Early Warning Of Disease Research Articles

Early warning system for nocardiosis in largemouth bass (Micropterus salmoides) based on multimodal information fusion

The high-density culture of the recirculating aquaculture system (RAS) makes bacterial and parasitic diseases in fish likely to spread within these systems. Therefore, preventing and controlling the occurrence of fish diseases in RAS is crucial for the future development of aquaculture. However, the current circulating water system lacks early warning measures to prevent and control diseases, resulting in poor accuracy in early warning and detection of diseases. To tackle this challenge, this study introduces an early warning system for largemouth bass (Micropterus salmoides) disease, which is based on You Only Look Once vision 8 (YOLOv8), ByteTrack, Long-Short-Term-Memory(LSTM), and Fuzzy Inference System (FIS). The system utilizes the water quality, surface characteristics, and behavioral traits of diseased fish to predict and prevent disease outbreaks. The system achieved an accuracy of 79.33% for identifying infected body surface features, 80.65% for identifying diseased water quality, and 81.08% for predicting diseased behavior. The experimental results indicate that the early warning system is highly reliable and effective, achieving integrated disease identification accuracy as high as 94.08%. This study enhances the accuracy of early disease warning in fish disease conditions, achieving early warning of nocardiosis in largemouth bass. The study provides crucial technical support for the sustainable and high-quality development of the aquaculture industry.

A Highly Sensitive and Rapid Enzyme Biosensor Based on AuNPs/CS@cMWCNTS for Detecting Fusarium Acid

Fusarium acid (FSA) serves as a highly sensitive biomarker for the early warning of bulb rot in Lanzhou lily caused by Fusarium oxysporum infection. Realizing its highly sensitive, accurate, and rapid detection is crucial for the early warning and control of lily bulb rot. Herein, an enzyme biosensor based on the enzyme inhibition principle was developed for the rapid detection of FSA. Firstly, chitosan-functionalized carboxylated multi-walled carbon nanotubes (CS@cMWCNTS) and gold nanoparticles (AuNPs) were modified on a bare electrode by drop coating and electrochemical deposition methods, respectively. Subsequently, dopamine β-hydroxylase (DBH) was firmly immobilized on the electrode surface through the gold-sulfur bond. Because the activity of DBH can be inhibited by FSA, which leads to a noticeable change in the signal response during the conversion of dopamine (DA) to norepinephrine (NA). Under optimized experimental conditions, the sensor exhibits an excellent linear relationship in the concentration range of 1.00 × 10−5 μg ml−1 ∼ 1.00 × 103 μg ml−1 with the correlation coefficient of 0.9856 and the detection limit is 4.60pg ml−1. Additionally, the sensor showed good stability, repeatability (RSD 1.85%), and selectivity. The method was applied to analyze FSA in the extraction from Lanzhou lily with a recovery higher than 97.59% and RSD less than 1.383%. This method enables the highly sensitive and rapid detection of FSA in real samples, and provides scientific basis and technical support for early warning of diseases and accurate implementation of prevention and control strategies in crop planting and production.

Respiration rate and intestinal microbiota as promising indicators for assessing starvation intensity and health status in Pacific oyster (Crassostrea gigas)

