Public Health Applications Research Articles

Application of artificial intelligence to study the causality in public health: a systematic review

Abstract Introduction In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association. Traditional methodologies often struggle to provide definitive causal conclusions due to confounding variables. However, leveraging artificial Intelligence (AI) and machine Learning (ML) offers a promising solution by modeling complex interactions among relevant factors. This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications. Methods A literature search was conducted in Pubmed, Web of Science, and Scopus, employing the following search strategy: (Causalit*[Title/abstract] OR Causation*[Title/abstract] OR Causal[Title/abstract]) AND (‘Machine Learning’[Title/abstract] OR “Deep Learning” [Title/abstract] OR ‘Artificial Intelligence’ [Title/abstract] OR Algorithm* [Title/abstract]). The search aimed to identify studies encompassing causal inference techniques utilizing ML and AI methodologies in public health. Results From a total of 28,230 articles published up to August 2023, after removing duplicates (11,158) and articles that did not meet the inclusion criteria (16,168 post title/abstract screening, and 856 post full-text screening), the systematic review included 48 articles. Bayesian additive regression trees, Bayesian network models, causal forests analysis, and graphical causal models emerged as the prevailing causal methodologies. AI-driven causal methodologies have been investigated across various domains within public health, encompassing disease prediction, evaluation of treatment efficacy, identification of risk factors, analysis of health behaviors, precision Public Health initiatives, and environmental health assessment. Discussion While our systematic review affirms the potential of AI-driven causal approaches across several public health domains, further research is warranted before these emerging techniques can be effectively integrated into real settings. Key messages • In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association and traditional methodologies often struggle to provide definitive causal conclusions. • This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications.

Enhancing Public Health: training an AI chatbot for complex tasks support

Abstract Background The evolution of Large Language Models (LLMs) such as GPT-4, Gemini, and Copilot has transformed the field of Natural Language Processing, demonstrating human-level capabilities and potential applications in Public Health. Here, we report on the training of a chatbot model based on GPT-4 designed to assist operators with relevant and complex public health tasks. Methods A scoping review of existing studies on the application of LLMs in Public Health settings was conducted to understand the landscape and identify gaps in their current implementations. We then proceeded to analyse the necessary skills and competencies required for public health professionals to adequately instruct the model on required tasks. Finally, we selected the Italian regulations and guidelines with the greatest relevance in the public health field to provide the knowledge base to the model. Results The review revealed no relevant documents on the use of LLMs in areas related to Public Health. Skills and competencies were retrieved from the Guidelines of the Italian Society of Hygiene for Public Health operators and used to instruct the model on how to address public health tasks. As for the knowledge base, we retrieved 25 documents regulating specific areas of the public health field, such as vaccinations, hygiene and safety at work, infection prevention, and school hygiene. Such documents were used to train the model, after formatting them appropriately. Conclusions Despite challenges with non-standardized and technically complex documentation, preliminary testing of our model has shown its versatility and utility in supporting daily public health operations. A validation phase is under definition and will involve a DELPHI process, in which products of the custom GPT model will be evaluated by a panel of public health operators to evaluate its performance in real-world settings, ensuring compliance with national health regulations. Key messages • Training a GPT-4-based chatbot to support complex tasks in Public Health, tailored by relevant national guidelines. • Our custom model, informed by public health guidelines, showed potential in preliminary tests.

Implementation of polygenic risk scores in secondary prevention of (breast) cancer

Abstract Utilizing polygenic risk scores to inform secondary prevention of breast cancer via population screening programs is one of the pioneering applications of genomics in public health applications. Over the last decade, polygenic risk scores for breast cancer have been developed and validated in various European populations (AUC ± 0.63, OR per Z unit ± 1.6, depending on the population). In some countries, the polygenic risk score has been integrated with decision modeling tools, such as CanRisk in the Netherlands, and low-level implementation into health and care systems has started. Implementation strategies vary, but generally consider initial implementation into high-risk populations, such as breast cancer families, in specialized clinical setting, such as academic medical centres, or specialized cancer clinics, referring relatives of patients to population screening based on comprehensive risk modelling, including polygenic risk scores. This route may be followed by other disease fields as well. The presentations outlines the current evidence and considerations on breast cancer genetic risk modelling, clinical pilot studies and the outline of step-wise implementation of such models to benefit secondary prevention programs. We start from the perspective of a single hospital, to the needed steps to upscale to national populations, both in diversity of these populations, as the throughput of genetic testing and counselling, until the harmonization and recalibration of various European healthcare models. We draw from examples of the Genotyping on all patients (GOALL), building cancer health platforms (CanHeal) and Genome of Europe (GoE) consortia.

