tuberculosis. For the last half-century, treating tuberculosis has adhered to a uniform management strategy in most patients. However, treatment ineffectiveness in some individuals with pulmonary tuberculosis presents a major challenge to the global tuberculosis control initiative. Unfavorable outcomes of tuberculosis treatment (including mortality, treatment failure, loss of follow-up, and unevaluated cases) may result in increased transmission of tuberculosis and the emergence of drug-resistant strains. Treatment failure may occur due to drug-resistant strains, non-adherence to medication, inadequate absorption of drugs, or low-quality healthcare. Identifying the underlying cause and adjusting the treatment accordingly to address treatment failure is important. This is where approaches such as artificial intelligence, genetic screening, and whole genome sequencing can play a critical role. In this review, we suggest a set of particular clinical applications of these approaches, which might have the potential to influence decisions regarding the clinical management of tuberculosis patients.

Drivers of inappropriate antibiotic use in low- and middle-income countries

Antimicrobial resistance (AMR) is a global security threat that accounts for about 700 000 deaths annually. Studies have shown that antimicrobial resistance could result in a 2% to 3.5% reduction in global Gross Domestic Product by 2050 and a loss of between 60 and 100 trillion US dollars, worth of economic output resulting in significant and widespread human suffering. Low- and middle-income countries (LMICs) will be worse hit by an unchecked rise of AMR. For example, it is predicted that AMR could kill about 4.1 million people in Africa by 2050 if it is not curbed. Similarly rising rates of AMR will lead to increased treatment costs and an inability to attain universal health coverage, in LMICs with fragile health systems. Sadly, AMR is driven by the inappropriate use of antimicrobials, especially antibiotics. Inappropriate antibiotic use is a pertinent problem in LMICs where regulatory frame works are weak. Inappropriate antibiotic use in LMICs is a multifaceted problem that cuts across clinical and veterinary medicine and agriculture. Therefore, efforts geared at curbing inappropriate antibiotic use in LMICs must identify the factors that drive this problem (i.e. inappropriate antibiotic use) in these countries. A clear knowledge of these factors will guide effective policy and decision making to curb inappropriate antibiotic use and ultimately AMR. The focus of this review is to discuss the factors that drive inappropriate antibiotic use in LMICs.

Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases

Controlling infectious diseases is a major health priority because they can spread and infect humans, thus evolving into epidemics or pandemics. Therefore, early detection of infectious diseases is a significant need, and many researchers have developed models to diagnose them in the early stages. This paper reviewed research articles for recent machine-learning (ML) algorithms applied to infectious disease diagnosis. We searched the Web of Science, ScienceDirect, PubMed, Springer, and IEEE databases from 2015 to 2022, identified the pros and cons of the reviewed ML models, and discussed the possible recommendations to advance the studies in this field. We found that most of the articles used small datasets, and few of them used real-time data. Our results demonstrated that a suitable ML technique depends on the nature of the dataset and the desired goal. Moreover, heterogeneous data could ensure the model's generalization, while big data, many features, and a hybrid model will increase the resulting performance. Furthermore, using other techniques such as deep learning and NLP to extract vast features from unstructured data is a powerful approach to enhancing the performance of ML diagnostic models.

Hand hygiene monitoring and compliance system using convolution neural networks

Hand hygiene monitoring and compliance systems play a significant role in curbing the spread of healthcare associated infections and the COVID-19 virus. In this paper, a model has been developed using convolution neural networks (CNN) and computer vision to detect an individual's germ level, monitor their hand wash technique and create a database containing all records. The proposed model ensures all individuals entering a public place prevent the spread of healthcare associated infections (HCAI). In our model, the individual's identity is verified using two-factor authentication, followed by checking the hand germ level. Furthermore, if required the model will request sanitizing/ hand wash for completion of the process. During this time, the hand movements are checked to ensure each hand wash step is completed according to World Health Organization (WHO) guidelines. Upon completion of the process, a database with details of the individual's germ level is created. The advantage of our model is that it can be implemented in every public place and it is easily integrable. The performance of each segment of the model has been tested on real-time images an validated. The accuracy of the model is 100% for personal identification, 96.87% for hand detection, 93.33% for germ detection and 85.5% for the compliance system respectively.

Challenges in the development of digital public health interventions and mapped solutions: Findings from a scoping review

Background

“Digital public health” has emerged from an interest in integrating digital technologies into public health. However, significant challenges which limit the scale and extent of this digital integration in various public health domains have been described. We summarized the literature about these challenges and identified strategies to overcome them.

