Early Warning System Research Articles

Examining primary healthcare services following earthquake shocks in Turkiye: A critical analysis of the initial 3months and the vital role of humanitarian policy transfer for rapid response.

The purpose of this article is to discuss the importance of policy transfer by humanitarian NGOs to post-disaster regions and the effectiveness of mobile Primary Health Care (PHC) services immediately after disasters. This study also focused on analysing the first 3months aftermath of the earthquake and assessed the changes in the access and needs of vulnerable groups in emergency response creation and systematic interventions after disasters. In disasters that require urgent response such as earthquakes, the importance of the existing NGOs (Such as MdM) capacity in the countries has emerged in terms of rapid response and experience sharing. During the field work, it is observed that MdM Mobile Medical Units (MMU) teams had played a key role in terms of tracking PHC needs, functioning as early warning system for epidemics, and prevention of communicable diseases in the EQ effected areas. In this aspect, the changes in diagnoses in the first trimester is examined using the comparative analysis methods. This study used a cross-sectional mixed-method approach in terms of objective evaluation. The results from a quantitative needs assessment were complemented by qualitative data. Herein, the data were collected in two stages: First, primary data was collected through the field activities of MMU, and second field-based assessments was conducted with health professionals who have been working in the MMU teams since the first moments of the earthquake.

Urban Resilience Index for Critical Infrastructure: A Scenario-Based Approach to Disaster Risk Reduction in Road Networks

Floods pose a significant threat to road networks globally, disrupting transportation, isolating communities, and causing economic losses. This study proposes a four-stage methodology (avoidance, endurance, recovery, and adaptability) to enhance the resilience of road networks. We combine analysis of constructed assets and asset system performance with multiple disaster scenarios (Reactive Flood Response, Proactive Resilience Planning, and Early Warning Systems). Advanced flood Geospatial-AI models and open data sources pinpoint high-risk zones affecting crucial routes. The study investigates how resilient assets and infrastructure scenarios improve outcomes within Urban Resilience Index (CRI) planning, integrating performance metrics with cost–benefit analysis to identify effective and economically viable solutions. A case study on the Lisbon Road network subjected to flood risk analyzes the effectiveness and efficiency of these scenarios, through loss and gain cost analysis. Scenario 2, Proactive Resilience Planning, demonstrates a 7.6% increase compared to Scenario 1, Reactive Flood Response, and a 3.5% increase compared to Scenario 3, Early Warning Systems Implementation. By considering asset performance, risk optimization, and cost, the study supports resilient infrastructure strategies that minimize economic impacts, while enabling communities to withstand and recover from flood events. Integrating performance and cost–benefit analysis ensures the sustainability and feasibility of risk reduction measures.

Enhancing Flood Risk Management: A Comprehensive Review on Flood Early Warning Systems with Emphasis on Numerical Modeling

During recent decades there has been an increase in extreme flood events and their intensity in most regions, mainly driven by climate change. Furthermore, these critical events are expected to intensify in the future. Therefore, the improvement of preparedness, mitigation, and adaptation counterparts is mandatory. Many scientific fields are involved in this task, but from a meteorological and hydrological perspective, one of the main tools that can contribute to mitigating the impact of floods is the development of Early Warning Systems. In this sense, this paper presents a scientific literature review of some of the most representative Flood Early Warning Systems worldwide, many of which are currently fully operational, with a special focus on the numerical modeling component when it is developed and integrated into the system. Thus, from basic to technically complex, and from basin or regional to continental or global scales of application, these systems have been reviewed. In this sense, a brief description of their main features, operational procedures, and implemented numerical models is also depicted. Additionally, a series of indications regarding the key aspects of the newly developed FEWSs, based on recent trends and advancements in FEWSs development found in the literature, are also summarized. Thus, this work aims to provide a literature review useful to scientists and engineers involved in flood analysis to improve and develop supporting tools to assist in the implementation of mitigation measures to reduce flood damage for people, goods, and ecosystems and to improve the community resilience.

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China.

