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Metodos Se incluyeron en el estudio las ciudades con mas de un millon de habitantes que informaron al Carbon Disclosure Project sobre sus acciones de adaptacion entre 2021 y 2023, y que describieron al menos una medida de adaptacion para un peligro climatico en al menos un ano. Se identificaron las ciudades que informaron sobre la existencia de sistemas de alerta temprana conforme al marco Alertas tempranas para todos de las Naciones Unidas, que comprende cuatro pilares: conocimiento del riesgo, monitoreo y prevision de peligros, comunicacion de alertas y preparacion. Asimismo, se analizo la participacion del sector sanitario en estos sistemas. [phrase omitted]

Climate change can trigger a sequence of events of significant magnitude with consequences for waterborne diseases. Heavy rainfall, flooding and hot weather are associated with waterborne diseases, but early warning systems could intercept these cascading risks.

The Noto Peninsula earthquake (Mj7.6), which occurred on New Year’s Day of 2024, had two characteristic features: multiple tremors at the initiation of the rupture and a long fault rupture exceeding 100 km. The source process included three significant tremors for 15 s: Mj ~ 3 event, Mj 5.9 event, and Mj 7.6 event. The rupture started at the tip of the Noto Peninsula and propagated bilaterally in northeast and southwest directions. We evaluated the performance of the Japanese earthquake early warning (EEW) issued to the public. The source determination process of the EEW was triggered by the preceding Mj ~ 3 event and the warning threshold was exceeded by the Mj 5.9 event, so there was at least a 13-s lead time before the S-arrival of the Mj 7.6 event, allowing many residents to take protective measures. The first warning was issued to only the Northern part of the Ishikawa prefecture. However, the second warning that was distributed to as far as a few hundred kilometers was issued 27.1 s after the first warning, which was longer than expected. This is because the magnitude was underestimated during the rupture process and the warning was issued based on the shaking observation of the Mj7.6 event. We recomputed the shaking estimation from the Integrated Particle Filter (IPF) method and the Propagation of Local Undamped Motion (PLUM) method used in the Japanese EEW, and additionally, the XYtracker method to evaluate the effect of fault finiteness. At the initial part of the rupture, the fault finiteness is difficult to capture, and the finite-source approach produced a similar shaking estimation to the point-source approach. As the rupture propagates, shakings in the western area near the fault were significantly underestimated by the point-source approach. For large earthquakes, considering fault finiteness may be able to capture the rupture directivity and improve the accuracy of shaking estimation. Graphical Abstract

Monitoring vital signs in hospitalized patients is crucial for evaluating their clinical condition. While early warning scores like the modified early warning score (MEWS) are typically calculated 3 to 4 times daily through spot checks, they might not promptly identify early deterioration. Leveraging technologies that provide continuous monitoring of vital signs, combined with an early warning system, has the potential to identify clinical deterioration sooner. This approach empowers health care providers to intervene promptly and effectively. This study aimed to assess the impact of a Remote Patient Monitoring System (RPMS) with an automated early warning system (R-EWS) on patient safety in noncritical care at a tertiary hospital. R-EWS performance was compared with a simulated Modified Early Warning System (S-MEWS) and a simulated threshold-based alert system (S-Threshold). Patient outcomes, including intensive care unit (ICU) transfers due to deterioration and discharges for nondeteriorating cases, were analyzed in Ramaiah Memorial Hospital's general wards with RPMS. Sensitivity, specificity, chi-square test for alert frequency distribution equality, and the average time from the first alert to ICU transfer in the last 24 hours was determined. Alert and patient distribution by tiers and vitals in R-EWS groups were examined. Analyzing 905 patients, including 38 with deteriorations, R-EWS, S-Threshold, and S-MEWS generated more alerts for deteriorating cases. R-EWS showed high sensitivity (97.37%) and low specificity (23.41%), S-Threshold had perfect sensitivity (100%) but low specificity (0.46%), and S-MEWS demonstrated moderate sensitivity (47.37%) and high specificity (81.31%). The average time from initial alert to clinical deterioration was at least 18 hours for RPMS and S-Threshold in deteriorating participants. R-EWS had increased alert frequency and a higher proportion of critical alerts for deteriorating cases. This study underscores R-EWS role in early deterioration detection, emphasizing timely interventions for improved patient outcomes. Continuous monitoring enhances patient safety and optimizes care quality.

