Effective Early Warning System Research Articles

Multi-Hazard Early Warning System (MHEWS) Assessment Methodology for Schools

Schools play an important role in any community because children spend most of their time in schools for their education. Unfortunately, schools are exposed to natural hazards such as earthquakes, typhoons, and floods like other structures. However, implementing an effective Early Warning System (EWS) can help school administrators, occupants, and communities prepare against these hazards. EWS is an essential component of a disaster risk reduction management (DRRM) plan. Its purpose is to generate and disseminate timely and meaningful information of possible extreme events to administrators and eventually to the students. However, it is difficult to ascertain the quality of a school’s EWS to address any upcoming hazards. With this, the authors designed a workshop with toolkits to guide school administrators in evaluating their organizational structure within the context of DRR. The toolkits assessed the soft and hard measures of the school, particularly its DRRM and EWS. A method was developed to measure the EWS in schools based on the toolkits. A radar scoring system was used to measure the school’s key elements: Risk Knowledge, Monitoring and Warning Services, Dissemination and Communication, Response Capabilities. Indices for each element and EWS as a whole were obtained. Modes of warning and communication were ranked. In Lipa City’s case, the results showed that the schools have an average of 74.050 EWS index, meaning the EWS is of good quality but can still be improved. This method can be applied to other schools to assess their EWS.

Revisiting the non-native insect fauna of Greece: Current trends and an updated checklist

Alien invasive species not only have adverse environmental impacts, they may also pose socioeconomic and human health threats. The increase in detected non-native (alien) and cryptogenic species, followed by the necessity for up-to-date scientific information on biological invasions, prompted a thorough literature investigation on the non-native entomofauna of Greece. Supplementing previous studies concerning Europe and Greece, a checklist for the non-native insect species of the country is provided, while their number is elevated from 266 to 469. Current trends regarding species richness and taxonomy, deliberate or accidental means of introduction, as well as their native range and trophic preferences are analysed and discussed. This publication illustrates the current state of non-native insects of Greece, highlighting the need for an effective early warning system integrated in national phytosanitary legislation, survey protocols and strategies towards the eradication and mitigation of their detrimental impacts.

Roles of detection, surveillance and early warning on outbreaks or epidemics of infectious diseases

Infectious diseases are still one of the leading causes of morbidity and death globally, affecting public health and life, social and economic development, and even national security. Early detection focuses on detecting the abnormal information of infectious disease outbreaks or epidemics in a timely and sensitive way to conduct field investigation and verification. It is also a precursor to effective surveillance and early warning system. The effective surveillance and early warning system can fully and accurately understand the real conditions, driving forces, and transmission chain of the occurrence of a specific infectious disease outbreak and epidemic and put forward scientific and effective prevention and control strategies and measures. Due to the measurement of the resources support and the particular data collection value, it is not easy to obtain epidemiological, etiological, and other data information in a timely, complete and accurate manner. This paper summarized the theory and technology on early detection, effective surveillance, and early warning information on infectious diseases. It also integrated and utilized the multi-source data, including effective infectious disease surveillance and the country's early warning system, to better understand the outbreak epidemic, causes, risks, processes, and driving forces. Thus, it is possible to set up a sensitive, specific staging measurement innovative technical system to monitor, early warning, and timely respond to acute infectious diseases through multidisciplinary cooperation in China. It provides the basis for strengthening the surveillance and early warning of new emerging and major infectious diseases and public health emergencies, avoiding the spread of inadequate response to infectious disease, and preventing the resources waste of over-response.

