Early Warning Capabilities Research Articles

An early warning indicator trained on stochastic disease-spreading models with different noises.

The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modelling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreaks by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.

Predicting Food‐Security Crises in the Horn of Africa Using Machine Learning

AbstractIn this study, we present a machine‐learning model capable of predicting food insecurity in the Horn of Africa, which is one of the most vulnerable regions worldwide. The region has frequently been affected by severe droughts and food crises over the last several decades, which will likely increase in future. Therefore, exploring novel methods of increasing early warning capabilities is of vital importance to reducing food‐insecurity risk. We present a XGBoost machine‐learning model to predict food‐security crises up to 12 months in advance. We used >20 data sets and the FEWS IPC current‐situation estimates to train the machine‐learning model. Food‐security dynamics were captured effectively by the model up to 3 months in advance (R2 > 0.6). Specifically, we predicted 20% of crisis onsets in pastoral regions (n = 96) and 20%–50% of crisis onsets in agro‐pastoral regions (n = 22) with a 3‐month lead time. We also compared our 8‐month model predictions to the 8‐month food‐security outlooks produced by FEWS NET. Over a relatively short test period (2019–2022), results suggest the performance of our predictions is similar to FEWS NET for agro‐pastoral and pastoral regions. However, our model is clearly less skilled in predicting food security for crop‐farming regions than FEWS NET. With the well‐established FEWS NET outlooks as a basis, this study highlights the potential for integrating machine‐learning methods into operational systems like FEWS NET.

A Combined Data-Driven and Model-Based Algorithm for Accurate Battery Thermal Runaway Warning.

With the increasingly widespread application of large-scale energy storage battery systems, the demand for battery safety is rising. Research on how to detect battery anomalies early and reduce the occurrence of thermal runaway (TR) accidents has become particularly important. Existing research on battery TR warning algorithms can be mainly divided into two categories: model-driven and data-driven methods. However, the common model-driven methods are often of high complexity, with poor versatility and low early warning capability; and the common data-driven methods are mostly based on neural networks, requiring substantial training costs, with better early warning capabilities but higher false alarm probabilities. To address the limitations of existing works, this paper proposes a combined data-driven and model-based algorithm for accurate battery TR warnings. Specifically, the K-Means algorithm serves as the data-driven module, capturing outliers in battery data, and the Bernardi equation serves as the model-driven module used to evaluate battery temperature. Ultimately, the outputs of the weighted model-driven module and data-driven module are combined to comprehensively assess whether the battery is abnormal. The proposed algorithm combines the advantages of model-driven and data-driven approaches, achieving a 25 min advance warning for thermal runaway, with a significantly reduced probability of false alarms.

Application of High-Frequency Intelligent Sensing Network in Monitoring and Early Warning of Water Quality Dynamic Change

By implementing a high-frequency intelligent network of sensors, this work explores continuous monitoring and alerting for dynamic changes in water quality. Life depends on water, yet pollution is a greater menace. For this reason, precautions and careful observation are necessary. Typically, the focus on conventional water quality system monitoring is too much on data collection and needs more on analysis and extraction, limiting its capacity to offer thorough solutions. Making informed decisions becomes more complicated when there are discrepancies like damaged data, loss from power outages, or transmission issues. The proposed High-Frequency Intelligent Sensing Network (HFISN) monitoring system uses cloud computing, IoT and Big data technologies for intelligent sensing. Researchers developed it to address various challenges. Researchers recommend Nephelometric Turbidity Unit (NTU) Sensor installation to enhance the system’s performance and facilitate better monitoring of sedimentation, particle issues, and water purity. This sensor makes it possible to make more informed decisions by expanding the platform’s dataset. The solution not only resolves data cleaning and analysis issues but also includes intelligent early-warning capabilities for timely alerts. Quantum Cloud (QC) technology is employed to enhance security and accessibility. Test findings confirm its robustness with extra features and a built-in turbidity sensor. Because the platform ensures data accuracy and dependability, it provides decision-makers with a solid foundation to protect water resources.

