Control Efforts Research Articles

Host Innate and Adaptive Immunity Against African Swine Fever Virus Infection

Africa swine fever virus (ASFV) is the causative agent of African swine fever (ASF), a highly contagious hemorrhagic disease that can result in up to 100% lethality in both wild and domestic swine, regardless of breed or age. The ongoing ASF pandemic poses significant threats to the pork industry and food security, with serious implications for the sanitary and socioeconomic system. Due to the limited understanding of ASFV pathogenesis and immune protection mechanisms, there are currently no safe and effective vaccines or specific treatments available, complicating efforts for prevention and control. This review summarizes the current understanding of the intricate interplay between ASFV and the host immune system, encompassing both innate and adaptive immune responses to ASFV infection, as well as insights into ASFV pathogenesis and immunosuppression. We aim to provide comprehensive information to support fundamental research on ASFV, highlighting existing gaps and suggesting future research directions. This work may serve as a theoretical foundation for the rational design of protective vaccines against this devastating viral disease.

Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data.

Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens based on features of known antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of only the unlabeled proteins from the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.

African Schistosomiasis: A Framework of Indicators Assessing the Transmission Risk and Intervention Effectiveness

Schistosomiasis, a parasitic disease with a complex transmission mechanism, requiring a snail intermediate host, is influenced by biology, the environment, human behavior and the prevailing socioeconomic situation. This study aimed to systematically investigate the importance and feasibility of indicators related to the factors influencing transmission and intervention measures for Schistosoma mansoni and S. haematobium. Based on a literature review and group discussions according to the Delphi method, a framework questionnaire was designed. A total of 33 experts on schistosomiasis were invited, and 27 were accepted, to rate the importance and feasibility of indicators for transmission with and the control of schistosomiasis, with a focus on intervention measures for S. mansoni and S. haematobium infections in Zimbabwe. After two rounds of Delphi consultations with these experts, calculated to have a high average authority coefficient (0.88), a consensus was reached on a framework that included 2 primary, 6 secondary and 39 tertiary indicators. The Delphi–entropy method was applied to assess the weight of each indicator. The key influencing factors included hazardous water exposure, accessibility to safe drinking water, sanitary facilities and the contamination of water bodies by outdoor defecation/urinary habits. The intervention measures involved improved diagnostics, health education, preventive chemotherapy, the presence of national control plans and the implementation of the strategy on water, sanitation and hygiene (WASH). While these factors are already well known, their detailed order of importance could help to improve the allocation of specific control efforts.

Factors Affecting Life Satisfaction Among People With Physical Disabilities During COVID-19: Observational Evidence from a Korean Cohort Study.

To determine the factors influencing the life satisfaction of people with physical disabilities during the COVID-19 pandemic, considering demographics, disability-related characteristics, health behaviors, and psychosocial characteristics. We used cross-sectional data from 301 respondents of the 2021/2022 survey of the Korean Health Cohort Study for People with Physical Disabilities. Descriptive statistics were used to analyze the research subjects' characteristics, and chi-square tests and multiple logistic regression were used to identify the determinants of life satisfaction. Among socio demographic variables, occupation had a strong association with life satisfaction. Significant health behavior variables included daily regular meals, weight control effort, and chronic pain. All psychosocial characteristics (perceived stress, depression, suicidal ideation, cognitive function assessment, subjective health status, family satisfaction, income satisfaction) were strongly associated with life satisfaction. Results of the analysis of factors affecting life satisfaction showed that unemployment, lack of regular exercise, elevated stress, suicidal thoughts, and dissatisfaction with family contributed to increased life dissatisfaction. Economic support through job creation that considers the specific characteristics of people with disabilities, and social access through community integration services that encourage participation in social activities, should be prioritized.