Methods

We adopted Arksey and O’Malley's framework (2005) integrating adaptations by Levac et al. (2010). OVID Medline, Embase, Google Scholar, and 14 government and intergovernmental agency websites were searched using terms related to “digital” and “public health.” We included conceptual and explicit descriptions of digital technologies in public health published in English between 2000 and June 2020. We excluded primary research articles about digital health interventions. Data were extracted using a codebook created using the European Public Health Association's conceptual framework for digital public health.

Results and analysis

Overall, 163 publications were included from 6953 retrieved articles with the majority (64%, n = 105) published between 2015 and June 2020. Nontechnical challenges to digital integration in public health concerned ethics, policy and governance, health equity, resource gaps, and quality of evidence. Technical challenges included fragmented and unsustainable systems, lack of clear standards, unreliability of available data, infrastructure gaps, and workforce capacity gaps. Identified strategies included securing political commitment, intersectoral collaboration, economic investments, standardized ethical, legal, and regulatory frameworks, adaptive research and evaluation, health workforce capacity building, and transparent communication and public engagement.

Conclusion

Developing and implementing digital public health interventions requires efforts that leverage identified strategies to overcome diverse challenges encountered in integrating digital technologies in public health.

Defining Digital Public Health and the Role of Digitization, Digitalization, and Digital Transformation: Scoping Review

Background

The recent proliferation and application of digital technologies in public health has spurred interest in digital public health. However, as yet, there appears to be a lack of conceptual clarity and consensus on its definition.

Objective

In this scoping review, we seek to assess formal and informal definitions of digital public health in the literature and to understand how these definitions have been conceptualized in relation to digitization, digitalization, and digital transformation.

Methods

We conducted a scoping literature search in Ovid MEDLINE, Embase, Google Scholar, and 14 government and intergovernmental agency websites encompassing 6 geographic regions. Among a total of 409 full articles identified, we reviewed 11 publications that either formally defined digital public health or informally described the integration of digital technologies into public health in relation to digitization, digitalization, and digital transformation, and we conducted a thematic analysis of the identified definitions.

Results

Two explicit definitions of digital public health were identified, each with divergent meanings. The first definition suggested digital public health was a reimagination of public health using new ways of working, blending established public health wisdom with new digital concepts and tools. The second definition highlighted digital public health as an asset to achieve existing public health goals. In relation to public health, digitization was used to refer to the technical process of converting analog records to digital data, digitalization referred to the integration of digital technologies into public health operations, and digital transformation was used to describe a cultural shift that pervasively integrates digital technologies and reorganizes services on the basis of the health needs of the public.

Conclusions

The definition of digital public health remains contested in the literature. Public health researchers and practitioners need to clarify these conceptual definitions to harness opportunities to integrate digital technologies into public health in a way that maximizes their potential to improve public health outcomes.

International Registered Report Identifier (IRRID)

RR2-10.2196/preprints.27686

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

The Application and Development of Deep Learning in Radiotherapy: A Systematic Review

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.

Expert commentary on the challenges and opportunities for surgical site infection prevention through implementation of evidence-based guidelines in the Asia-Pacific Region

INTRODUCTION

Surgical site infections (SSIs) are a significant source of morbidity and mortality in the Asia-Pacific region (APAC), adversely impacting patient quality of life, fiscal productivity and placing a major economic burden on the country's healthcare system. This commentary reports the findings of a two-day meeting that was held in Singapore on July 30-31, 2019, where a series of consensus recommendations were developed by an expert panel composed of infection control, surgical and quality experts from APAC nations in an effort to develop an evidence-based pathway to improving surgical patient outcomes in APAC.

METHODS

The expert panel conducted a literature review targeting four sentinel areas within the APAC region: national and societal guidelines, implementation strategies, postoperative surveillance and clinical outcomes. The panel formulated a series of key questions regarding APAC-specific challenges and opportunities for SSI prevention.

RESULTS

The expert panel identified several challenges for mitigating SSIs in APAC; (a) constraints on human resources, (b) lack of adequate policies and procedures, (c) lack of a strong safety culture, (d) limitation in funding resources, (e) environmental and geographic challenges, (f) cultural diversity, (g) poor patient awareness and (h) limitation in self-responsibility. Corrective strategies for guideline implementation in APAC were proposed that included: (a) institutional ownership of infection prevention strategies, (b) perform baseline assessments, (c) review evidence-based practices within the local context, (d) develop a plan for guideline implementation, (e) assess outcome and stakeholder feedback, and (f) ensure long-term sustainability.

CONCLUSIONS

Reducing the risk of SSIs in APAC region will require: (a) ongoing consultation and collaboration among stakeholders with a high level of clinical staff engagement and (b) a strong institutional and national commitment to alleviate the burden of SSIs by embracing a safety culture and accountability.

