Estimating Casualties for Large Earthquakes 

Worldwide Using an Empirical Approach 

By Kishor Jaiswal, David J. Wald, and Mike Hearne 

Open-File Report 2009–1136 

U.S. Department of the Interior 

U.S. Geological Survey 
U.S. Department of the Interior

KEN SALAZAR, Secretary 

U.S. Geological Survey

Suzette M. Kimball, Acting Director 

U.S. Geological Survey, Reston, Virginia 2009 

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Suggested citation:

Jaiswal, K.S., Wald, D.J., and Hearne, M., 2009, Estimating casualties for large earthquakes worldwide 

using an empirical approach: U.S. Geological Survey Open-File Report OF 2009–1136, 78 p. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright 

owners to reproduce any copyrighted material contained within this report.
iii

Contents 

Executive Summary ......................................................................................................................... 1

Introduction ...................................................................................................................................... 2

Earthquake Fatalities Worldwide ..................................................................................................... 6

Methodology .................................................................................................................................... 7

Goodness of Fit .............................................................................................................................. 10

Sources of Uncertainty ................................................................................................................... 11

Need for Regionalization ............................................................................................................... 13

Human Development Index ........................................................................................................ 14

Climate Classification ................................................................................................................ 15

Model Implementation ................................................................................................................... 18

Example Analysis .......................................................................................................................... 19

Indonesia..................................................................................................................................... 19

India ............................................................................................................................................ 20

Slovenia ...................................................................................................................................... 20

Albania ....................................................................................................................................... 21

Chile ........................................................................................................................................... 22

Georgia ....................................................................................................................................... 22

Greece ......................................................................................................................................... 23

Algeria ........................................................................................................................................ 23

Italy ............................................................................................................................................. 24

Japan ........................................................................................................................................... 25
iv

Pakistan....................................................................................................................................... 25

Peru ............................................................................................................................................. 26

Philippines .................................................................................................................................. 26

Romania ...................................................................................................................................... 27

Turkey......................................................................................................................................... 27

United States ............................................................................................................................... 28

Fatality Estimation for Recent Earthquakes .................................................................................. 29

Summary and Conclusions ............................................................................................................ 30

Acknowledgments ......................................................................................................................... 31

References Cited ............................................................................................................................ 32

Appendix I. PAGER Regionalization Scheme for the Empirical Model ....................................35 

Appendix II. PAGER Implementation of Empirical Model..........................................................44 

Appendix III. An Automated Alerts and Comments Development Methodology for the 

lossPAGER system ....................................................................................................................51 
v

Figures

1. A) Shaking-death distribution for earthquakes, 1900−2008 by country, and 

B) cumulative earthquake mortality recorded since 1900 for selected countries.. ............. 61

2. Fatality estimation using lognormal distribution and different norms for global 

earthquakes with 10 or more deaths recorded between 1973 and 2007. ............................ 62

3. Lilliefors goodness-of-fit test for lognormal distribution using L2G norm for global 

earthquakes between 1900 and 2008. ................................................................................. 63

4. Empirical model derived from fatal earthquakes in Indonesia. Earthquakes with 

zero recorded deaths were plotted at 0.1 deaths for viewing purposes... .......................... 64

5. Empirical model derived from fatal earthquakes in India. Earthquakes with zero 

recorded deaths were plotted at 0.1 deaths for viewing purposes.. .................................... 65

6. Fatality estimation using empirical loss modeling for Slovenia. Earthquakes with

zero recorded deaths were plotted at 0.1 deaths for viewing purposes... .......................... 66

7. Empirical model derived from fatal earthquakes in Chile. Earthquakes with zero 

recorded deaths were plotted at 0.1 deaths for viewing purposes... .................................. 67

8. Empirical model derived from fatal earthquakes in Georgia.. ............................................ 68

9. Empirical model derived from fatal earthquakes in Greece.. ............................................. 69

10. Empirical model derived from fatal earthquakes in Algeria. ............................................ 70

11. Empirical model derived from fatal earthquakes in Italy. ................................................ 71

12. Empirical model derived from fatal earthquakes in Japan. ............................................... 72

13. Empirical model derived from fatal earthquakes in Pakistan. .......................................... 73

14. Empirical model derived from fatal earthquakes in Peru... ............................................. 74

15. Empirical model derived from fatal earthquakes in Philippines ....................................... 75
vi

16. Empirical model derived from fatal earthquakes in Romania. ......................................... 76

17. Empirical model derived from fatal earthquakes in Turkey.. ........................................... 77

18. Comparison of fatality rate among different countries including the expert- 

 judgment-based fatality rates (v1.0) for the USA without California, Canada 

 and Australia group. .......................................................................................................... 78

Tables

1. List of countries with 10 or more fatalities due to any single earthquake 

 since 1900. For each country, it also shows the total number of earthquake 

shaking-related fatalities and the number of fatal earthquakes since 1900 ..................... 54 

2. Fatality estimation using empirical model for earthquakes since Jan 2008.......................56

Abbreviations Used in This Report

HDI human development index 

MMI Modified Mercalli Intensity

PAGER Prompt Assessment of Global Earthquakes for Response 

PGA peak ground acceleration

PGV peak ground velocity

PSA peak spectral acceleration

USGS U.S. Geological Survey
1

Estimating Casualties for Large Earthquakes 

Worldwide Using an Empirical Approach

By Kishor Jaiswal,1David J. Wald,

2

and Mike Hearne3

Executive Summary

We studied the earthquake mortality rates for more than 4,500 worldwide earthquakes since 

1973 and developed an empirical country- and region-specific earthquake vulnerability model to be 

used as a candidate for post-earthquake fatality estimation by the U.S. Geological Survey’s Prompt 

Assessment of Global Earthquakes for Response (PAGER) system. Earthquake fatality rate is 

defined as the ratio of the total number of shaking-related fatalities to the total population exposed 

at a given shaking intensity (in terms of Modified-Mercalli (MM) shaking intensity scale). An atlas 

of global Shakemaps developed for PAGER project (Allen and others, 2008) and the Landscan 

2006 population database developed by Oak Ridge National Laboratory (Dobson and others, 2000; 

Bhaduri and others, 2002) provides global hazard and population exposure information which are 

necessary for the development of fatality rate. Earthquake fatality rate function is expressed in 

terms of a two-parameter lognormal cumulative distribution function. The objective function 

(norm) is defined in such a way that we minimize the residual error in hindcasting past earthquake 

 

1

U.S. Geological Survey, P.O. Box 25046, M.S. 966, Lakewood, Denver, CO 80225-0046 (contracted through 

Synergetics Incorporated - http://www.synergetics.com). 2

U.S. Geological Survey, P.O. Box 25046, M.S. 966, Lakewood, Denver, CO 80225-0046. 3

Senior Software Engineer, U.S. Geological Survey, P.O. Box 25046, M.S. 966, Lakewood, Denver, CO 80225-0046

(contracted through Synergetics Incorporated - http://www.synergetics.com). 
2

fatalities. The earthquake fatality rate is based on past fatal earthquakes (earthquakes causing one 

or more deaths) in individual countries where at least four fatal earthquakes occurred during the 

catalog period. All earthquakes that have occurred since 1973 (fatal or non-fatal) were included in 

order to constrain the fatality rates for future estimations. Only a few dozen countries have 

experienced four or more fatal earthquakes since 1973; hence, we needed a procedure to derive 

regional fatality rates for countries that had not had enough fatal earthquakes during the catalog 

period. We propose a new global regionalization scheme based on idealization of countries that are 

expected to have similar susceptibility to future earthquake losses given the existing building stock, 

its vulnerability, and other socio-economic characteristics.

The fatality estimates obtained using an empirical country- or region-specific model will be 

used along with other selected engineering risk-based loss models (semi-empirical and analytical) 

in the U.S. Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) 

system for generation of automated earthquake alerts. These alerts could potentially benefit the 

rapid earthquake response agencies and governments for better response to reduce earthquake 

fatalities. Fatality estimates are also useful to stimulate earthquake preparedness planning and 

disaster mitigation. The proposed model has several advantages as compared with other candidate 

methods, and the country- or region-specific fatality rates can be readily updated when new data 

become available.

Introduction

The problem of earthquake casualty estimation has been studied by various researchers in 

the past, and it can be categorized into three broad approaches of casualty estimation: empirical, 

hybrid and analytical. The empirical approach consists of estimating aggregate historic earthquake 

statistics and estimating casualty rates (total casualties for a given population) in terms of ground 
3

shaking hazard (Samardjieva and Badal, 2002). The analytical approach consists of seismic hazard

analysis (estimating ground shaking defined in terms of peak ground acceleration (PGA), peak 

ground velocity (PGV), peak spectral acceleration (PSA) or seismic intensity and its likelihood), 

structural analysis (assessing response of structure given shaking hazard), damage analysis 

(estimating fragility characteristics for a given response of structural system), and loss analysis 

(estimating fatalities, injuries due to structural and nonstructural damage) (FEMA, 2006). In short, 

earthquake casualty estimation is performed at a building level by modeling the building damage 

using engineering-based ground motion parameters. The hybrid approach consists of estimating the 

fraction of the population killed due to the collapse of different types of structures at a given 

shaking hazard, generally represented in terms of macro-seismic intensities. Unlike the analytical 

approach, the hybrid approach does not attempt engineering-based structural and damage analyses 

but requires fewer parameters at building level and can be applied to regional or building-level 

casualty assessment (Coburn and others, 1989; Shiono and others, 1991a; Murakami, 1992; 

Yamazaki and others, 1996; Shakhramanian and others, 2000). Empirical modeling has been 

performed in a variety of different ways in the past depending upon the earthquake damage data 

available. Researchers in Japan attempted casualty estimation as early as the 1950s. Kawasumi 

(1951) estimated a measure of earthquake danger and expectation of maximum intensity in Japan. 

Similarly, early casualty estimation efforts in the United States were scenario specific and based on 

estimation of the casualty rate per 100,000 people and use of engineering judgment (Algermissen 

and others, 1972). Ohta and others (1983) developed an empirical relationship for estimating the 

number of casualties as a function of the number of completely destroyed houses. Oike (1991) 

proposed a relationship between earthquake magnitude and earthquake fatalities. A more recent 

attempt based on an analysis of 450+ global earthquakes obtains a log-linear relationship of 

fatalities as a function of magnitude and population density (Samardjieva and Badal, 2002). 
4

Nichols and Beavers (2003) studied the fatality catalogue of the twentieth century and established a 

bounding function using the fatality count, and the U.S. Geological Survey (USGS) assigned 

earthquake magnitude. 

Hybrid and analytical approaches involve objective assessment of casualties by 

incorporating various parameters such as structure types, occupancy characteristics, and state of 

building damage and level of shaking hazard. However such analysis requires a series of 

parameters (for example, knowledge of regional building inventory, structural vulnerability of each 

building type, occupancy at the time of earthquake, fatality rate given structural damage) which are 

often unavailable in certain countries or difficult to obtain in cases where it is available, due to 

inconsistent and poorly characterized historical earthquake casualty data. The empirical approach, 

on the other hand, is generally regression based, can effectively utilize the available quality and 

quantity of historical earthquake casualty data, and depends on very few free parameters of loss 

models. 

In the present investigation, we propose a global empirical model derived by using 

historical data of earthquake casualties by country and by using a fatality rate. While developing an 

empirical model, we derive the fatality rate as a function of shaking intensity, a spatially varying 

parameter and an indicator of impact of ground motion on built environment, instead of using an 

earthquake magnitude which indicates only the size of an earthquake and can be completely 

misleading in the extreme cases of population exposure and vulnerability of built environments. A 

population exposed to higher shaking intensity will tend to have higher losses than a population in 

lesser shaking intensities. Similarly a moderate-sized earthquake in terms of magnitude will have 

various levels of shaking intensity distribution patterns depending upon local geologic and 

seismotectonic conditions, ground motion attenuation, and local site amplification characteristics.

Magnitude-based empirical models are generally ineffective in capturing such variability unless 
5

they are derived for a unique seismogenic source. The ground shaking hazard map in the form of 

ShakeMap will incorporate earthquake source-specific parameters such as point source, fault 

finiteness (if any), and local soil characteristics to estimate the population exposure at different 

shaking intensities. The country-specific fatality rate estimation will be derived using historical 

earthquake data in the form of total shake-related deaths recorded for each earthquake and the 

associated population exposure at different shaking intensities at the time of earthquake. Allen and 

others (2009a) developed PAGER exposure database (called ExpoCat) by first recreating the 

ShakeMap for historical earthquakes, overlaying it on a map of the Landscan global population 

database developed by Oak Ridge National Laboratory (Bhaduri and others, 2002) and then 

correcting the 2006 population to the year of the earthquake by uniform reversal of population at 

different Modified Mercalli Intensity (MMI) levels using the country-specific population growth rates. 

For the forward application of the empirical model proposed here, the U.S. Geological 

Survey’s Prompt Assessment of Global Earthquake Response (PAGER) program already provides 

such estimates on a real-time basis for its global users (Wald and others, 2006). It is clear from 

recent earthquakes (for example, Bhuj, 2001; Kashmir, 2005; Wenchuan, 2008), and indeed for 

most earthquake disasters in the last few decades, that for large-scale disasters, it takes days or 

sometimes weeks before the actual scale of disaster is understood. PAGER estimates can be used 

not only to rapidly understand the size, location, and scale of catastrophe but also to inform 

national and international agencies about the assessment of post-earthquake needs in order to make 

decisions about humanitarian assistance based on the scale of the disaster. Clearly, the empirical 

loss estimation approach proposed for the PAGER system is designed to utilize existing casualty 

and exposure data and to have global capability with the possibility of real-time application.
6

Earthquake Fatalities Worldwide

We examined global earthquake fatality data since 1900, and Table 1 presents a list of 

countries that have experienced 10 or more shaking-related fatalities (not including other nonshaking related deaths—for example, deaths due to fire, land or mudslide, or ground failure). 

Clearly, countries like China, Pakistan, Iran, and Turkey dominate the list; Chinese earthquakes 

have caused 604,330 deaths since 1900 (fig. 1a). China experienced 122 fatal earthquakes since the 

year 1900. The Tangshan earthquake of 1976 caused nearly a quarter of a million deaths, and some 

researchers believe that the actual number might have exceeded half a million people. The entire 

city had to be rebuilt (Liu and others, 2002). On average, each fatal Chinese earthquake has caused 

nearly 5,000 deaths, clearly indicating China’s vulnerability to future earthquakes. Similarly, 

Pakistan has experienced the most devastating earthquake in recent times in 2005 in Kashmir, 

which killed more than 85,000 people. Seventy-five Iranian earthquakes have claimed 161,215 

lives, whereas Turkey has experienced 64 fatal earthquakes that killed more than 85,000 people. 