Implementing early disease warning is vital to prevent large-scale mortality of mollusks cultured in open waters. Starvation-induced emaciation syndrome is an undeniable prerequisite for mollusk mortality. Nevertheless, the indicators of mollusk response to starvation remain poorly understood. In this study, the effects of a 44-day starvation period on the physiological performance and symbiotic microbiota of oysters were investigated. Starvation resulted in reduced growth and survival and notable damage to intestinal structure, including shrinking lumens and shortened intestinal walls and villi. Analyses of the respiration rate and intestinal microbiota, including OTUs, taxonomy, and predicted functions, revealed a stepped change pattern with a tipping point on day 10, indicating that oysters employed distinct strategies to adapt to prolonged starvation. Biomarkers associated with nutrient digestion and uptake, such as Firmicutes and Staphylococcus abundance, as well as the Firmicutes/Bacteroidetes ratio, progressively declined with increasing starvation. Conversely, Proteobacteria, an indicator of energy disequilibrium and unstable gut microbiota, and pathogenic Vibrio, showed significant enrichment. As starvation progressed, bacterial competition intensified, as evidenced by the reduced network connectivity and the increasing percentage of negative correlations. Functional predictions demonstrated an early-stage enrichment of degradation functions followed by later-stage suppression during prolonged starvation. In contrast, biosynthesis functions exhibited the opposite trend, suggesting that intestinal microbiota adjusted their specific functions to cope with food deprivation. Collectively, a conceptual model of starvation stress tolerance limits (SSTL) was introduced to define critical thresholds and candidate markers for evaluating starvation intensity and oyster health. This study offers valuable insights into non-feeding mollusks' adaptation to prolonged starvation and provides potential indicators for mollusk mortality events.

Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from insufficient insulin secretion or reduced insulin activity. Epidemiological survey results show that about one third of diabetes patients have signs of diabetic retinopathy, and another third may suffer from serious retinopathy that threatens vision. However, the pathogenesis of diabetic retinopathy is still unclear, and there is no systematic method to detect the onset of the disease and effectively predict its occurrence. In this study, we used medical detection data from diabetic retinopathy patients to determine key biomarkers that induce disease onset through back propagation neural network algorithm and hierarchical clustering analysis, ultimately obtaining early warning signals of the disease. The key markers that induce diabetic retinopathy have been detected, which can also be used to explore the induction mechanism of disease occurrence and deliver strong warning signal before disease occurrence. We found that multiple clinical indicators that form key markers, such as glycated hemoglobin, serum uric acid, alanine aminotransferase are closely related to the occurrence of the disease. They respectively induced disease from the aspects of the individual lipid metabolism, cell oxidation reduction, bone metabolism and bone resorption and cell function of blood coagulation. The key markers that induce diabetic retinopathy complications do not act independently, but form a complete module to coordinate and work together before the onset of the disease, and transmit a strong warning signal. The key markers detected by this algorithm are more sensitive and effective in the early warning of disease. Hence, a new method related to key markers is proposed for the study of diabetic microvascular lesions. In clinical prediction and diagnosis, doctors can use key markers to give early warning of individual diseases and make early intervention.

Estimating the incidence of COVID-19, influenza and respiratory syncytial virus infection in three regions of Queensland, Australia, winter 2022: findings from a novel longitudinal testing-based sentinel surveillance programme

ObjectiveThe 2022 Australian winter was the first time that COVID-19, influenza and respiratory syncytial virus (RSV) were circulating in the population together, after two winters of physical distancing, quarantine and...

Surveillance of COVID-19 and influenza A(H1N1) prevalence in China via medicine-based wastewater biomarkers

The simultaneous monitoring of individual or multiple diseases can be achieved by selecting therapeutic medicines used to treat the primary symptoms of the condition as biomarkers in wastewater. This study proposes a novel approach to monitor the prevalence of COVID-19 and influenza A (H1N1) by selecting nine medicines to serve as biomarkers, including three antipyretics, three antivirals, and three cough suppressants. To verify our approach, wastewater samples were collected from seventeen urban and five rural wastewater treatment plants (WWTPs) in a Chinese city over a period of one year. The use of antipyretics increased notably during the COVID-19 pandemic, while the consumption of antivirals for influenza A (H1N1) rose in the post-COVID-19 pandemic period, indicating a minor spike in the occurrence of influenza A (H1N1) after the COVID-19 pandemic. Fever is a significant symptom of COVID-19 and can serve as a reliable indicator of disease prevalence. Our research found that the prevalence of COVID-19 in urban areas was significantly higher (at 78.5 %, 95 % CI: 73.4 % - 83.9 %) than in rural areas (with a prevalence of 48.1 %, 95 % CI: 42.4 % - 53.8 %). The prevalence of COVID-19 in urban areas in this study was consistent with the data reported by the Chinese center for Disease Control and Prevention (82.4 %). Continuous monitoring of WWTPs in urban areas with fluctuating populations and complex demographics can provide early disease warning. Our results demonstrate the feasibility of evaluating community disease prevalence by selecting major therapeutic medicines as biomarkers in wastewater.