The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue. Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence. We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)). Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.

Towards a semi-automatic classifier of malware through tweets for early warning threat detection

This paper presents a method for developing a malware ontology structure by detecting malware instances on Twitter. The ontology represents a semi-automatic classifier fed by the data extracted from tweets. In particular, the automatic part of the presented methodology relies on a pattern-based approach to detect trigger expressions leading to new information about malware, whilst the manual one covers the evaluation of the results by domain-experts, who also validate the reliability of the semantic relationships within the ontology framework. We present preliminary results on the application of our methodology to tweets extracted from MalwareBazaar database showing how the documents’ collection analysis, through Natural Language Processing (NLP) tasks, can support the knowledge retrieval and documents’ classification procedures for building early warning system of detected malware. Results obtained from this research paper within the time framework of 2023 are referred to the previous version of the current social network X.

Evolution and genetic characterization of Seoul virus in wild rats Rattus norvegicus from an urban park in Lyon, France 2020-2022.

Seoul virus (SEOV) is an orthohantavirus primarily carried by rats. In humans, it may cause hemorrhagic fever with renal syndrome (HFRS). Its incidence is likely underestimated and given the expansion of urban areas, a better knowledge of SEOV circulation in rat populations is called for. Beyond the need to improve human case detection, we need to deepen our comprehension of the ecological, epidemiological, and evolutionary processes involved in the transmission of SEOV. We performed a comprehensive serological and molecular characterization of SEOV in Rattus norvegicus in a popular urban park within a large city (Lyon, France) to provide essential information to design surveillance strategies regarding SEOV. We sampled rats within the urban park of 'La Tête d'Or' in Lyon city from 2020 to 2022. We combined rat population genetics, immunofluorescence assays, SEOV high-throughput sequencing (S, M, and L segments), and phylogenetic analyses. We found low structuring of wild rat populations within Lyon city. Only one sampling site within the park (building created in 2021) showed high genetic differentiation and deserves further attention. We confirmed the circulation of SEOV in rats from the park with high seroprevalence (17.2%) and high genetic similarity with the strain previously described in 2011 in Lyon city. This study confirms the continuous circulation of SEOV in a popular urban park where the risk for SEOV transmission to humans is present. Implementing a surveillance of this virus could provide an efficient early warning system and help prepare risk-based interventions. As we reveal high gene flow between rat populations from the park and the rest of the city, we advocate for SEOV surveillance to be conducted at the scale of the entire city.

Nurse by numbers: The impact of early warning systems on nurses' higher-order thinking, a quantitative study.

To evaluate registered nurses' perceptions of whether the mandated use of the early warning system vital signs tool impacts the development of nurses' higher-order thinking skills. A concurrent mixed methods study design. Using an online survey, registered nurses' perceptions were elucidated on whether early warning system algorithmic tools affected the development of their higher-order thinking. Likert-type matrix questions with additional qualitative fields were used to obtain information on nurse's perceptions of the tool's usefulness, clinical confidence in using the tool, compliance with escalation protocols, work environment and perceived compliance barriers. Most of the 305 (91%) participants included in the analysis had more than 5 years of nursing experience. Most nurses supported the early warning tool and were happy to comply with escalation protocols if the early warning score concurred with their assessment of the patient (63.6%). When the score and the nurse's higher-order thinking did not align, some had the confidence to override the escalation protocol (40.0%), while others omitted (69.4%) or inaccurately documented vital signs (63.3%) to achieve the desired score. Very few nurses (3.6%) believe using early warning tools did not impede the development of higher-order thinking. Although experienced nurses appreciate the support of early warning tools, most value patient safety above the tools and rely on their higher-order thinking. The sustained development and use of nurses' higher-order thinking should be encouraged, possibly by adding a critical thinking criterion to existing algorithmic tools. The study has implications for all nurses who utilize algorithmic tools, such as early warning systems, in their practice. Relying heavily on algorithmic tools risks impeding the development of higher-order thinking. Most experienced nurses prioritize their higher-order thinking in decision-making but believe early warning tools can impede higher-order thinking. Registered nurses participated as survey respondents.