Colorimetric aptasensing of microcystin-LR using DNA-conjugated polydiacetylene.

Polydiacetylene (PDA) holds promise as a versatile material for biosensing applications due to its unique optical properties and self-assembly capabilities. In this study, we developed a colorimetric detection biosensor system utilizing PDA and aptamer for the detection of microcystin-LR (MC-LR), a potent hepatotoxin found in cyanobacteria-contaminated environments. The biosensor was constructed by immobilizing MC-LR-specific aptamer on magnetic beads, where the aptamer was hybridized with a urease-labelled complementary DNA (cDNA-urease). Upon binding MC-LR, the aptamer undergoes a conformational change to release cDNA-urease. The released cDNA-urease is subsequently captured by PDA bearing a single-stranded DNA (ssDNA). The enzymatic reaction triggers a distinctive color transition of PDA from blue to red. The results demonstrate exceptional sensitivity, with a linear detection range of 5-100ng/mL and a limit of detection as low as 1ng/mL. The practicability of the colorimetric method was demonstrated by detecting different levels of MC-LR in spiked water samples. The recoveries ranged from 77.3 to 102% and the color change, visible to the naked eye, underscores the practical utility for on-site applications. Selectivity for MC-LR over other microcystin variants (MC-RR and MC-YR) was confirmed. The colorimetric detection platform capitalizes on the properties of PDA and nucleic acid, offering a robust method for detecting small molecules with potential applications in environmental monitoring and public health.

Digital health and some thematic shifts in bioethics in academic publications after the pandemic

Abstract Background Digital health (DH) presents vast opportunities, but also dynamic shift in viewpoints, which is reflected in academic works. Since DH concerns patient data, a major issue is complying with the bioethical principles, and ensuring patients’ privacy and security. The aim of this research is to explore avenues, in which ethics and privacy are reflected in DH, especially in terms of research, legislation, artificial intelligence (AI) and blockchain applications in medicine and public health. Methods Literature search for full text publications in PubMed, Scopus and ScienceDirect focused on ethics and privacy in DH in the period 2019-2023 found 325 articles in English authored by academicians. Six thematic categories were identified: ethics (fundamental principles; ethics in using novel DH tools); privacy (in use of apps/wearables; as a challenge/barrier to DH interventions/technologies); ethics/privacy in: DH research (data gathering, use, sharing); policy and legislation (for DH applications); AI and blockchains (in medicine and public health). Results Articles were from 32 countries: USA-24.3%; UK-13.5%; Germany-7.7%; Canada-7.4%; Australia-6.7%; Netherlands, Italy, Switzerland-4.7% each; China, India-3.0% each and 20.3% from 22 other countries; 86.5% in medical/DH journals. Only 10.2% were published in 2019; 17.8% - 2020; 19.4% - 2021; 27.1% - 2022 and 25.5% - 2023. Privacy was discussed in 26.5%; ethics-23.7%; research-19.4%; AI-15.1%; policy and legislation-9.5% and blockchains-5.5% of the articles. The publication activity of all six categories increased after the unfold of the pandemic in 2020. In 2023 publications for ethics (13%), privacy (5.8%) and research (6.3%) decreased; for legislation (12.9%), AI (14.3%) and blockchains in healthcare (16.8%) increased. Conclusions The pandemic and its aftermath present a change in academic interest. Traditional ethical fundaments in DH slightly lose position in favor of top notch technologies and apt legislation. Key messages • The pandemic and modern technologies enforced new digital health applications. Further research will reveal the needs of AI and blockchain applications respecting privacy and the ethical principles. • Besides existing legislation and regulations, in order to catch up with the dynamic developments in DH, the legal base needs to become more flexible. Technologies change, privacy importance does not.