Virome of high-altitude canine digestive tract and genetic characterization of novel viruses potentially threatening human health.

The majority of currently emerging infectious illnesses are zoonotic infections, which have caused serious public health and economic implications. The development of viral metagenomics has helped us to explore unknown viruses. We collected 1,970 canine feces from Yushu and Guoluo in the plateau region of China for this study to do a metagenomics analysis of the viral community of the canine digestive tract. Our analysis identified 203 novel viruses, classified into 11 known families and 2 unclassified groups. These viruses include the hepatitis E virus, first identified in dogs, and the astrovirus, coronavirus, polyomavirus, and others. The relationship between the newly identified canine viruses and known viruses was investigated through the use of phylogenetic analysis. Furthermore, we demonstrated the cross-species transmission of viruses and predicted new viruses that may cause diseases in both humans and animals, providing technical support for the prevention and control of diseases caused by environmental pollution viruses. IMPORTANCE Most emerging infectious diseases are due to zoonotic disease agents. Because of their effects on the security of human or animal life, agriculture production, and food safety, zoonotic illnesses and livestock diseases are of worldwide significance. Because dogs are closely related to humans and domestic animals, they serve as one of the important links in the transmission of zoonotic and livestock diseases. Canines can contaminate the environment in which humans live such as water and soil through secretions, potentially altering the human gut microbiota or causing diseases. Our study enriched the viral community in the digestive tract microbiome of dogs and found types of viruses that threaten human health, providing technical support for the prevention and control of early warning of diseases caused by environmental contaminant viruses.

Sensitive and rapid detection of bacterial endotoxin with a functional carbon nanotube field-effect transistor biosensor

Endotoxin in the blood can cause unexplained fever, and even death due to endotoxemia and bacteremia. Rapid and sensitive detection of endotoxin has become a priority event to intervene in the occurrence of dangerous diseases in time. In this context, a carbon nanotubes-based field-effect transistor (CNTs FET) nanosensor is developed to realize the rapid, label-free and sensitive detection of endotoxin. The CNTs FET was fabricated by assembling the polymer-sorted high-purity semiconductor CNT films onto the sensing channel. In order to improve the detection sensitivity, carboxylated graphene quantum dots (cGQDs) were coupled to the CNT surface via the poly-l-lysine (PLL). After that, polymyxin B (PMB), which is highly specific for endotoxin, was covalently combined with cGQDs, thus enabling the capture and detection of endotoxin. The method not only displayed an extremely low level of limitation of detection in PBS (4.6 fg/mL) and serum (30.3 fg/mL), respectively, with superior resistance to interference, but also enabled the analysis of Gram-negative bacterial infections in blood samples in a short duration of time. Meanwhile, the receiver operating characteristic curve (ROC) proved the superior diagnostic accuracy of this method (AUC = 0.990). Considering its excellent performance, the constructed CNTs FET biosensor is a promising tool to provide early warning of disease for clinical Gram-negative bacteremia and endotoxemia.