Surprisingly, in Indonesia, 62 fatal earthquakes have killed 10,870 people; more than 50 percent of 

the deaths are attributed to the Yogyakarta earthquake of May 26, 2006, which caused 5,749 

deaths. Countries such as Armenia, Nepal, Argentina, Romania, and Nicaragua have experienced 

very few deadly earthquakes, but the number of deaths in any single event is quite large compared 

to other countries. Although Japan and Taiwan have experienced 43 and 38 fatal earthquakes, 

respectively, the deadliest earthquakes in these countries contribute more than 80 percent and 40 

percent of total deaths, respectively. The great Kanto earthquake of 1923 in Japan took more than 

142,807 lives, although most of the deaths were due to the fire following the earthquake and other 

non-shaking-related deaths and thus is not included in the present analysis. The United States has 

experienced 18 fatal earthquakes, but remarkably they caused only 270 deaths, averaging 15 deaths 

per event during the last 100 years. 
7

On a global scale, 76 percent of the totals shaking related-deaths were in China, Iran, 

Pakistan, and Turkey, whereas 24 percent of the total deaths came from other countries. About 80 

percent of the total shaking-related deaths since 1900 were due to only 25 earthquakes which 

occurred in 11 countries: China, Pakistan, Iran, Turkey, Italy, Chile, Armenia, Guatemala, India, 

Tajikistan, and Nepal. Figure 1b shows the cumulative fatality rate for a few countries and clearly 

shows that most fatalities are due to a small number of large earthquakes in these countries. 

Methodology

Fatality rate ( ), which is a function of shaking intensity (S), can be expressed in terms of a 

two-parameter lognormal distribution function as follows: 

( ) 



 



 



 



 Φ=





S S ln 1 , (1)

where Φ is the standard normal cumulative distribution function, Sj is discrete value of shaking 

intensity (S is bounded between MMI V to X and is expressed in numeric values with 0.5 increments;

for example, 5.0, 5.5, 6.0,...10.0), and θ and β are parameters of the distribution. Let Pi

(Sj) denote an 

estimated population exposed to shaking intensity Sj for an event i. Then the expected number of fatalities 

Ei can be denoted as ( ) ( )ji

j

i ≈ ∑ ji . SPSE (2)

In order to estimate the total number of fatalities from any given earthquake, we need to 

find a population exposure at each shaking intensity level and a fatality rate associated with the 

shaking intensity. The fatality rate depends on the two free parameters of the cumulative 

distribution function of lognormal distribution, θ and β. For each country or a geographic location k, 

if there are N historical fatal earthquakes then each event-specific fatality number could be used to 

determine the fatality rate by reconstructing the Shakemap for each earthquake and estimating 
8

population exposure at each interval of shaking intensity. If we suppose that Oi is the number of 

recorded deaths for an earthquake i, then we can determine the parameter of the distribution 

function (that is, the estimated fatality rate) in such a way that the residual error (that is, the error 

estimate between estimated and recorded deaths) is minimized. It is assumed that the recorded 

number of deaths from an earthquake in the catalog is free from any errors and is generally 

obtained from a well documented, peer reviewed source of literature or dataset for a particular 

earthquake. Thus a residual error in the estimate could be written in a variety of ways, for example: 

orOE normL

N

i

k ii 1||

1

,1 ∑=

 −= (3a)

or 

( ) normLorOE

N

i

k ii 2

1

2

,2 ∑=

 −= (3b) 

or 

[ ] ( ) orOE residual normG

N

N

i

k ii ln / log )( 1

1

2

,3 ∑=

 = (3c)

Clearly, each of the above norms provides a window of search-space for determining the 

parameters of the distribution function that minimizes the residual error. The L1 norm (eq. 3a) 

provides a search space for parameters that result in an error estimate which is a minimum of total 

error between recorded and estimated deaths. In other words, in the error estimate in L1 norm all 

the earthquakes are treated equally, even though the search space is influenced by earthquakes with 

high fatality where the absolute deviation between the estimated and recorded deaths is much 

higher than the deviation associated with the low fatality earthquakes. Figure 2a shows the plot of 

recorded versus estimated deaths using the L1 norm for all the earthquakes globally that have 
9

caused 10 or more deaths since 1973. The estimated lognormal distribution parameters for L1 norm 

are θ= 21.44 and β = 0.30. The logarithmic mean of the ratio of recorded versus estimated deaths is 

0.41, whereas the logarithmic standard deviation is 0.99. The L2 norm as shown in equation 3b 

provides a search space that results in an error estimate which is a sum of squared differences 

between recorded and estimated deaths. Again, the search space for estimating the parameters of 

the distribution in L2 norm is such that in case of high-fatality earthquakes, the squared differences 

tend to dominate the overall contribution of squared error (fig. 2b). Between L1 and L2 norm, L2

norm generally provides a search space that better satisfies high fatality earthquakes (that is, 

minimizes the squared difference of amplitudes of the data). However, in the case of the G norm 

(eq. 3c), we take the natural logarithm of the squared difference between the recorded and 

estimated deaths, which tends to reduce the contribution of high-fatality earthquakes in the total 

error term and generally better satisfies the low-fatality earthquakes (fig. 2c). Clearly, none of the 

above norms satisfies the present requirement to minimize the error at both ends (low and high 

fatality earthquakes simultaneously). We need a norm that combines the advantages of both L2 and 

G norms (that is, provides a search space that satisfies both low and high fatality earthquakes 

simultaneously in the natural logarithm space) to estimate the parameters of the distribution 

function. The objective function to determine the residual error could be written using a 

combination of ln(L2) and G norms as 

( ) [ ] ( ) normGLorOE

N

OE

N

N

i

ii

N

i

k ii ln / 2 1 1 ln

1

2

1

2

,4 ∑ ∑ = =

+















 = − (4)

Note that we take the natural logarithm of the squared difference term of L2 norm which 

satisfies the criteria required for high fatality earthquakes in combination with G norm. The 

objective function ln(L2)+G or, say, the L2G norm defined in equation 4 can be used to evaluate 

the parameters of the distribution function, which in turn can be used to estimate country-specific 
10

earthquake fatality rates. We use a standard iterative search algorithm available in Matlab Ver., 

R2007a for minimizing the objective function with the two free parameters of the distribution 

function, θ and β. As expected, we obtain a better constraint on both ends of the fit (lower as well as 

higher amplitudes of the dataset) as shown in figure 2d. Clearly, we obtain a better fit to the data 

without sacrificing much in terms of logarithmic standard deviation of ratio of recorded versus

estimated deaths (0.97 as against 0.95 for G norm (fig. 2c)). We have a higher accuracy for large 

fatal events, and still we did not increase the number of unknowns (free parameters) in our 

procedure. The advantage of L2G norm as compared with the other two norms became evident as 

we developed a country-specific model and is discussed in subsequent sections. This approach is 

simple and suitable for countries with at least three or more fatal earthquakes in the catalog, and

thus it helps us to obtain earthquake fatality rates for a large number of countries. 

Goodness of Fit 

The Lilliefors goodness-of-fit test is a special and stricter case of the Kolmogorov-Smirnov 

test commonly used in statistics to test whether an observed distribution is consistent with 

normality. We have used the Lilliefors goodness-of-fit test (Lilliefors, 1967) at 5 percent

significance level in order to test the hypothesis that the residual error )/log( i = OE ii can be 

modeled using a lognormal distribution. We estimate the mean and variance of the data and then 

find the maximum discrepancy between the empirical distribution function and the cumulative 

distribution function of the normal distribution with the estimated mean and estimated variance. 

The observed cumulative distribution is estimated using FN (X) = i / (N+1). The data pass 

the Lilliefors test: N = 194; Max| F*(X) – FN(X) | = 0.0681, as shown in figure 3, which is less than 

the critical value D0.05 = 0.886N–0.5 = 0.0976. This value indicates that it is reasonable to model the 
11

residual error as lognormally distributed about the median estimate with a logarithmic standard 

deviation equal to the value of G calculated in equation 3c.

Sources of Uncertainty

In most of the countries, the available earthquake-fatality data are very limited and are often 

insufficient alone to derive an empirical earthquake fatality model. The fatality rate as a function of 

ground shaking intensity is defined by using a two-parameter lognormal cumulative distribution 

function. This rate is better constrained theoretically when we have sufficient earthquakes with a 

wide range of fatalities, meaning that the larger the number of small and large fatality earthquakes, 

the better is the constraint on the fatality rate. Also, if the epicenters of the past fatal earthquakes 

are widely distributed within a country across its various inhabited places in terms of their 

vulnerabilities, the empirical model will better estimate deaths during future earthquakes 

irrespective of their location compared to the model which is derived from earthquakes limited to a 

particular source zone of a country. However, it is for practical purposes difficult to have data that 

record both the spatial and temporal aspects of this problem. 

Similarly, there are a number of additional sources that can contribute to the uncertainty in 

the model’s fatality estimates. For example, input hazard from Shakemap, fatality variation due to 

day or night occupancy pattern, accuracy in estimated population exposure, and accuracy of fatality 

records of historical earthquakes may significantly affect the accuracy of the model’s estimation. 

While developing the country- or region-specific empirical model, most of these factors have 

already been accounted for collectively as a part of the datasets that span more than 35 years. 

In order to estimate the total uncertainty, the PAGER models (empirical, semi-empirical, 

and analytical) currently employ a country-specific error term determined from hindcasting of past 

losses as discussed by Porter and others (2008). Let ζ denote a residual error in loss L, a variable 
12

representing normalized standard deviation of the logarithmic ratio of expected to recorded losses, 

which can vary by country. As shown in the previous section, the lognormal probability 

distribution fit to ratio of loss OE ii / commonly passes a Lilliefors goodness-of-fit test. In the 

empirical model, we have estimated the two free parameters of the distribution function (θ and β ) 

from the historical data; hence we reduce the total sample by two and then estimate ζ as:

∑[ ( )] =

++ − =

N

i

i OE i N 1

2 ln 5.0/5.0

2

1  (5)

A constant value of 0.5 deaths is added to the numerator and the denominator when the expected or 

recorded deaths are zero. We note that the error estimated in hindcasting the total shaking deaths 

using the empirical model already incorporates the total variability that comes from the uncertainty 

in shaking hazard for each earthquake, the uncertainty in the population exposure, and also possible 

errors in the number of recorded deaths in the catalog for these events. Variability in each of these 

inputs may have different effects depending upon the country under consideration (countries that 

experience frequent fatal earthquakes or countries that have relatively low vulnerability) or the 

nature of the constraints for shaking hazard estimates. 

In the forward sense, we can use the uncertainty in hindcasting the median loss estimates 

(refer to appendix II) through use of a country-specific residual error term (ζ) to estimate the 

probability for the upper and lower bounds of losses. If we let P denote the probability that the 

actual loss will be within one order of magnitude of deaths D, we can express this probability as: 

( ) ( )( )



 



 − =   LED P loglog (6)

Estimation of probabilities within one order of magnitude of median deaths (that is, an 

actual value could be within 1/10 to 10 times the model’s median fatality estimation) along with the 
13

median (50 percent probability) fatality estimate generally provides a very useful range, especially 

considering the wide variety of PAGER user-base and their responses at a global scale. 

Alternatively, the deaths quantiles (that is, the deaths D associated with different probability ranges 

p ~ (10 percent, 20 percent, 90 percent)) can be represented by rearranging equation 6 as follows: 

( ) ( )( )

  + − D = log LEp 1 exp  (7)

For PAGER alert purposes, we also need to provide the probability of different alert levels 

(see appendix III) such that the actual deaths could exceed certain predefined alert thresholds. The 

probability P that the actual death d may be between predefined thresholds a and b is given as: 

( ) 



 



 − Φ−



 



 − Φ=≤<

 

eb ea )log()log()log()log( bdaP

(8) 

Need for Regionalization

As described above, the empirical model development consists of estimation of the two free 

parameters for the lognormal distribution function. Statistically, in order to develop a countryspecific empirical model, we need at least three fatal earthquakes in each country. However various 

uncertainties are associated with fatality records in the catalog (for example, the actual number of 

deaths for a particular earthquake is uncertain), so we considered the minimum number of fatal 

earthquakes to be four rather than three. 

Only 30 countries in our database have had at least four fatal earthquakes since 1973. For 

those countries with fewer fatal events, we devised an approach that aggregates fatal events from 

like-countries through a regionalization scheme that focuses on likely indicators of comparable 

country vulnerability. The proposed regionalization scheme (shown in fig. Ia in appendix I) is 

based primarily on geography, building inventory, and socio-economic similarities for the 213 
14

countries without the minimum number fatal earthquakes during the catalog period for properly 

constraining a country-specific model. The choices we made in aggregating countries by these 

indicators are discussed below.

Human Development Index 

The Human Development Index (HDI) is an index combining normalized measures of life 

expectancy, literacy, education, and gross domestic product per capita for countries worldwide. The 

HDI is a standard means of measuring human development, a concept that, according to the United 

Nations Development Program, refers to the process of widening the options of persons, giving 

them greater opportunities for education, health care, income, and employment. One general use of 

the HDI is to rank countries by level of "human development," which usually also implies whether 

a country is a developed, developing, or underdeveloped country (fig. Ib). 

Socio-economic conditions affect the way people live and also tend to influence building 

construction and maintenance practices. With some notable exceptions, the built infrastructure in 

developed countries has greatly improved with passing years and is generally engineered to 

withstand country-specific natural hazards. For example, the strong and persistent economic 

advancements in United States, Japan, New Zealand, and some Northern European countries have 

resulted in significant improvement of their building stock with consistent efforts on both 

maintenance and retrofitting of poor building stocks. This improvement is evident from the fact 

that strong earthquakes in these countries result in significantly fewer collapsed buildings and 

hence a significant reduction in the loss of lives. However, in developing countries such as 

Indonesia, Pakistan, China, and India, poorer socio-economic conditions affect the standard of 

living and hence also the way people build and maintain their houses. The existence of poor 

building stock in these countries results in a large number of building collapses and disruption of 
15

life after significant earthquakes (for example, the 2001 Gujarat earthquake in India, the Pakistan 

earthquake of 2005, and the Wenchuan, China earthquake of 2007). Despite relatively low 

earthquake hazards in central and east African countries, the low human development index is an 

indicator of poor socio-economic conditions within these countries. It results in building stocks in 

these countries that are, in general, poorly built and maintained and are therefore highly vulnerable 

to earthquake shaking. Similarity of human development indicator values among neighboring 

countries indicates a commonality among these countries concerning their socio-economic 

conditions and hence we group them together. However, even with similar HDI indices, countries 

with varying climates require further consideration. 

