Dataset Information

Spatial Performance of Location-Based Alerts in France.

ABSTRACT: This study experiments a method for estimating and mapping the alerting potential of two Location-Based Alerting Solutions (LBAS): messaging techniques (cell broadcast, CBC, or location-based SMS, LB-SMS) and the smartphone push notification. Experiments have been evaluated over metropolitan France, a heterogeneously populated and risky area, considering that individuals are located at home. The rate of alertable individuals, calculated at the municipal level, show very strong potential of CBC/LB-SMS. 94.21% of individuals can be alertable using CBC/LB-SMS and 74.15% using the smartphone push notification. There is a very strong homogeneity in the performance of LBAS at the national level, but also local weaknesses exist. Mapping this potential of performance contributes to the discussion about the use of LBAS in France and paves the way for a broader assessment of the effectiveness of alerting solutions in order to propose a multi-channel system that will consider the territorial characteristics.

SUBMITTER: Bopp E

PROVIDER: S-EPMC7545217 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Spatial performance of location-based alerts in France.

Bopp Esteban E Douvinet Johnny J

International journal of disaster risk reduction : IJDRR 20201009

This study experiments a method for estimating and mapping the alerting potential of two Location-Based Alerting Solutions (LBAS): messaging techniques (cell broadcast, CBC, or location-based SMS, LB-SMS) and the smartphone push notification. Experiments have been evaluated over metropolitan France, a heterogeneously populated and risky area, considering that individuals are located at home. The rate of alertable individuals, calculated at the municipal level, show very strong potential of CBC/L ...[more]

PMID: 33052302

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Project description:BackgroundMost evaluations of epidemic thresholds for influenza have been limited to internal criteria of the indicator variable. We aimed to initiate discussion on appropriate methods for evaluation and the value of cross-validation in assessing the performance of a candidate indicator for influenza activity.MethodsHospital records of in-patients with a diagnosis of confirmed influenza were extracted from the Canadian Discharge Abstract Database from 2003 to 2011 and aggregated to weekly and regional levels, yielding 7 seasons and 4 regions for evaluation (excluding the 2009 pandemic period). An alert created from the weekly time-series of influenza positive laboratory tests (FluWatch, Public Health Agency of Canada) was evaluated against influenza-confirmed hospitalizations on 5 criteria: lead/lag timing; proportion of influenza hospitalizations covered by the alert period; average length of the influenza alert period; continuity of the alert period and length of the pre-peak alert period.ResultsInfluenza hospitalizations led laboratory positive tests an average of only 1.6 (95% CI: -1.5, 4.7) days. However, the difference in timing exceeded 1 week and was statistically significant at the significance level of 0.01 in 5 out of 28 regional seasons. An alert based primarily on 5% positivity and 15 positive tests produced an average alert period of 16.6 weeks. After allowing for a reporting delay of 2 weeks, the alert period included 80% of all influenza-confirmed hospitalizations. For 20 out of the 28 (71%) seasons, the first alert would have been signalled at least 3 weeks (in real time) prior to the week with maximum number of influenza hospitalizations.ConclusionsVirological data collected from laboratories was a good indicator of influenza activity with the resulting alert covering most influenza hospitalizations and providing a reasonable pre-peak warning at the regional level. Though differences in timing were statistically significant, neither time-series consistently led the other.

Dataset Information

Spatial Performance of Location-Based Alerts in France.

Publications

Spatial performance of location-based alerts in France.

Similar Datasets

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