Identification of some avian diseases based on images obtained from a camera by convolutional neural networks

The research paper introduced several results achieved in the research and construction of an automatic system for early detection of various poultry diseases by computer vision technology, using artificial intelligence to analyse images from surveillance cameras. The experimental system used the deep learning neural network RepVGG as an inference model for detecting poultry diseases. Training data set and model evaluation for 3 diseases ORT, CRD, MAREK, including 500 images for each disease, were directly collected from surveillance cameras and from the internet. The training process used 80% of the sample images; the model reached an error of 0.02 after 5000 rounds of training. Evaluation of the model with the remaining 10% of samples achieved an accuracy of 99.94%. In the actual test with the surveillance camera (resolution 1902x1080 pixels), the system can detect and classify correctly diseases over 90%, especially for diseases with images involving multiple distinguishing symptoms, the accuracy was more than 98%.The obtained results showed that the system can be used to support early warning of diseases for poultry. The system can be trained to detect other poultry diseases such as Newcastle disease, fowl pox, pullorum disease, typhoid...

Hyperspectral Sensing of Plant Diseases: Principle and Methods

Pathogen infection has greatly reduced crop production. As the symptoms of diseases usually appear when the plants are infected severely, rapid identification approaches are required to monitor plant diseases at early the infection stage and optimize control strategies. Hyperspectral imaging, as a fast and nondestructive sensing technology, has achieved remarkable results in plant disease identification. Various models have been developed for disease identification in different plants such as arable crops, vegetables, fruit trees, etc. In these models, important algorithms, such as the vegetation index and machine learning classification and methods have played significant roles in the detection and early warning of disease. In this paper, the principle of hyperspectral imaging technology and common spectral characteristics of plant disease symptoms are discussed. We reviewed the impact mechanism of pathogen infection on the photo response and spectrum features of the plants, the data processing tools and algorithms of the hyperspectral information of pathogen-infected plants, and the application prospect of hyperspectral imaging technology for the identification of plant diseases.

Back Cover

The cover picture shows air pollution or bad life habits could expose people to various forms of diseases. Development of advanced techniques can lend a great help to warn air toxicity, however a detector for all is lacking. Increasing evidences show that exhaled breath contains numerous gaseous biomarkers that can be used to discern respiratory infections or alert a possible pathological change. Integration of breath analysis and machine learning algorithms with various breath‐borne biomarker databases could offer early disease warning. More details are discussed in the article by Yao on page 1471—1477.image

Breathable, Antibacterial, and Biocompatible Collagen Fiber Network Decorated with Zwitterionic Silver Nanoparticles for Plantar Pressure Monitoring.

Foot plantar pressure monitoring and gait analysis are of great significant in footwear design, sport biomechanics, injury prevention, and early warning of disease. Flexible and wearable smart insoles pave a feasible way for these application scenarios. However, the majority of the currently developed smart insoles are composed of synthetic polymers (e.g., plastics, rubbers, etc.), leading to inevitable problems associated with air permeability, hygiene condition, biocompatibility, and wearing comfort. Here, a new paradigm of a natural collagen fiber network (CFN) with soft and breathable features, which can be obtained by facilely treating animal hides via conventional leather pretreatment process, is selected as substrate material for constructing smart insoles due to its high permeability and porosity. Further, biocompatible zwitterionic silver nanoparticles (AgNPs) with both carboxybetaine and catechol groups on the interface were designed for firmly and uniformly immobilization onto the hierarchical micro-/nanoscale fibers of CFN through mussel-inspired catechol/amino chemistry, giving rise to both good antibacterial property and pressure sensing capability of the resultant material. The finally developed smart insole by using the AgNPs decorated CFN exhibits good capability for plantar pressure mapping and gait feature analysis. Especially, the smart insole will be very suitable for pressure monitoring and gait analysis of a diabetic foot with sensitive skin that requires a high biocompatible and antibacterial environment.

Ultrasensitive DNA-Biomacromolecule Sensor for the Detection Application of Clinical Cancer Samples.

