Large Datasets Research Articles

Structural, Functional, and Genetic Changes Surrounding Electrodes Implanted in the Brain.

ConspectusImplantable neurotechnology enables monitoring and stimulating of the brain signals responsible for performing cognitive, motor, and sensory tasks. Electrode arrays implanted in the brain are increasingly used in the clinic to treat a variety of sources of neurological diseases and injuries. However, the implantation of a foreign body typically initiates a tissue response characterized by physical disruption of vasculature and the neuropil as well as the initiation of inflammation and the induction of reactive glial states. Likewise, electrical stimulation can induce damage to the surrounding tissue depending on the intensity and waveform parameters of the applied stimulus. These phenomena, in turn, are likely influenced by the surface chemistry and characteristics of the materials employed, but further information is needed to effectively link the biological responses observed to specific aspects of device design. In order to inform improved design of implantable neurotechnology, we are investigating the basic science principles governing device-tissue integration. We are employing multiple techniques to characterize the structural, functional, and genetic changes that occur in the cells surrounding implanted electrodes. First, we have developed a new "device-in-slice" technique to capture chronically implanted electrodes within thick slices of live rat brain tissue for interrogation with single-cell electrophysiology and two-photon imaging techniques. Our data revealed several new observations of tissue remodeling surrounding devices: (a) there was significant disruption of dendritic arbors in neurons near implants, where losses were driven asymmetrically on the implant-facing side. (b) There was a significant loss of dendritic spine densities in neurons near implants, with a shift toward more immature (nonfunctional) morphologies. (c) There was a reduction in excitatory neurotransmission surrounding implants, as evidenced by a reduction in the frequency of excitatory postsynaptic currents (EPSCs). Lastly, (d) there were changes in the electrophysiological underpinnings of neuronal spiking regularity. In parallel, we initiated new studies to explore changes in gene expression surrounding devices through spatial transcriptomics, which we applied to both recording and stimulating arrays. We found that (a) device implantation is associated with the induction of hundreds of genes associated with neuroinflammation, glial reactivity, oligodendrocyte function, and cellular metabolism and (b) electrical stimulation induces gene expression associated with damage or plasticity in a manner dependent upon the intensity of the applied stimulus. We are currently developing computational analysis tools to distill biomarkers of device-tissue interactions from large transcriptomics data sets. These results improve the current understanding of the biological response to electrodes implanted in the brain while producing new biomarkers for benchmarking the effects of novel electrode designs on responses. As the next generation of neurotechnology is developed, it will be increasingly important to understand the influence of novel materials, surface chemistries, and implant architectures on device performance as well as the relationship with the induction of specific cellular signaling pathways.

Current Diagnosis and Treatment of Acute Pancreatitis in Korea: A Nationwide Survey.

: Acute pancreatitis (AP) is a leading cause of emergency hospitalization. We present the current diagnostic and therapeutic status of AP as revealed by analysis of a large multicenter dataset. : The medical records of patients diagnosed with AP between 2018 and 2019 in 12 tertiary medical centers in Korea were retrospectively reviewed. : In total, 676 patients were included; of these, were 388 (57.4%) males, and the mean age of all patients was 58.6 years. There were 355 (52.5%), 301 (44.5%), and 20 (3.0%) patients with mild, moderate, and severe AP, respectively, as assessed by the revised Atlanta classification. The most common etiologies of AP were biliary issues (41.6%) and alcohol consumption (24.6%), followed by hypertriglyceridemia (6.8%). The etiology was not identified in 111 (16.4%) patients at the time of initial admission. The overall mortality rate was 3.3%, increasing up to 45.0% among patients with severe AP. Notably, 70.0% (14/20) of patients with severe AP and 81.5% (154/189) of patients with systemic inflammatory response syndrome had received <4 L per day during the initial 24 hours of admission. Only 23.8% (67/281) of acute biliary pancreatitis patients underwent cholecystectomy during their initial admission. In total, 17.8% of patients experienced recurrent attacks during follow-up. However, none of the patients with acute biliary pancreatitis experienced recurrent attacks if they had undergone cholecystectomy during their initial admission. : This study provides insights into the current status of AP in Korea, including its etiology, severity, and management. reveal disparities between clinical guidelines and their practical implementation for AP treatment.