Virtual infection prevention-The next frontier

The coronavirus disease 2019 (COVID-19) pandemic has resulted in the acceleration of telehealth and remote environments as stakeholders and healthcare systems respond to the threat of this disease. How can infectious diseases and healthcare epidemiology expertise be adapted to support safe care for all?

Integration of Advanced Health Technology Within the Healthcare System to Fight the Global Pandemic: Current Challenges and Future Opportunities

The COVID-19 pandemic presents a significant challenge for providing adequate healthcare services in the context of patient isolation.

DISCUSSION

The ability of our current healthcare system to cope with the current situation is mainly dependent on advanced health technology, such as telehealth, chatbots, virtual reality (VR), and artificial intelligence (AI). Telehealth can be a novel tool for improving our current healthcare system and allowing for greater delivery of healthcare services during global crises (i.e., the COVID-19 pandemic). Technology, such as chatbots, VR, and AI, could be utilized to reduce the burden of both communicable and noncommunicable diseases, as well as to build a patient-centered decision-making healthcare system.

OBJECTIVES

Understanding the various methods of enhancing healthcare services using advanced health technology will help to develop new applications that can be integrated into regular healthcare and in time of healthcare crises.

CONCLUSION

Advanced health technology is a main tool to face a pandemic that decreased the burden on physicians and patients as well as the entire healthcare system.

COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq

The prediction of diseases caused by viral infections is a complex medical task where many real data that consists of different variables must be employed. As known, COVID-19 is the most dangerous disease worldwide; nowhere, an effective drug has been found yet. To limit its spread, it is essential to find a rational method that shows the spread of this virus by relying on many infected people’s data. A model consisting of three artificial neural networks’ (ANN) functions was developed to predict COVID-19 separation in Iraq based on real infection data supplied by the public health department at the Iraqi Ministry of Health. The performance efficiency of this model was evaluated, where its performance efficiency reached 81.6% when employed four statistical error criteria as mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R²), and Nash-Sutcliffe coefficient (NC). The severity of the virus’s spread across Iraq was assessed in a short term (in the next 6 months), where the results show that the spread severity will intensify in this short term by 17.1%, and the average death cases will increase by 8.3%. These results clarified by creating spatial distribution maps for virus spread are simulated by employing a Geographic Information System (GIS) environment to be used as a useful database for developing plans for combating viruses in Iraq.

COVID-19: Understanding the Pandemic Emergence, Impact and Infection Prevalence Worldwide

Coronavirus disease (COVID-19) has showed high transmission across the continents due to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) with total infected cases of around ~ 44 million people. This communicable virus that initiated from the Wuhan city of China in the month of December 2020 has now spread to 189 different countries with 1.1 million fatalities worldwide (till 28 Oct, 2020). The World Health Organization (WHO) declared this outbreak as Public Health Emergency of International Concern in January, 2020. The infection spreads mainly due to contact with infected droplets or fomites, highlighting flu like symptoms initially, which may further progress into severe pneumonia and respiratory failure, often observed in elderly patients with prehistory of other diseases. The diagnosis is based on detection of viral antigen, human antibody and viral gene (RT-PCR). Further, various other diagnostic tools including X-ray, CT-scan are used for imaging purpose, recently artificial intelligence based imaging (contactless scanning) gained popularity. Generally testing of existing drugs (repurposing) and development of new molecules are the main strategies adopted by researchers. However, as per initial findings, various drugs, monoclonal antibody and plasma therapy were found to show effectiveness against COVID-19. Further, many vaccine candidates have entered or will soon enter phase III clinical testing. This disease has further challenged the global economy. Thus, this review uniquely compares the strategies adopted by developed and developing countries worldwide including protective measures like lockdown, continuous testing, utilizing latest tools (artificial intelligence) in curbing this infection spread.

COVID-19: Advances in diagnostic tools, treatment strategies, and vaccine development

An unprecedented worldwide spread of the SARS-CoV-2 has imposed severe challenges on healthcare facilities and medical infrastructure. The global research community faces urgent calls for the development of rapid diagnostic tools, effective treatment protocols, and most importantly, vaccines against the pathogen. Pooling together expertise across broad domains to innovate effective solutions is the need of the hour. With these requirements in mind, in this review, we provide detailed critical accounts on the leading efforts at developing diagnostics tools, therapeutic agents, and vaccine candidates. Importantly, we furnish the reader with a multidisciplinary perspective on how conventional methods like serology and RT-PCR, as well as cutting-edge technologies like CRISPR/Cas and artificial intelligence/machine learning, are being employed to inform and guide such investigations. We expect this narrative to serve a broad audience of both active and aspiring researchers in the field of biomedical sciences and engineering and help inspire radical new approaches towards effective detection, treatment, and prevention of this global pandemic.