Diagnostic testing of biological macromolecules is of great significance for early warning of disease and cancer. Nevertheless, restricted by limited surface area and large steric hindrance, sensitive detection of macromolecules with interface‐based sensing method remains challenging. Here, a “biphasic replacement” electrochemical aptamer‐based (BRE‐AB) sensing strategy which placed capture reaction of the biomacromolecule in a homogeneous solution phase and replaced with a small diameter of single‐stranded DNA to attach to the interface is introduced. Using the BRE‐AB sensor, the ultrasensitive detection of luteinizing hormone (LH) with the detection limit of 10 × 10−12 m is demonstrated. Molecular Dynamics simulations are utilized to explore the binding mechanism of aptamer and target LH. Moreover, it is confirmed that the BRE‐AB sensor has excellent sensing performance in whole blood and undiluted plasma. Using the BRE‐AB sensor, the LH concentrations in 40 clinical samples are successfully quantified and it is found that LH is higher expressed in breast cancer patients. Furthermore, the sensor enables simple, low‐cost, and easy to regenerate and reuse, indicating potentially applicable for point‐of‐care biological macromolecules diagnostics.

基于网络弹性的早期疾病预警

基于网络弹性的早期疾病预警

Monitoring and early warning system of porcine pasteurellosis based on WebGIS

The incidence of porcine pasteurellosis in China is so widespread that it is difficult to clearly understand the prevalence and maintain continuous monitoring. In order to reduce immense negative economic impact on the livestock industry; monitoring, early warning, and visual management systems are highly desirable. In this study, a monitoring and early warning system for porcine pasteurellosis was established based on Web Geographical Information System (WebGIS) technology. By establishing a path analysis function, buffer analysis function, and hot spot analysis function, it can provide a method of support and control of infectious diseases. For early warning of disease, four common interpolation methods were tested, all of which showed that the affected area of porcine pasteurellosis in China was mainly concentrated in the south of the mainland. A cross-validation method was used to compare the four interpolation methods. The cross-validation showed that the inverse distance weighting (IDW) method was suitable for forecasting the occurrence of porcine pasteurellosis in China. Finally, using C sharp (C#) as the development language and WebGIS technology, a monitoring and early warning system based on Browser/Server structure was developed. This is the first monitoring and early warning system of porcine pasteurellosis based on WebGIS. The performance of the WebGIS technology indicated a great potential for animal infectious disease applications and provided a foundation for future work.

Clinical features and high resolutionCT imaging findings of preliminary diagnosis novel coronavirus pneumonia

Objective To summarize the clinical characteristics of 141 patients with novel coronavirus pneumonia (NCP) and the imaging characteristics of High Resolution CT(HRCT) in the chest. Methods From January 20, 2020 to 28, 141 NCP patients, 77 males and 64 females, with a median age of 49 (9,87), were retrospectively analyzed. The clinical features, laboratory examination indexes and HRCT findings of 141 NCP patients were analyzed. Results In 141 NCP patients, 38 (26.95%) had a decrease in leukocyte count and 71 (50.35%) had a decrease in lymphocyte ratio. Among 141 NCP patients, 139 (98.58%) had fever (over 37.5 ° C), 106 (75.18%) coughed, 11 (7.80%) had headache, 41 (29.08%) coughed up sputum, 93 (65.96%) had chest distress, and 4 (2.84%) had diarrhea. HRCT of 141 NCP patients were abnormal, 52 (36.88%) showed ground glass shadow (GGO) and patchy shadow, mainly subpleural distribution; 23 (16.31%) showed GGO with focal consolidation; 27 (19.15%) had small patchy blur; 20 (14.18%) had large patchy consolidation; 48 (34.04%) had bronchovascular bundle thickening and vascular perforator sign; 5 (3.55%) had Air bronchus sign; small nodule shadow in 7 cases (4.96%); fibrosis, grid shadow or strip shadow in 5 cases (3.55%); bilateral pleural effusion in 7 cases (4.96%); mediastinal or bilateral hilar lymphadenopathy in 4 cases (2.84%). Conclusions The clinical features and HRCT images of NCP are various. Under the specific epidemiological background of NCP, HRCT scan of chest should be carried out in time to make early warning of disease. Key words: Novel coronavirus pneumonia; Tomography, X-ray computed