Double edged tech: navigating the public health and legal challenges of digital twin technology

Abstract This research addresses the legal challenges posed by the implementation of digital twin technology in public health. Digital twins are advanced computational models which aim to replicate human organ functions and biological processes. Their goal is to enable the simulation of the entire human body and to personalize the outputs with individual patient’s health data. Digital twin technology promises to bridge gap between personalized medicine and precision public health. This research is based on a comprehensive review of the current European and Swiss legal frameworks for medical devices and data protection that apply to digital twin technology. Within these frameworks, digital twins are in a grey area, as they are only considered as medical device software when applied to individuals. They also incorporate artificial intelligence, machine learning, and data analytics, which are already a source of legal concern in a healthcare setting, but their effective use in public health raises additional issues. Preliminary findings suggest significant shortcomings in the legal frameworks regarding data privacy, and the potential for increased surveillance by health insurance systems, which could lead to human rights violations. The public health application of digital twins would require global data analysis of sensitive personal information, raising further questions about the secure storage, transmission, and processing of this data. The study concludes that current legal frameworks must be future proofed to manage the complexities introduced by digital twins in public health. In particular, it suggests that regulatory oversight of medical devices needs to be significantly improved in order to safely harness the potential of digital twin technologies for public health. Finally, the new European Union health data space and the forthcoming EU AI act will also play important roles in the implementation of digital twin technologies. Key messages • Applying digital twin technology in public health faces considerable challenges, particularly the issues around data privacy, potential discrimination, and increased surveillance. • We need to adapt and strengthen existing legal frameworks to effectively manage the complexities introduced by digital twins, ensuring that these technologies can be safely used for public health.

An effective non‐restarting control chart based on the joint judgment

AbstractThe cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts have been widely used in industry and public health applications. Still, few researchers have paid attention to the practical problem in public health applications, in which the control chart statistic cannot be reset to zero after an out‐of‐control (OC) signal. Some studies have addressed this issue by adding an upper limit to the statistic or using adaptive hypothesis testing (AHT). However, the control chart with an upper limit added to the testing statistic leads to a loss of information, and the post‐outbreak false alarm rate is still higher than the restarting control chart. The AHT control charts could not quickly alarm for the pandemic outbreak alarm as expected. To improve these drawbacks of control chart monitoring, two phase‐II non‐restarting (NR) CUSUM–EWMA control charts with the AHT framework, named NR–CUSUM–EWMA and AHUTNR–CUSUM–EWMA control charts, are proposed to well alarm for the pandemic outbreak. The AHTUNR–CUSUM–EWMA control chart uses the joint judgment of the UNR–CUSUM–EWMA (uncorrected NR–CUMSUM–EWMA) and NR–CUSUM–EWMA control charts, and the run rule to enhance the efficiency of the pandemic outbreak alarm. Simulation results show that the proposed NR–CUSUM–EWMA and AHUTNR–CUSUM–EWMA control charts can well maintain the outbreak alarm rate and post‐outbreak false alarm rate without losing cumulative information. Finally, the applicability and effectiveness of the proposed method are illustrated by an application to measles surveillance in North Rhine‐Westphalia, Germany.

Co-developing frameworks towards environmentally directed pharmaceutical prescribing in Scotland – A mixed methods study

