Prevent Disease Spread Research Articles

Machine learning for early detection of plant viruses: Analyzing post-infection electrical signal patterns

Plant viral diseases significantly threaten global agricultural productivity, necessitating rapid and non-invasive early detection methods to mitigate losses and prevent disease spread. This study presents a novel approach for early detection of viral infections in plants by analyzing electrical signal patterns, focusing on Alfalfa Mosaic Virus (AMV) infection in tobacco plants. A custom-designed, portable signal recording system was developed, incorporating multiple filtering stages at different steps to minimize noise in field conditions without the need for a Faraday cage, ensuring high-quality signal acquisition. Electrical signals were recorded from both healthy and AMV-infected plants before visible symptoms appeared. By extracting a comprehensive range of features in the low-frequency range of 0–4 Hz and carefully examining specific changes in the electrical signals post-infection, significant alterations were observed, characterized by weakened signal strength and increased irregularity. Through precise feature selection techniques, and using only three key features—Median, Autoregressive coefficients, and Autocorrelation—machine learning models, including Support Vector Machine, K-Nearest Neighbors, and Random Forest, achieved approximately 97% accuracy in detecting virus-infected plants even before the appearance of visual symptoms. This represents superior performance with minimal input data compared to existing methods. These results demonstrate the potential of electrical signal analysis combined with machine learning as a practical, rapid, non-invasive, and affordable tool for early virus detection in plants that is easy to use by non-specialists. Implementing this approach could significantly improve disease management strategies and contribute to sustainable agricultural practices by enabling timely interventions in real-world agricultural environments.

Analysis of stochastic epidemic model with awareness decay and heterogeneous individuals on multi-weighted networks.

In the current study, we present a stochastic SAIS (unaware susceptible-aware susceptible-infectious-unaware susceptible) epidemic dynamic model on complex networks with multi-weights. The disease dynamic is influenced by random perturbations to the force of the infection rates, as well as awareness rates. To analyze the problem of extinction, we discuss both the stochastic asymptotic stability in the large and almost surely exponential stability of the trivial solution. Then, we get some sufficient conditions, which guarantee the stochastic persistence of infectious disease. Based on the Erdös-Réyni random graph, the numerical simulations are given. These not only validate our conclusions but also obtain else significative results. Both theoretical results and numerical simulations further reflect that improvement of risk awareness and reduction of decay in awareness are highly effective in preventing disease spread. And then, environmental noises play a significant role in disease transmission.

Contact Tracing Different Age Groups During the COVID-19 Pandemic: Retrospective Study From South-West Germany.

Contact tracing was implemented in many countries during the COVID-19 pandemic to prevent disease spread, reduce mortality, and avoid overburdening health care systems. In several countries, including Germany, new systems were needed to trace potentially infected individuals. Using data collected in the Rhine-Neckar and Heidelberg (RNK/HD) districts in southwest Germany (population: 706,974), this study examines the overall effectiveness and efficiency of contact tracing in different age groups and stages of the pandemic. From January 27, 2020, to April 30, 2022, the RNK/HD Health Authority collected data on COVID-19 infections, quarantines, and deaths. Data on infection, quarantine, and death was grouped by age (young: 0-19 years; adult: 20-65 years; and senior citizens: >65 years) and pandemic phase (infectious wave plus subsequent lull periods) and analyzed for proportion, risk, and relative risk (RR). The overall effectiveness and efficiency of contact tracing were determined by calculating quarantine sensitivity (proportion of the infected population captured in quarantine), positive predictive value (PPV; proportion of the quarantined population that was infected), and the weighted Fβ-score (combined predictive performance). Of 706,974 persons living in RNK/HD during the study period, 192,175 (27.2%) tested positive for SARS-CoV-2, 74,810 (10.4%) were quarantined, and 932 (0.132%) died following infection. Compared with adults, the RR of infection was lower among senior citizens (0.401, 95% CI 0.395-0.407) and while initially lower for young people, was ultimately higher for young people across all 5 phases (first-phase RR 0.502, 95% CI 0.438-0.575; all phases RR 1.35, 95% CI 1.34-1.36). Of 932 COVID-19-associated deaths during the study period, 852 were senior citizens (91.4%), with no deaths reported among young people. Relative to adults, senior citizens had the lowest risk of quarantine (RR 0.436, 95% CI 0.424-0.448), while young people had the highest RR (2.94, 95% CI 2.90-2.98). The predictive performance of contact tracing was highest during the second and third phases of the pandemic (Fβ-score=0.272 and 0.338, respectively). In the second phase of the pandemic, 5810 of 16,814 COVID-19 infections were captured within a total quarantine population of 39,687 (sensitivity 34.6%; PPV 14.6%). In the third phase of the pandemic, 3492 of 8803 infections were captured within a total quarantine population of 16,462 (sensitivity 39.7%; PPV 21.2%). The use of quarantine aligned with increasing risks of COVID-19 infection and death. High levels of quarantine sensitivity before the introduction of the vaccine show how contact tracing systems became increasingly effective at capturing and quarantining the infected population. High levels of PPV and Fβ-scores indicate, moreover, that contact tracing became more efficient at identifying infected individuals. Additional analysis of transmission pathways is needed to evaluate the application of quarantine in relation to infection and death risks within specific age groups.

