Emergency Response Plan Research Articles

Preparing for stormy weather: building VA health system resilience for dialysis emergency preparedness in the era of climate change.

Climate change has been implicated as the driver for the increasing number, intensity, duration, and consequences of catastrophic weather events. As a result of extreme weather events, climate change has also been implicated as an important mediator of adverse kidney health outcomes, not only increasing the risk for the development of acute and chronic kidney diseases, but also disrupting the delivery of critical kidney health services. In particular, the delivery of dialysis services during major emergencies remains an ongoing and increasing problem, with a recognized need for improved emergency preparedness and disaster management (EP-DM) strategies to mitigate the increased risk of morbidity and mortality associated with missed dialysis treatment. There are increasing reports detailing the challenges of kidney dialysis care in times of crisis, to include those resulting from both man-made and natural disasters. Optimized management of the high-risk vulnerable dialysis patient population must include both facility-facing comprehensive continuity of operations and emergency response plans, and ongoing patient-facing emergency preparedness education. This review discusses the adverse impact of climate change-related natural disasters on the delivery of dialysis services, and the evolving EP-DM strategies developed and implemented by the Veterans Health Administration (VA) to optimize the care and well being of the vulnerable end stage kidney disease (ESKD) patient population.

Rapid large-scale building damage level classification after earthquakes using deep learning with Lidar and satellite optical data

ABSTRACT In post-earthquake scenarios, the swift assessment of building damage levels is pivotal for efficient emergency response and recovery planning. Nevertheless, conventional in-situ damage evaluations consume time. Current satellite-based deep learning methods save time but often lack detail, usually classifying damage as either collapsed or intact. This two-level information is not enough for rescue or recovery planning. Light Detection and Ranging (Lidar)-based deep learning methods, which provide three-dimensional (3D) information, could address this issue of damage details. Therefore, this paper proposes a deep learning-based building damage level classification method using both Lidar and satellite data. The proposed method classifies damage into four levels, including no/minor damage, partially collapsed, totally collapsed, and story failure. The developed network builds upon RandLA-Net, incorporating surface normal vectors to enhance accuracy. A colourised Lidar dataset was created for the network. The network underscores the advantage of incorporating surface normal information. A framework is also proposed based on the damage level outcomes of the developed network, which aids in emergency response efforts. Consequently, this paper demonstrates the practical utility of deep learning networks in rapidly assessing detailed building damage levels after earthquakes. Its practical contribution is guiding decision-making during the critical phases of post-earthquake response and recovery.

A qualitative study of the government’s engagement of the private health sector in the delivery of Ghana’s COVID-19 emergency response

IntroductionThere is a growing literature on the significance of private sector engagement and collaboration for optimal response to health emergencies. The current study examines how the private sector was engaged...

Air pollution modelling of accidents involving hazardous substances

Abstract Air pollution modeling for accidents involving hazardous substances is a critical aspect of emergency response and environmental protection. This modeling aims to predict the dispersion and impact of toxic substances released into the atmosphere during accidents, such as industrial spills, transport accidents, chemical plant incidents or acts of terrorism. Such dangers are among the challenges which the modern society is facing and has to overcome. In this study we propose a systematic approach for organizing monitoring, collecting data from mobile sensors, creating situation development scenarios, modelling the spread of potential toxic-element pollution in dependence with the current meteorological conditions. The UNI-DEM model for simulation of the pollutants’ dispersion and transport in the air, as well as models for spread of leaking gas and liquid vapors are involved in this study. Finally, depending on the comprehensive analysis of the modeling results, an adequate response to such severe situations can be proposed. Assessment of safety, healthcare and environmental impacts should be the final result and the most socially important application of our work. The calculated pollutant concentrations can be compared with regulatory standards or health-based guidelines to assess potential health risks to exposed populations and environmental impacts on ecosystems. The importance of this information for emergency response planning, public health interventions, and environmental management strategies is beyond dispute.