Macroeconomic Currency Crisis Early Warning Indicators in Emerging Countries

This study investigates the relationships between exchange rate and the main macroeconomic variables as GDP, inflation and unemployment on one hand and the ability of these variables in alerting about coming exchange rate crisis in emerging countries. The three variables have significant coefficients with exchange rate in line with literature signs except unemployment rate. The study uses signal approach, dealing specifically with the main macroeconomic variables, selected by system GMM method in emerging markets. The study develops macroeconomic pressure indices from these selected macroeconomic variables using the market pressure index methodology from Early Warning System literature. Based on the macroeconomic variables, a combined macroeconomic pressure index has been built. The results of the non-parametric early warning system indicate that the individual macroeconomic pressure indexes created are good warning tools of a currency crisis. The macroeconomic pressure indexes are better early warning indicators than market pressure index built from international reserves, in emerging countries for four quarters warning period window. Production pressure index appears more accurate followed by inflation but unemployment pressure index is the most sensitive. However, the number of effective indicators and the accuracy of the indexes are not the same for all the countries, changing from a country to another.

Affordable Sensors for Speditive and Accurate Documentation of Built Heritage: First Tests and Preliminary Results

Abstract. When facing unregulated human interventions, the tumultuous backdrop of wars, and the relentless impact of climate change, a significant effect is held on those structures that stand as custodians of our cultural identity and historical legacy. The need to document their status is an essential first step toward preservation and restoration. This contribution will analyze and compare diverse datasets acquired through three distinct methods: Terrestrial Laser Scanner, Apple’s iPhone 15 Pro with no additional tool, and iPhone 15 Pro using Pix4D viDoc RTK Rover to assess the quality and accuracy of point clouds, as well as to determine the feasibility of each technique for the expeditious documentation of endangered built heritage. The use of mass-distributed low-cost sensors provides a rapid and cost-effective means to capture detailed 3D models of built heritage while, at the same time, democratizing the process of documentation. Low-cost sensors not only facilitate documentation but also enhance the efficiency of conservation efforts. The precision of LiDAR sensors helps identify structural vulnerabilities, allowing conservationists to prioritize interventions and allocate resources wisely. Furthermore, detailed imagery will enable conservationists to monitor subtle structural changes over time, acting as an early warning system against potential threats. As an additional aid, the RTK Rover enables centimeter-level positioning accuracy while maintaining a compact and fast deployable design, and the direct measurement of Ground Control Points (GCPs) significantly streamlines the surveying process and equipment footprint.

Learning Material Organization for Disaster Resilient Villages

This study aims to express the best learning material organization for the Disaster Resilient Village Program (Program Desa Tangguh Bencana, DESTANA). It was delivered to the people of Pacitan Regency as a disaster-prone area. Various potential disasters exist in the area of Pacitan Regency. The learning material is organized in line with the revised Bloom’s Taxonomy, which applies six levels of thought processes. The learning process starts from a hierarchical structure that identifies lower-level thinking skills to higher levels of skills that are ready for operational implementation. The Lower Order Thinking Skills materials encompass understanding various disaster vulnerabilities based on geographical conditions, the nature of disaster threats and risks, participatory disaster management by the community, principles of evacuation planning, the role of Disaster Management Plans, the paradigm of disaster risk management, principles of the Early Warning System (EWS), the purpose of contingency plans, and principles of first aid in emergencies. The Higher Order Thinking Skills materials include the ability to measure indicators for disaster-resilient villages, establish volunteer working groups, design evacuation stages, develop Disaster Management Plans, Disaster Risk Management, Early Warning Systems (EWS), and Village Contingency Plans. The learning achievement is measured through community understanding and practice, the success of building a disaster response coordination structure, and activities in a disaster simulation. It is organized in seven discussion forums. The program resulted in significant achievements. The Pacitan Regency Government implemented the Disaster Resilient Village Program for the safety of its residents. Keywords: disaster, resilient village, learning material

