scholarly journals Using Digital Surveillance Tools for Near Real-Time Mapping of the Risk of International Infectious Disease Spread: Ebola as a Case Study

2019 ◽  
Author(s):  
Sangeeta Bhatia ◽  
Britta Lassmann ◽  
Emily Cohn ◽  
Malwina Carrion ◽  
Moritz U. G. Kraemer ◽  
...  
Keyword(s):  
Real Time ◽  
Disease Surveillance ◽  
Early Warning Systems ◽  
West African ◽  
Disease Spread ◽  
Warning Systems ◽  
Short Term ◽  
Incidence Trends ◽  
Digital Surveillance

AbstractIn our increasingly interconnected world, it is crucial to understand the risk of an outbreak originating in one country or region and spreading to the rest of the world. Digital disease surveillance tools such as ProMED and HealthMap have the potential to serve as important early warning systems as well as complement the field surveillance during an ongoing outbreak. Here we present a flexible statistical model that uses data produced from digital surveillance tools (ProMED and HealthMap) to forecast short term incidence trends in a spatially explicit manner. The model was applied to data collected by ProMED and HealthMap during the 2013-2016 West African Ebola epidemic. The model was able to predict each instance of international spread 1 to 4 weeks in advance. Our study highlights the potential and limitations of using publicly available digital surveillance data for assessing outbreak dynamics in real-time.

scholarly journals Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sangeeta Bhatia ◽  
Britta Lassmann ◽  
Emily Cohn ◽  
Angel N. Desai ◽  
Malwina Carrion ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Real Time ◽  
Disease Surveillance ◽  
West African ◽  
Disease Spread ◽  
Short Term ◽  
Incidence Trends ◽  
Use Of Data ◽  
Digital Surveillance ◽  
Risk Of Disease

AbstractData from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013–2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1–4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.

scholarly journals Innovative Monitoring Tools and Early Warning Systems for Risk Management: A Case Study

Geosciences ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 62 ◽  
Author(s):  
Andrea Segalini ◽  
Andrea Carri ◽  
Alessandro Valletta ◽  
Maurizio Martino
Keyword(s):  
Monitoring System ◽  
Early Warning ◽  
Slope Failure ◽  
Central Italy ◽  
Early Warning Systems ◽  
Critical Event ◽  
Warning Systems ◽  
Automatic Data ◽  
Area Of Interest

During recent years, the availability of innovative monitoring instrumentation has been a fundamental component in the development of efficient and reliable early warning systems (EWS). In fact, the potential to achieve high sampling frequencies, together with automatic data transmission and elaboration are key features for a near-real time approach. This paper presents a case study located in Central Italy, where the realization of an important state route required a series of preliminary surveys. The monitoring system installed on site included manual inclinometers, automatic modular underground monitoring system (MUMS) inclinometers, piezometers, and geognostic surveys. In particular, data recorded by innovative instrumentation allowed for the detection of major slope displacements that ultimately led to the landslide collapse. The implementation of advanced tools, featuring remote and automatic procedures for data sampling and elaboration, played a key role in the critical event identification and prediction. In fact, thanks to displacement data recorded by the MUMS inclinometer, it was possible to forecast the slope failure that was later confirmed during the following site inspection. Additionally, a numerical analysis was performed to better understand the mechanical behavior of the slope, back-analyze the monitored event, and to assess the stability conditions of the area of interest.

scholarly journals Time forecast of a break-off event from a hanging glacier

2016 ◽  
Vol 10 (3) ◽  
pp. 1191-1200 ◽  
Author(s):  
Jérome Faillettaz ◽  
Martin Funk ◽  
Marco Vagliasindi
Keyword(s):  
Real Time ◽  
Power Law ◽  
Surface Displacement ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Safety Measures ◽  
Periodic Oscillations ◽  
Ice Volume ◽  
Surface Displacements ◽  
Displacement Measurements

Abstract. A cold hanging glacier located on the south face of the Grandes Jorasses (Mont Blanc, Italy) broke off on the 23 and 29 September 2014 with a total estimated ice volume of 105 000 m3. Thanks to accurate surface displacement measurements taken up to the final break-off, this event was successfully predicted 10 days in advance, enabling local authorities to take the necessary safety measures. The break-off event also confirmed that surface displacements experienced a power law acceleration along with superimposed log-periodic oscillations prior to the final rupture. This paper describes the methods used to achieve a satisfactory time forecast in real time and demonstrates, using a retrospective analysis, their potential for the development of early-warning systems in real time.

scholarly journals Real-time inversion of tsunamis generated by landslides

2011 ◽  
Vol 11 (9) ◽  
pp. 2511-2520 ◽  
Author(s):  
C. Cecioni ◽  
A. Romano ◽  
G. Bellotti ◽  
M. Risio ◽  
P. de Girolamo
Keyword(s):  
Real Time ◽  
Laboratory Experiments ◽  
Early Warning Systems ◽  
Time Inversion ◽  
Warning Systems ◽  
Optimal Position ◽  
Tsunami Early Warning ◽  
Tsunami Waves ◽  
Inversion Procedure ◽  
Tsunami Early Warning Systems