Climate Classification 

Climate also is a considerable determinant of the way people live and build their places of 

shelter. Since ancient times, building architecture has been influenced by the local climate. The 

primary objective of the shelter was to protect the inhabitants from the weather elements. However 

in recent times, the climate-responsive architecture has evolved around the world to effectively tap 

into natural resources such as heat and light (Bensalem, 1997). Buildings constructed in cold 

climates tend to have large size openings in the direction of the Sun to exploit maximum exposure, 

and have low ceilings to minimize and reduce heat loss within the interior of the building. In hot 

climates, the buildings tend to have their peripheral system (outer walls) thicker whereas in cold 

climates the walls inside the structure are made thicker to insulate and keep the heat in. For 

example, the climate in the eastern Black Sea region, which lies in northern Turkey, plays an active 

role in the formation and diversity of the vernacular houses in the region (Engin and others, 2007). 

The warm, humid climate of the region has different effects on the spaces, elements and annexes of 

the vernacular houses. Similarly, in arid desert regions, buildings are designed with flat roofs, small 
16

openings, and heavy-weight materials. The configuration is such that the thick exterior roof and 

walls will absorb the temperature fluctuations and keep the internal temperature steady and lower 

than the outside temperature. The buildings in hot climate tend to have patios, verandas or 

courtyards. Vernacular architecture does vary between hot and cold regions but many of the same 

techniques are employed, which makes vernacular houses unique in each respective climate.

While having thicker walls and roof serve insulation purposes well for hot climates, 

seismically such configuration may not be sound if constructed using brittle material. In fact, the 

absence of an effective lateral load transfer mechanism may increase its vulnerability. The size and 

position of opening in the walls also significantly affect the lateral load resistance capacity of walls. 

Such design must be considered in the earthquake vulnerability of such structures. Some of 

architecture practices have evolved adapting to not only the local climate, social, and cultural 

patterns but also to the natural hazards. For example, the construction of traditional houses called 

bhongas in the western Kachchh region of India resists both the arid desert climate and natural 

hazards such as cyclones and earthquakes. With light-weight roofs, cylindrical walls, and adequate 

roof- wall connections, such structures can withstand the lateral shaking considerably better than

conventional architecture (Choudhary and others, 2002). Thus, local climate conditions do play a 

crucial role in determining common building configurations and, in certain cases, building 

configurations have evolved with passing years. We could not establish a direct relationship 

between building configuration and vulnerability to earthquakes. The effect of building 

configuration on seismic vulnerability is not easily quantifiable and is beyond the scope of the 

present investigation. Further research is necessary to establish a more coherent and direct 

relationship between local climate, building configuration, and associated vulnerability to 

earthquakes. Nevertheless, it is clear that the local climate affects the building configurations and 

architecture and, hence, indirectly influences the overall seismic vulnerability of the region’s built 
17

environment. We considered climate as one useful, broad indicator in understanding the seismic 

resilience at a regional scale in the absence of more detailed information. 

German scientist Wladimir Köppen in 1900 provided the first quantitative classification of 

world climates, which was later updated by Rudolf Geiger in 1954 and 1961. A large number of 

climate studies and subsequent publications adopted this scheme of climate classification (Kottek 

and others, 2006). Figure Ic of appendix I provides the most recently updated climate classification 

map, which we have referred to while developing the regionalization scheme. The hot, dry 

equatorial climate and low HDIs of central Africa affect its built environment. Buildings in central 

African countries are generally adobe, mud wall, and clay burnt-brick masonry constructions. Rural 

areas constitute 60percent of informal construction. In the absence of an adequate number of fatal 

earthquakes during the catalog period, we have grouped the countries in this region together (fig. Ia 

of appendix I) so as to develop a regional empirical model. Such a model can be used as a proxy 

empirical model for estimation of likely fatalities in future earthquakes in these countries. 

Appendix I details the regionalization scheme proposed for empirical model along with the list of 

countries in each region, their range of HDI, climate conditions and also notes about their built 

environments. The PAGER regionalization scheme is used mainly to develop the fatality model 

considering earthquake vulnerability of structures in these countries at a regional scale rather than 

at the country level. Thus, countries that have sufficient fatal earthquakes will still have their own 

country-specific fatality model; however their historical earthquakes will also be utilized in 

developing a regional model that can be used for countries with few or no fatal earthquakes during 

the past several decades.
18

Model Implementation

We have used the global Shakemap catalog developed by Allen and others (2008), which 

consists of 5,600 global earthquakes that occurred since 1973. In addition, we employ the PAGERCAT database (Allen and others, 2009b) that combines high-quality earthquake source information

(that is, hypocentral location and magnitude) and casualty data gathered from several published 

catalogs. Of the large earthquakes since 1973, only 700 earthquakes are known to be fatal and thus 

could be utilized for empirical model development. The Landscan 2006 population database 

developed by Oak Ridge National Laboratory (Bhaduri and others, 2002) has been used as a 

primary input for estimation of population exposure. By overlaying the Shakemap of a particular 

earthquake on the Landscan 2006 database, we retrieve the total population at each interval. In 

order to hindcast the year 2006 population of Landscan database to the year of an earthquake, we 

used population growth rates compiled by United Nations (United Nations, 2006) and applied a 

correction factor to the 2006 population to get the population exposure during the earthquake. Thus 

for each catalog earthquake ‘i’, we estimate population exposure Pi

(Sj) due to shaking intensity Sj, 

using a 0.5 intensity unit interval provided in the PAGER-CAT database. 

In order to estimate country-level fatality rates as a function of shaking intensity, we used a 

standard numerical minimization algorithm (Nelder-Mead, or modified simplex procedure) to 

estimate parameters θand β for each country. The development of country-specific empirical 

fatality rates to be used for the PAGER system is discussed in detail in the following section. We 

also discuss the comparison of minimizing different norms by first deriving empirical model 

parameters and comparing the estimation of the model for each norm for selected countries (figs. 4 

and 5). As discussed in the previous section, the L2G norm clearly provides the best estimates 

when one combines both low- and high-fatality events.
19

Example Analysis

We discuss the development of empirical fatality rates using historical earthquakes for 

selected countries to provide examples of models for the range of constraints and regionalization 

approaches necessary in our model. We discuss some historical events in these countries, but the 

loss models developed are limited to calibrations using exposure and fatality data for only the past 

35 years. 

Indonesia 

Indonesia is an earthquake-prone country; it has experienced 53 fatal earthquakes during the 

last 35 years. About 78 earthquakes with zero or more deaths that have occurred since 1973 were 

used to develop the empirical model shown in figure 4. Only shaking related deaths (not tsunami 

deaths) were used to constrain the empirical fatality model which estimates that approximately 1 in 

267 people will be killed at shaking intensity IX and about 1 in 2,782 at intensity VIII. We also 

compared other norms such as L2 and G norm of equation 3b and 3c respectively. Clearly, the 

estimated deaths are significantly overestimated for smaller earthquakes in the L2 norm; however 

the deaths for the largest earthquakes were estimated with higher accuracy. Similarly, the G norm 

significantly under-estimates total fatalities for larger earthquakes. The newly proposed 

combination norm (L2G) estimates both small- and large-fatality earthquakes with higher accuracy 

than the individual norms. The empirical fatality rate indicates 1 death per 270 people exposed to 

shaking intensity IX and it reduces to 1 death per 2,800 people at intensity VIII. The May 26, 2006, 

Yogyakarta earthquake in Indonesia, which occurred south-southeast of the city of Yogyakarta on 

Java, Indonesia (http://earthquake.usgs.gov/eqcenter/eqinthenews/2006/usneb6/) resulted in 5,749 

deaths. More than 127,000 houses were destroyed and an additional 451,000 were damaged in the 
20

area. About 75,100 people were exposed to shaking intensity X and about 856,900 people were 

exposed to intensity IX. 

India 

Earthquakes have claimed more than 50,000 lives in India during the last 107 years. More 

than 150 large earthquakes have struck the country since 1973, of which 28 were fatal and caused a 

total of 31,994 deaths. We have used 28 earthquakes to develop a country-specific empirical model 

as shown in figure 5. We also show the comparison of three norms (L2, G, and a combination norm 

L2G). Again, as expected, the L2 norm estimates the deadliest earthquakes with higher accuracy 

than the G norm, which provides a better fit to the smaller events. Although the L2 norm estimates 

deadlier earthquakes better, it estimates on the order of 1,000 deaths for an earthquake that had no 

fatalities. The combination norm provides a way to constrain both low- and high-fatality domains 

and suggests a model that can be used for future earthquake fatality estimates. The empirical 

fatality model for India indicates a rate of 1 death per 25 people exposed to shaking intensity IX 

and 1 death per 5250 people exposed to intensity VII. The Bhuj earthquake of 2001 in Gujarat state 

of India caused widespread damage and killed more than 20,000 people. The earthquake had a 

population exposure of 212,000 at shaking intensity IX and above, and about 982,600 at intensity 

VII.

Slovenia 

Slovenia has not experienced a fatal earthquake during the past 35 years although there 

were large earthquakes in 1974, 1998 and 2004, which caused damage but no fatalities. The April 

12, 1998, earthquake was the strongest earthquake in Slovenia in a century and caused billions of 

dollars in damage (http://www.ukom.gov.si/). In order to develop an empirical fatality model for 

Slovenia, we used the regionalization scheme to combine fatality data from neighboring countries 
21

of the group: Czech Republic, Slovenia, Slovakia, Hungary, Bosnia and Herzegovina, Croatia, 

Serbia, Montenegro, Romania, Albania, former Yugoslav Republic of Macedonia, Bulgaria, and 

Republic of Moldova (refer to appendix I). Most of these countries have similar construction 

practices although some variation might be expected due to the effect of World War II and its 

influence on local infrastructure and economies. We used 21 fatal earthquakes in this group along 

with 8 nonfatal events to construct the empirical lognormal fatality model as shown in figure 6. The 

estimated fatality rate for Slovenia (and the group as a whole) indicates that about 1 in 310 people 

exposed to Modified Mercalli shaking intensity IX will be killed; approximately 1 in 17,600 will be

killed when exposed to intensity VII. We use this model for all the countries within this group since 

individually, with the exceptions of Romania, they do not have a sufficient number fatal 

earthquakes to construct country-specific models. For Romania, which has had six fatal 

earthquakes, we developed a country-specific model as shown in appendix II. 

Albania 

In the past several decades, Albania experienced only one fatal earthquake, on Nov 16, 

1982. For that event, 1 person was killed, 12 were injured, and extensive damage (intensity VIII) 

was reported in the Fier, Berat, and Lushjne districts. It was felt at Titograd, Yugoslavia, and also 

in northwestern Greece and in southern Italy (see http://earthquake.usgs.gov/eqcenter/eqarchives

/significant/sig_1982.php). We estimate that more than 183,900 people were exposed to shaking 

intensity VII and above. We used the fatal earthquakes within the group of countries (Bulgaria, 

former Yugoslav Republic of Macedonia, Republic of Moldova, and others) to develop an 

empirical model for Albania. There have been 29 earthquakes within the group of which 21 were 

fatal, as discussed above in case of Slovenia. Both Slovenia and Albania have a group-based 

model. 
22

Chile 

The great earthquake of May 22, 1960, off the coast of south central Chile was one of the 

largest earthquakes in the twentieth century (magnitude 9.5) and caused a tsunami that killed 61 

people in Hawaii and 122 in Japan. Death estimates from the tsunami for the entire Peru-Chile 

coastline ranged from 330 to 2,000 people (see http://www.drgeorgepc.com/Tsunami1960.html). In 

addition, about 2,000 lives were lost there from the widespread shaking damage (Atwater and 

others, 1999). Chile has experienced more than 180 earthquakes since 1973, of which 11 were 

fatal. We used 26 earthquakes with zero or more deaths in Chile to develop the empirical fatality 

estimation model as shown in figure 7. The L2G norm fits both smaller and large size earthquakes 

and the estimated parameters are θ = 40.93 and β = 0.44 with log residual error (ζ) of 1.77. The 

estimated fatality rate is 1 per 3,800 people exposed at Modified Mercalli shaking intensity IX and 

1 per 10,800 at shaking intensity VIII. An earthquake of magnitude 7.8 struck offshore of 

Valparaiso in Chile. More than 5,433,200 people were estimated to have experienced shaking 

intensity VII for an event in which 177 people were killed, 2,575 injured, and extensive damage 

occurred in central Chile, including the cities of San Antonio, Valparaiso, Vina del Mar, Santiago 

and Rancagua (http://earthquake.usgs.gov/regional/world/events/1985_03_03.php). 

Georgia 

Georgia experienced 9 earthquakes since 1973; 7 of them were fatal, and the largest struck 

Racha-Java on April 29, 1991, causing an estimated 114 shaking fatalities. More than 105,000 

people were estimated to have experienced shaking intensity IX and above and about 547,300 

exposed at intensity VI and above. The estimated empirical model parameters for Georgia are θ= 

26.49 and β = 0.33 with log residual error (ζ) of 0.74 as shown in figure 8. 
23

We estimate a fatality rate of one per 2,180 people exposed to shaking intensity IX and one 

per 8500 people exposed to shaking intensity VIII. For intensity VII, the rate is much lower, 

approximately one per 45,600. 

Greece 

Twenty-five fatal earthquakes occurred in Greece in the past 3 decades resulting in 1,300 

fatalities. The largest earthquake since last century was an earthquake of magnitude 7.2 that 

occurred on Aug 12, 1953, causing an estimated 800 deaths. Thirty earthquakes have been used to 

develop an empirical fatality model and the estimated parameters are θ = 21.48 and β = 0.28 with 

log residual error (ζ) of 1.43 as shown in figure 9. The fatality rate developed for Greek 

earthquakes is one death per 1,270 people exposed to shaking intensity IX which reduces to one per 

43,300 at shaking intensity VII. The most recent deadly earthquake in Greece was the magnitude 

6.0 Athens earthquake of Sept 9, 1999 which resulted in 143 deaths and caused extensive damage 

(http://www.geo.uib.no/seismo/quakes_world/Athens-earthq/HTML/Pavlides1.htm). About 65 

buildings were reported collapsed killing 143 people and injuring about 7,000. The earthquake had 

an estimated exposure of 9,700 people at shaking intensity IX and 278,200 at shaking intensity 

VIII.