Vaccination Coverage by Age 24 Months Among Children Born During 2017-2021 - U.S.-Affiliated Pacific Islands.

Childhood vaccination is one of the most successful public health interventions to improve life expectancy, decrease health care costs, and reduce the spread of preventable diseases. Using data from jurisdictional immunization information systems, vaccination coverage by age 24 months among children born during 2017-2021 in the U.S.-affiliated Pacific Islands was estimated for all vaccines included in jurisdictional programs. Progress toward the U.S. Healthy People 2030 and World Health Organization Immunization Agenda 2030 vaccination goals of ≥90% coverage by age 24 months with recommended vaccines was inconsistently met across jurisdictions. For example, coverage by age 24 months with ≥1 dose of measles, mumps, and rubella vaccine ranged from 68.2% to 91.6% by birth cohort in Federated States of Micronesia and from 87.4% to 96.6% in Palau; coverage with ≥4 doses of diphtheria and tetanus toxoids and acellular pertussis vaccine (DTaP) ranged from 39.6% to 60.6% in Federated States of Micronesia and from 73.4% to 85.4% in Palau. Coverage as of June 1, 2024, increased for all vaccines across all jurisdictions and birth cohorts, indicating catch-up vaccination after age 24 months. For example, coverage with ≥4 doses of DTaP by June 1, 2024, ranged from 74.0% to 84.4% in American Samoa by birth cohort and from 91.6% to 94.8% in Palau. This report is the first comprehensive analysis of trends in childhood vaccination coverage in the U.S.-affiliated Pacific Islands; data in this report can be used to determine where additional efforts are needed to assess reasons for delayed vaccination of children and strategies to mitigate vaccination delays, specific to each jurisdiction.

Diagnostics for detection and surveillance of priority epidemic-prone diseases in Africa: an assessment of testing capacity and laboratory strengthening needs.

In 2023, Africa experienced 180 public health emergencies, of which 90% were infectious diseases and 75% were related to zoonotic diseases. Testing capacity for epidemic-prone diseases is essential to enable rapid and accurate identification of causative agents, and for action to prevent disease spread. Moreover, testing is pivotal in monitoring disease transmission, evaluating public health interventions and informing targeted resource allocation during outbreaks. An online, self-assessment survey was conducted in African Union Member States to identify major challenges in testing for epidemic-prone diseases. The survey assessed current capacity for diagnosing priority epidemic-prone diseases at different laboratory levels. It explored challenges in establishing and maintaining testing capacity to improve outbreak response and mitigate public health impact. Survey data analysed diagnostic capacity for priority infectious diseases, diagnostic technologies in use, existing surveillance programmes and challenges limiting diagnostic capacity, by country. The survey result from 15 Member States who responded to the survey, showed high variability in testing capacity and technologies across countries and diverse factors limiting testing capacity for certain priority diseases like dengue and Crimean-Congo haemorrhagic fever. At the same time diagnostic capacity is better for coronavirus disease 2019 (COVID-19), polio, and measles due to previous investments. Unfortunately, many countries are not utilizing multiplex testing, despite its potential to improve diagnostic access. The challenges of limited laboratory capacity for testing future outbreaks are indeed significant. Recent disease outbreaks in Africa have underscored the urgent need to strengthen diagnostic capacity and introduce cost-effective technologies. Small sample sizes and differing disease prioritisation within each country limited the analysis. These findings suggest the benefits of evaluating laboratory testing capacity for epidemic-prone diseases and highlight the importance of effectively addressing challenges to detect diseases and prevent future pandemics.