Short-Term Passenger Flow Prediction in Urban Rail Transit based on Hybrid Deep Learning Models

Urban rail transit systems are essential for efficient, reliable, and environmentally friendly transportation in modern cities. Correct and effective short-term passenger flow prediction is crucial for optimizing operational efficiency, enhancing service quality, and ensuring passenger safety. Traditional prediction methods often fail to capture the complex, non-linear, and dynamic patterns in urban rail transit systems. This study uses a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to predict short-term passenger flow in urban rail transit. By integrating the strengths of CNN in capturing spatial features and LSTM in modeling temporal dependencies, the hybrid model aims to improve prediction accuracy. Using historical data from Hangzhou Metro, the study demonstrates the model's effectiveness in predicting passenger flow, which reasonably has a good fit. Additionally, models with different convolutional layers show different performances. These improved predictions offer valuable insights for transit authorities, enabling them to make more informed decisions regarding train scheduling, resource allocation, and emergency response planning. By anticipating passenger demand more accurately, authorities can optimize the deployment of trains, reduce waiting times, enhance passenger comfort, and improve overall service reliability.

National Burden and Epidemiological Features of Mass Casualty Incidents in Spain, from 2014 to 2022.

Mass Casualty Incidents (MCIs) pose significant challenges to health care systems, especially regarding emergency preparedness and response. This study aims to analyze the epidemiological characteristics and burden of MCIs in Spain from 2014 to 2022, focusing on the type, frequency, and impact of these incidents on public health and emergency services. A population-based retrospective observational study examined MCIs in Spain between January 2014 and December 2022. Data were collected from various emergency services. Incidents involving 4 or more victims requiring medical assistance and ambulance mobilization were included. The study categorized MCIs into 5 types: road traffic accidents, fires and explosions, chemical poisonings, maritime accidents, and others. A total of 1618 MCIs resulting in 8556 victims were identified, averaging 15 (95% CI, 11-19) incidents per month, with 79% due to road traffic accidents and 13% to fires and explosions, which also had the highest average of 7.6 victims per incident. Despite maritime accidents comprising only 1.9% of incidents, they had the highest fatality rate. MCIs were more frequent on weekends, in January and July, and between 3:00 PM and 9:00 PM. The average response time was 38 minutes, with 35% of victims sustaining severe injuries. Despite a slight decrease in annual MCIs from 2014 to 2022 in Spain, the trend is not statistically significant. The study highlights the need for a national registry and standardized data collection to enhance emergency preparedness and response planning and facilitate the reduction of the MCI burden.

Identification and ranking of oil pollution by developing a preventive management and emergency response model against pollution: A case study of a mega port on the northern coast of the Persian Gulf

Oil pollution remains a critical environmental challenge in maritime transportation, significantly impacting marine ecosystems and coastal communities. This study investigates oil pollution incidents at the loading and unloading docks of Imam Khomeini Port and aims to develop a comprehensive prevention and emergency response management model. We conducted a thorough evaluation of environmental components, specifically Total Petroleum Hydrocarbons (TPHs), and utilized the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to identify and prioritize risks. Key findings revealed four emergency pollution scenarios, highlighting critical areas that require immediate intervention. Our analysis underscored essential agenda items for effective emergency response planning, scoring 4.4 for environmental resource conservation and 4.35 for pre-incident responsibility assignment. Furthermore, a McKinsey Gap Analysis identified seven strategic priorities for the emergency response plan, indicating substantial weaknesses in the strategy (1.65) and skills (1.75) dimensions, demonstrating inadequate crisis preparedness. These results emphasize the urgent need for a robust emergency response model tailored to mitigate oil pollution risks. By addressing identified gaps, this study provides a strategic framework for enhancing environmental management practices at the port, fostering more sustainable operations in the Persian Gulf region. Ultimately, our findings advocate for collaborative efforts among stakeholders, including national and international organizations, to implement rigorous environmental policies and effective monitoring systems to safeguard marine environments against oil pollution.

A new scoring system to predict fatal accidents in General Aviation and to facilitate emergency control centre response