Algeria 

Earthquakes have caused devastating effects in Algeria during the last few centuries. 

Recently, the magnitude 6.8 May 21, 2003, earthquake struck Boumerdes and Algiers, caused 

widespread damage in the epicentral region, claimed 2,271 human lives, injured about 10,000, 

damaged approximately 20,000 housing units, and left about 160,000 homeless (Bouhadad and 

others, 2004). In the past three decades, there were 23 significant earthquakes in Algeria, of which 

12 caused one or more fatalities. The El Asnam earthquake of Algeria occurred on Oct 10, 1980, 
24

and was the deadliest since 1973; it killing an estimated 3,500 people. In our calculations, about 

29,000 people were exposed to shaking intensity IX and an estimated 320,000 people exposed to 

intensity VIII. 

Eighteen earthquakes since 1973, were used to develop an empirical fatality model for 

Algeria by considering recorded shaking deaths and associated population exposure at different 

shaking intensity levels (fig. 10). We estimate a fatality rate of one in 190 people exposed to 

shaking intensity IX and it decreases to one death per 8,940 people exposed at shaking intensity 

VII.

Italy 

Earthquakes have claimed more than 36,000 human lives in Italy since beginning of 1900. 

About 32,610 people were killed in a single magnitude 7.0 earthquake that struck on Jan 13, 1915 

that devastated buildings in Rome and Chieti (Davison, 1915). Historically there are several 

earthquakes that killed more than 200,000 (Jan 11, 1693 killed 60,000; Feb 4, 1783 killed 50,000; 

Dec 16, 1857 killed 11,000; Dec 28, 1908 killed 70,000 people) from a USGS compilation of 

historical earthquakes (http://earthquake.usgs.gov/regional/world/historical_country.php#italy).

Forty-three earthquakes, of which fifteen were fatal, were used to estimate the empirical 

model parameters for Italy. The largest earthquake that struck Italy since 1973 was the magnitude 

6.9 Irpinia earthquake on Nov 23, 1980, which caused 2,483 deaths. The estimated population 

exposure was 37,200 people at shaking intensity IX and above and 250,180 at shaking intensity 

VIII. The empirical model parameters estimated are θ = 13.23 and β = 0.18, with log residual error 

(ζ) of 1.60 as shown in figure 11. The model corresponds to a fatality rate of one death per 68 

people exposed to shaking intensity IX which reduced to one death per 6310 people exposed at 

shaking intensity VII. 
25

Japan 

Earthquakes are more common in Japan than most other countries of the world. There are 

22 fatal earthquakes recorded in Japan since 1973 that have killed 5,945 people, and the deadliest 

one was the Jan 16, 1995, Kobe earthquake which alone took 5,502 lives. The Kobe earthquake had 

an estimated population exposure of 1,740,200 at shaking intensity IX and about 3,176,200 at 

shaking intensity VIII. We used 108 earthquakes in Japan since 1973 with zero or more deaths to 

estimate empirical model parameters as θ = 11.93 and β = 0.10 with log residual error (ζ) of 1.49 

(fig. 12). We estimate a fatality rate that corresponds to an estimated one death in every 330 people 

exposed at shaking intensity IX and one in every 20,100 at shaking intensity VIII. 

Pakistan 

Pakistan is one of the most seismically vulnerable countries of the World and has already 

witnessed several devastating earthquakes in the last century which in total have killed more than 

150,000 people. There are 84 earthquakes with magnitude 5.5 and above that have occurred in 

Pakistan in the last 35 years; 16 of them were fatal and claimed more than 93,000 lives. The 

magnitude 7.6 Kashmir earthquake of 2005 was the largest and most lethal in recent times, causing 

very heavy damage in the Muzaffarabad area and in the Kashmir region of north Pakistan, where 

entire villages were destroyed in the epicentral areas 

(http://earthquake.usgs.gov/eqcenter/eqinthenews/2005/usdyae/#summary). The Kashmir event 

caused 87,351 deaths and more than 69,000 injuries. The earthquake had an estimated 290,200 

people exposed to shaking intensity IX and about 769,000 people exposed to shaking intensity 

VIII. The empirical model, developed using 23 fatal earthquakes since 1973, indicates a death rate 

of 1 per 4 people exposed to shaking intensity IX and about 1 per 1,850 people at shaking intensity 
26

VII (fig. 13). These fatality rates are extremely high, confirming the extreme vulnerability of the 

region’s structures and population to earthquakes. 

Peru 

Located on a circum-Pacific seismic belt, an active seismotectonic region which witnesses 

more than two-third of the world’s large-magnitude earthquakes, Peru has experienced dozens of 

fatal earthquakes in the past several decades. The Ancash earthquake of November 10, 1946, was

the deadliest and caused 1,400 fatalities. Widespread destruction to the building stock was reported 

in this earthquake near the Sihuas-Quiches-Conchucos area of Ancash, an area also affected by

landslides (http://earthquake.usgs.gov/regional/world/events/1946_11_10.php). 

Despite experiencing 122 earthquakes with magnitude 5.5 or greater since 1973, Peru

surprisingly had reported fatalities from only 27 of them. We used 33 events that have experienced 

zero or more fatalities since 1973 to estimate the model parameters as shown in figure 14. The 

estimated parameters correspond to a fatality rate of 1 death per 4,180 people exposed to intensity 

IX shaking and 1 death per 31,000 at shaking intensity VII. The recent magnitude 8.0 August 15, 

2007, Pisco earthquake, which killed 514 people, affected an estimated 493,400 people at shaking 

intensity VIII and 307,200 at shaking intensity VII. 

Philippines 

The Philippines has a long history of earthquake occurrence (Bautista and Bautista, 2004), 

and the earliest earthquake reported was as far back as 1589. The magnitude 7.7 earthquake of July 

16, 1990, in Luzon was the strongest earthquake in the Philippines in recent times; it caused 1,621 

fatalities. For that event, an estimated 892,500 people were exposed to shaking intensity IX and 

above and 1,217,700 were exposed at intensity VIII. 
27

The Philippines has experienced more than 300 earthquakes of magnitude 5.5 or greater of 

which 20 were fatal, totaling an estimated 1,773 shaking-related deaths. We used all the 

earthquakes since 1973 with zero or more deaths to develop an empirical fatality model. The 

estimated rate corresponds to 1 death per 1,700 people exposed at intensity IX and 1 death per 

22,100 people at shaking intensity VIII, as shown in figure 15. 

Romania 

The magnitude 7.4 Bucharest earthquake of March 4, 1977, one of the most destructive 

earthquakes in Vrancea, Romania, in recent times, killed 1,581 people and injured 7,576. Thirtytwo 8–12-story buildings collapsed and another 150 old buildings (4–6 stories) were heavily 

damaged (Mandrescu and others, 2007). The empirical model developed using six earthquakes with 

zero or more deaths since 1973 provides θ = 17.50 and β = 0.24 with slightly higher log residual 

error (ζ) of 2.60 as shown in figure 16. The estimated fatality rate corresponds to 1 death per 360 

people exposed at shaking intensity IX and 1 death per 15,200 at intensity VII. 

Turkey 

Earthquakes in Turkey during the 20th century have caused enormous loss of life and 

property with a total of 110,000 deaths and 250,000 injuries while destroying more than 600,000 

housing units (Erdik, 2003). The Marmara region (the western portion of the North Anatolian fault 

zone) in Turkey has been the site of numerous destructive earthquakes (Erdik and others, 2004). 

There have been 64 fatal earthquakes in Turkey since 1900; 40 of them struck the country since 

1973 killing more than 27,000 people. The Aug 17, 1999, Kocaeli earthquake caused an estimated 

17,439 shaking-related deaths with an estimated population exposure of 572,400 at shaking 

intensity IX and above. The empirical model developed for Turkey as shown in figure 17 indicates 
28

a death rate of 1 per 38 people exposed at shaking intensity IX; about 1 death per 1,000 exposed at 

shaking intensity VIII. 

United States 

The PAGER regionalization scheme proposed in this investigation treats California 

differently than rest of the United States. The existence of seismically resistant building stock with 

stringent building code enforcement and construction practice, and sustained efforts towards 

seismic risk reduction for future earthquakes, demands such demarcation. The rest of the 

conterminous United States is less prone to frequent, large earthquakes compared to California. 

Due to lack of fatal earthquakes in United States, it was not possible to deduce an empirical fatality 

model from past earthquake data alone. In the first internal release of empirical model (v1.0), we 

have used expert judgment to develop the fatality rate model shown in figure 18. It is mainly based 

on comparing the fatality rate among several groups of countries. The fatality rate derived from 

past earthquake data in Taiwan appears to be slightly higher than California but lower than 

Northern Europe. The proposed empirical fatality rate for the rest of the United States lies between 

Northern Europe and Taiwan with estimated death rate of 1 person per 12,470 exposed at shaking 

intensity IX and above. The rate reduces to 1 person per 61,300 exposed at shaking intensity VIII 

and almost no deaths at shaking intensity VI or below. 

In the current release (v1.1), we have used all fatal as well as nonfatal earthquakes from a 

group of countries including Canada, Australia, Mexico, and others (refer to appendix I) to deduce 

the empirical fatality rate. Despite the higher vulnerability of overall Mexican building stock, both 

Mexico and the rest of the United States (without California) have a substantial amount of 

unreinforced masonry buildings, which are extremely vulnerable at higher intensities. The 

estimated empirical fatality rate for a group of countries indicates 1 death per 23,400 people 
29

exposed at intensity IX, and it reduces to 1 death per 52,900 at intensity VIII. The newly estimated 

rate is lower at intensity IX than estimated by the v1.0 model but slightly higher at lower intensity. 

We think this difference is partially due to the influence of earthquakes from Mexico on the group 

model. Further investigations are necessary in order to estimate the validity of a regional or expertjudgment model to be used for fatality estimation in the rest of United States. Other candidate lossmodeling approaches, such as semi-empirical and analytical models being developed for PAGER 

casualty assessment, will also be used along with the empirical model (v1.1) for future casualty 

estimates.

Fatality Estimation for Recent Earthquakes

We have implemented the PAGER empirical model to test the fatality estimation for recent 

earthquakes not used in the calibration process. We provide a summary of estimated earthquake 

deaths of all the earthquakes of magnitude 5.5 and above that occurred from January to June 2008 

and compare the estimated with the deaths recorded by credible reporting agencies. As shown in 

table 2, more than 77 percent of the smaller events which had zero recorded deaths were estimated 

correctly. For events with few shaking deaths, the estimated deaths were within ±1⁄2 order of 

magnitude. For large earthquakes such as Sichuan in China, the model computation was based on 

data prior to 2007 and we estimated 51,000 deaths. Since this earthquake was such a profound 

catastrophe, and it is well documented, we have now included it in recalibration of the empirical 

model for China. 

The current PAGER system that runs internally at the USGS has implemented the empirical 

model discussed in this report as well as semi-empirical and analytical models (Wald and others, 

2008). We are currently monitoring the performance of the system (stability in terms of triggering 
30

events, performing automatic casualty estimates, alarming, and distribution) before making fatality 

estimates public.

Summary and Conclusions

We studied global earthquake fatality data (1973−2007) and propose a new approach for 

estimating earthquake fatalities worldwide. We use a two-parameter empirical lognormal 

distribution to express country-specific mean fatality rate as a function solely of MMI, without 

reference to other earthquake parameters (for example, magnitude, location, or time of day). Our 

model development compares the total recorded shaking-related deaths for each earthquake in our 

catalog to the estimated populations exposed to each MMI intensity level determined using the 

ShakeMap and LandScan population database. For countries with low seismic hazard and thus 

limited fatality data, we combined fatality data from neighboring countries that have similar 

vulnerabilities. The regionalization scheme proposed in this investigation is preliminary and based 

on qualitative analysis. Further investigations are necessary in order to validate the applicability of 

regional empirical model for countries where there are few or no fatal earthquakes. 

For more than 200 countries, we employed our regionalization scheme, combining data 

from several different countries (appendix II). We envision that the addition of more-recent 

earthquakes (both fatal and nonfatal) and the incorporation of additional constraints (for example, 

in terms of macroseismic intensities, choice of appropriate ground motion prediction equations, or 

new PGA-MMI conversion rules) will be necessary to put boundaries on the empirical fatality rates 

globally. This new information may require re-creation of Shakemaps of past fatal earthquakes and 

frequent recalibration of the empirical model parameters. In order to include such changes, we plan 

to update the electronic version of appendix II (http://earthquake.usgs.gov/eqcenter/pager/) 

regularly as new data trigger the updating of fatality rates for a particular country or region. 
31

We also presented a comparison of fatality estimations based on the empirical model with 

the actual recorded fatalities for recent (2008) earthquakes and found a very good match in more 

than 95 percent of the events. Using this initial model, PAGER could estimate total event-level 

fatalities in future earthquakes within an average 1⁄2 to 1 order of magnitude, with higher accuracy 

in highly fatal events. 

One obvious limitation of the empirical model is the paucity of data in low-seismic 

countries or few fatal earthquakes in large countries during the limited time period for which 

quality hazard, loss, and population data are available. This empirical approach will therefore be 

supplemented with other engineering-based models for the PAGER casualty estimation system. In 

addition, for larger countries which warrant sub-country level fatality models, given their diversity 

of regional construction practices, we will investigate the potential for countries or regions with 

sufficient empirical data.

Acknowledgments

We thank Paul Earle, Keith Porter, Trevor Allen, and Robin Spence and for helpful 

discussions during the research phase of this investigation. Special thanks to Margaret Hopper and 

Dina D’Ayala for reviewing the manuscript. 
32

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35

Appendix I. PAGER Regionalization Scheme for the Empirical Model

[The following table describes the regionalization scheme with respect to data on the human development index (HDI), climate 

characteristics, and common building types in the group of countries. Refer to figure Ia for the map of proposed regionalization 

scheme with colors indicating individual regions. Figure Ib shows a global map of countries based on the HDI, and figure Ic shows a 

map associated with climate characteristics]

 

Region List of Countries

Human development and climatic 

characteristics 

(refer to figures Ib & Ic)

Description

1. Australia, USA, 

and Canada 

Australia, Canada, and United States 

(without California), Mexico, Saint Pierre 

and Miquelon, United States Minor 

Outlying Islands

HDI: Mostly high, more than 0.95 with 

higher end for United States and lower of 

0.84 in Mexico.