Serotypes and Genotypes of Streptococcus pneumoniae in an Unvaccinated Population in Suzhou, China.

Streptococcus pneumoniae is a significant etiological agent of infection and commonly inhabits the human nasopharynx, alongside other potentially pathogenic bacteria. In this study, S. pneumoniae strains were obtained from a community population and subjected to investigation of their phenotypes, genotypes, and vaccine coverage. S. pneumoniae was isolated from nasopharyngeal swab samples of a healthy population in the Guangfu Community. Capsular serotypes and genotypes were identified using Quellung reaction and multilocus sequence typing (MLST), respectively. The antimicrobial susceptibility was tested using minimum inhibitory concentrations. In total, 500 unvaccinated people were sampled. Ninety-four S. pneumoniae strains were identified. Common serotypes were 19F, 6A, and 9V. The strain coverages of PCV13 and PPV23 were 61.7% and 58.5%, respectively. About 27.6% isolates were non-susceptible to penicillin, and over 80% were resistant to erythromycin and doxycycline. Among 27 novel sequence types (STs) identified in all strains, the most common STs were ST236 (6/94, 6.4%) and ST12669 (6/94, 6.4%). Nearly half of the strains were grouped into four clone complexes (CC12665, CC271, CC6011, and CC180), of which CC271 showed the highest resistance to PEN. In our study, various drug-resistant clone complexes of Streptococcus pneumoniae were found in the healthy population, the elderly, and children. Consequently, pneumococcal vaccines should be included in the national immunization schedule to prevent disease spread.

A detection method for dead caged hens based on improved YOLOv7

In large-scale laying hen farms, daily inspection of dead hens is a relevant task to monitor the health of the flock and prevent disease spreading. The current manual inspection method used in caged hen farms is inefficient, costly, and particularly difficult for high-rise cages. To address this issue, a dead caged hen detection method based on improved You Only Look Once version 7 (YOLOv7) was proposed in this study, which was optimized to improve detection performance and speed in complex farming environments, such as cage wire mesh occlusion and crowded hen occlusion. First, the Convolutional Block Attention Module was used to enable the model to learn target features accurately. Second, the Distance Intersection over Union Non-maximum Suppression and repulsion loss were introduced to improve crowded hen occlusion and reduce missed detections. Additionally, to facilitate the deployment of the proposed method on mobile devices, the MobileNetv3 lightweight network was used to replace the backbone of YOLOv7. Furthermore, the lightweight model was trained using the knowledge distillation method to enhance its performance. Finally, a comparison experiment of different object detection networks and an ablation experiment were conducted to evaluate the proposed method. The experimental results reveal that the improved YOLOv7 model proposed in this study performs optimally. Its precision, recall, F1 score, and mAP@0.5 for the dead hens in the test set are 95.7 %, 86.8 %, 0.910, and 86.2 %, respectively. Compared with the original YOLOv7 model, precision, recall, and mAP@0.5 were increased by 6 %, 10 %, and 13.4 %, respectively. The model parameters and Giga Floating-point Operations were decreased by 31.95 % and 60.56 %, respectively, resulting in a detection speed increase of 43 Frames Per Second. Furthermore, with the assistance of an inspection robot, the proposed dead hen detection model was deployed in the actual farming environments. Compared with methods proposed by other researchers, the proposed model is more suitable for complex actual farming environments and achieves higher detection accuracy, which can offer a reference for automated caged hen detection.

ICS-ResNet: A Lightweight Network for Maize Leaf Disease Classification

The accurate identification of corn leaf diseases is crucial for preventing disease spread and improving corn yield. Plant leaf images are often affected by factors such as complex backgrounds, climate, light, and sample data imbalance. To address these issues, we propose a lightweight convolutional neural network, ICS-ResNet, based on ResNet50. This network incorporates improved spatial and channel attention modules as well as a deep separable residual structure to enhance recognition accuracy. (1) The residual connections in the ResNet network prevent gradient loss during deep network training. (2) The improved channel attention (ICA) and spatial attention (ISA) modules fully utilize semantic information from different feature layers to accurately localize key features of the network. (3) To reduce the number of parameters and lower computational costs, we replace traditional convolutional computation with a depth-separable residual structure. (4) We also employ cosine annealing to dynamically adjust the learning rate, enhancing the network’s training stability, improving model convergence, and preventing local optima. Experiments on the corn dataset in Plant Village compare the proposed ICS-ResNet with eight popular networks: CSPNet, InceptionNet_v3, EfficientNet, ShuffleNet, MobileNet, ResNet50, ResNet101 and ResNet152. The results show that the ICS-ResNet achieves an accuracy of 98.87%, which is 5.03%, 3.18%, 1.13%, 1.81%, 1.13%, 0.68%, 0.44% and 0.60% higher than the other networks, respectively. Furthermore, the number of parameters and computations are reduced by 69.21% and 54.88%, respectively, compared to the original ResNet50 network, significantly improving the efficiency of corn leaf disease classification. The study provides strong technical support for sustainable agriculture and the promotion of agricultural science and technology innovation.