The early warning systems about landslide hazards in Ukraine

Early warning systems are an effective tool for preventing and mitigating the risks associated with the occurrences of various types of threats (including landslides). The paper presents and describes the concept and practical implementation of the new integrated methodology for early warning systems based on the integration of modern monitoring technologies and comprehensive numerical modeling of an object under study. Designing, testing and operation of monitoring systems of complex and unique construction objects have a lot of difficulties, need system knowledge in several spheres of science and engineering: construction, informational technologies, measuring instruments, systems and algorithms of data processing, programming etc. This information is known only to narrow range of highly qualified specialists that directly participated in designing and installing of the particular monitoring system at the particular construction object. The basic concept of Early Warning System installed on landslides is that the elements at risk, especially people being close from the dangerous area, must have sufficient time to evacuate, if an imminent collapse is expected. Therefore, an effective Early Warning System shall include such four main sets of actions: monitoring of the activity of the observed object, i.e. the data collection and transmission, as well as the equipment maintenance; the analysis and modeling of the observed and studied object; warning, i.e. the dissemination of simple and clear information about the observed object; the effective response of risk exposed elements; full understanding of risks. The examples of the practical application of the proposed integrated methodology to various construction projects and natural and technological systems are given, including 1) Central Livadia Landslide System and Livadia Palace; 2) a system for landslide hazard areas monitoring in the Kharkiv region; and 3) landslides Early Warning System using unmanned aerial vehicles as a specialized monitoring system for shearing deformations.

Earthquake Early Warning System Using Ncheck and Hard-Shared Orthogonal Multitarget Regression on Deep Learning

Realizing an effective earthquake early warning system (EEWS) in the case of extensive regions and noisy signals is challenging, particularly in East Java, Indonesia. This letter proposes the rapid detection of the p-wave arrival and determination of the earthquake’s hypocenter and magnitude using deep learning. The Ncheck algorithm is used for noise handling for picking the p-arrival on a multistation waveform as a form of picking target window prediction (PTWP). Then, multitarget regression (MTR) with a hard-shared orthogonal optimization model is proposed for earthquake parameter determination. The data sets used contained data of earthquakes recorded at three stations from the Indonesian seismic network in East Java; 2009–2017 data were used for training and validation, and 2019 data were used for real-time testing. The results show that the PTWP for picking p-arrival has a mean absolute error (MAE) of 0.12 s, and the MTR for earthquake magnitude, longitude, latitude, depth, and origin time detection shows MAEs of 0.21 M, 9.44, 18.72, 27.81 km, and 2.78 s, respectively.

The practice of event-based surveillance: concept and methods

ABSTRACT Event-based surveillance (EBS) is the organised approach to the detection and reporting of ‘signals,’ defined as information that may represent events of public health importance, often through channels outside of routine surveillance systems. Signals can be designed to detect patterns of disease, such as clusters of similar illness in a community, or clusters of disease or death in animals. Signals can also include single cases of suspected high-priority events such a patient with viral haemorrhagic fever. EBS can be a key component of an effective early warning system, which enables countries to be better prepared for endemic and pandemic illness outbreaks. EBS uses an all-hazards approach that includes the principles of One Health. This review covers the concept and process of EBS, different sources for EBS data, and methods to obtain information from these sources. This overview will aid countries in implementing this important form of surveillance.

Design and Development of Real-time landslide early warning system through low cost soil and rainfall sensors

Development of cost effective early warning systems is of paramount importance for real time monitoring of natural hazards such as Landslides, Floods, Earthquake etc. The present research work discusses about the design and development of cost effective early warning system for monitoring shallow debri landslides. The warning system consist of field unit and remote network interface. The field system includes the rainfall, temperature, humidity, soil temperature, soil moisture and Tiltmeter as primary sensors for monitoring the landslide movements with an independent power supply of 50 W solar panel. The datasets that are received are stored in the local data logger with the help of microsd card. Real time monitoring of the datasets and analysis was made possible by storing the datasets in remote Database Management System. These datasets are transmitted via the Global System for Mobile communication module installed at the field unit. In order to convey the alerts during emergency time a simple visual message has been adopted for the current model. Level I, Level II and Level III, Level I being the threshold values are within the desired limit and actions are not necessary and Level III denotes threshold values have exceeded the set limit and warning are issued to local stake holders. A pilot study of the Early Warning System has been installed on site for quality checks of the sensor & data and the results were monitored. The model developed not only can address the early warning signs of mass movement but also can be calibrated to predict flood in major streams based on rainfall intensity.