Numerous accidents occur with General Aviation aircraft every year. To date, pre-emptive prediction of survival or death is impossible. The current study aims to identify significant factors elementary to predict survival after General Aviation (GA) aircraft accidents. The Implementation of a scoring system, including these factors, may facilitate emergency control centre response. Data of flight accidents over a 20-year period (extracted from the German Federal Bureau of Aircraft Accident Investigation [BFU]) was analysed for fixed-wing motorized small aircrafts below 5,700 kg MTOW. Factors of interest were analysed using Chi2- and Mann–Whitney-U-Tests. Logistic regression was used to establish a score to calculate the probability of a fatal outcome after an aircraft accident. The BFU lists 1,595 GA aircraft accidents between 2000 and 2019. The factors “third quarter of the year” (p = 0.04), “last quarter of the year” (p = 0.002), “fire” (p < 0.0001), “distance from airport > 10 km” (p < 0.0001), “landing” (p < 0.0001) and “cruise” (p < 0.0001), significantly correlated positively or negatively with a fatal outcome. “Take-off”, “approach”, “month”, “day of the week”, “persons on board above three”, “night-time” and “icing conditions” showed no significant correlation. Using logistic regression “third quarter of the year” and “cruise” were excluded when using the B-STEP method. Including the four significant parameters, the score showed a strong effect with f2 = 0.709. The analysis of GA aircraft accidents in Germany enabled the identification of relevant factors and establishment of a new scoring system for survival prediction after small aircrafts accidents below 5,700 kg MTOW. The implementation of the scoring system in emergency control centres in the context of digital development and artificial intelligence can improve emergency response planning and distribution.

Simulation of landslide dam failure due to overtopping considering wide-graded soil erosion

Landslide dams, as a particular type of secondary geological disaster, can cause serious flood disasters. Therefore, accurately predicting potential dam failure processes is crucial for developing reasonable emergency response plans. Currently, several landslide dam failure models have been proposed, but most of these models do not appropriately consider the wide gradation of landslide dam materials, which is essential for accurate erosion calculations. Therefore, this research proposes a relative exposure formula under three-dimensional conditions to account for the concealment and exposure effects between large and small particles in wide-graded soil. Based on this, the incipient velocity of wide-graded soil is derived, and a mathematical model of overtopping failure of landslide dams is established. As part of the model, a numerical solution process is also developed to ensure convergence. To evaluate the performance of this new mathematical model, the discharge process of Tangjiashan landslide dam was used as a real-world case for dam failure calculation. The calculated results of key dam failure outcomes were compared with actual measurements, showing that the results of this model are largely consistent with actual measurements, with the error controlled within 10%. Additionally, the mathematical model proposed in this article was compared with two other existing dam failure models, revealing that the model proposed here has advantages in controlling the error of key dam failure outcomes, mainly due to the comprehensive consideration of erosion calculation in wide-graded soil. To further evaluate the stability of the model, the breach processes of the “11.3” Baige landslide dam and the Yigong landslide dam were also calculated, and the results showed reasonable consistency. Based on the model presented in this article, the influence of the grading width of landslide dam materials on key dam failure outcomes was discussed. It was found that grading width significantly affects the development process of breach flow. Specifically, wider grading width leads to smaller peak values of breach flow and later peak times. Additionally, the final size of the breach is affected by grading width, as it has a significant impact on erosion strength, although the sensitivity of this parameter is relatively weak. Given the significant impact of grading width on the dam failure process, especially for landslide dams containing wide-graded soil, it is essential to fully consider the wide grading of materials for accurate dam failure calculations.

Research on the Calculation Method and Diffusion Pattern of VCE Injury Probability in Oil Tank Group Based on SLAB-TNO Method

Accidental leakage from oil–gas storage tanks can lead to the formation of liquid pools. These pools can result in vapor cloud explosions (VCEs) if combustible vapors encounter ignition energy. Conducting accurate and comprehensive consequence analyses of such explosions is crucial for quantitative risk assessments (QRAs) in industrial safety. In this study, a methodology based on the SLAB-TNO model to calculate the overpressure resulting from a VCE is presented. Based on this method, the consequences of the VCE accident considering the gas cloud concentration diffusion are studied. The probit model is employed to evaluate casualty probabilities under varying environmental and operational conditions. The effects of key parameters, including gas diffusion time, wind speed, lower flammability limit (LFL), and environment temperature, on casualty diffusion are systematically investigated. The results indicate that when the diffusion time is less than 100 s, the VCE consequences are significantly more severe due to the rapid spread of the gas cloud. Furthermore, increasing wind speed accelerates gas dispersion, reducing the spatial extent of casualty isopleths. The LFL is shown to have a direct impact on both the mass and diffusion of the flammable gas cloud, with higher LFL values shifting the explosion’s epicenter upward. The environmental temperature promotes gas diffusion in the core area and increases the mass of the combustible gas cloud. These findings provide critical insights for improving the safety protocols in oil and gas storage facilities and can serve as a valuable reference for consequence assessment and emergency response planning in similar industrial scenarios.