Intelligent-based approach for parameters identification and control of fractional order systems

The optimization algorithms for identification and control processes take more and more place in the industrial domain. However, determining a control law for fractional systems remains challenging for researchers today. This paper presents a simple method for using a fractional PID (FOPID) corrector to regulate a class of fractional systems that show a stable step response. The procedure is based on an algorithmic identification to assimilate the fractional system into a simple model. Optimization methods were used. Using techniques like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO) to ascertain the parameters of the basic model, a first-order system with delay. Then, the FOPID controller will be calculated using Ziegler–Nichols methods and optimization algorithms to stabilize the real process. Better performance Overshoot and Settling time as well as minimal Energy control effort are benefits of the proposed design. To confirm that the fractional regulator is robust which is optimized by the best-chosen method (PSO), the parameters of the chosen process will be randomly varied. Through two numerical simulations, the efficiency of the suggested method has been confirmed.

Abstract 4138273: Acceptability and Gain of Knowledge of Community Educational Tools About Rheumatic Heart Disease Integrated With Screening In Low-Income Settings

Background: Rheumatic heart disease (RHD) causes 305,000 premature annual deaths, and education is one of the strategies to diminish disease burden. International RHD foundations aim do provide preventive and control efforts for RHD. We aimed to assess the acceptability and gain of knowledge of a series of education flipcharts presented during screening programs in high-burden areas of Brazil. Methods: Four flipcharts (“Introduction to rheumatic fever (RF) and RHD”, “RHD and pregnancy”, “RHD and surgery” and “RHD community awareness”) were developed over 3 years and taught during 36 months to patients, community, health and education professionals in Minas Gerais state. Training included in-person interactions and virtual workshops. Pre and post-training questionnaires were applied through an online and printed surveys in 2021 and 2022, and post-education evaluations were conducted from January 2023 to April, 2024. Results: Flipchart training was successfully delivered to 112 education professionals, 574 health providers and 598 community members (N=1284): 899 (70%) were enrolled in primary care, and 1109 (86%) responded the surveys. Among respondents of the survey for health and education professionals (N=589), 240 (41%) had been educated about RHD in the previous year. 569 (96%) learned any new information; the content was all new for 21 (4%). Nearly all professionals reported that flipcharts could improve patients’ lives (571, 97%) and felt confident to use the tool with someone with no knowledge about RHD (533, 91%); 86% of the teachers said they would use flipcharts as educational tools. In the survey for community / schoolchildren (N=520) only 128 (25%) respondents had previous education on RHD, 510 (98%) reported that learned new information, and content was completely new for 242 (47%). A total of 430 (83%) individuals reported that they will discuss RHD with families and community. All qualitative written reports were positive. In 2021/2022, 218/485 (45%) health and education professionals responded the pre/post questionnaire. Knowledge about RHD increased after training: RF as the cause of RHD (56% vs 86%), use of Benzathine Penicillin G (50% vs 97%), frequency of antibiotic prophylaxis (32% vs 90%) and overall moderate or expert understanding of RF or RHD (30% vs 82%). Conclusion: Flipchart educational sessions about RHD had a very positive acceptability in high-risk Brazilian populations, with remarkable gain of knowledge for health professionals.

Rural-Urban Disparities and Trends in Cancer Screening: An Analysis of Behavioral Risk Factor Surveillance System Data (2018-2022).

Despite evidence of the benefit of routine cancer screenings, data show a concerning decline in cancer screening uptake for multiple cancer screenings. This analysis aimed to examine rural-urban differences in recent trends for being up to date with screenings for breast, cervical, and colorectal cancers. We used 2018, 2020, and 2022 Behavioral Risk Factor Surveillance System data to assess up-to-date cancer screening status among eligible U.S. adults. We calculated weighted prevalence estimates overall and stratified by county-level rural-urban classification. We used survey-weighted multivariable logistic regression models to examine rural-urban disparities in cancer screening up-to-date status by year. Prevalence of being up to date with each cancer screening was lower in 2022 than it was in 2018. The largest decline in screening overall was for cervical cancer that dropped from 81.89% in 2018 to 47.71% in 2022. Rural-urban disparities were observed for breast cancer screening from 2018 to 2022 with the odds of up-to-date screening being 14% to 27% lower for rural populations than urban populations. For colorectal and cervical cancer, the odds of being up to date with screenings were lower for rural populations in 2018 and 2020, but there was no significant difference observed in 2022 (colorectal screening OR = 0.96; 95% CI: 0.90, 1.02) (cervical screening OR = 0.97; 95% CI: 0.93, 1.03). There is a concerning trend of decreasing uptake of cancer screenings, which will challenge future efforts in cancer prevention and control efforts. Efforts are needed to better understand factors contributing to the declining uptake of cancer screenings.

Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques

This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier’s capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications.

Tuberculosis in Times of War and Peace

This chapter provides a comprehensive analysis of tuberculosis (TB) across different historical contexts of war and peace, underscoring the disease’s resilience and adaptability. It highlights the exacerbation of TB transmission due to war-related factors such as mass displacement, malnutrition, and compromised healthcare systems, while also noting periods of peace as opportunities for advancing TB control measures. The chapter examines the impact of major conflicts on TB incidence and management, including World Wars I and II and more recent conflicts like the Ukraine war. It also discusses the evolution of TB control efforts, from the discovery of Mycobacterium tuberculosis to modern strategies against drug-resistant TB, emphasizing the importance of international collaboration and integrated public health strategies to combat this enduring global health challenge.

An overview of outdoor low-cost gas-phase air quality sensor deployments: current efforts, trends, and limitations

Abstract. We reviewed 60 sensor networks and 17 related efforts (sensor review papers and data accessibility projects) to better understand the landscape of stationary low-cost gas-phase sensor networks deployed in outdoor environments worldwide. This study is not exhaustive of every gas-phase sensor network on the globe but rather exists to categorize types of sensor networks by their key characteristics and explore general trends. This also exposes gaps in monitoring efforts to date, especially regarding the availability of gas-phase measurements compared to particulate matter (PM) and geographic coverage gaps (the Global South, rural areas). We categorize ground-based networks that measure gas-phase air pollutants into two main subsets based on their deployment type – quasi-permanent (long term) and campaign (short to medium term) – and explore commonplace practices, strengths, and weaknesses of stationary monitoring networks. We conclude with a summary of cross-network unification and quality control efforts. This work aims to help scientists looking to build a sensor network explore best practices and common pathways and aid end users in finding low-cost sensor datasets that meet their needs.

Rising orthotospovirus incidence in India: Challenges and advances in management

Orthotospoviruses (OV) have emerged as a significant agricultural threat in India, posing a severe risk to critical crops, including tomato, chilli, watermelon and groundnut. This review explores the rising incidence of OV, including Groundnut Bud Necrosis Orthotospovirus (GBNV), Watermelon Bud Necrosis Orthotospovirus (WBNV), Capsicum Chlorosis Orthotospovirus (CaCV), Peanut Yellow Spot Orthotospovirus (PYSV), Iris yellow spot Orthotospovirus (IYSV) and Tomato Spotted Wilt Orthotospovirus (TSWV), along with the challenges in their management. These viruses have led to severe yield losses, sometimes causing complete crop failure due to their wide host range and the polyphagous nature of thrips. This further complicates control efforts by facilitating rapid spread across diverse crops and regions. The review highlights the multifaceted challenges in managing OV and thrips, including the lack of durable host resistance, limited diagnostic capabilities, and difficulties in controlling thrips populations. Current management strategies, including cultural practices, chemical control, biological control and resistant genotype development, have shown limited efficacy in providing long-term solutions. Recent advancements in biotechnological approaches, such as RNA interference, are discussed as promising pathways for improved virus management. The review underscores the need for genome editing techniques, such as CRISPR/Cas9, which offer the capacity to develop virus-resistant plants by targeting essential viral or vector genes to disrupt transmission cycles. Additionally, using nanoparticles for targeted delivery of antiviral compounds and novel detection tools presents another innovative solution to effectively mitigate the impact of OV on Indian agriculture.