Enhancing Extreme Weather Early Warning Systems in Upper Bekasi River Basin Through Coupled Hydro-meteorological Model

Flooding is frequently emerging events in the Upper Bekasi river basin and causes economic loss, property damage, loss of life, and even hinders sustainable development. Torrential rain is one of the natural hazards that often triggers flooding, especially in watersheds that have undergone land use changes. One of the mitigation efforts that can be carried out by the vicinity community, government, private enterprises and other stakeholders is by utilizing an early warning system. This study will delineate the successful use of coupled hydro-meteorological models to predict flooding in various regions of the world with different climatic and terrestrial characteristics. However, in order for an early warning system to be effective and improve community resilience, four vital elements of early warning systems are recommended to be fulfilled, namely risk knowledge, monitoring and warning services, dissemination and communication, and response capability. Therefore, this study will compile how coupled hydro-meteorological model is utilized properly to generate an effective early warning system so that thriving sustainability can be achieved.

Emergency flood detection using multiple information sources: Integrated analysis of natural hazard monitoring and social media data

Extreme weather events are occurring more frequently as a result of climate change. In October 2019, eastern Japan was hit by Hagibis, a large and high-speed typhoon. This unprecedented typhoon caused the evacuation of over 4000 people, injured more than 300 people, and damaged more than 98,000 dwellings throughout the affected area. Because floods are one of the most devastating natural disasters in Asia, providing an effective early warning system (EWS) is critical to reducing disaster impacts. However, warnings based only on natural hazard monitoring do not offer sufficient protection. Integrating natural hazard monitoring and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. We analyzed time-series data including rainfall intensity, 90-min-effective rainfall, and river water level as well as Twitter data related to disaster events during the 5-day period from 11 to 15 October, focusing on the most affected areas in Japan. The analysis included more than 60,000 tweets. Our analysis confirmed the utility of the statistical approach of outbreak detection with social media data in the early detection and local identification of multiple-flood events, and the results from the municipality-level analyses show that tweet frequencies related to the flood disaster ontological categories were significantly correlated to temporal variations in the hazard monitoring data. Thus, flood detection at the administrative level using social media data combined with current hazard monitoring data can enable a decision-driven EWS design. Interactive approaches for decision-making and knowledge production should continue to be considered in the face of climate-change-induced disasters.

Construction solid waste landfills: risk assessment and monitoring by fibre optic sensing technique

The development of underground spaces generates large amounts of construction wastes, which are generally disposed for the landfill outside of the city in China. This study briefly reviewed a landslide tragedy of a CSW landfill in China; Lessons learned from the failure of risk assessment and preventions were discussed, which showed that two challenging strategies are significant for the risk identification and mitigation. One is the effective early warning system based on field large-deformation measurement; the other is the efficient risk assessment with internal deformation data. To this end, a new sensing method is developed for the large-deformation measurement inside CSW landfills. The proposed fibre-optic based transducer can measure large deformation of up to 1200 mm with a high accuracy. Finally, the risk assessment of CSW landfill was proposed with the combined use of field monitoring data and risk evaluation model. The theoretical equations for stability analysis and potential application framework for the application of this technique in the risk assessment of CSW landfill landslide are presented. This approach possesses the advantages of capturing excessive and irregular deformation of solid waste landfills and of efficient response for the risk mitigation with the help of early warning system.

Anticipating an imminent large rock slope failure at the Hochvogel (Allgäu Alps)

AbstractCosts for installation and maintenance of protective structures are increasing while alpine hazards progressively threaten alpine communities, infrastructure and economics. Thus, reliable process‐based anticipation and early warning strategies offer a cost‐effective and smart solution for alpine societies in the near future. However, only few comprehensive pre‐failure observations of alpine rock slopes have been reported so far. This paper demonstrates pre‐failure observations of a rapidly deforming rock mass (potentially 260,000 m3) at the Hochvogel (Allgäu Alps, 2,592 m a.s.l.) and a geotechnical monitoring and warning concept. This is implemented in the complementary multi‐method approach of the AlpSenseBench project and the basis for an effective and reliable early warning system. Since 2014, overall displacement rates in the range of 2 to 10 mm/month in the main decametre deep fracture are observed. It is expected that predictive acceleration patterns will appear in the final pre‐failure stage. A detailed knowledge of multiple anticipative signals in correlation with accelerating rock slope deformations will contribute to an advance in accuracy and reliability of rock slide early warning.