Assessing Crisis Management Tools for Sustainability of Industrial Safety

This study presents a comprehensive risk assessment of ammonia leaks, focusing on the quantitative modelling of hazardous area ranges, concentration dynamics, and thermal radiation effects under varying leakage scenarios using the ALOHA 5.4.7 software. The analysis involves two key scenarios: an ammonia gas leak and a pool fire, each modelled under distinct atmospheric conditions. For the gas leak scenario, ammonia concentrations were mapped across ERPG-defined hazard zones, ranging from low-level irritation zones (ERPG-1) to life-threatening exposure levels (ERPG-3), with maximum concentrations reaching 1500 ppm within a 110 m radius. The second scenario examined the impact of thermal radiation from a pool fire, identifying critical radiation zones where exposure to heat fluxes exceeding 10 kW.m−2 could cause fatal outcomes within 12 m. Despite ALOHA’s strengths in modelling acute exposure risks and providing valuable input for emergency response planning, the study identifies several limitations, particularly regarding the long-term environmental and health impacts of chemical releases and the effects of varying meteorological conditions. These findings suggest that integrating ALOHA with advanced real-time monitoring and AI-based prediction systems could significantly improve its capacity to manage dynamic, rapidly evolving industrial hazards.

Deep Learning-Based Method for Predicting Emergency Resources Demand at Hydrogen Refueling Stations

The transition towards green energy has intensified the focus on hydrogen as a clean energy, with hydrogen refueling stations (HRSs) becoming increasingly prevalent. Despite the potential benefits, the inherent risks associated with high-pressure hydrogen storage pose significant safety challenges. Effective emergency response planning is critical to mitigate the consequences of any potential accidents at HRSs. This study developed a deep learning-based model to predict the demand for emergency resources following an accident at an HRS, thereby enhancing the efficiency of emergency responses. A HRS case library was constructed encompassing a variety of accident scenarios, including characteristics, potential casualties, and corresponding emergency resource demands. By employing deep learning algorithms, correlations between accident consequences and emergency resource demands were established, enabling rapid predictions post-accident. The study evaluated various neural network models and found the Random Forest model to be the most accurate for predicting casualties and resource demands. To facilitate user accessibility, a graphical user interface was developed allowing non-technical emergency responders to input accident parameters and receive immediate predictions on casualties and emergency resource demands. The integration of deep learning with emergency response planning presents a significant advancement in risk mitigation strategies for HRSs.

Ensure your emergency response plans comply with NATA's updated EAP position statement

Having the right plans in place can help your athletics department respond promptly and effectively to emergencies while limiting liability and preventing those emergencies from turning into out‐of‐control crises. To help college athletics departments prepare for any emergency, the National Athletic Trainers’ Association released new guidance earlier this year, the 2024 “National Athletic Trainers’ Association Position Statement: Emergency Action Plan Development and Implementation in Sport.” This updated plan outlines substantial advancements from the 2002 “National Athletic Trainers’ Association Position Statement: Emergency Planning in Athletics.” The emergency action plan is a written document that details the steps for athletic trainers, team physicians, and other health care professionals and stakeholders to take in order to ensure they’re prepared for on‐site emergency response to catastrophic or potentially catastrophic injuries in athletics‐related settings. The EAP is designed to facilitate emergency preparedness via an efficient and coordinated emergency response, and shouldn’t be mistaken for being the same as policies and procedures. Instead, an outgrowth of the EAP is a protocol in the form of a document that provides guidance for steps, decisions, and actions in sports medicine.

Gas Leakage Risk Management Of Biogas Powerplant Using Aloha Gas Dispersion Modelling And Bowtie Analysis

Biogas, a fuel derived from organic waste, has gained popularity as a sustainable energy source. The adaptability of biogas allows its utilization for electricity and heating. This study aims to evaluate the risk of biogas leakage using ALOHA gas dispersion modeling and Bowtie analysis to identify hazard zones and evaluate the effectiveness of preventive and mitigation measures. The research method using Gas dispersion modeling in ALOHA software can simulate the spread of leaked biogas, identify potential hazard zones, and inform emergency response planning. The results show that gas dispersion reaches up to 296 meters from the leak source in a worst-case scenario without ignition, with significant implications for safety. The ignition scenario produces high thermal radiation, further exacerbating the risk. In conclusion, these findings highlight the intolerable risk of biogas leakage, requiring comprehensive risk mitigation measures. This study recommends the implementation of a robust control system, routine maintenance, and emergency response plan to ensure the safety and sustainability of biogas facility operations