RETRACTION: H. Schmidt, M. Daseking, C. Gawrilow, J. Karbach, and J. Kerner auch Koerner, "Self-Regulation in Preschool: Are Executive Function and Effortful Control Overlapping Constructs?," Developmental Science 25, no. 6 (2022): e13272, https://doi.org/10.1111/desc.13272.

RETRACTION: H. Schmidt, M. Daseking, C. Gawrilow, J. Karbach, and J. Kerner auch Koerner, "Self-Regulation in Preschool: Are Executive Function and Effortful Control Overlapping Constructs?," Developmental Science 25, no. 6 (2022): e13272, https://doi.org/10.1111/desc.13272.

Kelimpahan Serangga akibat Beberapa Teknik Pengendalian pada Tanaman Kacang Panjang (Vigna sinensis L.)

The decline in the production of long beans in Indonesia is one of the causes of a pest attack. The majority of farmers in Indonesia, still use synthetic pesticides as a pest control effort. The aim of this research is to obtain a pest control technique that gives the lowest result against insect abundance in long bean crops. (Vigna sinensis L). The study used a single factor random group design (RAK) method with 6 repetitions and 4 treatments including: No Treatment/Control (K), Biointensive Control (PB), Combined Control (PK) and Synthetic Control. (PS). The sampling of insects is done using 3 traps: yellow traps, pitfall traps and insect net traps. Sampling is done 10 times, starting from 1 – 10 mst. The data analyzed using a scale fingerprint, on a different real result will be performed a further trial of the Minimum Real Difference with a degree of 5%. PS control shows the results of the analysis with the lowest average abundance on insect net trap (SNT) of (130.17) and pitfall trap (PFT) of (147.50) different real from other treatments, but no real difference on yellow board trap (YST) of size (104,50).

A Comparison Of Mosquito Species Assemblages Sampled With Three Commonly Used Traps And A Novel Design.

In nuisance or vector mosquito management, effective sampling is a stepping stone to efficient use of resources, targeted control efforts, and successful reduction of disease transmission. Experimental evidence indicates that there are species biases for certain traps, which in turn implies that the traps used will influence the species make-up of samples collected. A comparative study between 3 CO2-baited light traps and 2 hay-infusion baited gravid traps-Centers for Disease Control and Prevention (CDC) light trap, American BioPhysics Company (ABC) light trap, Reiter-Cummings (RC) gravid trap, and a convertible gravid/light trap with a novel design-was conducted to test for species specificity of each trap type. It was found that different species of mosquitoes are more likely to be collected in greater numbers with certain traps compared to others, even between the different light traps or gravid traps. In particular, Coquillettidia perturbans tended to be collected in greater numbers with CDC light traps, Culex salinarius tended to be collected in greater numbers with the experimental convertible light traps, and Culex pipiens tended to be collected in greater numbers with the Reiters-Cummings gravid traps over the other traps included in the study (P ≤ 0.05). The overall species richness of samples was comparable among trap types, with similar performance of the new trap designs as established designs (P ≤ 0.05).

Stability of steady-state solutions of Jeffcott rotor with varying rotational speed

AbstractThe behavior of flexible rotating systems with varying rotational speeds is essential in engineering applications. Analysis methods that consider linear dynamic models and many existing nonlinear analysis approaches assume constant rotational speed. These approaches are unsuited to study the dynamic interaction between driving torque and whirling motion in this class of applications. In this paper, an analysis of the stability and control of a Jeffcott rotor under varying operational conditions is presented. A nonlinear dynamic model of the system is formulated to enable a detailed stability and parametric analysis. A proportional-integral (PI) torque command is employed to achieve a steady-state rotational speed. Assuming constant lateral control effort, system equilibrium points and their stability characteristics as functions of the system’s parameters are analyzed. A control law that minimizes the lateral effort is derived. A feedback proportional lateral control strategy is introduced to enhance the system’s region of stability, particularly in the supercritical speed range. Finally, a simulation study is conducted to validate the analytical findings. Simulation results demonstrate the effectiveness of the proposed approach for defining stable operating conditions and improving system performance.