Climate: Varies significantly within 

country. For example, within Australia, the 

climate varies from warm temperate, to arid 

with desert precipitation and mostly hot arid 

temperature. In the United States, it is warm 

and temperate, with high humidity in the 

East and dry in West. In Canada, the main 

climate is snow, with high humidity 

precipitation and cool summer temperatures 

in the North to hot in southern Canada.

This group consists of countries which have 

a) most of its building stock engineered b) 

very few vulnerable structures and c) 

stringent building design and construction 

standards. Except Mexico, all the other 

countries in this region have fewer fatal 

earthquakes during the catalog period to 

have country-specific fatality models. 

Most of the residential building stock in 

North America (except California) is of 

unreinforced masonry construction and 

wood frame construction. Mexican building 

stock, although comparatively more 

vulnerable given its performance in recent 

earthquakes, provides a useful basis for 

developing a regional vulnerability/fatality 

model.

2. New Zealand 

and California

New Zealand and California state of USA HDI: Mostly high, in the range of 0.94. For 

California, a country level index is 

representative.

Climate: Varies significantly. In New 

Zealand, it is temperate oceanic climate with 

heavy winter snows. California has 

Mediterranean climate (warm temperate) 

with summer dry precipitation. Summers are 

hot and dry, due to domination of 

subtropical high pressure system. 

Through sustainable developments, both 

California and New Zealand have achieved 

higher earthquake safety standards than rest 

of the world. Most of the residential 

building stock in this region is a singlefamily wood-frame construction designed to 

resist earthquake shaking. Historical 

earthquakes in this region have caused very 

few fatalities relative to other countries for 

similar-sized earthquakes.
36

3. Central 

America 

Costa Rica and Panama HDI: 0.85 in Costa Rica and 0.83 in 

Panama.

Climate: Tropical rain forest climate that is 

monotonously wet throughout the year.

Both Costa Rica and Panama have 

experienced substantial losses during past 

fatal earthquakes. Earthquakes from Costa 

Rica contribute to the development of 

regional empirical mode to be used for 

Panama.

4. South Central 

America

Dominican Republic, Jamaica, Guadeloupe, 

El Salvador

HDI: Mostly in the range of 0.75–0.77 with 

high of 0.92 in Guadeloupe.

Climate: Tropical rain forest climate with 

monotonously wet throughout the year.

The building stock and construction practice 

in this group suits the local conditions 

(predominant use of wood/mud/concrete for 

construction of houses).

5. Caribbean and 

Central America

Guatemala, Belize, Honduras, Nicaragua, 

Haiti, Puerto Rico, Cayman Islands, Turks 

and Caicos Islands, Anguilla, Montserrat, 

Cuba, Bahamas, Saint Kitts and Nevis, Saint 

Lucia, Antigua and Barbuda, Trinidad and 

Tobago, Aruba, Netherlands Antilles, 

Dominica, Grenada, Saint Vincent and the 

Grenadines, Martinique, British Virgin 

Islands, U.S. Virgin Islands, Barbados, Saint 

Barthelemy, Saint Martin (France)

HDI: Medium in the range of 0.69–0.71 in 

Nicaragua, Honduras, Guatemala with 0.77 

in Belize and 0.52 in Haiti. High in the 

range of 0.8–0.9 with higher in Barbados, 

Bahamas, Cuba.

Climate: Equatorial climate with fully 

humid or monsoonal precipitation 

throughout the region.

This is a group of islands and oceanic 

countries in Pacific and Atlantic oceans.

None of the countries except Guatemala has 

enough fatal earthquakes to generate 

country-specific empirical models. It is 

assumed that these places have similar 

vulnerability to earthquakes primarily due to 

similar geographic and weather conditions.

6. Western South 

America

Colombia, Ecuador, Peru, Chile, Argentina

HDI: Mostly high in the range of 0.75–0.9 

with 0.85 in Uruguay and 0.8 in Brazil, and 

0.76 in Paraguay.

Climate: Mixed climate zone with 

equatorial and winter dry precipitation in 

Central Brazil. Equatorial with fully humid 

to monsoonal precipitation in Northern 

Brazil. Mostly warm temperate with hot and 

warm summer temperature in Southern 

Brazil.

Unreinforced clay brick/block masonry 

construction, confined masonry, and adobe 

construction are most common in this group 

of countries. Some of the countries in this 

group may have significantly different 

building stock; however none of the 

countries have adequate earthquake casualty 

data to develop a country-specific empirical 

casualty model.

7. Eastern South 

America

Venezuela, Bolivia, Brazil, Uruguay, 

Guyana, Suriname, Paraguay, French 

Guiana

HDI: Mostly high in the range of 0.85–0.9. 

In the medium range, it is 0.79 in Colombia.

Climate: It varies from arid to warm 

temperate from east to west and temperature 

is mostly between hot to warm summer.

Countries in this group have similar 

construction practices and building stock.

8. North Africa Algeria, Egypt, Tunisia, Western Sahara HDI: Medium to high level with 0.77 for

Tunisia, and 0.73 for Algeria.

Climate: Mixed climate condition with 

slight variation within a country. Most parts 

Countries like Algeria and Egypt have 

experienced earthquakes in the past 

centuries and have vulnerable construction 

as a part of its building stock.
37

of Algeria and Western Sahara has arid 

climate with desert precipitation and hot 

arid temperature. Northern parts of Morocco 

Tunisia and Algeria have warm temperate 

and summer dry precipitation with hot 

summer temperatures.

9. South-central

Africa

Botswana, Namibia, South Africa, 

Swaziland, Zimbabwe, Morocco, Sudan, 

Chad, Central African Republic, Cameroon, 

Congo, DRP Congo, Gabon, Equatorial 

Guinea, Sao Tome and Principe, Angola, 

Mauritania, Senegal, Gambia, GuineaBissau, Sierra Leone, Liberia, Cote d'Ivoire, 

Ghana, Togo, Benin, Niger, Nigeria, Mali, 

Burkina Faso, Guinea, Yemen, Eritrea, 

Djibouti, Ethiopia, Somalia, Kenya, 

Uganda, Rwanda,Burundi, United Republic 

of Tanzania, Malawi, Madagascar, 

Mozambique, Zambia, Lesotho

HDI: Mostly in low range (less than 0.5) in 

the east coast except in Kenya and 

Madagascar with lower medium range that 

is, between 0.50–0.55. No HDI index data is 

available for Somalia. Gabon being highest 

at 0.68.

Climate: Mostly equatorial climate except 

in Somalia and parts of Ethiopia where it is 

arid desert and high arid temperatures. 

Equatorial climate throughout central part 

with winter dry precipitation and arid desert 

climate in northern part of Central Africa. 

Parts of Central Africa receive monsoonal 

precipitation.

Buildings in central African countries are 

generally adobe, mud wall, and clay burnt 

brick masonry constructions. Rural areas 

constitute 60% of informal construction. 

This is mainly due to cheaper locally 

available material, climatic condition, ageold construction practices and lack of 

adequate infrastructure.

10. Italy Italy, Holy See, Malta, San Marino HDI: Mostly high in the range of 0.94–0.95 

for Italy and other countries but slightly 

lower value of 0.88 for Malta.

Climate: Warm temperate climate with 

fully humid precipitation in northern part to 

summer dry in southern part of the Italy. 

Concrete, moment-frame construction and 

masonry construction constitute more than 

80% of the building stock in this region. 

11. Northern 

Europe

Norway, Sweden, Finland, Denmark, 

Germany, Belgium, France, Austria, 

Switzerland, Aland Islands, Monaco, 

Poland, Bouvet Island, United Kingdom, 

Ireland, Guernsey, Isle of Man, Jersey, 

Falkland Islands (Malvinas), Saint Helena, 

South Georgia and the South Sandwich 

Islands, Iceland, Faroe Islands, Greenland, 

Svalbard and Jan Mayen, Liechtenstein, 

Luxembourg, Netherlands, Greece, Spain, 

Portugal, Gibraltar, Cape Verde, Andorra

HDI: High index through northern Europe 

with highest in Norway, Ireland and Sweden 

around 0.96–0.98. HDI is 0.89 in Portugal 

and 0.95 for Spain but slightly lower in the 

range of 0.87 for Poland.

Climate: Warm temperate climate with 

fully humid precipitation and warm summer 

throughout northern Europe except Norway, 

Sweden, and Finland where it is mostly 

snow climate and fully humid with cool 

summer temperature. 

These countries have not experienced large 

fatal earthquakes in the past. The building 

stock in these countries generally consists of 

ancient European-style massive stone 

masonry and block masonry constructions 

which are vulnerable to shaking. Most of the 

buildings constructed after 1960s are 

concrete and steel moment frame 

constructions. 

12. Eastern 

Europe

Czech Republic, Slovenia, Slovakia, 

Hungary, Bosnia and Herzegovina, Croatia, 

Serbia, Montenegro, Romania, Albania, 

Former Yugoslav Republic of Macedonia, 

HDI: Mostly high, in the range of 0.8–0.9 

with higher in Slovenia. No HDI data is 

available for Serbia and Montenegro. HDI is 

0.8 in Macedonia, Bulgaria and Albania. 

This group of countries which have 

experienced deadly earthquakes in the past. 

Block masonry, rubble/dressed stone 

masonry, and concrete framed constructions 
38

Bulgaria, Republic of Moldova Medium in the range of 0.7 for Moldova.

Climate: Snow climate, fully humid 

precipitation with warm summer 

temperatures throughout Eastern European 

countries.

are common in this group of countries; they 

also share similarities in terms of geography 

and socio-economic characteristics. Both 

Spain and Portugal have experienced 

significant earthquakes in the past but they 

do not have enough earthquakes to develop 

country-specific models.

13. Baltic States 

and Russia

Estonia, Latvia, Lithuania, Belarus, 

Ukraine, Russian Federation, Georgia, 

Armenia, Azerbaijan

HDI: Mostly high, in the range of 0.8–0.85. 

It varies slightly within the Baltic states with 

the upper end in Russia and the lower end 

for Ukraine in the range of 0.75–0.8.

Climate: The main climate within the Baltic 

states is snow with fully humid 

precipitation; temperature that varies from 

warm summer in Ukraine and Belarus to 

cool summer in the northern and central 

Russian Federation, with the Polar Tundra 

temperature in parts of northern Russia 

close to the north pole.

These countries that have experienced fatal 

earthquakes during the last few centuries 

and have similar building stock. Some of the 

building stock is extremely vulnerable to 

earthquake shaking. The region is part of 

former Soviet Union and has vulnerable 

construction (particularly, precast concrete 

framed and block masonry construction 

which performed poorly in the 1988 Spitak, 

Armenia earthquake). 

14. Central Asia Kazakhstan, Uzbekistan, Turkmenistan, 

Kyrgyzstan, Tajikistan

HDI: Medium to high index, in the range of 

0.67–0.71with lowest in Tajikistan and 

highest in Turkmenistan.

Climate: Arid Steppe climate with cold arid 

temperature throughout the region and 

desert precipitation in central part. Warm 

temperate with summer dry conditions in 

the pockets of southeastern parts of 

Tajikistan and Kyrgyzstan.

Common buildings types in this group 

consist of precast concrete, stone/concrete 

block masonry construction and steel 

moment frame with infill masonry 

construction. Construction practices were 

strongly influenced during the former Soviet 

Union era. Rural area consists of 

predominant adobe and wood construction.

15. Arabian 

Peninsula

Turkey, Oman, United Arab Emirates, 

Qatar, Saudi Arabia, Bahrain, Kuwait, 

Lebanon, Jordan, Palestinian Territory, 

Syrian Arab Republic, Israel, Cyprus, 

Libyan Arab Jamahiriya

HDI: Mostly high, in the range of 0.8 in 

Libya with higher end in Saudi Arabia. 

Medium in Syria and high in Israel. HDI is

in the range of 0.7–0.8 for other countries.

Climate: Arid, desert and hot arid and 

slightly cold arid temperature. Warm 

temperate with steppe and hot summer 

condition in northern pockets of Libya. Arid 

climate with steppe and cold arid 

temperatures in eastern part of the countries.

This group consists of countries which 

potentially have low seismic hazard and 

have experienced very few fatal earthquakes 

during the catalog period. The most 

common building construction is adobe, 

brick, and stone masonry construction for 

residential and reinforced concrete 

construction for workplaces. With low 

seismic hazard, the building stock is 

generally not considered to be designed for 

earthquake resistant characteristics 

(Petrovski, 1983). 

16. Iran & Iraq Iran, Iraq, Afghanistan and Pakistan HDI: Lowest in Afghanistan, Iraq and Predominantly vulnerable construction 
39

Pakistan in the range of 0.5–0.6 and 0.76 in 

Iran.

Climate: Arid climate with desert 

precipitation through the region with hot 

arid temperatures. The northern parts of 

these countries have mostly cold arid 

temperatures. 

includes traditional adobe, unburnt brick, 

and block-masonry constructions which 

have experienced severe damage during past 

earthquakes. About 70-80% of rural 

building stock is made of traditional 

constructions in these countries.

17. Chinese 

Peninsula

Brunei Darussalam, China, North Korea, S. 

Korea, Macao, Mongolia 

HDI: High in the range of 0.75–0.8 with 

medium (0.7–0.75) for Mongolia.

Climate: Mixed climate zone within 

countries. In China, it varies from arid 

desert to polar tundra climate in the central 

and southern part and warm temperate 

climate with humid and hot summer 

temperature in southern and eastern Coast of 

China. In Southern Mongolia, the climate is 

mostly arid with winter dry precipitation to 

snow climate with cool summers in the 

northern part of the country. Central parts of 

S. Korea have snow climate with winter dry 

precipitation and hot summer temperature.

During the catalog period only a few deadly 

earthquakes occurred in one or two 

countries in this group. These countries have 

significant vulnerable building stock. 

18. Philippines 

and Malaysian 

Peninsula

Singapore, Thailand, Hong Kong, Malaysia, 

Philippines

HDI: Mostly high 0.82–0.92 in Malaysia 

and Singapore and medium in the range of 

0.75–0.78 in Philippines and Thailand 

respectively.

Climate: In Thailand, Malaysia, and 

Philippines, the main climate is mostly 

equatorial but the precipitation varies from 

winter dry to fully humid in Malaysia.

This region consists of a group of countries 

that are relatively similar in terms of their 

urban building stock, but rural areas may 

have variable construction practices. 