Abstract. In this paper, we test a method for forecasting in real-time the properties of offshore propagating tsunami waves generated by landslides, with the aim of supporting tsunami early warning systems. The method uses an inversion procedure, that takes input data measurements of water surface elevation at a point close to the tsunamigenic source. The measurements are used to correct the results of pre-computed numerical simulations, reproducing the wave field induced by different landslide scenarios. The accuracy of the method is evaluated using the results of laboratory experiments, aimed at studying tsunamis generated by landslides sliding along the flank of a circular shoreline island. The paper investigates what the optimal position is of where to measure the tsunamis, what the effects are, the accuracy of the results, and of uncertainties on the landslide scenarios. Finally, the method is successfully tested using partial input time series, simulating the behaviour of the system in real-time during the tsunami event when forecasts are updated, as the measurements become available.

scholarly journals Landslide early warning based on failure forecast models: the example of Mt. de La Saxe rockslide, northern Italy

2015 ◽  
Vol 3 (2) ◽  
pp. 1511-1525 ◽  
Author(s):  
A. Manconi ◽  
D. Giordan
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Northern Italy ◽  
Warning Systems ◽  
Surface Displacements ◽  
Forecast Models ◽  
Landslide Early Warning ◽  
Critical Phases ◽  
Temporal Windows

Abstract. We investigate the use of landslide failure forecast models by exploiting near-real-time monitoring data. Starting from the inverse velocity theory, we analyze landslide surface displacements on different temporal windows, and apply straightforward statistical methods to obtain confidence intervals on the estimated time of failure. Here we describe the main concepts of our method, and show an example of application to a real emergency scenario, the La Saxe rockslide, Aosta Valley region, northern Italy. Based on the herein presented case study, we identify operational thresholds based on the reliability of the forecast models, in order to support the management of early warning systems in the most critical phases of the landslide emergency.

scholarly journals Ethics, Integrity, and Retributions of Digital Detection Surveillance Systems for Infectious Diseases: Systematic Literature Review (Preprint)

2021 ◽  
Author(s):  
Ivy Y Zhao ◽  
Ye Xuan Ma ◽  
Man Wai Cecilia Yu ◽  
Jia Liu ◽  
Wei Nan Dong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Disease ◽  
Civil Rights ◽  
Infectious Diseases ◽  
Early Warning ◽  
Ethical Issues ◽  
Early Warning Systems ◽  
Surveillance Systems ◽  
Warning Systems ◽  
Digital Surveillance

BACKGROUND The COVID-19 pandemic has increased the importance of the deployment of digital detection surveillance systems to support early warning and monitoring of infectious diseases. These opportunities create a “double-edge sword,” as the ethical governance of such approaches often lags behind technological achievements. OBJECTIVE The aim was to investigate ethical issues identified from utilizing artificial intelligence–augmented surveillance or early warning systems to monitor and detect common or novel infectious disease outbreaks. METHODS In a number of databases, we searched relevant articles that addressed ethical issues of using artificial intelligence, digital surveillance systems, early warning systems, and/or big data analytics technology for detecting, monitoring, or tracing infectious diseases according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and further identified and analyzed them with a theoretical framework. RESULTS This systematic review identified 29 articles presented in 6 major themes clustered under individual, organizational, and societal levels, including awareness of implementing digital surveillance, digital integrity, trust, privacy and confidentiality, civil rights, and governance. While these measures were understandable during a pandemic, the public had concerns about receiving inadequate information; unclear governance frameworks; and lack of privacy protection, data integrity, and autonomy when utilizing infectious disease digital surveillance. The barriers to engagement could widen existing health care disparities or digital divides by underrepresenting vulnerable and at-risk populations, and patients’ highly sensitive data, such as their movements and contacts, could be exposed to outside sources, impinging significantly upon basic human and civil rights. CONCLUSIONS Our findings inform ethical considerations for service delivery models for medical practitioners and policymakers involved in the use of digital surveillance for infectious disease spread, and provide a basis for a global governance structure. CLINICALTRIAL PROSPERO CRD42021259180; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=259180

scholarly journals P-wave picking for earthquake early warning: Refinement of a T pd method

2021 ◽  
Author(s):  
Masumi Yamada ◽  
Jim Mori
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Arrival Time ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Warning Systems ◽  
Wave Detection ◽  
Earthquake Early Warning Systems ◽  
Wave Arrival

Summary Detecting P-wave onsets for on-line processing is an important component for real-time seismology. As earthquake early warning systems around the world come into operation, the importance of reliable P-wave detection has increased, since the accuracy of the earthquake information depends primarily on the quality of the detection. In addition to the accuracy of arrival time determination, the robustness in the presence of noise and the speed of detection are important factors in the methods used for the earthquake early warning. In this paper, we tried to improve the P-wave detection method designed for real-time processing of continuous waveforms. We used the new Tpd method, and proposed a refinement algorithm to determine the P-wave arrival time. Applying the refinement process substantially decreases the errors of the P-wave arrival time. Using 606 strong motion records of the 2011 Tohoku earthquake sequence to test the refinement methods, the median of the error was decreased from 0.15 s to 0.04 s. Only three P-wave arrivals were missed by the best threshold. Our results show that the Tpd method provides better accuracy for estimating the P-wave arrival time compared to the STA/LTA method. The Tpd method also shows better performance in detecting the P-wave arrivals of the target earthquakes in the presence of noise and coda of previous earthquakes. The Tpd method can be computed quickly so it would be suitable for the implementation in earthquake early warning systems.

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