The presence of human pharmaceuticals in the aquatic environment is recognised internationally as an important public health and environmental issue. In Scotland, healthcare sustainability targets call for improvements to medicine prescribing and use to reduce healthcare's impact on the environment. This proof-of-concept study aimed to develop a framework on the environmental impact of pharmaceuticals to use as a knowledge support tool for healthcare professionals, focussing on pharmaceutical pollution. Nominal Group Technique was applied to achieve consensus on pharmaceuticals and modelling factors for the framework, working with a panel of cross-sector stakeholders. Bayesian Belief Network modelling was applied to predict the environmental impact (calculated from hazard and exposure factors) of selected pharmaceuticals, with Scotland-wide mapping for visualisation in freshwater catchments. The model calculated the pollution risk score of the individual pharmaceuticals, using the ratio of prescribed mass vs. mass that would not exceed the predicted no-effect concentration in the freshwater environment. The pharmaceuticals exhibited different risk patterns, and spatial variation of risk was evident (generally related to population density), with the most catchments predicted to exceed the pollution risk score for clarithromycin (probability >80 % in 35 of 40 modelled catchments). Simulated risk scores were compared against observed risk calculated as the ratio of measured environmental concentrations from national regulatory and research monitoring and predicted no-effect concentrations. The model generally overpredicted risk, likely due to missing factors (e.g. solid-phase sorption, temporal variation), low spatial resolution, and low temporal resolution of the monitoring data. This work demonstrates a novel, trans-disciplinary approach to develop tools aiding collation and integration of environmental information into healthcare decision-making, through application of public health, environmental science, and health services research methods. Future work will refine the framework with additional clinical and environmental factors to improve model performance, and develop electronic interfaces to communicate environmental information to healthcare professionals.

Impacts of PM2.5 exposure near cement facilities on human health and years of life lost: A case study in Brazil

Cement factories significantly contribute to atmospheric pollution by generating fine particulate matter (PM2.5), which can potentially increase the mortality risk. The lack of information on the health impacts of PM2.5 pollution from cement operations in Brazil prompted this investigation. We used corrected PM2.5 measurements from low-cost sensors from March 2021 to October 2022 in Rio Branco do Sul, city in the southern region of the country and home to Latin America's largest cement plant, to assess exposure data. Disability-adjusted life years (DALY) method was applied to estimate the years of life lost (YLL) and cost estimate due to deaths from non-accidental causes, cardiovascular and respiratory diseases. The total YLL attributable to PM2.5 concentration was estimated by calculating the attributable fraction (AF) through relative risk. We also collected PM2.5 using a Harvard impactor to evaluate health risks from toxic metals components. During the study period, the analysis of chemical characterization of PM2.5 showed enrichment factors for most elements and the possible influence of the calcination process facilities on the PM2.5 levels. The mean concentration of PM2.5 exceeded the annual WHO air quality guideline (AQG) level, accounted for 3.5%, 4.7%, and 4.3% of total YLL from all causes, cardiovascular, and respiratory diseases, which corresponded to 0.23 (95% CI: 0.17–0.26), 0.06 (95% CI: 0.05–0.07) and 0.03 (95% CI: 0.01–0.06) years loss in life expectancy, respectively. An indirect health cost attributable to PM2.5 resulted in US$ 1.4 million, equivalent to about 3.5% of the total local annual health costs in Rio Branco do Sul, underscoring the significant financial burden of PM2.5 exposures. The greatest economic loss was found in the male age group of 40–69 years and among those with cardiovascular disease, rather than those with respiratory disease. Despite this, the carcinogenic and non-carcinogenic risks from inhalation of hazardous elements were within safe ranges. This work demonstrated PurpleAir's potential for air quality and public health applications. Our findings indicate health and economic benefits from reducing PM2.5 levels by adopting WHO air pollution standards. The results can guide policies toward delivering more effective health care.

Rapid Detection of Single Bacteria Using Filter-Array-Based Hyperspectral Imaging Technology.

Rapid and accurate detection of bacterial pathogens is crucial for preventing widespread public health crises, particularly in the food industry. Traditional methods are often slow and require extensive labeling, which hampers timely responses to potential threats. In response, we introduce a groundbreaking approach using filter-array-based hyperspectral imaging technology, enhanced by a super-resolution demosaicking technique. This innovative technology streamlines the detection process and significantly enhances the resolution of mosaic hyperspectral imaging. By utilizing a snapshot hyperspectral camera with a 15 ms integration time, it facilitates the identification of bacteria at the single-cell level without requiring chemical labels. The integration of a 3D convolutional neural network optimizes the recognition of pathogenic bacteria, achieving an impressive accuracy of 91.7%. Our approach dramatically improves the efficiency and effectiveness of bacterial detection, providing a promising solution for critical applications in public health and the food industry.