Bioethical and Human Right Considerations during COVID-19 Pandemic Period: Reflections of Integrated Oncology Clinical Services from India

AbstractEver since the outbreak of COVID-19, the global health care systems are overwhelmed to cope up with the rapidly evolving disease paradigm through implementation of action plans at societal and medical domains. As per the directives from the World Health Organization and learned professional organizations, the international governments and states have formulated different protocols to prevent disease spread, for diagnosis and treatment of the disease and associated comorbidities, and to educate citizens during this crisis phase. Health care services across the world followed a “prioritizing strategy” for hospital population wherein the non-COVID cases were given less focus. Many hospitals opted for a conscious staff-sparing strategy to minimize exposure and protection of clinically valuable staff. Oncology services across the world reported a decline in the provision of clinical services to patients. There were medical concerns such as missed diagnosis, delayed diagnosis, delayed treatment, stoppage of screening programs, and differed follow-ups during the last 2 years of pandemic. The multidisciplinary oncology teams aim to ensure that cancer patients in the continuum of integrated cancer care pathway get globally accepted standards of optimum care. However, Beauchamp's ethical principles of autonomy, beneficence, nonmaleficence, and distributive justice were arguably compromised during the pandemic period. The articles of UNESCO Universal Declaration on Bioethics and Human Rights (UDBHR) declaration were possibly violated in cancer patients as a vulnerable population. This article analyses the bioethical and human right concerns with respect to medical and societal domains in oncology during the COVID-19 pandemic period.

Metoda pomiaru temperatury ciała człowieka za pomocą kamery termowizyjnej

Measurement and monitoring of human physiological parameters play an important role in many applications such as health care, sports training and prevention of disease spread. Dynamic changes in physiological parameters can reveal not only changes in a patient’s physiological state and function, but also be used to assess a person’s activity status, fitness and fatigue. Body temperature is among the most important human physiological parameters for assessing basic vital functions, apart from heart rate, blood pressure and respiratory rate. In medical practice, various types of measuring instruments are used to measure temperature, such as liquid thermometers, electronic thermometers, non-contact ear thermometers, non-contact forehead thermometers. Liquid and electronic thermometers require the appropriate sensors to be connected to a person, which may be undesirable or impossible as, for example, in newborns or during sports training. Non-contact thermometers operate over short distances and often force a specific position of the person during the measurement. In addition, the above body temperature measurement techniques require direct supervision by medical personnel, which often reduces the effectiveness and efficiency of screening temperature measurement. In screening temperature measurements of a large number of people, especially those on the move, a measuring thermal imaging camera works well. The article presents a method for measuring human body temperature using a thermal imaging camera. The presented method is characterized by high accuracy of temperature measurement, which allows medical use of the obtained measurements. The measurement method has been tested on a test stand and for a selected test sample of people. The measurements and tests carried out confirmed the possibility of obtaining temperature measurement accuracy with an expanded uncertainty of less than 0.05 K with a resolution of less than 0.1 K.

A novel plant type, leaf disease and severity identification framework using CNN and transformer with multi-label method.