Targeted Formation of Biofilms on the Surface of Graphite Electrodes as an Effective Approach to the Development of Biosensors for Early Warning Systems

Biofilms based on bacteria Pseudomonas veronii (Ps. veronii) and Escherichia coli (E. coli) and yeast Saccharomyces cerevisiae (S. cerevisiae) were used for novel biosensor creation for rapid biochemical oxygen demand (BOD) monitoring. Based on the electrochemical measurement results, it was shown that the endogenous mediator in the matrix of E. coli and Ps. veronii biofilms and ferrocene form a two-mediator system that improves electron transport in the system. Biofilms based on Ps. veronii and E. coli had a high biotechnological potential for BOD assessment; bioreceptors based on such biofilms had high sensitivity (the lower limits of detectable BOD5 concentrations were 0.61 (Ps. veronii) and 0.87 (E. coli) mg/dm3) and high efficiency of analysis (a measurement time 5–10 min). The maximum biosensor response based on bacterial biofilms has been observed in the pH range of 6.6–7.2. The greatest protective effect was found for biofilms based on E. coli, which has high long-term stability (151 days for Ps. veronii and 163 days for E. coli). The results of the BOD5 analysis of water samples obtained using the developed biosensors had a high correlation with the results of the standard 5-day method (R2 = 0.9820, number of tested samples is 10 for Ps. veronii, and R2 = 0.9862, number of tested samples is 10 for E. coli). Thus, biosensors based on Ps. veronii biofilms and E. coli biofilms could be a novel analytical system to give early warnings of pollution.

Deformation Analysis and Prediction of a High-Speed Railway Suspension Bridge under Multi-Load Coupling

High-speed railway suspension bridges (HSRSBs) have been constructed with the new advancements in technology. The deformation prediction for HSRSBs is essential to their safety and maintenance. The conventional prediction methods are developed for bridges without high-speed railway. Different factors, including temperature (TEMP), time delay compensation (TDC), train live load (TLL), are considered in these methods. However, the train side (TS) and train instantaneous position (TIP) have a significant impact on deformation for HSRSBs, and they are not used in the prediction. More importantly, the coupling issue among different factors is so significant that it cannot be neglected. In this study, we propose a deformation prediction model based on a backpropagation (BP) neural network. This model uses different factors as model input, including TEMP, TDC, TLL, TS, and TIP. The coupling issue is addressed by using the new model. The new model was evaluated using a dataset of 10-day field measurements. It achieves a mean absolute error (MAE) of 8.81 mm, a mean relative error (MRE) of 9.82%, and coefficient of determination (R2) of 0.94. The new model will provide high-precision prediction for deformation and will be used in the development of an early warning system.

Evaluation of the single-frequency variometric approach based on low-cost GNSS observations and different satellite combinations for detecting short-term dynamic behaviors

Abstract This study presents the capability of the single-frequency (SF) variometric approach (VA) technique with low-cost GNSS observations to detect short-term dynamic behaviors. Harmonic oscillations with amplitudes between 5 and 20 mm and frequencies between 0.3 and 5.0 Hz were generated employing a single-axis shake table to investigate the performance of the SF-VA technique in the Structural Health Monitoring (SHM) system. Besides, a Mw 6.9 Kobe, 1995 earthquake simulation was generated using the shake table to analyze the SF-VA performance for the Earthquake Early Warning (EEW) system. A low-cost u-blox ZED-F9P GNSS receiver and ANN-MB-00 patch antenna were used to collect GNSS observations at a 20 Hz sampling rate during the experiments. The observations were processed using the MATLAB-based open-source PPPH-VA software in real-time mode, considering eight different satellite combinations. The capability of the SF-VA technique to detect horizontal dynamic behaviors in real-time mode was investigated in the frequency and time domains, accepting the displacements from the Linear Variable Differential Transformer (LVDT) sensor as a reference. The results in the frequency domain demonstrate that the SF-VA technique with low-cost GNSS observations can successfully detect the peak frequency value of short-term harmonic oscillations up to 5 Hz. Moreover, time domain findings emphasize that the short-time dynamic oscillations can be determined with the SF-VA technique with an accuracy ranging from 0.8 to 6.4 mm. Earthquake simulation experiment results demonstrate that the strong ground motions caused by mega earthquakes can be determined at mm-level by the SF-VA method. The results of both experiments show that multi-GNSS observations contribute to the SF-VA technique considerably. Overall, the findings reveal that the SHM and EEW systems can be operated with low-cost GNSS receivers, and the natural frequency of the man-made structures and accurate displacement values of seismic waveforms can be determined in real-time with the SF-VA technique.