Proposed Framework for Adopting Privacy-Preserving Record Linkage for Public Health Action.

To propose a framework for adoption of privacy-preserving record linkage (PPRL) for public health applications. Twelve interviews with subject matter experts (SMEs) were conducted virtually and coded using an inductive approach. A collaborative session was conducted with SMEs to identify key steps in the PPRL project lifecycle which informed development of a PPRL implementation checklist. This framework has 2 decision-making levels: the organization level and the project or program level. Organization-level considerations include PPRL governance, the optimal choice among approved PPRL solutions, the need for longitudinal linkages, the potential issue of vendor lock-in, and costs. Program-level considerations include characteristics of the PPRL use case, linkage quality and accuracy, data privacy and use, security thresholds, compatibility with data owners' data architecture, and trade-offs between open-source and commercial PPRL solutions. A PPRL implementation checklist was developed to guide public health practitioners considering PPRL for data linkage. The framework may be considered by public health entities to guide adoption and implementation of PPRL in public health research and surveillance. Public health experts may refer to this framework and the PPRL implementation checklist when determining the appropriateness of PPRL for specific use cases and implementation planning.

Towards a comprehensive COVID-19 non-pharmaceutical interventions’ index for the province of Québec

ObjectivesThe primary objective of this project was to develop a comprehensive COVID-19 non-pharmaceutical interventions’ index for the province of Québec (QCnPI-Index). The resulting database systematically categorizes, multiple non-pharmaceutical interventions implemented in the 17 administrative regions (AR) of the province of Québec to mitigate the spread of COVID-19 in the form of an index.Data descriptionData represent interventions and groups of interventions implemented during the COVID-19 period in Québec. They are a compilation of policies, guidelines, and governmental interventions related to COVID-19, considering temporal and geographical dimensions. Data were collected for all 17 AR of Québec using dates as unit of analysis, from March 2020 to April 2022. They were first collected and then coded by an interdisciplinary research team to form the foundation of the QCnPI-Index.ContributionThis quantitative instrument offers the necessary granularity for nuanced spatial and temporal studies within the province of Québec, using AR, for instance, as unit of analysis. With this database, pre-, during-, and post-COVID periods can thus be better analyzed. Additionally, the innovative methodologies employed for data collection, coding, and weighting offer valuable insights that may have broader applications in public health, epidemiology, and other research domains. The QCnPI-Index could be instrumental for public health, epidemiology, and transportation researchers investigating the multifaceted impacts of non-pharmaceutical interventions on various societal domains, such as road safety, alcohol and cannabis consumption, and/or mental health, in the province of Québec.

ILKD: An Incremental Learning and Knowledge Distillation Framework for Coronavirus Risk Prediction

Abstract The coronavirus pandemic has seriously affected public health and social order. Prediction methods based on machine learning can identify the infectivity phenotype and pandemic risk of coronavirus. Currently, six types of coronaviruses that infect humans have been discovered, with significant differences in viral genome sequences. Continuous genetic variation of the virus will lead to reduced performance of machine learning models and potential learning forgetting. To solve this challenge, we propose an incremental learning and knowledge distillation framework (ILKD). First, we employ Dna2Vec to extract virus features and encode the virus sequence into virus feature vector. Second, we use hierarchical clustering to continuously identify new coronavirus groups. Third, ILKD employ a combined strategy of incremental learning and knowledge distillation to transform the Back Propagation (BP) neural network to continuously learn and predict the phenotypes of human-to-human coronavirus infection. Experimental results show that ILKD can effectively alleviate the learning forgetting phenomenon. Further analysis reveals ILKD has better performance than other incremental learning models, and has important public health application value.

Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model

BackgroundWrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts.MethodsWe developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL.ResultsIn OPTIMISE cohort, STEPHEN’s estimates of the proportion of time spent sedentary had significantly (p < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06–0.25] vs. 0.23 [0.13–0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06–0.26] vs. 0.42[0.32–0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03–0.25] vs. 0.36[0.15–0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes).ConclusionsSTEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/. The package includes trained models, but users have the flexibility to train their own models.