Forecast and early warning of hand, foot, and mouth disease based on meteorological factors: Evidence from a multicity study of 11 meteorological geographical divisions in mainland China

BackgroundHand, foot, and mouth disease (HFMD) is a significant public health issue in China. Early warning and forecasting are one of the most cost-effective ways for HFMD control and prevention. However, relevant research is limited, especially in China with a large population and diverse climatic characteristics. This study aims to identify local specific HFMD epidemic thresholds and construct a weather-based early warning model for HFMD control and prevention across China. MethodsMonthly notified HFMD cases and meteorological data for 22 cities selected from different climate zones from 2014 to 2018 were extracted from the National Notifiable Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. A generalized additive model (GAM) based on meteorological factors was conducted to forecast HFMD epidemics. The receiver operator characteristic curve (ROC) was generated to determine the value of optimal warning threshold. ResultsThe developed model was solid in forecasting the epidemic of HFMD with all R square (R2) in the 22 cities above 85%, and mean absolute percentage error (MAPE) less than 1%. The warning thresholds varied by cities with the highest threshold observed in Shenzhen (n = 7195) and the lowest threshold in Liaoyang (n = 12). The areas under the curve (AUC) was greater than 0.9 for all regions, indicating a satisfied discriminating ability in epidemics detection. ConclusionsThe weather-based HFMD forecasting and early warning model we developed for different climate zones provides needed information on occurrence time and size of HFMD epidemics. An effective early warning system for HFMD could provide sufficient time for local authorities to implement timely interventions to minimize the HFMD morbidity and mortality.

An Empirical Study on the Impact of Coastal Areas on China's Financial Stability

Wang, L. and Jiang, J.J., 2020. An empirical study on the impact of coastal areas on China's financial stability. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 393-397. Coconut Creek (Florida), ISSN 0749-0208.With the improvement of China's openness, the free flow of international capital is more and more free, but at the same time, it also puts forward higher requirements for the stability of the financial system. Coastal areas have the highest degree of openness in China, with more developed economy and finance. It can be said that coastal areas are “systemically important” areas that determine the degree of financial stability in China. This paper first puts forward the hypothesis of “systematic importance” of coastal areas to China's financial stability, then constructs the model of “interest rate total amount of credit”, and studies the influence degree of interest rate changes on the total amount of credit in China's major provinces by using the method of time series analysis, and then draws the conclusion that coastal areas are the “systemically important” areas of China's financial stability. Finally, this paper points out that coastal areas are the “systemically important” areas of China's financial stability Suggestions: first, coastal areas should establish corresponding financial stability assessment system while improving financial openness; second, build an effective risk early warning system; third, build an effective risk supervision system; fourth, build an effective emergency system.

Evaluation of Ethiopian early warning system: the case of Dera and Jabithenana Zuria Districts

Early warning is a question of saving life and property. Its importance is unquestionable in the context of multi-hazard exposed country like Ethiopia. This study analyzes the Ethiopian early warning system in terms of design and practice with the case of two districts. Effective early warning system is expected to generate information, monitor and forecast hazards, disseminate and communicate information, and enhance response capacity. The study found that the systems’ achievement is promising in terms of relief oriented short term objectives, but the developmental long term objectives have a long way to go in the study area. The system has a characteristic of generic EWS which is difficult to manage the features of all hazards at a time. The system has a shortage of experts beside lack of permanent responsible person at kebele level. Indicators collected by the system are late and stress indicators which are not best for the purpose. The system is found not suitable for rapid onset hazards. Also, it emphasizes on hazards while vulnerability and capacity which are important factors of risk are underestimated. Early warning messages do not include all important components of a quality messages, and false messages are usual leading majority of the people not to respect the warning. The system is subjected to political interest and responses of food aids are not enough in terms of quality and quantity. The article revealed that the Ethiopian early warning system is problematic across the four components of UNISDR effective early warning framework. The study recommends changes on problematic aspects of the system on the study areas.