Preparedness and Public Health: an Italian Local Health Authority plan for a mass gathering in 2023

Abstract Issue Mass gatherings represent an important Public Health challenge. The increased number of people present in a specific area puts local health systems under stress (i.e.workload of emergency services, environmental health and food safety) and at the same time it rises the risks of a potential mass casualty incident. Effective planning and disaster preparedness have to be achieved through collaboration among local health authorities(LHA) and other stakeholders (event planners, law enforcement, local transportation services). Problem Every year, for 3 days, the Italian Alpine troops gather in a different city to remember First World War fallen. During those days, the city centre transit is not allowed to car (in specific areas even to emergency vehicles).In May 2023 this event occurred in Udine, a city in the North East of Italy with almost 100.000 inhabitants. For that year at least 300.000 participants were expected. LHA had to reorganize preparing itself to manage a temporary quadrupled catchment area. Results In the previous months, local authorities met local health representatives and other stakeholders in order to define a single emergency response plan. For healthcare services it was decided to: institute first aid points within the city; reorganise patients’ emergency transport system; increase hospitals’ treatment capacity (beds, life support equipment); increase healthcare professional availability; empower drugstores (opening times, drugs procurement); strengthen human and animal health surveillances;activate alternative communication systems. During the event, LHA updated other authorities involved three times a day about hospital and emergency system situation. At the end of the event,a multiprofessional debriefing meeting was held and no critical situations were reported. Lessons Preparedness requires a great commitment in terms of resources and time, but it is confirmed to be essential to guarantee adequate assistance even in extraordinary conditions. Key messages • Mass gathering s events put under stress all the main areas of public health system. • Public health preparedness cannot be effective without a strong involvement of other public and private stakeholders.

From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases

The outbreak of emerging infectious diseases poses significant challenges to global public health. Accurate early forecasting is crucial for effective resource allocation and emergency response planning. This study aims to develop a comprehensive predictive model for emerging infectious diseases, integrating the blending framework, transfer learning, incremental learning, and the biological feature Rt to increase prediction accuracy and practicality. By transferring features from a COVID-19 dataset to a monkeypox dataset and introducing dynamically updated incremental learning techniques, the model's predictive capability in data-scarce scenarios was significantly improved. The research findings demonstrate that the blending framework performs exceptionally well in short-term (7-day) predictions. Furthermore, the combination of transfer learning and incremental learning techniques significantly enhanced the adaptability and precision, with a 91.41% improvement in the RMSE and an 89.13% improvement in the MAE. In particular, the inclusion of the Rt feature enabled the model to more accurately reflect the dynamics of disease spread, further improving the RMSE by 1.91% and the MAE by 2.17%. This study underscores the significant application potential of multimodel fusion and real-time data updates in infectious disease prediction, offering new theoretical perspectives and technical support. This research not only enriches the theoretical foundation of infectious disease prediction models but also provides reliable technical support for public health emergency responses. Future research should continue to explore integrating data from multiple sources and enhancing model generalization capabilities to further enhance the practicality and reliability of predictive tools.

Is Anyone Ready to Save a Life? An Examination of Cardiac Emergency Preparedness in Schools.

This study explored the cardiac emergency preparedness of school employees in Illinois, as well as their attitudes toward cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) training. One thousand two hundred seventy-six school employees completed an online survey regarding their school's cardiac emergency preparedness, as well as their access to CPR/AED training, confidence and willingness to perform CPR/AED, and attitudes toward CPR policies and mandates. In total, results from 1273 school employees were analyzed. School employees in Illinois are not prepared to respond to a cardiac emergency, but desire training, plans, and drills. Demographic analyses revealed statistically significant differences in cardiac emergency preparedness between individuals of different personal and school characteristics. School employees in Illinois are not prepared to respond to a cardiac emergency. To increase preparedness for cardiac emergencies at school, schools should implement CPR/AED training for all employees, cardiac emergency response plans, and cardiac emergency response drills. Policies should be implemented at the state and local level to support cardiac emergency preparedness in schools, including mandated CPR/AED training for all school employees, cardiac emergency response plans for every building, and required cardiac emergency response drills.