Seismic and radon signatures: A multiparametric approach to monitor surface dynamics of a hazardous 2021 rock–ice avalanche, Chamoli Himalaya

AbstractThe observation of precursory signals of the 2021 Chamoli rock–ice avalanche provides an opportunity to investigate the multidisciplinary analysis approach of rock failure. On 7 February 2021, a huge rock–ice mass detached from the Raunthi peak at Chamoli district in Uttarakhand, India. The tragic catastrophe resulted in more than 200 deaths and significant economic losses. Here, we analyse radon concentration and seismic signals to characterise the potential precursory anomalies prior to the detachment. Continuous peaks of radon anomalies were observed from the afternoon of 5 to 7 February and decreased suddenly after the event, while a cumulative number of seismic tremors and amplitude variations are more intensified ~2.30 h before the main event, indicating a static to dynamic phase change within the weak zone. This study not only characterises abnormal signals but also models the rock failure mechanisms. The analysis unveils three time‐dependent nucleation phases, physical mechanisms of signal generation and a complete scenario of physical factors that affected the degree of criticality of slope failure. The results of this study suggest gradual progression of rock cracks/joints, subsequent material creep and slip advancement acceleration preceded the final failure. Furthermore, the study highlights the importance of an early warning system to mitigate the impact of events like the 2021 Chamoli rock–ice avalanche.

GRAPES: Earthquake Early Warning by Passing Seismic Vectors Through the Grapevine

AbstractEstimating an earthquake's magnitude and location may not be necessary to predict shaking in real time; instead, wavefield‐based approaches predict shaking with few assumptions about the seismic source. Here, we introduce GRAph Prediction of Earthquake Shaking (GRAPES), a deep learning model trained to characterize and propagate earthquake shaking across a seismic network. We show that GRAPES’ internal activations, which we call “seismic vectors”, correspond to the arrival of distinct seismic phases. GRAPES builds upon recent deep learning models applied to earthquake early warning by allowing for continuous ground motion prediction with seismic networks of all sizes. While trained on earthquakes recorded in Japan, we show that GRAPES, without modification, outperforms the ShakeAlert earthquake early warning system on the 2019 M7.1 Ridgecrest, CA earthquake.

Development of an early warning system for higher education institutions by predicting first‐year student academic performance

AbstractIn this study, an early warning system predicting first‐year undergraduate student academic performance is developed for higher education institutions. The significant factors that affect first‐year student success are derived and discussed such that they can be used for policy developments by related bodies. The dataset used in experimental analyses includes 11,698 freshman students' data. The problem is constructed as classification models predicting whether a student will be successful or unsuccessful at the end of the first year. A total of 69 input variables are utilized in the models. Naive Bayes, decision tree and random forest algorithms are compared over model prediction performances. Random forest models outperformed others and reached 90.2% accuracy. Findings show that the models including the fall semester CGPA variable performed dramatically better. Moreover, the student's programme name and university placement exam score are identified as the other most significant variables. A critical discussion based on the findings is provided. The developed model may be used as an early warning system, such that necessary actions can be taken after the second week of the spring semester for students predicted to be unsuccessful to increase their success and prevent attrition.