Combating infections under siege: Healthcare challenges amidst the military assault in Gaza

AbstractThe ongoing Israeli military assault on Gaza has caused a devastating collapse of healthcare infrastructure, resulting in an unprecedented surge in infectious diseases. The destruction of sanitation systems, restrictions on medical supplies, and displacement of civilians have created an environment conducive to the rapid spread of waterborne, vector‐borne, and antibiotic‐resistant infections. This paper draws from the insights of local Palestinian healthcare workers and recent medical missions conducted by FAJR Scientific, the Palestine American Medical Association, and Doctors Without Borders, examining infection control efforts under extreme constraints. The missions encountered major challenges, such as the absence of sterile equipment, inadequate access to clean water, and disrupted healthcare services. These conditions have exacerbated the spread of diseases such as hepatitis, meningitis, and polio, with severe impacts on Gaza's pediatric population. Efforts to implement infection control measures, such as telemedicine consultations, wound care protocols, and antibiotic stewardship, were heavily impeded by the ongoing blockade. The paper underscores the critical importance of lifting the siege to allow humanitarian access and to address the root causes of the infectious disease crisis in Gaza. It also highlights the resilience of Palestinian healthcare workers who continue to provide care under these dire circumstances. The recommendations outlined aim to support future medical teams working in conflict zones by providing practical strategies for infection prevention and control amidst such sustained violence. These efforts are contingent on international intervention and an end to the systematic blockade that has weaponized healthcare access.

Association between toxin-antitoxin system mutations and global transmission of MDR-TB.

The emergence of Multidrug-Resistant Tuberculosis (MDR-TB) poses a significant threat to global tuberculosis control efforts. This study aimed to examine the influence of mutations in Toxin-Antitoxin system genes on the global transmission of MDR-TB caused by Mycobacterium tuberculosis (M. tuberculosis). Whole-genome sequencing was conducted on 13,518 M. tuberculosis isolates. Genes of the Toxin-Antitoxin system were obtained from the National Center for Biotechnology Information (NCBI) Gene database. Techniques such as Random Forest, Gradient Boosting Decision Tree, and Generalized Linear Mixed Models were employed to identify mutation sites in Toxin-Antitoxin system-related genes that facilitated the transmission of MDR-TB. 4,066 (30.08%) were identified as MDR-TB strains of all analyzed isolates. We found significant associations between specific gene mutations and MDR-TB transmission clusters including mutations in Rv0298 (G213A), Rv1959c (parE1, C88T), Rv1960c (parD1, C134T), Rv1991A (maze, G156A), Rv2547 (vapB, C54G), Rv2862A (vapB23, T2C), and Rv3385c (vapB46, G70A). Additionally, several gene mutations associated with MDR-TB transmission clades such as Rv1956 (higA, G445T), Rv1960c (parD1, C134T), and Rv1962A (vapB35, G99A) were noted. Certain gene mutations including vapB35 (G99A), higA (G445T), and parD1 (C134T) correlated with cross-regional transmission clades. This study highlights the significant association between specific gene mutations in the Toxin-Antitoxin system and the global transmission of MDR-TB, providing valuable insights for developing targeted interventions to control MDR-TB.

A different approach to identifying thermal parameters for invasive species.