Biological characteristics and efficacy of Bacillus thuringiensis var. thuringiensis against the cotton leaf roller, Syllepte derogata (Fabricius, 1775) (Lepidoptera: Crambidae)

Cotton (Gossypium hirsutum L.) is an important fiber crop cultivated in > 50 countries of the world. The cotton leaf roller Syllepte derogata (Fabricius, 1775) (Lepidoptera: Crambidae) is considered as an invasive and widespread species in many geographic regions of the world and pose significant damages to cotton crop. This study reported the first record of S. derogata and its biological characteristics in Azerbaijan. The results indicated that S. derogata was only observed on cotton crop from the Absheron region of the country. The larvae only attacked on cotton leaves and did not damage the reproductive organs (i.e., bolls). S. derogata completed one generation in 31–42 days in the Absheron region. Larvicidal efficacy of Bacillus thuringiensis var. thuringiensis (Btt) against S. derogata was also tested under laboratory and field conditions. Three different Btt concentrations (0.3, 0.4, 0.5 g/l) were tested against 2nd and 4th larval instars of S. derogata. The 0.5 g/l concentration of Btt caused 72.88% larval mortality under field conditions. However, under laboratory conditions, 0.3, 0.4, and 0.5 g/l concentrations caused 50, 66.67, and 100% mortality of the 2nd instar larvae on the 7th day, respectively. The 0.5 g/l suspension of Btt proved the most effective against S. derogata; therefore, it can be recommended for the management of S. derogata in the infested region of the country. This study further warns that S. derogata could spread to the other cotton-producing regions of the country; thus, an effective early warning and rapid response system must be developed for the pest in the country.

Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area

In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (TGR) area is selected, and a displacement prediction model of Extreme Learning Machine with Gray Wolf Optimization (GWO-ELM model) is proposed. By analyzing the monitoring data of landslide displacement, the time series of landslide displacement is decomposed into trend displacement and periodic displacement by using the moving average method. First, the trend displacement is fitted by the cubic polynomial with a robust weighted least square method. Then, combining with the internal evolution rule and the external influencing factors, it is concluded that the main external trigger factors of the periodic displacement are the changes of precipitation and water level in the reservoir area. Gray relational degree (GRG) analysis method is used to screen out the main influencing factors of landslide periodic displacement. With these factors as input items, the GWO-ELM model is used to predict the periodic displacement of the landslide. The outcomes are compared with the nonoptimized ELM model. The results show that, combined with the advantages of the GWO algorithm, such as few adjusting parameters and strong global search ability, the GWO-ELM model can effectively learn the change characteristics of data and has a better and relatively stable prediction accuracy.

Construction and Application of Early Warning System for Soil Environmental Quality

Early warning of soil environmental quality is an important basis for implementing classified and graded soil risk management measures. To quickly understand the regional soil environmental quality and take effective measures in time to prevent continuous soil pollution before deterioration of soil environmental quality, a simple, effective, and quantifiable early warning system for soil environmental quality of agricultural land and development land was respectively established based on environmental capacity and pollutant input-output flux theory. Furthermore, corresponding method and mechanism for early warning were defined based on soil environmental quality standards, food safety standards, and carcinogenic risk coefficients. The agricultural land in Youxian county and the development area within the fifth-ring in Beijing were chosen to assess the soil environmental quality and predict risks of heavy metals exceeding standards in different scenarios. The results show that the soil environmental quality of the agricultural land in Wangling and Taoshui Town both can be classified to the fifth early warning level. Compared with other remediation measures, the Cd contents of soil can be lowered to risk screening levels in the short term by the scenario of "paddy straw not returned to the field". The soil quality in the development area within the fifth-ring in Beijing belongs to the first early warning level under both the "no intervention" and the "decreased input" scenarios, which means that Cd, Cu, Pb, and Zn all need more than 50 years to reach their threshold values to pose potential health risks.

IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas.

In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seasons (2017–2019), and the data from the sensors were compared with the field observations and rainfall data for the evaluation. The relationship between change in tilt rate, volumetric water content, and rainfall are explored in the study, and the records prove the significance of considering long-term rainfall conditions rather than immediate rainfall events in developing rainfall thresholds for the region.

Machine-Learning-Based Early-Warning System Maintains Stable Production

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 197365, “A Novel Early Warning System of Oil Production Based on Machine Learning,” by Kang Ma, Hanqiao Jiang, and Junjian Li, China University of Petroleum-Beijing, et al., prepared for the 2019 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11–14 November. The paper has not been peer reviewed. After long-term waterflooding, a number of mature oil fields in China have entered the high-water-cut stage, and abnormal production decline has become the primary problem for stable production. This paper describes an accurate, three-step, machine-learning-based early warning system (EWS) that has been used to monitor production and guide strategy in the Shengli field. Adding artificial samples to the training process improved the system’s prediction accuracy greatly (Fig. 1). Introduction For conventional Chinese oil fields that have entered the high-water-cut stage after decades of waterflooding, stabilizing production has become increasingly difficult. After stimulation treatments throughout the field’s history, abnormal decline rates—that is, exceeding 5%—occurred more frequently. Production declined dramatically in 2004 and has not been maintained since then. To prevent future abnormal production declines, an effective EWS was needed that could release a production alarm to enable engineers to take preventive measures in advance. The complete paper includes a discussion of various early-warning models and their limitations. The paper discusses an EWS based on a neural network method using a previously established data set. Factors that can affect the abnormal decline were selected. The index sets of production composition and injected and produced water obtained from practical statistics were considered as the main assessment indicators. Grey relational analysis was used to evaluate the importance of the different indicators and to eliminate redundant parameters. Machine learning was adopted to build the EWS. Using the degree of deviation from normal as the input data for the prediction model provided the highest accuracy. However, the basic machine-learning model contains many input parameters that cannot be obtained easily. The number of input parameters was optimized on the basis of the variation of accuracy under different input parameter numbers. To improve prediction accuracy, artificial samples were added to the training process. The prediction accuracy of the final optimization model can reach 92%. The result reveals the possibility of the occurrence of anomalous decline in different reservoirs, which can guide oilfield production strategy effectively. The EWS was verified by oilfield production. Fundamentals of Neural-Network Classification Neural networks use existing data to determine the implicit model between input and output data. Classification and prediction are the most common applications. Neural networks are used in the industry to solve classification and regression problems.

Toward International Harmonization of Breast Implant Registries: International Collaboration of Breast Registry Activities Global Common Data Set.

The Poly Implant Prothèse incident and breast implant-associated anaplastic large cell lymphoma have pointed to the need for uniform registries for breast implants as key features to monitoring the outcomes of breast implant surgeries internationally. The purpose of this study was to identify and harmonize common data elements collected by breast implant registries across the International Collaboration of Breast Registry Activities (ICOBRA) global consortium. The authors convened an international group of surgeons, consumers, nurses, registry experts, and regulators to review the data points. A modified Delphi approach was applied, to rate the importance of each point on a six-point Likert scale. Data points from six national breast implant registries were divided into categories: clinical, implant-related, patient-reported findings; operation details and implanting technique details; patient characteristics; unique device identifiers; unique patient identifiers; and clinical demographics. A total of 52 data points collected by over 33 percent of national registries were identified. After five rounds, 34 data points formed the final set with agreed definitions. The group recognized the critical importance of additional elements that are currently not uniformly collected (e.g., patient-reported outcomes and long-term data) and set out the process for the dynamic global set updates driven by evidence gaps. The authors defined internationally agreed on common data elements and definitions used in breast implant registries. This collaboration will allow data sets to be combined, enabling an effective global early warning system of implant-related problems and further work on data sets.