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia.

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.

Determination of Clinically Acceptable Analytical Variation of Cardiac Troponin at Decision Thresholds.

Clinical decision-making for risk stratification for possible myocardial infarction (MI) uses high-sensitivity cardiac troponin (hs-cTn) thresholds that range from the limit of detection to several-fold higher than the upper reference limit (URL). To establish a minimum analytical variation standard, we can quantify the effect of variation on the population clinical measures of safety (sensitivity) and effectiveness [proportion below threshold, or positive predictive value (PPV)]. From large datasets of patients investigated for possible MI with the Abbott hs-cTnI and Roche hs-cTnT assays, we synthesized datasets of 1 000 000 simulated patients. Troponin concentrations were randomly varied several times based on absolute deviations of 0.5 to 3 ng/L and relative changes of 2% to 20% around the low-risk threshold (5 ng/L) and URLs, respectively. For both assays at the low-risk thresholds, there were negligible differences in sensitivity (<0.3%) with increasing analytical variation. The proportion of patients characterized as low risk reduced by 30% to 29% (Roche) and 53% to 44% (Abbott). At the URL, increasing analytical variation also did not change sensitivity; the PPV fell by less than 3%. For risk stratification, increased delta thresholds (change between serial troponin concentrations) increased sensitivity at the cost of a decreased percentage of patients below the delta threshold, with the largest changes at the greatest analytical variation. At the low-risk threshold, analytical variation up to 3 ng/L minimally impacted the safety metric (sensitivity) but marginally reduced effectiveness. Similarly, at the URL even relative variation up to 25% minimally impacted safety metrics and effectiveness. Analytical variation for delta thresholds did not negatively impact sensitivity but decreased effectiveness.

Insomnia symptom prevalence in England: a comparison of cross-sectional self-reported data and primary care records in the UK Biobank

ObjectivesWe aimed to use a large dataset to compare self-reported and primary care measures of insomnia symptom prevalence in England and establish whether they identify participants with similar characteristics.DesignCross-sectional study...

Machine Learning Classification Algorithms for Traffic Stops—A Comparative Study

The application of machine learning algorithms across various fields is gaining momentum, and the results increasingly emphasize the need for further testing and implementation. This is driven by the potential to streamline and expedite numerous processes. In this paper, we have employed five algorithms: KNN, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes, and these algorithms have been tested in three large datasets. On average, their performance ranges from a minimum of 80% to a maximum of 90%. Data preprocessing has been completed, and concurrently, we have implemented the SMOTE algorithm to address the challenge of unbalanced data in this research. Simultaneously, the Naïve Bayes algorithm yields the most favorable results of Accuracy, Precision, Recall, and F1 Score, for the “is_arrested” class. Furthermore, to assess the performance of each algorithm, we employed metrics including Accuracy, Precision, Recall, and F1 Score. These metrics allowed us to decide which algorithm achieved the most effective classification.

Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning.

We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.

SHAPEwarp-web: sequence-agnostic search for structurally homologous RNA regions across databases of chemical probing data.

RNA molecules perform a variety of functions in cells, many of which rely on their secondary and tertiary structures. Chemical probing methods coupled with high-throughput sequencing have significantly accelerated the mapping of RNA structures, and increasingly large datasets of transcriptome-wide RNA chemical probing data are becoming available. Analogously to what has been done for decades in the protein world, this RNA structural information can be leveraged to aid the discovery of structural similarity to a known RNA (or RNA family), which, in turn, can inform about the function of transcripts. We have previously developed SHAPEwarp, a sequence-agnostic method for the search of structurally homologous RNA segments in a database of reactivity profiles derived from chemical probing experiments. In its original implementation, however, SHAPEwarp required substantial computational resources, even for moderately sized databases, as well as significant Linux command line know-how. To address these limitations, we introduce here SHAPEwarp-web, a user-friendly web interface to rapidly query large databases of RNA chemical probing data for structurally similar RNAs. Aside from featuring a completely rewritten core, which speeds up by orders of magnitude the search inside large databases, the web server hosts several high-quality chemical probing databases across multiple species. SHAPEwarp-web is available fromhttps://shapewarp.incarnatolab.com.

Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces

ABSTRACT The utmost issue in Motor Imagery Brain-Computer Interfaces (MI-BCI) is the BCI poor performance known as ‘BCI inefficiency’. Although past research has attempted to find a solution by investigating factors influencing users’ MI-BCI performance, the issue persists. One of the factors that has been studied in relation to MI-BCI performance is gender. Research regarding the influence of gender on a user’s ability to control MI-BCIs remains inconclusive, mainly due to the small sample size and unbalanced gender distribution in past studies. To address these issues and obtain reliable results, this study combined four MI-BCI datasets into one large dataset with 248 subjects and equal gender distribution. The datasets included EEG signals from healthy subjects from both gender groups who had executed a right- vs. left-hand motor imagery task following the Graz protocol. The analysis consisted of extracting the Mu Suppression Index from C3 and C4 electrodes and comparing the values between female and male participants. Unlike some of the previous findings which reported an advantage for female BCI users in modulating mu rhythm activity, our results did not show any significant difference between the Mu Suppression Index of both groups, indicating that gender may not be a predictive factor for BCI performance.

Toward confident prostate cancer detection using ultrasound: a multi-center study.

Deep learning-based analysis of micro-ultrasound images to detect cancerous lesions is a promising tool for improving prostate cancer (PCa) diagnosis. An ideal model should confidently identify cancer while responding with appropriate uncertainty when presented with out-of-distribution inputs that arise during deployment due to imaging artifacts and the biological heterogeneity of patients and prostatic tissue. Using micro-ultrasound data from 693 patients across 5 clinical centers who underwent micro-ultrasound guided prostate biopsy, we train and evaluate convolutional neural network models for PCa detection. To improve robustness to out-of-distribution inputs, we employ and comprehensively benchmark several state-of-the-art uncertainty estimation methods. PCa detection models achieve performance scores up to average AUROC with a 10-fold cross validation setup. Models with uncertainty estimation obtain expected calibration error scores as low as , indicating that confident predictions are very likely to be correct. Visualizations of the model output demonstrate that the model correctly identifies healthy versus malignant tissue. Deep learning models have been developed to confidently detect PCa lesions from micro-ultrasound. The performance of these models, determined from a large and diverse dataset, is competitive with visual analysis of magnetic resonance imaging, the clinical benchmark to identify PCa lesions for targeted biopsy. Deep learning with micro-ultrasound should be further studied as an avenue for targeted prostate biopsy.

Analysis and Visualization of Advertising Sales Data Using Python Software Through an Internship Program

This research discusses the use of data visualization as a tool for analyzing and presenting information effectively. The main goal of this research is visualization that allows better understanding of complex data. In this context, research explores various data visualization techniques, including the use of bar charts, correlation heatmaps, and interactive technology to present information intuitively. In improving decision-making abilities, identifying patterns or trends, and exploring relationships and insights contained in large and heterogeneous datasets. The research methodology includes comparative analysis of various visualization methods, user experiments, and the application of new techniques to evaluate the effectiveness and usefulness of data visualization in advertising video sales data at PT XXX. Based on the research results, it was found that data visualization involves presenting data information in graphic or image form to facilitate understanding. This helps in explaining the facts and determining the steps that need to be taken. The research results are expected to provide in-depth insight into the development of data visualization techniques. The results of this data visualization can be widely applied in various fields, especially creative, marketing and sales management.

Sex differences in histopathological markers of cerebral amyloid angiopathy and related hemorrhage.