19. Indian 

Peninsula 

India, Sri Lanka, Bangladesh, Nepal, 

Bhutan, Myanmar 

HDI: Varies from 0.62 in India to 0.74 in 

Sri Lanka.

Climate: Mixed climatic condition within a 

country. Polar tundra climate in northern 

Nepal and equatorial climate with winter 

dry precipitation . In India, it is mostly 

warm temperate in central India to 

equatorial southern India and arid in western 

parts of northern India. In Sri Lanka, it is 

equatorial with winter dry precipitation in 

north to fully humid condition in southern 

Sri Lanka. 

Most of the buildings in Nepal, Bhutan and 

Myanmar are constructed using adobe, 

rubble stones, and clay burnt bricks. 

Countries in this group have experienced 

several fatal earthquakes during the last few 

decades and are believed to be similar in 

terms of their susceptibility to earthquake 

losses.

20. Indonesian Cambodia, Laos, Timor-Leste Viet Nam, HDI: Mostly medium range but lowest in Countries in this group have experienced 
40

Peninsula Papua New Guinea, American Samoa, 

Samoa, Tokelau, Tuvalu, Fiji, Tonga, 

Vanuatu, Wallis and Futuna, Niue, Nauru, 

New Caledonia, Solomon Islands, Palau, 

Guam, Northern Mariana Islands, Marshall 

Islands, Federated States of Micronesia, 

Kiribati, Cook Islands, French Polynesia, 

Norfolk Island, Pitcairn, British Indian 

Ocean Territory, Christmas Island, Cocos 

(Keeling) Islands, French Southern 

Territories, Heard Island and McDonald 

Islands, Maldives, Comoros, Mauritius, 

Mayotte, Reunion, Seychelles, Bermuda, 

Antarctica, Indonesia 

Laos, Timor in the range of 0.5–0.55 and in 

the range of 0.72–0.75 in other countries.

Climate: Mostly equatorial and varies 

within a country. In Indonesia, it is fully 

humid precipitation in the central islands of 

Indonesia, and winter dry to monsoonal 

precipitation in Cambodia and Viet Nam.

numerous deadly earthquakes in the past. 

They share analogous construction practices 

(predominant clay brick masonry, wood, 

and block masonry constructions) and have 

vulnerable building stock prone to 

earthquake damage. 

21. Japan & 

South Korea

Japan, Taiwan HDI: Mostly high index ranges from 0.92–

0.95.

Climate: Main climate is a warm temperate 

condition with fully humid precipitation and 

hot summer. The northern tip of the 

Japanese islands has snow climate with fully 

humid condition and warm summer. 

Traditional as well as modern wood-frame 

constructions are the most common forms of 

residential construction in both rural as well 

as urban areas. However most recent 

buildings (constructed after 1980s) in urban 

areas are generally either concrete shear 

wall or steel moment-frame constructions. 
Figure Ia. Proposed regionalization scheme (groups shown with different colors) for empirical loss modeling. 

41
42

High

 0.950 and over 

 0.900–0.949 

 0.850–0.899

 0.800–0.849 

 0.750–0.799

Medium

 0.700–0.749 

 0.650–0.699 

 0.600–0.649 

 0.550–0.599 

 0.500–0.549

Low

 0.450–0.499 

 0.400–0.449 

 0.350–0.399 

 under 0.350 

 not available

Figure Ib. Map showing spatial variation of Human Development Index (HDI) (Source: http://hdrstats.undp.org/indicators/1.html and 

http://en.wikipedia.org/wiki/List_of_countries_by_Human_Development_Index). 
43

Figure Ic. Map showing Koppen-Geiger climatic classification. 
44

Appendix II. PAGER Implementation of Empirical Model*

 

Country Name ISO code Theta Beta

ζ

(residual 

error)

Number

of 

earthquakes

Status

Afghanistan AF 31.44 0.43 2.24 26 Country

Aland Islands AX 18.63 0.24 1.41 47 Group

Albania AL 16.47 0.22 1.82 29 Group

Algeria DZ 15.91 0.22 2.17 18 Country

American Samoa AS 13.71 0.16 1.89 110 Group

Andorra AD 18.63 0.24 1.41 47 Group

Angola AO 15.05 0.19 2.18 29 Group

Anguilla AI 12.56 0.13 1.52 29 Group

Antarctica AQ 13.71 0.16 1.89 110 Group

Antigua and Barbuda AG 12.56 0.13 1.52 29 Group

Argentina AR 75.99 0.57 1.71 97 Group

Armenia AM 29.74 0.36 2.43 21 Group

Aruba AW 12.56 0.13 1.52 29 Group

Australia AU 100.00 0.61 1.63 49 Group

Austria AT 18.63 0.24 1.41 47 Group

Azerbaijan AZ 29.74 0.36 2.43 21 Group

Bahamas BS 12.56 0.13 1.52 29 Group

Bahrain BH 11.05 0.10 1.61 87 Group

Bangladesh BD 11.01 0.11 2.38 44 Group

Barbados BB 12.56 0.13 1.52 29 Group

Belarus BY 29.74 0.36 2.43 21 Group

Belgium BE 18.63 0.24 1.41 47 Group

Belize BZ 12.56 0.13 1.52 29 Group

Benin BJ 15.05 0.19 2.18 29 Group

Bermuda BM 13.71 0.16 1.89 110 Group

Bhutan BT 11.01 0.11 2.38 44 Group

Bolivia BO 100.00 0.63 1.83 13 Group

Bosnia and Herzegovina BA 16.47 0.22 1.82 29 Group

Botswana BW 15.05 0.19 2.18 29 Group

Bouvet Island BV 18.63 0.24 1.41 47 Group

Brazil BR 100.00 0.63 1.83 13 Group

British Indian Ocean 

Territory IO 13.71 0.16 1.89 110 Group

Brunei Darussalam BN 10.40 0.10 2.03 119 Group

Bulgaria BG 16.47 0.22 1.82 29 Group

Burkina Faso BF 15.05 0.19 2.18 29 Group

Burundi BI 15.05 0.19 2.18 29 Group
45

Cambodia KH 13.71 0.16 1.89 110 Group

Cameroon CM 15.05 0.19 2.18 29 Group

Canada CA 100.00 0.61 1.63 49 Group

Cape Verde CV 18.63 0.24 1.41 47 Group

Cayman Islands KY 12.56 0.13 1.52 29 Group

Central African Republic CF 15.05 0.19 2.18 29 Group

Chad TD 15.05 0.19 2.18 29 Group

Chile CL 40.93 0.44 1.77 26 Country

China CN 10.40 0.10 2.03 119 Country

Christmas Island CX 13.71 0.16 1.89 110 Group

Cocos (Keeling) Islands CC 13.71 0.16 1.89 110 Group

Colombia CO 48.07 0.47 2.29 22 Country

Comoros KM 13.71 0.16 1.89 110 Group

Congo CG 15.05 0.19 2.18 29 Group

Democratic Republic of 

the Congo CD 15.05 0.19 2.18 29 Group

Cook Islands CK 13.71 0.16 1.89 110 Group

Costa Rica CR 27.61 0.36 1.62 3 Country

Cote d'Ivoire CI 15.05 0.19 2.18 29 Group

Croatia HR 16.47 0.22 1.82 29 Group

Cuba CU 12.56 0.13 1.52 29 Group

Cyprus CY 11.05 0.10 1.61 87 Group

Czech Republic CZ 16.47 0.22 1.82 29 Group

Denmark DK 18.63 0.24 1.41 47 Group

Djibouti DJ 15.05 0.19 2.18 29 Group

Dominica DM 12.56 0.13 1.52 29 Group

Dominican Republic DO 33.77 0.37 1.86 13 Group

Ecuador EC 100.00 0.64 1.74 12 Country

Egypt EG 16.16 0.23 1.99 22 Group

El Salvador SV 26.62 0.32 1.95 7 Country

Equatorial Guinea GQ 15.05 0.19 2.18 29 Group

Eritrea ER 15.05 0.19 2.18 29 Group

Estonia EE 29.74 0.36 2.43 21 Group

Ethiopia ET 15.05 0.19 2.18 29 Group

Falkland Islands 

(Malvinas) FK 18.63 0.24 1.41 47 Group

Faroe Islands FO 18.63 0.24 1.41 47 Group

Fiji FJ 13.71 0.16 1.89 110 Group

Finland FI 18.63 0.24 1.41 47 Group

France FR 18.63 0.24 1.41 47 Group

French Guiana GF 100.00 0.63 1.83 13 Group

French Polynesia PF 13.71 0.16 1.89 110 Group

French Southern 

Territories TF 13.71 0.16 1.89 110 Group

Gabon GA 15.05 0.19 2.18 29 Group

Gambia GM 15.05 0.19 2.18 29 Group
46

Georgia GE 26.49 0.33 0.74 7 Country

Germany DE 18.63 0.24 1.41 47 Group

Ghana GH 15.05 0.19 2.18 29 Group

Gibraltar GI 18.63 0.24 1.41 47 Group

Greece GR 21.48 0.28 1.43 30 Country

Greenland GL 18.63 0.24 1.41 47 Group

Grenada GD 12.56 0.13 1.52 29 Group

Guadeloupe GP 33.77 0.37 1.86 13 Group

Guam GU 13.71 0.16 1.89 110 Group

Guatemala GT 12.25 0.13 1.83 15 Country

Guernsey GG 18.63 0.24 1.41 47 Group

Guinea GN 15.05 0.19 2.18 29 Group

Guinea-Bissau GW 15.05 0.19 2.18 29 Group

Guyana GY 100.00 0.63 1.83 13 Group

Haiti HT 12.56 0.13 1.52 29 Group

Heard Island and 

McDonald Islands HM 13.71 0.16 1.89 110 Group

Holy See (Vatican City 

State) VA 13.23 0.18 1.6 43 Group

Honduras HN 12.56 0.13 1.52 29 Group

Hong Kong HK 16.04 0.18 1.51 32 Group

Hungary HU 16.47 0.22 1.82 29 Group

Iceland IS 18.63 0.24 1.41 47 Group

India IN 11.53 0.14 1.93 28 Country

Indonesia ID 14.05 0.17 1.74 78 Country

Islamic Republic of Iran IR 9.58 0.10 2.71 93 Country

Iraq IQ 9.70 0.10 2.46 143 Group

Ireland IE 18.63 0.24 1.41 47 Group

Isle of Man IM 18.63 0.24 1.41 47 Group

Israel IL 11.05 0.10 1.61 87 Group

Italy IT 13.23 0.18 1.6 43 Country

Jamaica JM 33.77 0.37 1.86 13 Group

Japan JP 11.93 0.10 1.49 108 Country

Jersey JE 18.63 0.24 1.41 47 Group

Jordan JO 11.05 0.10 1.61 87 Group

Kazakhstan KZ 16.37 0.2 1.63 15 Group

Kenya KE 15.05 0.19 2.18 29 Group

Kiribati KI 13.71 0.16 1.89 110 Group

D. People's Republic of 

Korea KP 10.40 0.10 2.03 119 Group

Republic of Korea KR 10.40 0.10 2.03 119 Group

Kuwait KW 11.05 0.10 1.61 87 Group

Kyrgyzstan KG 16.37 0.20 1.63 15 Group

Lao People's Democratic 

Republic LA 13.71 0.16 1.89 110 Group

Latvia LV 29.74 0.36 2.43 21 Group
47

Lebanon LB 11.05 0.10 1.61 87 Group

Lesotho LS 15.05 0.19 2.18 29 Group

Liberia LR 15.05 0.19 2.18 29 Group

Libyan Arab Jamahiriya LY 11.05 0.10 1.61 87 Group

LiechtensteinLI 18.63 0.24 1.41 47 Group

Lithuania LT 29.74 0.36 2.43 21 Group

Luxembourg LU 18.63 0.24 1.41 47 Group

Macao MO 10.40 0.10 2.03 119 Group

Former Yugoslav R. of 

Macedonia MK 16.47 0.22 1.82 29 Group

Madagascar MG 15.05 0.19 2.18 29 Group

Malawi MW 15.05 0.19 2.18 29 Group

Malaysia MY 16.04 0.18 1.51 32 Group

Maldives MV 13.71 0.16 1.89 110 Group

Mali ML 15.05 0.19 2.18 29 Group

Malta MT 13.23 0.18 1.6 43 Group

Marshall Islands MH 13.71 0.16 1.89 110 Group

Martinique MQ 12.56 0.13 1.52 29 Group

Mauritania MR 15.05 0.19 2.18 29 Group

Mauritius MU 13.71 0.16 1.89 110 Group

Mayotte YT 13.71 0.16 1.89 110 Group

Mexico MX 100.00 0.72 2.73 5 Country

Federated States of 

Micronesia FM 13.71 0.16 1.89 110 Group

Republic of Moldova MD 16.47 0.22 1.82 29 Group

Monaco MC 18.63 0.24 1.41 47 Group

Mongolia MN 10.40 0.10 2.03 119 Group

Montenegro ME 16.47 0.22 1.82 29 Group

Montserrat MS 12.56 0.13 1.52 29 Group

Morocco MA 10.04 0.10 2.43 3 Country

Mozambique MZ 15.05 0.19 2.18 29 Group

Myanmar MM 11.01 0.11 2.38 44 Group

Namibia NA 15.05 0.19 2.18 29 Group

Nauru NR 13.71 0.16 1.89 110 Group

Nepal NP 11.01 0.11 2.38 44 Group

Netherlands NL 18.63 0.24 1.41 47 Group

Netherlands Antilles AN 12.56 0.13 1.52 29 Group New Caledonia NC 13.71 0.16 1.89 110 Group