Detection and Analysis of Behavior Trajectory for Sea Cucumbers Based on Deep Learning

The motion trajectory of sea cucumbers reflects the behavior of sea cucumbers, and the behavior of sea cucumbers reflects the status of the feeding and individual health, which provides the important information for the culture, status detection and early disease warning. Different from the traditional manual observation and sensor-based automatic detection methods, this paper proposes a detection, location and analysis approach of behavior trajectory based on Faster R-CNN for sea cucumbers under the deep learning framework. The designed detection system consists of a RGB camera to collect the sea cucumbers' images and a corresponding sea cucumber identification software. The experimental results show that the proposed approach can accurately detect and locate sea cucumbers. According to the experimental results, the following conclusions are drawn: (1) Sea cucumbers have an adaptation time for the new environment. When sea cucumbers enter a new environment, the adaptation time is about 30 minutes. Sea cucumbers hardly move within 30 minutes and begin to move after about 30 minutes. (2) Sea cucumbers have the negative phototaxis and prefers to move in the shadows. (3) Sea cucumbers have a tendency to the edge. They like to move along the edge of the aquarium. When the sea cucumber is in the middle of the aquarium, the sea cucumber will look for the edge of the aquarium. (4) Sea cucumbers have unidirectional topotaxis. They move along the same direction with the initial motion direction. The proposed approach will be extended to the detection and behavioral analysis of the other marine organisms in the marine ranching.

Epidemiologic and Clinical Characteristics of 42 Deaths Due to SARS-CoV-2 Infection in Wuhan, China: A Retrospective Study

Background: In December 2019, 2019-nCoV appeared in Wuhan, China. Some of these cases led to death. Here, we describe the epidemiologic and clinical characteristics of patients who died from SARS-CoV-2 infection, and the potential risk factors associated with a fatal outcome. Methods: This retrospective case analyzed data from 42 death cases due to 2019-nCoV infection at Tongji Hospital Affiliated to Huazhong University of Science and Technology, Wuhan, China, including demographics, clinical detection, laboratory findings, and treatments. Findings: The mean time to admission from symptom onset was11±5 days, the mean hospital stay was 5±4 days. Among the 42 patients, 71% were male, 60% had ≥1 co-morbidity. All patients had fever and bilateral pneumonia on computed tomography, abnormal nfection-related biomarkers, and renal impairment. Abnormal blood coagulation parameters seen in more than half of patients may be consistent with disseminated intravascular coagulation. All these patients were treated in the ICU. Interpretation: SARS-CoV-2 infection carries a risk of mortality; based on this case series older men with underlying co-morbidities are likely at increased risk. Impaired consciousness level, markers of renal impairment and coagulation abnormalities may be poor prognostic factors. Funding Statement: This work was supported by grants from the Epidemiology, Early Warning, and Response Techniques of Major Infectious Diseases in the Belt and Road Initiative (#2018ZX10101002), National Natural Science Foundation of China (#81871699), and Foundation of Jilin Province Science and Technology Department (#172408GH010234983). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Tongji Hospital Affiliated to Huazhong University of Science and Technology, in Wuhan, Hubei Province, China, approved this retrospective study. Ethical approval was granted by the Ethics Commission of Tongji Hospital Affiliated to Huazhong University of Science and Technology.

Noninvasive and Wearable Respiration Sensor Based on Organic Semiconductor Film with Strong Electron Affinity.