Epidemiological Profile of Major Persons with Alzheimer's Disease Care in Home Care and Physiotherapeutic Interventions

Objective: To characterize the epidemiological profile of elderly people with Alzheimer's Disease (AD) treated in home care and the physiotherapeutic interventions carried out. Theoretical Framework: AD is a neurodegenerative pathology inserted within a multifactorial syndrome, being progressive, chronic and aggravating in advanced stages and requiring constant and specific care, being a problem in the health service. Method: This is a documentary, descriptive and quantitative study, with medical records of 75 elderly people with AD, from 2019 to 2022, from a home care service, the variables studied were: sociodemographic data, general health status and stage of the disease and physiotherapeutic procedures. Results and Discussion: The average age was 86 years old, female (68.0%), widowed (40.0%), retired (93.3%) and 66.6% lived with their children. In a severe stage of the disease (42.6%), with high blood pressure (53.3%) and 46.7% in home care for less than 1 year. The general health status is regular (56.0%), but disoriented (70.6%). Speech changes (69.4%) and 73.3% need assistance with mobility. They needed motor/respiratory physiotherapeutic procedures (77.3%), and 12.0% needed invasive mechanical ventilation and 6.7% with an oxygen catheter. Research Implications: The implications of this research are discussed according to the results presented with practical application in public health, associated with neurodegenerative diseases, especially AD, knowing the profile of the elderly and the need for a multidisciplinary team, especially the physiotherapist, to improve the quality of life of this population public. Originality/Value: This study contributes to the literature regarding knowledge of the characteristics of elderly people with AD who receive home care and the physiotherapeutic interventions that are carried out.

Transforming Healthcare in Africa: The Role of Artificial Intelligence in Combatting Infectious Diseases, Neglected Tropical Diseases, and Antimicrobial Resistance

This review article focuses on the role of Artificial Intelligence (AI) in transforming healthcare in Africa, specifically in combatting infectious diseases, Neglected Tropical Diseases (NTDs), and antimicrobial resistance. We provide a comprehensive overview of the significance of AI in the healthcare industry, highlighting its urgency and importance in addressing these specific health challenges in Africa. We begin by discussing the role of AI in infectious disease surveillance and outbreak detection. We explore how AI technology can be employed for real-time tracking and prediction of outbreaks, providing examples of successful AI applications in infectious disease surveillance within the African context. Next, we examine the potential of AI-enabled diagnosis and treatment for faster and more accurate diagnoses of infectious diseases and NTDs. We highlight specific examples of AI applications in diagnosing and treating these diseases in Africa, showcasing the potential of AI to improve clinical outcomes and save lives. Furthermore, we focus on how AI-driven drug discovery and development can expedite the search for new treatments for infectious diseases and combat antimicrobial resistance. We present examples of AI applications in drug discovery within the African context, illustrating the potential for AI to revolutionize the development of effective therapeutics. In addition, we delve into how AI-powered public health interventions can enhance the design and implementation of targeted interventions. We explore how AI can optimize resource allocation and facilitate data-driven decision-making processes, providing examples of AI applications in public health in Africa. Finally, we address the challenges and limitations of implementing AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. We discuss potential barriers and ethical concerns surrounding AI applications in healthcare, aiming to encourage informed and responsible utilization of AI technologies. Overall, this review emphasizes the importance and potential of AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. It positions AI as a catalyst for revolutionizing healthcare in the region, leading to more effective disease surveillance, diagnosis, treatment, drug discovery, and public health interventions.

Identifying Impacts of Contact Tracing on Epidemiological Inference from Phylogenetic Data.