Men with cerebral amyloid angiopathy (CAA) may have an earlier onset of intracerebral hemorrhage, and a more hemorrhagic disease course compared to women. In this cohort study we investigated sex differences in histopathological markers associated with amyloid-β burden and hemorrhage in cognitively impaired individuals and patients with CAA, using neuropathological data from two autopsy databases. First, we investigated presence of parenchymal (Thal score) and vascular amyloid-β (CAA severity score) in cognitively impaired individuals from the National Alzheimer's Coordinating Center (NACC) neuropathology database. Next, we examined sex differences in hemorrhagic ex vivo MRI-markers and local cortical iron burden, and the interaction of sex on factors associated with cortical iron burden (CAA percentage area and vessel remodelling) in patients with pathologically confirmed clinical CAA from the Massachusetts General Hospital (MGH) CAA neuropathology database. In 6120 individuals from the NACC database (45% women, mean age 80y), the presence of parenchymal amyloid-β (OR [95%CI]=0.68 [0.53-0.88]) but not vascular amyloid-β was less in men compared to women. In 19 patients with definite CAA from the MGH CAA database (35% women, mean age 75y), a lower microbleed count (p<0.001), but a higher proportion of cortical superficial siderosis and a higher local cortical iron burden was found in men (p<0.001) compared to women. CAA percentage area was comparable in men and women (p=0.732). Exploratory analyses demonstrated a possible stronger negative relation between cortical CAA percentage area and cortical iron density in men compared to women (p=0.03). Previously observed sex differences in hemorrhage onset and progression in CAA patients are likely not due to differences in global CAA severity between men and women. Other factors, such as vascular remodelling, may contribute, but future studies are necessary to replicate our findings in larger datasets and to further investigate the underlying mechanisms behind these complex sex differences.

A Dataset for Evaluating Contextualized Representation of Biomedical Concepts in Language Models

Due to the complexity of the biomedical domain, the ability to capture semantically meaningful representations of terms in context is a long-standing challenge. Despite important progress in the past years, no evaluation benchmark has been developed to evaluate how well language models represent biomedical concepts according to their corresponding context. Inspired by the Word-in-Context (WiC) benchmark, in which word sense disambiguation is reformulated as a binary classification task, we propose a novel dataset, BioWiC, to evaluate the ability of language models to encode biomedical terms in context. BioWiC comprises 20’156 instances, covering over 7’400 unique biomedical terms, making it the largest WiC dataset in the biomedical domain. We evaluate BioWiC both intrinsically and extrinsically and show that it could be used as a reliable benchmark for evaluating context-dependent embeddings in biomedical corpora. In addition, we conduct several experiments using a variety of discriminative and generative large language models to establish robust baselines that can serve as a foundation for future research.

Simulation of gamma spectra from soil samples by using MCNP: A case study

A comparison between the MCNP simulated gamma spectrums based on the nuclear data from NuDat with the current version 3.0 and Nucléide-Lara against measured spectra based on the IAEA reference samples has been performed to assess the influence of nuclear data of photon decay processes and cross-section of photon interactions on the quality of the simulated spectra. As the results, the appearance of some abnormal energy peaks; namely 215 keV, 571 keV, 675 keV, 1227 keV of the NuDat-based simulated spectra and 90 keV, 94 keV, 106 keV, 416 keV of the Nucléide-Lara-based simulated spectra, which were present in neither the measured spectrum nor remaining simulated spectra, indicating issues with accuracy and completeness of these dataset. In addition, the good correlation between the combined dataset-based simulated spectra and reference samples-based measured spectra within the range of 50 keV to 2620 keV suggests that this MCNP simulation configuration can be used to generate a large simulated dataset for Machine Learning (ML) models that automatically identify and qualify radioactive isotope from gamma-ray spectra, overcoming the practical limitation of number of reference samples to sufficiently generate data for training and testing ML algorithms in the field of environmental radiation [1].