The growth of plants is threatened by numerous diseases. Accurate and timely identification of these diseases is crucial to prevent disease spreading. Many deep learning-based methods have been proposed for identifying leaf diseases. However, these methods often combine plant, leaf disease, and severity into one category or treat them separately, resulting in a large number of categories or complex network structures. Given this, this paper proposes a novel leaf disease identification network (LDI-NET) using a multi-label method. It is quite special because it can identify plant type, leaf disease and severity simultaneously using a single straightforward branch model without increasing the number of categories and avoiding extra branches. It consists of three modules, i.e., a feature tokenizer module, a token encoder module and a multi-label decoder module. The LDI-NET works as follows: Firstly, the feature tokenizer module is designed to enhance the capability of extracting local and long-range global contextual features by leveraging the strengths of convolutional neural networks and transformers. Secondly, the token encoder module is utilized to obtain context-rich tokens that can establish relationships among the plant, leaf disease and severity. Thirdly, the multi-label decoder module combined with a residual structure is utilized to fuse shallow and deep contextual features for better utilization of different-level features. This allows the identification of plant type, leaf disease, and severity simultaneously. Experiments show that the proposed LDI-NET outperforms the prevalent methods using the publicly available AI challenger 2018 dataset.

Association between stringency of lockdown measures and emergency department visits during the COVID-19 pandemic: A Dutch multicentre study.

The COVID-19 outbreak disrupted regular health care, including the Emergency Department (ED), and resulted in insufficient ICU capacity. Lockdown measures were taken to prevent disease spread and hospital overcrowding. Little is known about the relationship of stringency of lockdown measures on ED utilization. This study aimed to compare the frequency and characteristics of ED visits during the COVID-19 outbreak in 2020 to 2019, and their relation to stringency of lockdown measures. A retrospective multicentre study among five Dutch hospitals was performed. The primary outcome was the absolute number of ED visits (year 2018 and 2019 compared to 2020). Secondary outcomes were age, sex, triage category, way of transportation, referral, disposition, and treating medical specialty. The relation between stringency of lockdown measures, measured with the Oxford Stringency Index (OSI) and number and characteristics of ED visits was analysed. The total number of ED visits in the five hospitals in 2019 was 165,894, whereas the total number of visits in 2020 was 135,762, which was a decrease of 18.2% (range per hospital: 10.5%-30.7%). The reduction in ED visits was greater during periods of high stringency lockdown measures, as indicated by OSI. The number of ED visits in the Netherlands has significantly dropped during the first year of the COVID-19 pandemic, with a clear association between decreasing ED visits and increasing lockdown measures. The OSI could be used as an indicator in the management of ED visits during a future pandemic.

Understanding of Nanofiber Face Mask as Corona Virus Disease Prevention in Human

Background: Corona virus has become a global issue. It makes various countries have taken this outbreak very seriously. One of the necessary precautions is by using a face mask. The nanofiber technology on face masks greatly helps the public and government to increase the prevention of disease spread. The purpose of this study was to conduct a systematic literature review on nanofiber face masks as human corona virus disease prevention. Methods: Stages of systematic literature review of 632 documents was carried out using text mining techniques, while hierarchical cluster analysis were carried out for the extraction of terms in documents. Results: The terms "mask" and "nanofiber" were the most words that appear (more than 40 times) in the WoS and PubMed nanofiber mask document form. On the other hand, the terms "disease" and "respiration" mostly appeared in human corona virus disease prevention. Both of these terms were used to obtain specific articles as a basis for the study of nanofiber mask as human corona disease prevention. Conclusion: This study is very important since prevention measures against corona disease (corvid-19) are a very high concern. The next study is expected to bring this review literature into an experimental study of nanofiber applied to face masks.

A novel study of the impact of vaccination on pneumonia via fractional approach

Substantial advancements have been made in applying fractional calculus to understanding the epidemiology of viral diseases. However, it remains troubling that over 2,000,000 children, particularly those under the age of 5 and elderly individuals over 65, succumb to pneumonia annually in developing nations. This paper uses a deterministic SVEIR (susceptible, vaccinated, exposed, infected, and recovered) model to investigate pneumonia disease dynamics from a mathematical perspective. The governing model has been generalized from Caputo fractional derivatives, and the generalized Euler’s method has been used to calculate the approximate solution of the governing model. The dynamical systems theory examines the model’s stability analysis, basic reproduction number, and equilibrium points. The linearization method for spatial equilibrium and the Lyapunov functional method demonstrate that the model is locally asymptotic stable for disease-free equilibrium. Graphical results are showcased through MATLAB21 to illustrate the resolution of the problem. The research findings suggest that the disease will eventually disappear from the population if vaccination coverage rises over the necessary vaccination threshold. According to sensitivity analysis, the most sensitive factors are the transmission rate and the pace at which exposed individuals become contagious. According to the study, we must strengthen treatment effectiveness to prevent disease spreading.