Robust sampling methods are foundational to inferences using phylogenies. Yet the impact of using contact tracing, a type of non-uniform sampling used in public health applications such as infectious disease outbreak investigations, has not been investigated in the molecular epidemiology field. To understand how contact tracing influences a recovered phylogeny, we developed a new simulation tool called SEEPS (Sequence Evolution and Epidemiological Process Simulator) that allows for the simulation of contact tracing and the resulting transmission tree, pathogen phylogeny, and corresponding virus genetic sequences. Importantly, SEEPS takes within-host evolution into account when generating pathogen phylogenies and sequences from transmission histories. Using SEEPS, we demonstrate that contact tracing can significantly impact the structure of the resulting tree, as described by popular tree statistics. Contact tracing generates phylogenies that are less balanced than the underlying transmission process, less representative of the larger epidemiological process, and affects the internal/external branch length ratios that characterize specific epidemiological scenarios. We also examined real data from a 2007-2008 Swedish HIV-1 outbreak and the broader 1998-2010 European HIV-1 epidemic to highlight the differences in contact tracing and expected phylogenies. Aided by SEEPS, we show that the data collection of the Swedish outbreak was strongly influenced by contact tracing even after downsampling, while the broader European Union epidemic showed little evidence of universal contact tracing, agreeing with the known epidemiological information about sampling and spread. Overall, our results highlight the importance of including possible non-uniform sampling schemes when examining phylogenetic trees. For that, SEEPS serves as a useful tool to evaluate such impacts, thereby facilitating better phylogenetic inferences of the characteristics of a disease outbreak. SEEPS is available at github.com/MolEvolEpid/SEEPS.

Large-Scale Genomics Applications in Public Health and Precision Medicine Using Big Data and Predictive Analytics

After gaining experience from large population sequencing projects, there is a significant gap between the promise of genomics in Precision Medicine (PM) and the existing state of affairs. To fully exploit this potential, Public Healthcare (PH) must adopt procedures, regulations, and technology that are unfamiliar to them. The development needs to be improved by several historical procedures and regulatory hurdles. In the future, PM will include making genetic big data easily accessible at the point of treatment and implementing tools to govern its use effectively. Significant modifications need to be made in billing, payment, and monitoring to accomplish this goal. New systemic and technological structures must also be developed inside the healthcare sector. The responsibilities of clinical geneticists will expand to include overseeing PM systems. Genetic counselors will guide and assist PM by establishing and upholding PM structures. The trajectory of the research is now hindered by several impediments, resulting in avoidable fatalities. Revamping medical facilities to accommodate genomics has the potential to influence the treatment of patients significantly and enable the achievement of long-awaited advancements in PM.

Analysis of Pharmaceutical Sciences and Public Health Applications

Growing in frequency in both rural and urban parts of India, growing percentage of people at risk of diabetes, Indian population's genetic susceptibility to diabetes risk factors related to the environment, Globalisation causing a decline in physical exercise Some of the challenges that the disease poses in India are low patient awareness of hypertension, undiagnosed hypertension due to a variety of factors such as low healthcare facilities and economic status, inadequate disease control, and an increasing incidence of complications related to hypertension. Although there are solutions, the healthcare team will need to work in tandem with the clinician who is battling the illness on an individual basis. This will prevent the patient from receiving the proper care, the clinician from being able to listen to the patient's problems and find solutions, the patient from receiving the psychological support they need for a chronic illness, and the nation from suffering from economic hardship after the patient's illness. The purpose of this study was to identify the services provided by pharmaceutical care and evaluate the barriers to pharmaceutical care in various settings in order to provide relevant recommendations.

Multiomics: Functional Molecular Biomarkers of Micronutrients for Public Health Application.

Adequate micronutrient intake and status are global public health goals. Vitamin and mineral deficiencies are widespread and known to impair health and survival across the life stages. However, knowledge of molecular effects, metabolic pathways, biological responses to variation in micronutrient nutriture, and abilities to assess populations for micronutrient deficiencies and their pathology remain lacking. Rapidly evolving methodological capabilities in genomics, epigenomics, proteomics, and metabolomics offer unparalleled opportunities for the nutrition research community to link micronutrient exposure to cellular health; discover new, arguably essential micronutrients of microbial origin; and integrate methods of molecular biology, epidemiology, and intervention trials to develop novel approaches to assess and prevent micronutrient deficiencies in populations. In this review article, we offer new terminology to specify nutritional application of multiomic approaches and encourage collaboration across the basic to public health sciences to advance micronutrient deficiency prevention.