Assessing and Enhancing the CWD Emergency Response Plan: Incorporating Lessons from Typhoon Odette using Integrated Regulatory Frameworks

This research aims to evaluate the effectiveness of the existing CWD emergency response plan and advocate enhancements based totally at the lessons found out from Typhoon Odette. By analyzing the regulatory frameworks governing CWD control and emergency response, the examiner seeks to pick out gaps and inconsistencies that hindered the performance of the reaction to the typhoon. Through an integrated method, the research will examine the regulatory panorama, investigate the preparedness and effectiveness of the CWD emergency reaction plan, and endorse recommendations for boosting its skills. The findings will make contributions to the improvement of greater robust and complete CWD emergency reaction plans that could better mitigate the effects of future disasters.

Nursing home leader response during COVID-19: a qualitative descriptive study about use of external resources during the pandemic

BackgroundUS nursing homes were ground zero for COVID-19 and nursing home leaders faced multiple challenges to keep residents and staff safe. Understanding the leader’s role and their use of external resources to rapidly respond to the pandemic is important to better prepare for the next infectious disease outbreak emergency. The purpose of this study is to describe Missouri nursing home leaders’ use of external resources to manage challenges encountered during the pandemic.MethodsThis qualitative descriptive study uses data from semi-structured interviews conducted with leaders from 24 Midwestern nursing homes between March 2022 and March 2023. Interviews were transcribed verbatim and analyzed using Dedoose software. Directed content analysis, guided by Donabedian’s Structure, Process, Outcome framework, was used for analysis. Interviews were conducted as part of a larger mixed-methods study focused on developing knowledge and recommendations to improve US nursing homes’ capacity to respond to infectious disease outbreaks.ResultsForty-three interviews were conducted across the 24 homes. Participants included administrators (n = 24), nurse leaders (n = 19), and infection preventionists (n = 16). Six sub-categories of external resources/support were used by leaders to manage challenges during the pandemic:1) corporate support and communications, 2) statewide resources, 3) community-based resources, 4) health care coalitions focused on emergency response planning, 5) existing affiliations with local organizations i.e., hospitals, and 6) community members and families. Corporate support was a primary resource; however, it was limited to chain-based homes. Leaders from standalone homes seemed most reliant on statewide agencies, existing affiliations, and other community-based resources due to their lack of corporate connections. Health care coalitions were few, but when available, helped nursing homes prepare for the pandemic onset. Family and community members were vital despite being off-site from nursing homes at the pandemic onset.ConclusionLeaders played a pivotal role in accessing and using external resources to manage challenges during the pandemic. Statewide and community-based agencies and existing affiliations were particularly critical for standalone homes who otherwise had little to no means of support. Federal, state and local agencies must consider opportunities to build multi-agency regional collaborations, local health care coalitions and community-based partnerships that include nursing homes as member. Finally, community members and family were important in providing support, thus closing visitation is a double-edged sword that needs careful, future consideration.

Long-term validation of inner-urban mobility metrics derived from Twitter/X

Urban mobility analysis using Twitter as a proxy has gained significant attention in various application fields; however, long-term validation studies are scarce. This paper addresses this gap by assessing the reliability of Twitter data for modeling inner-urban mobility dynamics over a 27-month period in the. metropolitan area of Rio de Janeiro, Brazil. The evaluation involves the validation of Twitter-derived mobility estimates at both temporal and spatial scales, employing over 1.6 × 1011 mobile phone records of around three million users during the non-stationary mobility period from April 2020 to. June 2022, which coincided with the COVID-19 pandemic. The results highlight the need for caution when using Twitter for short-term modeling of urban mobility flows. Short-term inference can be influenced by Twitter policy changes and the availability of publicly accessible tweets. On the other hand, this long-term study demonstrates that employing multiple mobility metrics simultaneously, analyzing dynamic and static mobility changes concurrently, and employing robust preprocessing techniques such as rolling window downsampling can enhance the inference capabilities of Twitter data. These novel insights gained from a long-term perspective are vital, as Twitter - rebranded to X in 2023 - is extensively used by researchers worldwide to infer human movement patterns. Since conclusions drawn from studies using Twitter could be used to inform public policy, emergency response, and urban planning, evaluating the reliability of this data is of utmost importance.