Interventional medical detection techniques require expensive devices and cause inconvenience and discomfort to the human body, which restricts their application to the frequency and duration of measurements. A noninvasive respiration test is urgently required for the next-generation medical technologies in early disease warning and postoperative monitoring. This article describes a noninvasive and wearable sensing device that shows high sensitivity toward acetone in respiratory gases with excellent stability, low energy consumption, and reliable flexibility. To obtain such a sensor, the organic semiconductor compound La(TBPP)(TBNc) (TBPP = tetrakis(4-tert-butylphenyl)porphyrin; TBNc = tetrakis(4-tert-butylphenyl)naphthalocyanine) was synthesized and further self-assembled into a highly ordered flexible film via a simple solution-vapor annealing method. The fabricated flexible film was deposited on an interdigitated electrode with poly(ethylene terephthalate) substrate and employed as an electrical identification component for a respiration sensor. Thanks to the attractive electron-transfer properties of highly ordered films and strong electron affinity of La(TBPP)(TBNc) molecules, the as-prepared sensor shows a low detection limit (200 ppb) and acceptable selectivity. The wrinkled/rippled structure of films endows the fabricated sensors with the ability of mechanical flexibility. More importantly, the experimental results suggest the potential application of acetone identification in real respiratory gases and provide a new concept for the development of noninvasive and wearable medical diagnostic devices.

Epi Archive: Automated Synthesis of Global Notifiable Disease Data

ObjectiveAutomatically collect and synthesize global notifiable disease data and make it available to humans and computers. Provide the data on the web and within the Biosurveillance Ecosystem (BSVE) as a novel data stream. These data have many applications including improving the prediction and early warning of disease events.IntroductionGovernment reporting of notifiable disease data is common and widespread, though most countries do not report in a machine-readable format. This is despite the WHO International Health Regulations stating that “[e]ach State Party shall notify WHO, by the most efficient means of communication available.” 1Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health. While most nations likely store incident data in a machine-readable format, governments can be hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational2.A survey conducted by LANL of notifiable disease data reporting in over fifty countries identified only a few websites that report data in a machine-readable format. The majority (>70%) produce reports as PDF files on a regular basis. The bulk of the PDF reports present data in a structured tabular format, while some report in natural language or graphical charts.The structure and format of PDF reports change often; this adds to the complexity of identifying and parsing the desired data. Not all websites publish in English, and it is common to find typos and clerical errors.LANL has developed a tool, Epi Archive, to collect global notifiable disease data automatically and continuously and make it uniform and readily accessible.MethodsA survey of the national notifiable disease reporting systems is periodically conducted notating how the data are reported and in what formats. We determined the minimal metadata that is required to contextualize incident counts properly, as well as optional metadata that is commonly found.The development of software to regularly ingest notifiable disease data and make it available involves three to four main steps: scraping, detecting, parsing and persisting.Scraping: we examine website design and determine reporting mechanisms for each country/website, as well as what varies across the reporting mechanisms. We then design and write code to automate the downloading of data for each country. We store all artifacts presented as files (PDF, XLSX, etc.) in their original form, along with appropriate metadata for parsing and data provenance.Detecting: This step is required when parsing structured non-machine-readable data, such as tabular data in PDF files. We combine the Nurminen methodology of PDF table detection with in-house heuristics to find the desired data within PDF reports3.Parsing: We determine what to extract from each dataset and parse these data into uniform data structures, correctly accommodating the variations in metadata (e.g., time interval definitions) and the various human languages.Persisting: We store the data in the Epi Archive database and make it available on the internet and through the BSVE. The data is persisted into a structured and normalized SQL database.ResultsEpi Archive currently contains national and/or subnational notifiable disease data from thirty-nine nations. When a user accesses the Epi Archive site, they are able to peruse, chart and download data by country, subregion, disease and time interval. Access to a cached version of the original artifacts (e.g. PDF files), a link to the source and additional metadata is also available through the user interface. Finally, to ensure machine-readability, the data from Epi Archive can be reached through a REST API. http://epiarchive.bsvgateway.org/ConclusionsLANL, as part of a currently funded DTRA effort, is automatically and continually collecting global notifiable disease data. While thirty-nine nations are in production, more are being brought online in the near future. These data are already being utilized and have many applications, including improving the prediction and early warning of disease events.