The brown marmorated stinkbug, Halyomorpha halys Stål (Hemiptera: Pentatomidae), is a polyphagous invasive insect found in the eastern United States in 1998 but became a major agricultural pest in 2010. Environmental temperatures regulate the location of invasive species establishment in new locations. To determine those areas where an invasive species might establish it is essential to understand the metabolic response of all life stages to temperature. Differential scanning calorimetry is a useful tool to monitor living organisms' metabolism at different temperatures, providing vital information related to the ability of the species to survive in new environments. The information obtained from isothermal and scanning calorimetric experiments on all the life stages of H. halys indicates that the third instar is the most thermoresponsive stage and eggs and fifth instar are the least thermoresponsive, whereas the third instars exhibit a broad range of thermoresponsiveness as compared to all other developmental stages. The recorded values for lower, optimal, and upper developmental temperatures in this study were similar to those reported by other researchers using laboratory and field data to develop degree-day models. This method can help in the rapid development of degree day models to improve and synchronize control efforts for newly invasive species.

The new geopolitics of EU cybersecurity: security, economy and sovereignty

Abstract The European Union (EU) is currently experiencing significant geopolitical shifts and is concerned that its dependence on externally produced or foreign-owned technologies risks its strategic autonomy, with implications for its security and economy. In response, it has placed sovereignty at the forefront of its agenda and at the centre of its relations with the world. Academic literature has so far paid limited attention to understanding the interactions between geopolitics, perceptions of technological dependence, and the impact on policy governance in cybersecurity. Bearing this gap in mind, the article asks how the EU's discourse of sovereignty and reducing external dependencies is shaping its approach to cybersecurity. Applying regulatory mercantilism and policy analysis to three case-studies—focusing on control of semiconductors, data server location, and cybersecurity certification— the article finds that the EU's depiction of its cybersecurity as impacted by dependence on externally manufactured products resulted in the merging of security and economic rationales. This has translated into efforts for greater control, either through promoting production within the European physical space through security-influenced industrial policies or—when this is not possible—by exerting regulatory influence beyond Europe's borders. Where globalization is believed to have failed, regulatory mercantilism becomes more likely as a policy approach.

Enhancing SARS-CoV-2 Lineage Surveillance through the Integration of a Simple and Direct qPCR-Based Protocol Adaptation with Established Machine Learning Algorithms.

Emerging and evolving Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) lineages, adapted to changing epidemiological conditions, present unprecedented challenges to global public health systems. Here, we introduce an adapted analytical approach that complements genomic sequencing, applying a cost-effective quantitative polymerase chain reaction (qPCR)-based assay. Viral RNA samples from SARS-CoV-2 positive cases detected by diagnostic laboratories or public health network units in Ceará, Brazil, were tracked for genomic surveillance and analyzed by using paired-end sequencing combined with integrative genomic analysis. Validation of a key structural variation was conducted with gel electrophoresis for the presence of a specific open reading frame 7a(ORF7a) gene deletion within the "BE.9" lineages tracked. The analytical innovation of our method is the optimization of a simple intercalating dye-based qPCR assay through repositioning primers from the ARTIC v4.1 amplicon panel to detect large molecular patterns. This assay distinguishes between "BE.9" and "non-BE.9" lineages, particularly BQ.1, without the need for expensive probes or sequencing. The protocol was validated against lineage predictions from next-generation sequencing (NGS) using 525 paired samples, achieving 93.3% sensitivity, 95.1% specificity, and 92.4% agreement, as measured by Cohen's Kappa coefficient. Machine learning (ML) models were trained using the melting curves from intercalating dye-based qPCR of 1724 samples, enabling highly accurate lineage assignment. Among them, the support vector machine (SVM) model had the best performance and after fine-tuning showed ∼96.52% (333/345) accuracy in comparison to the test data set. Our integrated approach provides an adapted analytical method that is both cost-effective and scalable, suitable for rapid assessment of emerging variants, especially in resource-limited settings. In this work, the protocol is applied to improve the monitoring of SARS-CoV-2 sublineages but can be extended to track any key molecular signature, including large insertions and deletions (indels) commonly observed in pathogenic agent subtypes. By offering a complement to traditional sequencing methods and utilizing easily trainable machine learning algorithms, our methodology contributes to enhanced molecular surveillance strategies and supports global efforts in pandemic control.