Early warning response to rainstorm: Designing a model with incentive and supervision mechanisms based on the principal-agent theory

Owing to the increasing frequency of rainstorm disasters, strengthening the link between meteorological warning and emergency response that is early warning response has become crucial. However, early warning response usually fail, the underlying reasons behind this phenomenon and their improvement measures need to be urgently explored. This study uses the principal–agent theory to establish an incentive-constraining model based on hierarchical government to explore this problem. Considering the interest game between the superior and local governments, we mainly discuss how to design the optimal incentive mechanism under the condition of information asymmetry to enhance the local government's enthusiasm for providing an early warning response to rainstorm, thereby maximizing the benefits for both parties in the model. Furthermore, based on actual conditions, we use numerical simulation to present the model results more clearly. The results indicate that incentive and supervision mechanisms are mutually substitutable. Strong early warning capability and active public cooperation can improve the local government's enthusiasm for providing an early warning response. Excessive costs lead the local government to adopt a negative attitude toward warning, regardless of supervision is introduced. Only when the local government exhibits strong risk aversion and its investment is affected by numerous random factors, introducing supervision is effective. Meanwhile, too many external factors, excessive or insufficient supervision's intensity also affect the optimization effect of supervision on incentive contract. To maximize the effectiveness of supervision, the superior government should improve the supervision team's capability, and maintain the supervision's intensity within a reasonable range.

Construction and application of a novel WGAN-CNN-based predicting approach for dust concentration at underground coal mine working faces.

To enhance the real-time monitoring and early-warning capabilities for dust disasters in underground coal mine, this paper presents a novel WGAN-CNN-based prediction approach to predict the dust concentration at underground coal mine working faces. Dust concentration, wind speed, temperature, and methane concentration were collected as the original data due to their nonlinear relationship. The consistency between the generated and original data distributions was verified through PCA dimensionality reduction analysis. The predictive performance of this approach was assessed using five metrics (R2, EVS, MSE, RMSE, and MAE) and compared with three other algorithms (Random Forest Regressor, MLP Regressor, and LinearSVR). The findings indicate that a majority of the generated data falls within the distribution range of the real dataset, exhibiting reduced levels of volatility and dispersion. The R2 values of prediction results are all above 98%, and the MSE values are between 0.0007 and 0.0106. The proposed approach exhibits superior predictive accuracy and robust model generalization capabilities compared to alternative algorithms, thereby enhancing the real-time monitoring and early-warning level of dust disasters in underground coal mine. This will facilitate the realization of advanced prevention and control measures for dust disasters, showcasing a wide range of potential applications.

Multifunctional polylactic acid sensing fabric based on biomass flame retardants for intelligent fire early-warning

Today, building materials emit many hazardous gases in the event of a fire, causing great harm to human health and the environment. Therefore, it is of great significance to develop bio-based flame retardant materials and to realize preventive measures to reduce fires or their damage. In this work, we fabricated a novel multifunctional fire early-warning polylactic acid-based fabric (MFR-PBF) by coating MXene nanosheet, phytic acid @ furfurylamine (PA@FA) and ammonium polyphosphate (APP) via an eco-friendly layer-by-layer assembly method. MFR-PBF showed outstanding flame retardancy including a limiting oxygen index value of 35 % and better char formation capacity. More importantly, MFR-PBF exhibited sensitive fire early-warning capability (∼1 s) and excellent cyclic alarm stability (>15 cycles) due to the excellent semiconductor responsiveness (light and heat) and the significant catalytic char formation effect. Moreover, MFR-PBF is comfortable, flexible and strong enough to sew onto firefighter uniform to detect a variety of human motions, which can be monitored in the internet by using a LoRa emitter and a gateway. In addition, the controllable heating performance rendered MFR-PBF as a potential portable heater. This work provides new insights into the preparation and application of intelligent fire early-warning fabrics in the smart fire protection and Internet of Things.

Analysis on Emergency Management of Urban Public Safety in the Era of Big Data

With the continuous development of computer technology and Internet technology, the world has entered the era of big data. The advent of the era of big data has brought opportunities and challenges to the development of all walks of life in China, and it is also true for urban emergency management, which not only helps the government to improve the efficiency of emergency management, but also effectively solves the adverse social consequences caused by the government’s lack of corresponding early warning capabilities. Of course, as the so-called opportunities and challenges coexist, the era of big data brings opportunities to the development of urban emergency management, but it also brings great challenges to relevant departments. This paper aims to comprehensively analyze the development of urban emergency management in the era of big data by consulting relevant literature and investigating a large number of actual cases of urban emergency management, so as to provide reference for relevant workers.

