Bayesian Updating of Earthquake Vulnerability Functions with Application to Mortality Rates

Noh, H; Kiremidjian, Anne S.; Ceferino, Luis; So, Emily

doi:10.1193/081216eqs133m

Cited by 28 publications

(18 citation statements)

References 12 publications

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“…Next, the model uses information on building occupancy to provide probabilistic estimates of the spatial distribution of injuries and fatalities in the city. The validity of the model results was verified 16 by comparing the casualties and fatality levels in the city to empirical formulas 55 and with fatality-to-collapse building data from the 2005 Pakistan earthquake 56 .…”

Section: Methodsmentioning

confidence: 93%

Effective plans for hospital system response to earthquake emergencies

et al. 2020

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Hospital systems play a critical role in treating injuries during disaster emergency responses. Simultaneously, natural disasters hinder their ability to operate at full capacity. Thus, cities must develop strategies that enable hospitals' effective disaster operations. Here, we present a methodology to evaluate emergency response based on a model that assesses the loss of hospital functions and quantifies multiseverity injuries as a result of earthquake damage. The proposed methodology can design effective plans for patient transfers and allocation of ambulances and mobile operating rooms. This methodology is applied to Lima, Peru, subjected to a disaster scenario following a magnitude 8.0 earthquake. Our results show that the spatial distribution of healthcare demands mismatches the post-earthquake capacities of hospitals, leaving large zones on the periphery significantly underserved. This study demonstrates how plans that leverage hospital-system coordination can address this demand-capacity mismatch, reducing waiting times of critically injured patients by factors larger than two.

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Section: Methodsmentioning

confidence: 93%

Effective plans for hospital system response to earthquake emergencies

et al. 2020

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“…When the number of structures is limited to a few hundreds, simple methods are often used, which usually consist in simple evaluations of a limited number of parameters (e.g., Fikri et al, 2019). Larger building populations have to be dealt with using probabilistic methods (e.g., Noh et al, 2017) or extremely indirect techniques (Geiß et al, 2014).…”

Section: Vulnerability and Damage Distributionmentioning

confidence: 99%

Vulnerability and Site Effects in Earthquake Disasters in Armenia (Colombia). II – Observed Damages and Vulnerability

Chávez-García¹,

Monsalve-Jaramillo²,

Vila-Ortega³

2020

Preprint

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Abstract. Damage in Armenia, Colombia, for the 1999 (Mw6.2) event was disproportionate. We analyse the damage report as a function of number of storeys and construction age. We recovered two vulnerability evaluations made in Armenia in 1993 and in 2004. We compare the results of the 1993 evaluation with damages observed in 1999 and show that the vulnerability evaluation made in 1993 could have predicted the relative frequency of damage observed in 1999. Our results show that vulnerability of the building stock was the major factor behind damage observed in 1999. Moreover, it showed no significant reduction between 1999 and 2004.

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“…In conventional Bayesian approaches, each observation of a random variable quantity of interest can be used to update the prior distribution of the parameters (e.g. median and standard deviation) describing the likelihood distribution of that random variable; this is done in order to obtain its posterior distribution (Devore and Farnum, 2007; Noh et al, 2017, 2013). However, this approach often makes strong assumptions about the data (for example, independent and/or identically distributed) and thus tends to result in a posterior distribution heavily biased toward data (which may be based on early anecdotal reports from the field immediately following an earthquake) (Wyss, 2017).…”

Section: Introductionmentioning

confidence: 99%

An efficient Bayesian framework for updating PAGER loss estimates

et al. 2020

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We introduce a Bayesian framework for incorporating time-varying noisy reported data on damage and loss information to update near real-time loss estimates/alerts for the U.S. Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) system. Initial loss estimation by PAGER immediately following an earthquake includes several uncertainties. Historically, the PAGER’s alerting on fatality and economic losses has not incorporated location-specific reported data on physical damage or casualties for a given earthquake. The proposed framework provides the ability to include early reports on fatalities at any given time and improve the overall impact forecast for the earthquake. The reported data on fatalities or damage are generally incomplete and noisy, especially in the early hours of the disaster. To address these challenges, we develop a recursive Bayesian updating framework that takes into account the loss projection model and the measurement and model uncertainties. The framework is applied to loss data for three example earthquakes, and the results show that the proposed updating improves the loss estimates and alert level to the correct level within the first day of the earthquake.

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Bayesian Updating of Earthquake Vulnerability Functions with Application to Mortality Rates

Cited by 28 publications

References 12 publications

Effective plans for hospital system response to earthquake emergencies

Effective plans for hospital system response to earthquake emergencies

Vulnerability and Site Effects in Earthquake Disasters in Armenia (Colombia). II – Observed Damages and Vulnerability

An efficient Bayesian framework for updating PAGER loss estimates

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