New Zealand NZ 38.33 0.36 0.8 41 Group

Nicaragua NI 12.56 0.13 1.52 29 Group

Niger NE 15.05 0.19 2.18 29 Group

Nigeria NG 15.05 0.19 2.18 29 Group

Niue NU 13.71 0.16 1.89 110 Group

Norfolk Island NF 13.71 0.16 1.89 110 Group

Northern Mariana 

Islands MP 13.71 0.16 1.89 110 Group
48

Norway NO 18.63 0.24 1.41 47 Group

Oman OM 11.05 0.10 1.61 87 Group

Pakistan PK 9.71 0.10 2.34 23 Country

Palau PW 13.71 0.16 1.89 110 Group

Occupied Palestinian 

Territory PS 11.05 0.1 1.61 87 Group

Panama PA 29.45 0.38 1.14 4 Group

Papua New Guinea PG 13.71 0.16 1.89 110 Group

Paraguay PY 100.00 0.63 1.83 13 Group

Peru PE 51.50 0.50 1.62 33 Country

Philippines PH 15.95 0.18 1.53 31 Country

Pitcairn PN 13.71 0.16 1.89 110 Group

Poland PL 18.63 0.24 1.41 47 Group

Portugal PT 18.84 0.30 0.64 Country

Puerto Rico PR 12.56 0.13 1.52 29 Group

Qatar QA 11.05 0.10 1.61 87 Group

Reunion RE 13.71 0.16 1.89 110 Group

Romania RO 17.50 0.24 2.6 6 Country

Russian Federation RU 29.74 0.36 2.43 21 Group

Rwanda RW 15.05 0.19 2.18 29 Group

Saint Helena SH 18.63 0.24 1.41 47 Group

Saint Kitts and Nevis KN 12.56 0.13 1.52 29 Group

Saint Lucia LC 12.56 0.13 1.52 29 Group

Saint Pierre and 

Miquelon PM 100.00 0.61 1.63 49 Group

Saint Vincent and the 

Grenadines VC 12.56 0.13 1.52 29 Group

Samoa WS 13.71 0.16 1.89 110 Group

San Marino SM 13.23 0.18 1.6 43 Group

Sao Tome and Principe ST 15.05 0.19 2.18 29 Group

Saudi Arabia SA 11.05 0.10 1.61 87 Group

Senegal SN 15.05 0.19 2.18 29 Group

Serbia RS 16.47 0.22 1.82 29 Group

Seychelles SC 13.71 0.16 1.89 110 Group

Sierra Leone SL 15.05 0.19 2.18 29 Group

Singapore SG 16.04 0.18 1.51 32 Group

Slovakia SK 16.47 0.22 1.82 29 Group

Slovenia SI 16.47 0.22 1.82 29 Group

Solomon Islands SB 13.71 0.16 1.89 110 Group

Somalia SO 15.05 0.19 2.18 29 Group

South Africa ZA 15.05 0.19 2.18 29 Group

South Georgia and the 

South Sandwich Islands GS 18.63 0.24 1.41 47 Group

Spain ES 18.63 0.24 1.41 47 Group

Sri Lanka LK 11.01 0.11 2.38 44 Group

Sudan SD 15.05 0.19 2.18 29 Group
49

Suriname SR 100.00 0.63 1.83 13 Group

Svalbard and Jan Mayen SJ 18.63 0.24 1.41 47 Group

Swaziland SZ 15.05 0.19 2.18 29 Group

Sweden SE 18.63 0.24 1.41 47 Group

Switzerland CH 18.63 0.24 1.41 47 Group

Syrian Arab Republic SY 11.05 0.10 1.61 87 Group

Taiwan TW 12.54 0.10 1.36 27 Country

Tajikistan TJ 16.37 0.20 1.63 15 Group

United Republic of 

Tanzania TZ 15.05 0.19 2.18 29 Group

Thailand TH 16.04 0.181.51 32 Group

Timor-Leste TL 13.71 0.16 1.89 110 Group

Togo TG 15.05 0.19 2.18 29 Group

Tokelau TK 13.71 0.16 1.89 110 Group

Tonga TO 13.71 0.16 1.89 110 Group

Trinidad and Tobago TT 12.56 0.13 1.52 29 Group

Tunisia TN 16.16 0.23 1.99 22 Group

Turkey TR 10.97 0.10 1.52 81 Country

Turkmenistan TM 16.37 0.20 1.63 15 Group

Turks and Caicos Islands TC 12.56 0.13 1.52 29 Group

Tuvalu TV 13.71 0.16 1.89 110 Group

Uganda UG 15.05 0.19 2.18 29 Group

Ukraine UA 29.74 0.36 2.43 21 Group

United Arab Emirates AE 11.05 0.10 1.61 87 Group

United Kingdom GB 18.63 0.24 1.41 47 Group

United States US 100.00 0.61 1.63 49 Group

United States Minor 

Outlying Islands UM 100.00 0.61 1.63 49 Group

Uruguay UY 100.00 0.63 1.83 13 Group

Uzbekistan UZ 16.37 0.20 1.63 15 Group

Vanuatu VU 13.71 0.16 1.89 110 Group

Venezuela VE 100.00 0.67 1.89 8 Group

Viet Nam VN 13.71 0.16 1.89 110 Group

British Virgin Islands VG 12.56 0.13 1.52 29 Group

U.S. Virgin Islands VI 12.56 0.13 1.52 29 Group

Wallis and Futuna WF 13.71 0.16 1.89 110 Group

Western Sahara EH 16.16 0.23 1.99 22 Group

Yemen YE 15.05 0.19 2.18 29 Group

Zambia ZM 15.05 0.19 2.18 29 Group

Zimbabwe ZW 15.05 0.19 2.18 29 Group

Saint Barthelemy BL 12.56 0.13 1.52 29 Group

Saint Martin (France) MF 12.56 0.13 1.52 29 Group

U.S. Earthquake Region 

California XF 38.53 0.36 0.80 39 Country
50

* Refer to the PAGER website (http://earthquake.usgs.gov/eqcenter/pager/) 

 for the most recent version of appendix II.
Appendix III. An Automated Alerts and Comments Development 

Methodology for the lossPAGER System

The USGS PAGER system currently provides the estimates of total population exposed to different 

levels of shaking intensity along with maps presented in an expanded form on the USGS Earthquake 

Hazards Program website (http://earthquake.usgs.gov/pager/). Although the population exposure estimates 

provide a useful indicator of an earthquake’s potential impact, adding the fatality estimate based alert would 

provide more actionable information for emergency response. In order to provide this information, we 

propose the development of alert schema as described below. The uncertainty associated with median 

fatality estimates is represented in terms of probabilistic assessment of the fatalities being in different alert 

threshold on a global scale. We propose the color schema in terms of Green, Yellow, Orange and Red which 

are also commonly used for other natural perils. In addition to alert levels which are assigned based on 

median fatality deaths, we also suggest confidence levels denoting the level of uncertainty associated with 

the model’s fatality estimates. The uncertainty estimates are presented using a bar scale of Green, Yellow, 

Orange and Red alerts indicating the probability of different alerts for a given earthquake. 

G Green No deaths

Y Yellow 1 to 100 deaths

O Orange 100 to 1,000 deaths

R Red > 1,000 deaths 

While developing the comments for internal lossPAGER system, we propose a combination of alert 

levels and confidence levels to generate automated comments. These comments will be based on a range of 

factors apart from the fatality estimates obtained using the empirical model. These factors include

population exposure, country or group based model, and fatality estimates from other models. The comment 

development algorithm is flexible; including accommodating results from the other two PAGER loss models 

(semi-empirical and analytical) when applicable. 

• Uncertainty Estimation and illustration: 

a) Estimating upper and lower bound ranges for actual deaths

If Ω = median estimated deaths from the model and ‘ξ’ is the standard deviation of log-residual error 

(logarithmic ratio of estimated death and recorded deaths) which is normally distributed, then the 

probability of actual deaths d being less than certain bound b is

− ( ) log(b) log(Ω) P d ≤ b = Φ    

Implementation:

Probability P of actual death ‘d’ is

P = Φ ((log(D)-log(Ω))/ ) 

If D = Upperbound deaths, then we get P= Probability of upper bound deaths

If D = Lowerbound deaths, then we get P= Probability of lower bound deaths 

It is desirable to allow D to span an order of magnitude. We can estimate the probability associated 

with one order of magnitude above or below for the median estimate of Ω= 500, by substituting

D(lower) = Ω /10 = 50, and D(higher)= Ω *10= 5000.

51
b) Estimating the probability of actual deaths being in different thresholds:

Probability that the actual death d may be between a and b is given as

P(a < d ≤ b) log(b) − log(Ω) log(a) − log(Ω) = Φ  − Φ       

Implementation:

For alert level purposes, we can use the predefined alert threshold and estimate the probability of 

being in different alert levels using the followingdeathrange = [ 1 10 100 1000 10000 100000] is the range to be used for defining the alert levels 

then,

P(k) = Φ ((log(deathrange(k+1))-log(Ω))/ ξ) - Φ ((log(deathrange(k))-log(Ω))/ ξ);

The median death estimate Ω will determine the alert symbol and we can choose two alert thresholds (one 

above and one below, based on Ω) to estimate the probability of actual deaths being in those (upper and 

lower) alert thresholds. We illustrate the impact level assessment using the median fatality estimate and 

associated uncertainty in Figure III. The plot shows an alert symbol with color indicating level of impact 

along the vertical scale showing 0 to 1,000,000+ fatality range with green, yellow, orange and red levels. On 

the left hand side of scale, we show the probability of deaths being in a given alert threshold. For example, 

for an earthquake with zero median estimated deaths, there is 64% likelihood that the earthquake will have

green alert diminishing to 36% for yellow alert. Figure III (a to d) shows different alert thresholds and 

demonstrate the likelihood estimates. The definition of alert thresholds used herein (0 deaths for green level; 

1 to 10 & 10 to 100 for yellow; 100 to 1,000 for orange level, and 1,000 or greater for red alert level) are 

preliminary. Marano et al.(2009) are using past fatal earthquake data to investigate the development of alert 

thresholds that are not just limited to the fatality ranges but rather provide a more comprehensive 

assessment including exposure and financial losses. The development of such alert schema applicable at a 

global scale is not only crucial for future PAGER alerts and associated uncertainty estimation but also for 

the international disaster response agencies for their response planning.

52
(a) (b)

(c) (d)

Figure III. Illustration of various impact levels and associated uncertainty depiction for 

hypothetical scenarios. 

53
54

Table 1. List of countries with 10 or more fatalities due to any single earthquake since 1900. For 

each country, it also shows the total number of earthquake shaking-related fatalities and the 

number of fatal earthquakes since 1900.

Serial 

no. Country

Maximum

shaking 

deaths (10 or 

more) due to 

any single 

earthquake

since 1900 

Total shaking 

deaths by all 

earthquakes 

since 1900 

Total fatal

(one or more 

deaths)

earthquakes 

since 1900 

Average 

shaking 

deaths per

earthquake 

since 1900 

1 China 242,800 604,330 122 4,954 

2 Pakistan 87,351 153,586 21 7,314 

3 Iran 45,000 161,215 75 2,150 

4 Turkey 32,968 85,182 64 1,331 

5 Italy 32,610 36,169 18 2,009 

6 Chile 28,000 28,718 24 1,197 

7 Armenia 25,000 25,000 1 25,000 

8 India 20,023 52,189 17 3,070

9 Tajikistan 15,000 27,050 7 3,864 

10 Nepal 10,700 12,330 4 3,083 

11 Nicaragua 10,000 10,017 3 3,339

12 Mexico 9,500 11,941 33 362 

13 Argentina 8,000 8,147 8 1,018 

14 Ecuador 6,000 7,269 15 485 

15 Indonesia 5,749 10,870 62 175 

16 Japan 5,502 6,499 43 151 

17 Afghanistan 4,000 8,404 27 311 

18 Algeria 3,500 7,422 14 530 

19 Taiwan 3,276 7,850 38 207 

20 Turkmenistan 3,257 3,668 3 1,223

21 Yemen 2,800 2,811 2 1,406 

22 Guatemala 2,000 25,103 12 2,092 

23 Russian Federation 1,989 1,997 3 666 

24 Philippines 1,621 2,980 27 110 

25 Romania 1,581 2,598 5 520 

26 Peru 1,400 2,566 41 63 

27 Colombia 1,185 2,643 19 139 

28 El Salvador 950 1,584 7 226 

29 Greece 800 1,313 25 53 

30 Morocco 631 635 3 212 

31 Egypt 552 576 4 144 

32 Bulgaria 500 713 4 178 

33 Kazakhstan 450 467 4 117 

34 Guinea 443 443 1 443 

35 Venezuela 240 340 8 43 

36 Congo; DR 200 210 3 70 

37 Serbia 156 157 2 79 
55

38 Georgia 114 132 6 22 

39 Bolivia 105 108 3 36 

40 Costa Rica 75 114 7 16

41 United States 65 270 18 15 

42 Kyrgyzstan 61 102 2 51 

43 Portugal 56 80 4 20 

44 Bangladesh 50 61 5 12 

45 Ethiopia 40 70 2 35 

46 Myanmar 38 81 7 12 

47 Macedonia 35 35 1 35 

48 

Bosnia and 

Herzegovina 20 29 2 15 

49 Iraq 20 20 1 20 

50 Albania 18 19 2 10 

51 Papua New Guinea 15 37 9 4 

52 Australia 12 12 1 12 

53 South Africa 12 18 3 6 

54 Panama 11 15 3 5 
56

Table 2. Fatality estimation using empirical model for earthquakes since January 2008.