Macroeconomic Prediction with High Frequency Data of Electricity Consumption based on MIDAS model

In recent years, China’s macroeconomic landscape has been characterized by a confluence of challenges, including intensified global economic headwinds and the complexities associated with domestic structural economic transformation. Consequently, the macroeconomic environment has become increasingly intricate, with heightened sensitivity and rapid responsiveness to external shocks, posing formidable tests for China’s macroeconomic early warning capabilities. Traditional macroeconomic forecasting methodologies, predominantly reliant on monthly and quarterly economic and industrial statistics, exhibit inherent limitations due to their low frequency and sluggish data updates, rendering them inadequate for accurately predicting short-term macroeconomic fluctuations. Electricity consumption, being a pivotal component of terminal energy utilization across most industries, holds robust representativeness for the broader macroeconomic landscape. Leveraging the salient characteristics of electricity consumption data, this study amalgamates it with other high-frequency data to forecast industrial added value through the employment of the MIDAS (Mixed Data Sampling) model. The findings indicate that the integration of daily electricity consumption data into the MIDAS model can significantly enhance short-term prediction accuracy.

Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model

Climatic factors influence malaria transmission via the effect on the Anopheles vector and Plasmodium parasite. Modelling and understanding the complex effects that climate has on malaria incidence can enable important early warning capabilities. Deep learning applications across fields are proving valuable, however the field of epidemiological forecasting is still in its infancy with a lack of applied deep learning studies for malaria in southern Africa which leverage quality datasets. Using a novel high resolution malaria incidence dataset containing 23 years of daily data from 1998 to 2021, a statistical model and XGBOOST machine learning model were compared to a deep learning Transformer model by assessing the accuracy of their numerical predictions. A novel loss function, used to account for the variable nature of the data yielded performance around + 20% compared to the standard MSE loss. When numerical predictions were converted to alert thresholds to mimic use in a real-world setting, the Transformer’s performance of 80% according to AUROC was 20–40% higher than the statistical and XGBOOST models and it had the highest overall accuracy of 98%. The Transformer performed consistently with increased accuracy as more climate variables were used, indicating further potential for this prediction framework to predict malaria incidence at a daily level using climate data for southern Africa.

High fire safety thermal protective composite aerogel with efficient thermal insulation and reversible fire warning performance for firefighting clothing

High fire safety thermal protective aerogels serve as a thermal insulation barrier in firefighting clothing are urgently needed to improve personal protective technology. However, fabricating fire protection aerogels that can simultaneously hold efficient insulation and early fire warning capabilities to provide high fire safety for firefighters still remains grand challenging. Herein, a novel lightweight and high fire safety smart thermal protective composite aerogel (APFP) composed of aramid nanofibers (ANFs), polyethylene glycol (PEG), Fe3O4 NWs, and polyaniline (PANI) was fabricated via freeze-drying and a chemical in-situ polymerization method. The resultant APFP achieved an efficient thermal buffer performance in response to high temperature with a latent heat of 162.5 J/g due to the absorption and storage thermal capability of PEG.Furthermore, based on the resistance change of Fe3O4 NWs under high temperature and flame, adding 25 wt% Fe3O4 NWs to ANFs-based aerogel, APFP aerogel can achieve resistance sensitive transition from an electrically insulated state to a highly conductive state. Under a flame attack, the APFP demonstrated an ultrasensitive and repeatable fire warning response (1.3 s), which contributes to acquiring active fire protection for APFP before the thermal barrier layer malfunctions in fire. In addition, the APFP exhibited impressive flexibility, heat insulation, lightweight, and flame retardancy, which self-extinguished within 1.5 s after exposure to fire for 10 s. This work proposed an ingenious strategy for the construction of high fire safety and thermal insulation fireproof aerogels, which revealed enticing prospects as thermal insulation barrier in the field of firefighting clothing.