Driver Distraction Detection Using Machine Learning

The problem of driver distraction has become serious, and this is due to physical distractions that can be dangerous on the roads. Convolutional Neural Networks (CNNs) are used in this research to introduce a new approach to identifying physical distractions. To train the designed CNN model, a large dataset of labeled images that depict different physical distractions such as eating, texting, and manipulating controls is utilized. We make use of the spatial hierarchies learned by CNNs that enable us to extract important information for distraction identification with high efficiency. The experimental results validate the proposed method’s ability to recognize physical distractions in real driving scenarios effectively. Incorporating such models into in-car systems could therefore result in reduced distractions and consequently fewer accidents and additionally offer immediate warnings or interventions thus improving driver safety considerably. Key Words: Distraction, Machine Learning, Convolution Neural Network, Road accidents.

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

Forestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number is increasing rapidly. This serves as a catalyst for us to implement more stringent measures for workplace safety in order to achieve the sustainability objective of SDG 3, which focuses on health and well-being. This study contributes to the analysis of occupational accidents and focuses on two large real-world datasets from both the Austrian Federal Forests (ÖBf) and the Austrian Workers’ Compensation Board (AUVA). Decision trees, random forests, and fully connected neural networks are used for the analysis. By exploring different interpretation methods, this study sheds light on the decision-making processes ranging from basic association to causal inference and emphasizes the importance of causal inference in providing actionable insights for accident prevention. This paper contributes to the topic of explainable AI, specifically in its application to occupational safety in forestry. As a result, it introduces novel aspects to decision support systems in this application domain.

Timing of Red Blood Cell Transfusions and Occurrence of Necrotizing Enterocolitis

Observational studies often report that anemia and red blood cell (RBC) transfusions are associated with a higher risk of necrotizing enterocolitis (NEC) among extremely low-birthweight (ELBW) infants. To evaluate whether there is a temporal association between 72-hour hazard periods of exposure to RBC transfusions and NEC among ELBW infants randomized to either higher or lower hemoglobin transfusion thresholds. This post hoc secondary analysis of 1690 ELBW infants who survived to postnatal day 10 enrolled in the Transfusion of Prematures (TOP) randomized multicenter trial between December 1, 2012, and April 12, 2017, was performed between June 2021 and July 2023. First, the distribution of RBC transfusions and the occurrence of NEC up to postnatal day 60 were examined. Second, 72-hour posttransfusion periods were categorized as hazard periods and the pretransfusion periods of variable duration as control periods. Then, the risk of NEC in posttransfusion hazard periods was compared with that in pretransfusion control periods, stratifying the risk based on randomization group (higher or lower hemoglobin transfusion threshold group). The primary outcome was incidence of NEC stage 2 or 3. Secondary outcomes included the incidence rates of NEC within five 10-day intervals, taking into account the number of days at risk. Of 1824 ELBW infants randomized during the TOP trial, 1690 were included in the present analysis (mean [SD] gestational age, 26.0 [1.5] weeks; 899 infants [53.2%] were female). After categorizing 4947 hazard periods and 5813 control periods, we identified 133 NEC cases. Fifty-nine of these cases (44.4%) occurred during hazard periods. Baseline and clinical characteristics of infants with NEC during hazard periods did not differ from those of infants with NEC during control periods. The risk of NEC was 11.9 per 1000 posttransfusion hazard periods and 12.7 per 1000 control periods (adjusted risk ratio, 0.95; 95% CI, 0.68-1.32; P = .74). This risk did not differ significantly between randomization groups, but the incidence rate of NEC per 1000 days peaked between postnatal days 20 and 29 in the lower hemoglobin transfusion threshold group. The findings of this post hoc analysis suggest that, among ELBW infants with the hemoglobin ranges occurring in the TOP trial, exposure to RBC transfusions was not temporally associated with a higher risk of NEC during 72-hour posttransfusion hazard periods. Given that the incidence rate of NEC peaked between postnatal days 20 and 29 among infants with lower hemoglobin values, a more in-depth examination of this at-risk period using larger data sets is warranted. ClinicalTrials.gov Identifier: NCT01702805.