Development and evaluation of a multiplex real-time RT-PCR assay for simultaneous detection of H5, H7, and H9 subtype avian influenza viruses

H5, H7 and H9 are the major subtypes of avian influenza virus (AIV) that cause economic losses in the poultry industry and sporadic zoonotic infection. Early detection of AIV is essential for preventing disease spread. Therefore, molecular diagnosis and subtyping of AIV via real-time RT-PCR (rRT-PCR) is preferred over other classical diagnostic methods, such as egg inoculation, RT-PCR and HI test, due to its high sensitivity, specificity and convenience. The singleplex rRT-PCRs for the Matrix, H5 and H7 gene used for the national surveillance program in Korea have been developed in 2017; however, these methods were not designed for multiplexing, and does not reflect the sequences of currently circulating strains completely. In this study, the multiplex H5/7/9 rRT-PCR assay was developed with sets of primers and probe updated or newly designed to simultaneously detect the H5, H7 and H9 genes. Multiplex H5/7/9 rRT-PCR showed 100% specificity without cross-reactivity with other subtypes of AIVs and avian disease-causing viruses or bacteria, and the limit of detection was 1–10 EID50/0.1 ml (50% egg infectious dose). Artificial mixed infections with the three different subtypes could be detected accurately with high analytical sensitivity even under highly biased relative molecular ratios by balancing the reactivities of each subtype by modifying the concentration of the primers and probes. The multiplex H5/7/9 rRT-PCR assay developed in this study could be a useful tool for large-scale surveillance programs for viral detection as well as subtyping due to its high specificity, sensitivity and robustness in discriminating viruses in mixed infections, and this approach would greatly decrease the time, cost, effort and chance of cross-contamination compared to the conventional method of testing three subtypes by different singleplex rRT-PCR methods in parallel or in series.

International Migration, Refugees, and Spread of Tuberculosis in Brazil: Analysis of Clusters, Trends, and Associated Factors (2010-2021).

International migration is a global phenomenon with significant implications on the health-disease process due to exposures along transit routes and local/destination epidemiological indicators. We aimed to analyze the transmission and spread of tuberculosis among international migrants and refugees from a spatiotemporal perspective and the associated factors. This was an ecological study of cases of tuberculosis in international migrants in Brazil, between 2010 and 2021. Annual incidence rates were calculated and spatiotemporal scan techniques were used to identify municipalities at risk. Multiple logistic regression was used to identify factors associated with tuberculosis in international migrants. A total of 4037 cases of tuberculosis were reported in Brazil in international migrants. Municipalities at risk for this event were identified using the spatiotemporal scan technique, and a cluster was identified with ITT: +52.01% and ETT: +25.60%. A higher probability of TB infection was identified in municipalities with a TB incidence rate >14.40 cases/100 inhabitants, population >11,042 inhabitants, Gini index >0.49, and illiteracy rate >13.12%. A lower probability was found in municipalities with average per capita household income >BRL 456.43. It is recommended that health authorities implement monitoring and rigorous follow-up in affected areas to ensure proper diagnosis and treatment completion for international migrants, preventing disease spread to other communities.

Comparison of efficacy and reliability of six commercial COVID-19 diagnostic PCR kits

Abstract Objectives All countries have been deeply affected by the coronavirus disease 2019 pandemic, both economically and in situations that strain health systems, such as workforce and workload. Therefore, various measures should be taken to control the disease and prevent its spread. Since the disease onset, real-time PCR tests have been used as the gold standard for disease diagnosis. Owing to the rapid progress of the pandemic and the spread of the disease, validation, consistency, and optimization tests of some commercial kits have been conducted directly in the field. Therefore, it is important to compare the results of these kits and improve the existing ones. Methods We compared five kits (Bioexen, Polgen, Coronex, Diagen, and Anatolia) donated to the TOBB Economics and Technology University Hospital PCR laboratory with the KrosGen kit to detect severe acute respiratory syndrome coronavirus 2. A total of 244 samples were selected and analyzed using five different severe acute respiratory syndrome coronavirus 2 PCR detection kits. Results Positive and negative results from the six kits were compared using the working protocols of the kits, primers, and cycle threshold (Ct) values. Five of the six kits have reliable compatibility for Ct<30 but decreases for Ct≥30. Therefore, it is important to evaluate the performance of these kits for reduced viral loads. Conclusions Using a suitable kit with high compatibility for Ct≥30 is important for detecting patients with a low viral load and helping prevent disease spread.