Evaluation of hailstorm early warning capability in Taiwan's summer

<p>本研究以中央氣象署於預報作業使用之對流監測平台系統(System for Convection Analysis and Nowcasting, SCAN)提供的歷史對流胞及冰雹預警資訊為基礎，結合由民眾或新聞媒體所記錄下來的歷史冰雹目擊事件，針對臺灣近年夏季冰雹的即時預警狀況進行統整評估。分析資料方面，為能涵蓋臺灣南北地區之事件，故分別選用五分山雷達、七股雷達作為北部與南部地區的資料來源。統計結果顯示，臺灣夏季冰雹多發生於弱綜觀天氣條件的下午時段，且實際目擊的冰雹尺寸平均值小於現行預警門檻。冰雹相應對流胞的持續時間比一般夏季對流更長，其中又以較低海拔的七股雷達訊號更完整。然而，更高的雷達掃描時間解析度，使五分山雷達的預警表現明顯更好。整體而言，過去SCAN系統於臺灣冰雹事件發生前可提供的預警時間多在15分鐘以內，核心問題則包含(一)部分目擊事件未成功預警以及(二)現有系統假警報過多二者。本研究針對上述情況，分別提供(一)以冰雹目擊個案特徵統計結果下修預警門檻與(二)以臺灣探空資料修正SCAN系統背景預設之環境0℃、-20℃高度等改善建議。</p> <p> </p><p>This study is based on the historical storm cell and hail warning information provided by the System for Convection Analysis and Nowcasting (SCAN), which is utilized by the Central Weather Bureau (CWB) for operational forecasting. By combining hail records from recent years as reported in the news, the real-time early warning capabilities for Taiwan’s summer hail can be integrated and improved. In terms of the analysis data, Wu-Fan San (RCWF) and Chi-Gu (RCCG) were selected as radar data sources to cover events in the northern and southern regions, respectively.<br />The results indicate that most of the events occur in the afternoon during weak synoptic weather conditions, and the size of witnessed hail is mostly below the warning threshold. Hailstorms have a longer duration compared to general summer storm cells, and the RCCG at lower altitudes provides a more complete signal. However, the higher time resolution of the radar scan gives RCWF a significantly better early warning performance. In summary, SCAN can provide a lead time of approximately 15 minutes for hail warnings, but two major issues are identified: (1) some hail events were not detected by the SCAN system, and (2) there are too many false alarms. This research proposes two solutions: (1) lowering the warning threshold based on the statistical analysis of hail reports, and (2) correcting the default value of the SCAN system using sounding data from Taiwan to address these problems.</p> <p> </p>

Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability

Acute myocardial infarction dominates coronary artery disease mortality. Identifying bio-signatures for plaque destabilization and rupture is important for preventing the transition from coronary stability to instability and the occurrence of thrombosis events. This computational systems biology study enrolled 2,235 samples from 22 independent bulks cohorts and 14 samples from two single-cell cohorts. A machine-learning integrative program containing nine learners was developed to generate a warning classifier linked to atherosclerotic plaque vulnerability signature (APVS). The classifier displays the reliable performance and robustness for distinguishing ST-elevation myocardial infarction from chronic coronary syndrome at presentation, and revealed higher accuracy to 33 pathogenic biomarkers. We also developed an APVS-based quantification system (APVSLevel) for comprehensively quantifying atherosclerotic plaque vulnerability, empowering early-warning capabilities, and accurate assessment of atherosclerosis severity. It unraveled the multidimensional dysregulated mechanisms at high resolution. This study provides a potential tool for macro-level differential diagnosis and evaluation of subtle genetic pathological changes in atherosclerosis.

Skill and lead time of vegetation drought impact forecasts based on soil moisture observations

Timely and skilful forecasts of vegetation drought impacts should enable more proactive drought preparedness, management, and mitigation. Numerous previous studies found a temporal correlation between soil moisture and vegetation condition. However, a correlation across the full range of soil moisture and vegetation condition does not automatically translate into skill in forecasting infrequent events such as agricultural droughts. Here, we develop a threshold- or impact-based forecasting framework to assess early warning capability (EWC). We analysed the skill and lead time achieved using soil moisture observations at multiple depths as predictors of subsequent vegetation drought impacts inferred from MODIS satellite observations at 93 sites across the United States. Forecast thresholds were expressed in terms of seasonally-adjusted standardised anomalies (z-scores) to distinguish climate-related impacts from any seasonal vegetation cycle. Different combinations of soil moisture integration depth, satellite vegetation indices and threshold levels were tested. Near-Infrared Reflectance of vegetation (NIRv) yielded a marginally better EWC than other indicators of vegetation drought impact. The optimal soil moisture integration depth varied between land cover types, from 0 to 10 cm for cropping systems to 0–100 cm for grassland and savanna. The greatest skill improvements were achieved using similar z-score threshold values for the soil moisture trigger and vegetation impact, producing typical lead times of two to four weeks. Further research is recommended to combine the framework developed here with spatially continuous soil moisture analyses or forecasts available from remote sensing and land surface models.

