Report estimated quake death tolls to save lives

Wyss, Max

doi:10.1038/545151a

Cited by 13 publications

(9 citation statements)

References 8 publications

(6 reference statements)

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“…The history of casualty underestimates by news media is not known to us in the case of the 1980 Irpinia earthquake, but we assume it was similar to the cases documented [16]. Figure 4 shows the delay of assessing the size of the disaster in the case of the L'Aquila M6.3 earthquake on 6 April, 2009, compared to the mean value estimates by QLARM 22 min after the earthquake.…”

Section: Discussionmentioning

confidence: 83%

Near-Real-Time Loss Estimates for Future Italian Earthquakes Based on the M6.9 Irpinia Example

Wyss¹,

Rosset²

2020

Geosciences

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The number of fatalities and injured was calculated, using the computer code QLARM and its data set and assuming information about the Irpinia 1980 earthquake became available in near-real-time. The casualties calculated for a point source, an approximate line source and a well-defined line source would have become available about 30 min, 60 min and years after the main shock, respectively. The first estimate would have been satisfactory, indicating the seriousness of the disaster. The subsequent loss estimate after 60 min would have defined the human losses accurately, and the ultimate estimate was most accurate. In 2009, QLARM issued a correct estimate of the number of fatalities within 22 min of the M6.3 L’Aquila main shock. These two results show that the number of casualties and injuries in large and major earthquakes in Italy can be estimated correctly within less than an hour by using QLARM.

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Section: Discussionmentioning

confidence: 83%

Near-Real-Time Loss Estimates for Future Italian Earthquakes Based on the M6.9 Irpinia Example

Wyss¹,

Rosset²

2020

Geosciences

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“…5). More distant villages never had roads, with access only on foot, which posed problems for remote villagers, who were relatively poor, in a context where mortality rates were also probably highest (Wyss, 2017).…”

Section: Inaccessibilitymentioning

confidence: 99%

“…These include coordination difficulties among various different actors providing relief services (Khan et al, , Watson and Clarke, ), insufficient targeting of remote affected areas (Khan et al, ) environmental protection (Sudmeier‐Rieux et al, ) and the need to minimise gaps, overlaps and fragmentation (Stephenson, ). Overall, the greatest challenge is how to assist effectively, and to recognise the scale of the problem, since time is crucial (Wyss, ). In some part that depends on past practice and expertise, the networks of relief agencies and their ability to mobilise social capital.…”

Section: Disaster Responsementioning

confidence: 99%

Social capital in a crisis: NGO responses to the 2015 Nepalese earthquakes

Hillig

Connell

2018

Asia Pacific Viewpoint

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Two major earthquakes hit Nepal in April and May 2015 causing widespread devastation. Many NGOs, including CARE Nepal, International Nepal Fellowship and Richa Bajimaya Memorial Foundation, responded to the crisis in diverse ways. In the relief phase, the three NGOs faced many challenges as a result of inadequate planning for a large‐scale disaster, including access to information, coordination and inaccessibility. NGOs were able to partly overcome these problems through their ability to draw on social capital, networks and trust, values typical of Nepalese society, which is largely structured by informal social relations. Bonding and bridging social capital, and necessary linking social capital at a different scale, all posed certain problems for equity and efficiency. Although using social networks enabled a more rapid response, this could not easily combat inaccessibility and emphasised uneven development.

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“…Providing accurate and timely forecasts of an earthquake’s overall impact is critical for emergency response. To fully understand the scope of the impact of any devastating earthquake generally takes days, weeks, and sometimes months; for example, see earthquake death tolls reported over time in Wyss (2017). This timeline depends upon the country where the earthquake occurred and can be affected by secondary hazards (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…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). The information contained in the prior distribution about the total loss from PAGER gets ignored unless careful consideration is given to uncertainty modeling.…”

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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Report estimated quake death tolls to save lives

Abstract: Rescue workers in Kathmandu, where a magnitude-7.8 earthquake killed 10,000 people in April 2015.© 2 0 1 7 M a c m i l l a n P u b l i s h e r s L i m i t e d , p a r t o f S p r i n g e r N a t u r e . A l l r i g h t s r e s e r v e d .

Cited by 13 publications

References 8 publications

Near-Real-Time Loss Estimates for Future Italian Earthquakes Based on the M6.9 Irpinia Example

Near-Real-Time Loss Estimates for Future Italian Earthquakes Based on the M6.9 Irpinia Example

Social capital in a crisis: NGO responses to the 2015 Nepalese earthquakes

An efficient Bayesian framework for updating PAGER loss estimates

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