Machine Learning-Driven SERS Nanoendoscopy and Optophysiology.

A frontier of analytical sciences is centered on the continuous measurement of molecules in or near cells, tissues, or organs, within the biological context in situ, where the molecular-level information is indicative of health status, therapeutic efficacy, and fundamental biochemical function of the host. Following the completion of the Human Genome Project, current research aims to link genes to functions of an organism and investigate how the environment modulates functional properties of organisms. New analytical methods have been developed to detect chemical changes with high spatial and temporal resolution, including minimally invasive surface-enhanced Raman scattering (SERS) nanofibers using the principles of endoscopy (SERS nanoendoscopy) or optical physiology (SERS optophysiology). Given the large spectral data sets generated from these experiments, SERS nanoendoscopy and optophysiology benefit from advances in data science and machine learning to extract chemical information from complex vibrational spectra measured by SERS. This review highlights new opportunities for intracellular, extracellular, and in vivo chemical measurements arising from the combination of SERS nanosensing and machine learning.

MPIGAN: An end-to-end deep based generative framework for high-resolution magnetic particle imaging reconstruction.

Magnetic particle imaging (MPI) is a recently developed, non-invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X-space are two commonly used MPI reconstruction methods, where the former is extremely time-consuming and the latter usually produces blurry images. Currently, we proposed an end-to-end machine learning framework to reconstruct high-resolution MPI images from 1-D voltage signals directly and efficiently. The proposed framework, which we termed "MPIGAN", was trained on a large MPI simulation dataset containing 291597 pairs of high-resolution 2-D phantom images and each image's corresponding voltage signals, so that it was able to accurately capture the nonlinear relationship between the spatial distribution of SPIONs and the received voltage signal, and realized high-resolution MPI image reconstruction. Experiment results showed that, MPIGAN exhibited remarkable abilities in high-resolution MPI image reconstruction. MPIGAN outperformed the traditional methods of system matrix and X-space in recovering the fine-scale structure of magnetic nanoparticles' spatial distribution and achieving enhanced reconstruction performance in both visual effects and quantitative assessments. Moreover, even when the received signals were severely contaminated with noise, MPIGAN could still generate high-quality MPI images. Our study provides a promising AI solution for end-to-end, efficient, and high-resolution magnetic particle imaging reconstruction.

Estimating the risk of SARS-CoV-2 infection in New Zealand border arrivals

BackgroundTravel restrictions and border controls were used extensively during the COVID-19 pandemic. However, the processes for making robust evidence-based risk assessments of source countries to inform border control policies was in many cases very limited.MethodsBetween April 2020 and February 2022, all international arrivals to New Zealand were required to spend 14 days in government-managed quarantine facilities and were tested at least twice. The infection rates among arrivals in the years 2020, 2021 and 2022 were respectively 6.3, 9.4 and 90.0 cases per thousand arrivals (487, 1064 and 1496 cases). Test results for all arrivals were linked with travel history, providing a large and comprehensive dataset on the number of SARS-CoV-2-positive and negative travellers from different countries over time. We developed a statistical model to predict the country-level infection risk based on infection rates among recent arrivals and reported cases in the country of origin. The model incorporates a country-level random effect to allow for the differences between the infection risk of the population of each country and that of travellers to New Zealand. A time dependent auto-regressive component of the model allows for short term correlation in infection rates.ResultsA model selection and checking exercise found that the model was robust and reliable for forecasting arrival risk for 2 weeks ahead. We used the model to forecast the number of infected arrivals in future weeks and categorised countries according to their risk level. The model was implemented in R and was used by the New Zealand Ministry of Health to help inform border control policy during 2021.ConclusionsA robust and practical forecasting tool was developed for forecasting infection risk among arriving passengers during a period of controlled borders during the COVID-19 pandemic. The model uses historical infection rates among arrivals and current infection rates in the source country to make separate risk predictions for arrivals from each country.