Getting ahead of Flash Drought: From Early Warning to Early Action

Abstract Flash droughts, characterized by their unusually rapid intensification, have garnered increasing attention within the weather, climate, agriculture, and ecological communities in recent years due to their large environmental and socioeconomic impacts. Because flash droughts intensify quickly, they require different early warning capabilities and management approaches than are typically used for slower-developing “conventional” droughts. In this essay, we describe an integrated research-and-applications agenda that emphasizes the need to reconceptualize our understanding of flash drought within existing drought early warning systems by focusing on opportunities to improve monitoring and prediction. We illustrate the need for engagement among physical scientists, social scientists, operational monitoring and forecast centers, practitioners, and policy-makers to inform how they view, monitor, predict, plan for, and respond to flash drought. We discuss five related topics that together constitute the pillars of a robust flash drought early warning system, including the development of 1) a physically based identification framework, 2) comprehensive drought monitoring capabilities, and 3) improved prediction over various time scales that together 4) aid impact assessments and 5) guide decision-making and policy. We provide specific recommendations to illustrate how this fivefold approach could be used to enhance decision-making capabilities of practitioners, develop new areas of research, and provide guidance to policy-makers attempting to account for flash drought in drought preparedness and response plans.

Citywide wastewater SARS-CoV-2 levels strongly correlated with multiple disease surveillance indicators and outcomes over three COVID-19 waves

Public health surveillance systems for COVID-19 are multifaceted and include multiple indicators reflective of different aspects of the burden and spread of the disease in a community. With the emergence of wastewater disease surveillance as a powerful tool to track infection dynamics of SARS-CoV-2, there is a need to integrate and validate wastewater information with existing disease surveillance systems and demonstrate how it can be used as a routine surveillance tool. A first step toward integration is showing how it relates to other disease surveillance indicators and outcomes, such as case positivity rates, syndromic surveillance data, and hospital bed use rates. Here, we present an 86-week long surveillance study that covers three major COVID-19 surges. City-wide SARS-CoV-2 RNA viral loads in wastewater were measured across 39 wastewater treatment plants and compared to other disease metrics for the city of Houston, TX. We show that wastewater levels are strongly correlated with positivity rate, syndromic surveillance rates of COVID-19 visits, and COVID-19-related general bed use rates at hospitals. We show that the relative timing of wastewater relative to each indicator shifted across the pandemic, likely due to a multitude of factors including testing availability, health-seeking behavior, and changes in viral variants. Next, we show that individual WWTPs led city-wide changes in SARS-CoV-2 viral loads, indicating a distributed monitoring system could be used to enhance the early-warning capability of a wastewater monitoring system. Finally, we describe how the results were used in real-time to inform public health response and resource allocation.

Advancing early warning capabilities with CHIRPS-compatible NCEP GEFS precipitation forecasts

CHIRPS-GEFS is an operational data set that provides daily bias-corrected forecasts for next 1-day to ~15-day precipitation totals and anomalies at a quasi-global 50-deg N to 50-deg S extent and 0.05-degree resolution. These are based on National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System version 12 (GEFS v12) precipitation forecasts. CHIRPS-GEFS forecasts are compatible with Climate Hazards center InfraRed Precipitation with Stations (CHIRPS) data, which is actively used for drought monitoring, early warning, and near real-time impact assessments. A rank-based quantile matching procedure is used to transform GEFS v12 “reforecast” and “real-time” forecast ensemble means to CHIRPS spatial-temporal characteristics. Matching distributions to CHIRPS makes forecasts better reflect local climatology at finer spatial resolution and reduces moderate-to-large forecast errors. As shown in this study, having a CHIRPS-compatible version of the latest generation of NCEP GEFS forecasts enables rapid assessment of current forecasts and local historical context. CHIRPS-GEFS effectively bridges the gap between observations and weather predictions, increasing the value of both by connecting monitoring resources (CHIRPS) with interoperable forecasts.