Analisis Sanitasi Lingkungan dan Pengendalian Nyamuk di Kota Tarutung

Tarutung City, as the center of government and commerce in North Tapanuli Regency, faces environmental sanitation issues that contribute to the increase in mosquito populations. This study aims to analyze the condition of environmental sanitation and mosquito control efforts in Tarutung City. A descriptive-analytical approach was used to assess environmental cleanliness, the availability of sanitation facilities, and vector control programs implemented by the local government. The results of the study indicate that environmental sanitation in some areas remains inadequate, with many water storage sites found to be breeding grounds for Aedes aegypti mosquitoes, the primary cause of dengue fever. Control efforts, such as fogging and the elimination of mosquito breeding sites (PSN), have not been optimal due to the lack of community participation and awareness of the importance of maintaining environmental cleanliness. This study recommends enhancing public education programs, improving waste management, and conducting regular monitoring of high-risk areas to reduce mosquito populations and prevent disease spread. Strengthening cooperation between the government and the community is crucial to achieving a healthier environment free from mosquito threats

Individual and temporal variation in movement patterns of wild alpine reindeer and implications for disease management

Animal behaviour is important for prevalence and outbreaks of infectious diseases, for instance by affecting individual interactions. Increasing the knowledge of individual movement patterns can provide better insight into disease prevalence and spread, helping to target efforts to minimise disease outbreaks. Chronic wasting disease (CWD) is a fatal prion disease affecting cervids. CWD is transmitted by animal‐to‐animal contact and through the environment, thus individual variation in space use and social associations may influence disease transmission patterns and infection risk. CWD was detected in Norwegian alpine reindeer Rangifer tarandus tarandus in 2016, and eradication of the infected population was implemented. A 3:1 infection rate between males and females suggests sex‐specific behavioural drivers. We utilised an extensive individual‐based dataset of 149 GPS‐marked wild reindeer to investigate individual variation in movement patterns in terms of inter‐ and intra‐annual home range size and site fidelity, and variation in home range overlap and distances between individuals. We aimed to identify patterns which could indicate higher potential disease risk. Females had larger annual and seasonal home ranges than males, except during calving and rut. Greater home range overlaps and shorter between‐individual distances were found between same‐sex individuals than different‐sex individuals, except during the rut. Accordingly, the rut season stands out with greater male home ranges, greater home range overlap and shorter distances between males and between males and females, which could indicate that this season is critical for disease transmission. Measures to prevent disease spread should lower contact rates, e.g. by reducing the abundance of adult males before they mix with other groups during the rut. This can be achieved for instance by allowing earlier hunt on adult males when they are distributed in small male groups, to reduce the transmission risk and keep disturbance of other individuals low.

A user-centered smart inhaler algorithm for targeted drug delivery in juvenile onset recurrent respiratory papillomatosis treatment integrating computational fluid particle dynamics and machine learning

Recurrent respiratory papillomatosis (RRP) is a chronic condition primarily affecting children, known as juvenile onset RRP (JORRP), caused by a viral infection. Antiviral medications have been used to reduce the need for frequent surgeries, slow the growth of papillomata, and prevent disease spread. Effective treatment of JORRP necessitates targeted drug delivery (TDD) to ensure that inhaled aerosolized drugs reach specific sites, such as the larynx and glottis, without harming healthy tissues. Using computational fluid particle dynamics (CFPD) and machine learning (ML), this study (1) investigated how drug properties and individual factors influence TDD efficiency for JORRP treatment and (2) developed personalized inhalation therapy using an ML-empowered smart inhaler control algorithm for precise medication release. This algorithm optimizes the inhaler nozzle position and diameter based on drug and patient-specific data, enhancing drug delivery to the larynx and glottis. CFPD simulations show that particle size significantly affects deposition fractions in the upper airway, emphasizing the importance of particle size selection. Additionally, optimal nozzle diameter and delivery efficiency depend on particle size, inhalation flow rate, and release time. The ML-based TDD strategy, employing a classification and regression tree model, outperforms conventional inhalation therapy by achieving a higher delivery efficiency to the larynx and glottis. This innovative concept of an ML-empowered smart inhaler represents a promising step toward personalized and precise pulmonary healthcare through inhalation therapy. It demonstrates the potential of AI-driven smart inhalers for improving the treatment outcomes of lung diseases that require TDD at designated lung sites.