Comrade Kryuchkov’s “War Scare” (1983), or the Bureaucratic Origins of the “Able Archer” War-Scare Thesis

Many historians argue that the world came to the brink of nuclear war during the North Atlantic Treaty Organization’s Able Archer nuclear-release exercise in November 1983. This war-scare thesis originated with Soviet defector Oleg Gordievsky, who made this assertion based on several telegrams that he had seen at the State Committee for Security (KGB) residency in London in 1983. The article contextualizes and analyzes the telegrams based on the statements of leading officers of the KGB’s First Chief Directorate (FCD), responsible for foreign intelligence, to their Soviet-bloc colleagues in the years 1981 to 1983. Based on this analysis, the telegrams that Gordievsky cited had not “created a vicious spiral which was steadily and dangerously raising tension in Moscow,” as he later asserted. Instead, they reflected the bureaucratic strivings of the head of the FCD, Vladimir Kryuchkov, to finally develop an early-warning system against Western military attack, which he had narrowed down to potential nuclear-missile attack (Raketno-Yadernoe Napadenie).

Performance of a machine learning-based early warning system in hospitalized patients with diabetes mellitus

Performance of a machine learning-based early warning system in hospitalized patients with diabetes mellitus

Measuring the state of aquatic environments using eDNA-upscaling spatial resolution of biotic indices.

Aquatic macroinvertebrates, including many aquatic insect orders, are a diverse and ecologically relevant organismal group yet they are strongly affected by anthropogenic activities. As many of these taxa are highly sensitive to environmental change, they offer a particularly good early warning system for human-induced change, thus leading to their intense monitoring. In aquatic ecosystems there is a plethora of biotic monitoring or biomonitoring approaches, with more than 300 assessment methods reported for freshwater taxa alone. Ultimately, monitoring of aquatic macroinvertebrates is used to calculate ecological indices describing the state of aquatic systems. Many of the methods and indices used are not only hard to compare, but especially difficult to scale in time and space. Novel DNA-based approaches to measure the state and change of aquatic environments now offer unprecedented opportunities, also for possible integration towards commonly applicable indices. Here, we first give a perspective on DNA-based approaches in the monitoring of aquatic organisms, with a focus on aquatic insects, and how to move beyond traditional point-based biotic indices. Second, we demonstrate a proof-of-concept for spatially upscaling ecological indices based on environmental DNA, demonstrating how integration of these novel molecular approaches with hydrological models allows an accurate evaluation at the catchment scale. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Environmental data in epidemic forecasting: Insights from predictive analytics

Epidemic forecasting plays a critical role in public health preparedness and response, enabling proactive measures to mitigate the impact of infectious diseases. Environmental data, encompassing factors such as temperature, humidity, air quality, and geographical features, holds valuable insights for predicting and identifying areas prone to epidemics. This paper explores the integration of predictive analytics with environmental data to enhance epidemic forecasting capabilities. By leveraging predictive analytics techniques, researchers and public health officials can analyze environmental data to identify regions at higher risk of experiencing epidemic outbreaks. Through statistical modeling, machine learning algorithms, and computational simulations, predictive analytics utilize environmental indicators to forecast the likelihood and spread of diseases. For example, areas with high temperatures and humidity may be conducive to mosquito-borne diseases, while regions with poor air quality may experience increased rates of respiratory infections. Case studies highlight the application of predictive analytics in various contexts, including forecasting mosquito-borne diseases in tropical regions and tracking respiratory infections in urban areas with poor air quality. Early warning systems, informed by environmental data, provide timely alerts to potential epidemic threats, enabling proactive interventions and resource allocation. While the integration of environmental data into epidemic forecasting offers significant benefits, challenges remain, including data quality, availability, and ethical considerations. Continued research and collaboration are essential to address these challenges and further enhance the effectiveness of predictive analytics in identifying and mitigating epidemic risks. In conclusion, this paper underscores the importance of leveraging environmental data and predictive analytics for epidemic forecasting, emphasizing their potential to improve public health outcomes and enhance preparedness efforts in the face of emerging infectious diseases and climate change. Keywords: Environmental Data, Epidemic Forecasting, Predictive Analytics.