Wind Turbine Gearbox Failure Detection Through Cumulative Sum of Multivariate Time Series Data

The wind energy industry is continuously improving their operational and maintenance practice for reducing the levelized costs of energy. Anticipating failures in wind turbines enables early warnings and timely intervention, so that the costly corrective maintenance can be prevented to the largest extent possible. It also avoids production loss owing to prolonged unavailability. One critical element allowing early warning is the ability to accumulate small-magnitude symptoms resulting from the gradual degradation of wind turbine systems. Inspired by the cumulative sum control chart method, this study reports the development of a wind turbine failure detection method with such early warning capability. Specifically, the following key questions are addressed: what fault signals to accumulate, how long to accumulate, what offset to use, and how to set the alarm-triggering control limit. We apply the proposed approach to 2 years’ worth of Supervisory Control and Data Acquisition data recorded from five wind turbines. We focus our analysis on gearbox failure detection, in which the proposed approach demonstrates its ability to anticipate failure events with a good lead time.

Maternal Deaths During a Pilot Study Using Digitized Maternal Early Warning System

Background: India accounts for nearly one in six maternal deaths and over the last two decades, maternal mortality in India has decreased rapidly and faster than the global rate. However, the rate of decline has been slowing and further progress calls for new interventions and improvements in existing programs and the care-delivery process. Objective(s): We developed and tested a telehealth solution that included an early warning system and a clinical decision support tool for timely detection of clinical deterioration and appropriate management of women in labor at a large general hospital in India. The pilot study with 15,184 patients was associated with a significant decrease in maternal mortality and in-hospital eclampsia. The results were published earlier this year. Here we examine and analyze the maternal deaths that occurred during that study period and discuss reasons why preventable deaths occurred despite the telehealth early warning system and recommend possible approaches to further reduce the maternal mortality rate. Study Design: We carefully reviewed medical records of all maternal deaths during the two-year pilot from admission until death or transfer of these patients to a tertiary care center. We deconstructed the events leading to the adverse outcome and evaluated each based on the three delay modules for maternal deaths, namely seeking care, reaching the facility, and receiving care after reaching the facility. Results: Twelve maternal deaths occurred during the period of the study, six deaths occurred at the study sites and six deaths occurred after transport to a tertiary institution. Nine deaths were determined to be preventable. In five cases although multiple alerts were created indicating a clinical deterioration of the patients’ condition, lack of adequate knowledge and insufficient training on the part of the staff contributed to delays in initiating treatment and/or delays in timely transport. In all cases where the deaths occurred after the patient was transported, the warning system had identified the acute risk appropriately prior to the initiation of the transport. Considering all cases, the telehealth early warning system generated red alerts in every case, indicating an acute emergency (66.7%) and/or yellow alerts requiring continued observation (33.3%). Conclusion(s): Telehealth solutions incorporating early warning capability for identifying clinical deterioration among patients can play a crucial role in resource-constrained settings. Telehealth early warning systems have the potential to accelerate the care-delivery process and expose gaps in an organization’s operating procedures as well as in the knowledge base of providers. Successful implementation of such telehealth systems requires strong referral networks and appropriate protocols to take advantage of the system’s early warning capabilities. In addition, it may be necessary that the early warning system be implemented in all referring and receiving institution within the system to ensure no fallout in patient care.

Dynamic Early Warning Method of Multiclassification Financial Crisis of Listed Companies Based on Particle Swarm Optimization

In the Internet plus era, the environment of the world’s economy is changing towards globalization and information. The development of Chinese enterprises is facing hitherto unknown opportunities. Meanwhile, the financial risks facing enterprises are increasing day by day, because the constraints of environmental factors, economic and market factors, laws and regulations, social and cultural factors, policy environmental factors, and other unpredictable environmental factors have brought uncertainty to the financial situation of enterprises. The way a company handles its own financial risk has a big impact on its overall success. Many enterprises fall into economic crisis early because they do not pay enough attention to financial problems in the early stages and do not take effective measures to deal with the crisis situation in a timely manner, resulting in internal management confusion, deterioration of the external environment, capital chain problems, and serious asset losses. It is difficult to recover, and individuals who are experiencing a more significant financial crisis are at risk of becoming bankrupt. As a result, the success or failure of an enterprise’s early development is determined by the success or failure of its subsequent development. As a result, in order to devote more resources to the study of enterprise financial risk, this paper develops a variety of dynamic early warning models of financial crises in publicly traded companies by analyzing relevant theories of financial risk and combining the particle swarm optimization algorithm. This model has great efficiency and scientific early warning capabilities, and it can better forecast business financial crises.