[...]a search of the keywords “landslide”, “warning” and “system” in the abstracts of all articles that have ever been published in the Division's NHESS journal produced 698 results. Within this framework, this special issue was initially conceived to collect the most relevant works presented to the session SSS9.5/NH3.13 on “Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception” within the 2017 General Assembly of the European Geosciences Union. A broad range of conditions is covered, ranging from case studies characterized by scarcity (Shi et al., 2018) to the abundance of data (Devoli et al., 2018; Vaz et al., 2018) and, in some cases, also the measurement of hydrologic (Segoni et al., 2018a) or geotechnical variables (Canli et al., 2018; Salvatici et al., 2018) to strengthen the forecasting models. [...]the works included in the special issue describe early warning systems at very different stages of employment. The most debated unresolved issues in rainfall threshold research include the following the definition of objective and automatic procedures to define the thresholds (Staley et al., 2013; Segoni et al., 2014; Iadanza et al., 2016; Vessia et al., 2016; Rossi et al., 2017; Melillo et al., 2018); the need for taking into account the hydrological conditions of the hillslope system with more complex approaches (Posner et al., 2015; Bogaard and Greco, 2018); the evaluation and quantification of diverse uncertainties (Nikolopoulos et al., 2015; Destro et al., 2017; Marra et al., 2017; Rossi et al., 2017; Marra, 2018; Peres et al., 2018); the importance of validation procedures (Staley et al., 2013; Gariano et al., 2015; Lagomarsino et al., 2015); the use of rainfall data gathered from ground-based radars or satellites (Robbins, 2016; Destro et al., 2017; Rossi et al., 2017; Brunetti et al., 2018); the implementation of the thresholds into hazard management procedures and early warning systems (Kirshbaum et al. 2015; Rosi et al., 2015; Piciullo et al., 2017, 2018; Krøgli et al., 2018; Wei et al., 2018; Pecoraro et al., 2018).

Formal infectious disease surveillance in Ukraine has been disrupted by Russia's 2022 invasion, leading to challenges with tracking and containing epidemics. To analyze the effects of the war on infectious disease epidemiology, we used open-source data from EPIWATCH, an artificial intelligence early-warning system. We analyzed patterns of infectious diseases and syndromes before (November 1, 2021-February 23, 2022) and during (February 24-July 31, 2022) the conflict. We compared case numbers for the most frequently reported diseases with numbers from formal sources and found increases in overall infectious disease reports and in case numbers of cholera, botulism, tuberculosis, HIV/AIDS, rabies, and salmonellosis during compared with before the invasion. During the conflict, although open-source intelligence captured case numbers for epidemics, such data (except for diphtheria) were unavailable/underestimated by formal surveillance. In the absence of formal surveillance during military conflicts, open-source data provide epidemic intelligence useful for infectious disease control.

To improve the performance of earthquake early warning (EEW) systems, we propose an approach that utilizes the time-dependence of P-wave displacements to estimate the earthquake magnitude (M) based on the relationship between M and the displacement. The traditional seismological understanding posits that this relationship achieves statistical significance when the displacement reaches its final peak value, resulting in the adoption of time-constant coefficients. However, considering the potential for earlier establishment of the relationship’s significance than conventionally assumed, we analyze waveforms observed in Japan and determine the intercept in the relationship as a function of time from the P-wave onset. We demonstrate that our approach reduces the underestimation of M in the initial P-wave stages compared to the conventional technique. Consequently, we find a significant rise in the number of earlier warnings in the Japanese railway EEW system. Due to the inherent trade-off between the immediacy and accuracy of alarm outputs, the proposed method unavoidably leads to an increase in the frequency of alerts. Nonetheless, if deemed acceptable by system users, our approach can contribute to EEW performance improvement.

Climate change exacerbates wildfire risks in regions like the Mediterranean, where rising temperatures and prolonged droughts create ideal fire conditions. Adapting to this scenario requires implementing advanced risk management strategies that leverage cutting-edge technologies. Wildfire early warning systems are crucial tools for detecting fires at an early stage, helping prevent potential future damage. This paper proposes a smart CO[sub.2] sensor network-based early warning system, relying on a platform that enables the connection, management, and processing of data from the devices through the cloud. The wildfire early warning system was tested in a real controlled experiment, in which 44 sensors were deployed in strategically selected locations at varying distances from the fire. To enhance early detection, three Artificial Intelligence (AI) models were developed using AutoEncoders (AEs) and Long-Short-Term Memory (LSTM), and these were compared to a simple threshold-based (NO-AI) model. All AI models, especially the LSTM-based model, were able to extract more valuable information from the CO[sub.2] records, activating up to 56% more sensors than the NO-AI model in less time and tracking potential fire front propagation based on wind patterns. Therefore, the system not only improves early fire detection models but also effectively supports firefighting operations.

This paper assessed existing EWS challenges and opportunities in cloud computing through the PSALSAR framework for systematic literature review and meta-analysis. The research used extant literature from Scopus and Web of Science, where a total of 2516 pieces of literature were extracted between 2004 and 2022, and through inclusion and exclusion criteria, the total was reduced to 98 for this systematic review. This review highlights the challenges and opportunities in transferring in-house early warning systems (that is, non-cloud) to the cloud computing infrastructure. The different techniques or approaches used in different kinds of EWSs to facilitate climate-related data processing and analytics were also highlighted. The findings indicate that very few EWSs (for example, flood, drought, etc.) utilize the cloud computing infrastructure. Many EWSs are not leveraging the capability of cloud computing but instead using online application systems that are not cloud-based. Secondly, a few EWSs have harnessed the computational techniques and tools available on a single platform for data processing. Thirdly, EWSs combine more than one fundamental tenet of the EWS framework to provide a holistic warning system. The findings suggest that reaching a global usage of climate-related EWS may be challenged if EWSs are not redesigned to fit the cloud computing service infrastructure.