 

Date/time Country Magnitude Highest 

intensity

Total 

exposure

(>VI)

Recorded 

total deaths

Estimated 

shaking

deaths

Jan 01, 2008 at 06:32:33 Kyrgyzstan 5.7 VI 1,184,285 0 0 

Jan 01, 2008 at 18:55:03 Papua New Guinea 6.3 VII 60,745 0 0 

Jan 01, 2008 at 19:13:08 Papua New Guinea 5.8 VI 9,360 0 0 

Jan 03, 2008 at 11:15:48 Indonesia 5.5 VI 257 Unknown* 0 

Jan 04, 2008 at 07:29:18 Indonesia 6 VII 209,263 0 2 

Jan 06, 2008 at 05:14:17 Greece 6.1 VII 108,533 0 0 

Jan 07, 2008 at 03:12:26 Indonesia 5.9 VIII 86,940 Unknown 1

Jan 08, 2008 at 19:23:37 Indonesia 5.8 VIII 5,392 Unknown 0

Jan 09, 2008 at 08:26:45 China 6.4 IX 2,303 Unknown 1

Jan 13, 2008 at 12:15:40 Philippines 5.7 VII 247,585 Unknown 0

Jan 14, 2008 at 13:38:38 India 5.8 VI 2,263 0 0

Jan 16, 2008 at 11:54:44 China 5.9 VII 666 0 0

Jan 22, 2008 at 07:55:53 Wallis and Futuna 6 VII 189 Unknown 0

Jan 22, 2008 at 10:49:26 Wallis and Futuna 6.1 VII 189 Unknown 0

Jan 22, 2008 at 17:14:57 Indonesia 6.2 VIII 343,275 1 11

Jan 22, 2008 at 18:43:33 China 5.4 VII 174 Unknown 0

Jan 24, 2008 at 22:29:52 Papua New Guinea 5.9 VII 103,954 Unknown 0

Jan 30, 2008 at 07:32:47 Indonesia 6.2 VII 3,796 Unknown 0

Feb 03, 2008 at 07:34:12 Congo, D.R. 5.9 VIII 1,051,474 44 5 

Feb 04, 2008 at 17:01:29 Chile 6.3 VII 222,838 Unknown 2 

Feb 07, 2008 at 07:50:55 Indonesia 5.8 VII 40,084 Unknown 0 

Feb 09, 2008 at 07:12:06 Mexico 5.1 VII 104,068 Unknown 1 

Feb 11, 2008 at 18:29:30 Mexico 5.1 VII 76,664 Unknown 1 

Feb 12, 2008 at 01:29:40 Indonesia 5.5 VI 30,939 Unknown 0 

Feb 12, 2008 at 04:32:39 Mexico 5 VII 44,605 0 1 

Feb 12, 2008 at 12:50:20 Mexico 6.4 VII 179,622 0 2 

Feb 13, 2008 at 19:58:44 Indonesia 6.2 IX 1,241 Unknown 0

Feb 14, 2008 at 10:09:23 Greece 6.9 VII 46,340 Unknown 0
57

Feb 14, 2008 at 12:08:56 Greece 6.2 VIII 100,050 Unknown0

Feb 15, 2008 at 10:36:19 Lebanon 5 VI 93,474 Unknown0

Feb 19, 2008 at 17:01:29 Indonesia 5.3 VII 11,026 Unknown0

Feb 20, 2008 at 08:08:32 Indonesia 7.4 VIII 105,6793 18

Feb 20, 2008 at 18:27:11 Greece 6.2 VII 28,742 Unknown0

Feb 21, 2008 at 21:41:41 Indonesia 5.6 VII 53,959 Unknown0

Feb 21, 2008 at 23:55:36 Indonesia 5.7 VIII 33,506 Unknown1

Feb 23, 2008 at 07:17:09 Indonesia 5.4 VI 10,073 Unknown0

Feb 24, 2008 at 04:36:28 Indonesia 5.5 VI 284,588 Unknown0

Feb 24, 2008 at 08:53:38 Indonesia 5.4 VII 29,974 Unknown0

Feb 24, 2008 at 14:46:23 Indonesia 6.4 VIII 63,342 Unknown4

Feb 24, 2008 at 14:57:31 Indonesia 5.5 VII 42,711 Unknown 0 

Feb 25, 2008 at 08:36:35 Indonesia 7 VIII 338,143 0 11

Feb 25, 2008 at 18:06:05 Indonesia 6.4 VIII 60,821 Unknown 3 

Feb 25, 2008 at 21:02:20 Indonesia 6.6 VIII 68,502 Unknown 7 

Feb 26, 2008 at 18:18:31 Indonesia 5.8 VI 110 Unknown 0 

Mar 01, 2008 at 19:51:59 Chile 5.7 VII 119,991 Unknown 1 

Mar 03, 2008 at 02:37:27 Indonesia 6.1 VIII 41,409 Unknown 4 

Mar 12, 2008 at 11:23:34 Vanuatu 6.4 VIII 10,273 Unknown 0 

Mar 12, 2008 at 11:36:55 Vanuatu 6.3 VII 11,001 Unknown0

Mar 20, 2008 at 14:10:39 Philippines 6 VI 4,365 Unknown0

Mar 20, 2008 at 22:33:01 China 7.2 VII 32,165 Unknown0

Mar 20, 2008 at 23:12:02 China 5.5 VII 3,618

Unknown

0

Mar 26, 2008 at 20:06:05 Guam 5.6 VI 6,670 Unknown0

Mar 28, 2008 at 22:41:32 Philippines 5.8 VIII 7,254 Unknown0

Mar 29, 2008 at 08:09:46 Philippines 5.5 VII 4,74500

Mar 29, 2008 at 17:30:51 Indonesia 6.3 VIII 28,506 Unknown0

Apr 02, 2008 at 08:48:49 Indonesia 5.7 VI 51,895 Unknown0

Apr 09, 2008 at 11:13:20 Vanuatu 6.4 VI 11,918 Unknown0

Apr 09, 2008 at 11:23:40 Vanuatu 6.3 VI 5,272 Unknown0

Apr 09, 2008 at 12:46:12 Vanuatu 7.3 VII 26,47000

Apr 09, 2008 at 14:47:50 Vanuatu 6.3 VI 20,277 Unknown 0 

Apr 15, 2008 at 03:03:10 Guatemala 6.1 VI 107,135 Unknown 0 

Apr 19, 2008 at 03:12:28 Indonesia 6.1 VIII 31,538 Unknown 0 
58

Apr 19, 2008 at 10:21:12 Indonesia 6 VII 14,155 Unknown0

Apr 20, 2008 at 13:01:21 Indonesia 5.6 VI 4,844 Unknown0

Apr 23, 2008 at 18:28:42 Taiwan6 VI 11,372 Unknown0

Apr 23, 2008 at 14:05:42 Indonesia 5.5 VI 3,768 Unknown0

Apr 28, 2008 at 18:33:30 Vanuatu 6.4 VI 5,99200

Apr 29, 2008 at 05:26:04 Japan 5.8 VI 3,223 Unknown0

May 03, 2008 at 03:53:34 Indonesia 5.3 VI 19,421 Unknown0

May 03, 2008 at 19:01:46 Papua New Guinea 5.8 VII 13,239 Unknown0

May 07, 2008 at 16:45:19 Japan 6.8 VI24,19900

May 12, 2008 at 06:28:01 China 7.9X 33,506,961 87,652 66,191

May 12, 2008 at 06:43:14 China 6 VIII 752,642 Unknown3

May 12, 2008 at 09:42:25 China 5.5 VII 410,714 Unknown 0 

May 12, 2008 at 06:54:18 China 5.7 VII 1,145,759 Unknown 3 

May 12, 2008 at 11:11:02 China 5.8 VIII 392,136 Unknown 0 

May 12, 2008 at 20:08:48 China 5.6 VII 116,642 Unknown 0 

May 13, 2008 at 07:07:08 China 5.8 VIII 63,284 Unknown 0 

May 13, 2008 at 10:29:19 Indonesia 5.4 VI 2,740 Unknown 0 

May 14, 2008 at 02:54:38 China 5.5 VII 27,598 Unknown 0 

May 16, 2008 at 05:25:47 China 5.6 VII 28,997 Unknown 0 

May 17, 2008 at 17:08:25 China 5.7 VII 152,591 Unknown0

May 18, 2008 at 12:17:23 Indonesia 5.7 VII 117,664 Unknown0

May 19, 2008 at 14:26:45 Indonesia 6 VIII 246,257 Unknown2

May 20, 2008 at 17:08:00 Indonesia 5.7 VII 145,628 Unknown1

May 24, 2008 at 13:24:06 Solomon Islands 5.9 VIII 4,880 Unknown0

May 24, 2008 at 19:20:47 Colombia 5.9 VII 998,1716 14

May 25, 2008 at 08:21:49 China 6 VIII 273,2136 32

May 27, 2008 at 08:37:51 China 5.7 VII 109,024 Unknown0

May 26, 2008 at 15:01:36 Costa Rica 5.6 VII 251,273 Unknown0

May 29, 2008 at 15:46:00 Iceland 6.3 VIII 12,15500

Jun 01, 2008 at 01:57:23 Philippines 6.3 VII 10,29500

Jun 01, 2008 at 09:42:32 Indonesia 5.5 VI 198 Unknown0

Jun 01, 2008 at 10:33:28 Indonesia 5.8 VIII 5,574 Unknown 0 

Jun 03, 2008 at 16:20:51 Solomon Islands 6.2 VI 14,545 0 0 

Jun 03, 2008 at 17:31:31 Indonesia 5.9 VI 25,197 Unknown 0 
59

Jun 03, 2008 at 21:03:46 Indonesia 5.8 VI 4,412 Unknown0

Jun 03, 2008 at 22:04:27 Indonesia 6 VI 6,970 Unknown0

Jun 03, 2008 at 22:49:57 Indonesia 5.5 VII 15,034 Unknown0

Jun 06, 2008 at 20:02:57 Algeria 5.5 VI 897,210 Unknown5

Jun 08, 2008 at 12:25:30 Greece 6.3 IX 504,0612 11

Jun 13, 2008 at 23:43:46 Japan 6.9 IX 2,083,576 13 22

Jun 14, 2008 at 00:20:13 Japan 5.5 VII 73,871 Unknown0

Jun 18, 2008 at 13:13:08 Indonesia 5.4 VI 1,375 Unknown0

Jun 22, 2008 at 07:22:06 Solomon Islands 5.8 VIII 7,485 Unknown0

Jun 25, 2008 at 02:53:29 Indonesia 5.6 VII 120,301 Unknown0

Jun 25, 2008 at 15:41:27 Papua New Guinea 5.7 VI 5,840 Unknown0

Jul 01, 2008 at 00:17:29 Peru 5.5 VII 6,597 Unknown 0 

Jul 08, 2008 at 07:42:10 Japan 6 VI 15,391 6 0 

Jul 14, 2008 at 04:44:55 Indonesia 5.5 VI 10,348 Unknown 0 

Jul 15, 2008 at 03:26:36 Greece 6.4 VI 14,271 Unknown 0 

Jul 23, 2008 at 15:26:19 Japan 6.8 VII 3,861,300 1 0 

Jul 23, 2008 at 19:54:45 China 5.5 VII 78,854 Unknown 0 

Jul 24, 2008 at 01:43:17 Russian Federation 6.2 VII 455 Unknown 0 

Jul 24, 2008 at 07:09:30 China 5.7 VII 95,969 1 0 

Jul 29, 2008 at 18:42:15

U.S. Earthquake Region 

California 5.4 VI 1,172,066 Unknown0

Aug 01, 2008 at 08:32:43 China 5.7 VII 209,50321

Aug 04, 2008 at 15:16:53 Papua New Guinea 5.5 VI 3,361 Unknown0

Aug 05, 2008 at 09:49:17 China 6 VIII 171,0693 15

Aug 06, 2008 at 22:41:01 Indonesia 5.9 VIII 14,35200

Aug 12, 2008 at 05:25:58 Solomon Islands 5.9 VII 1,260 Unknown0

Aug 15, 2008 at 10:25:16 Philippines 6 VII 154,743 Unknown0

Aug 16, 2008 at 04:01:10 Russian Federation 5.7 VII 495 Unknown0

Aug 21, 2008 at 12:24:31 China 6 VIII 45,499 3 1 

Aug 25, 2008 at 13:21:59 China 6.7 IX 3,657 Unknown 4 

Aug 26, 2008 at 03:07:30 Indonesia 5.7 VI 852 Unknown 0 

Aug 25, 2008 at 02:43:09 Philippines 5.8 VI 526,993 0 0 

Aug 25, 2008 at 11:25:16 New Zealand 5.5 VII 15,630 0 0 

Aug 27, 2008 at 01:35:32 Russian Federation 6.2 VIII 44,829 0 3 
60

Aug 27, 2008 at 21:52:38 Iran 5.8 VIII 47,534 Unknown 18

Aug 30, 2008 at 06:54:07 Papua New Guinea 6.4 VI 133,825 0 0

Aug 30, 2008 at 08:30:54 China 5.9 VIII 166,839 38 2

Aug 31, 2008 at 08:31:10 China 5.5 VII 103,560 Unknown 0

Sep 08, 2008 at 03:03:16 Vanuatu 6.2 VI 22,582 0 0

Sep 08, 2008 at 18:52:08 Vanuatu 6.9 VI 6,157 0 0

Sep 10, 2008 at 11:00:34 Iran 6.1 IX 43,310 7 295

Sep 10, 2008 at 16:12:04 Chile 5.8 VII 4,346 0 0

Sep 11, 2008 at 00:00:03 Indonesia 6.6 VI 164,487 0 0

Sep 11, 2008 at 00:20:52 Japan 6.9 VI 378 0 0

*Unknown- Referred as unknown fatalities at the time of this investigation.
61

(A) 

1900

1921

1942

1963

1984

2005

Afghanistan

China

India

Indonesia

Iran

Japan

Pakistan

Philippines

Turkey

1

10

100

1,000

10,000

100,000

1,000,000

Year

Cumulative 

Fatalities

Afghanistan China India Indonesia Iran

Japan Pakistan Philippines Turkey

(B) 

Figure 1. A) Shaking-death distribution for earthquakes, 1900-2008 by country, and B) 

cumulative earthquake mortality recorded since 1900 for selected countries. 
62

(a) (c) 

(b) (d)

Figure 2. Fatality estimation using lognormal distribution and different norms for global 

earthquakes with 10 or more deaths recorded between 1973 and 2007.
63

Figure 3. Lilliefors goodness of fit test for lognormal distribution using L2G norm for global 

earthquakes between 1900 and 2008. 
64

Figure 4. Empirical model derived from fatal earthquakes in Indonesia. Earthquakes with zero 

recorded deaths were plotted at 0.1 deaths for viewing purposes. 
65

Figure 5. Empirical model derived from fatal earthquakes in India. Earthquakes with zero 

recorded deaths were plotted at 0.1 deaths for viewing purposes. 
66

Figure 6. Fatality estimation using empirical loss modeling for Slovenia. Earthquakes with zero 

recorded deaths were plotted at 0.1 deaths for viewing purposes.
67

Figure 7. Empirical model derived from fatal earthquakes in Chile. Earthquakes with zero 

recorded deaths were plotted at 0.1 deaths for viewing purposes. 
68

Figure 8. Empirical model derived from fatal earthquakes in Georgia. 
69

Figure 9. Empirical model derived from fatal earthquakes in Greece. 
70

Figure 10. Empirical model derived from fatal earthquakes in Algeria. 
71

Figure 11. Empirical model derived from fatal earthquakes in Italy. 
72

Figure 12. Empirical model derived from fatal earthquakes in Japan. 
73

Figure 13. Empirical model derived from fatal earthquakes in Pakistan. 
74

Figure 14. Empirical model derived from fatal earthquakes in Peru. 
75

Figure 15. Empirical model derived from fatal earthquakes in Philippines. 
76

Figure 16. Empirical model derived from fatal earthquakes in Romania. 
77

Figure 17. Empirical model derived from fatal earthquakes in Turkey. 
78

Figure 18. Comparison of fatality rate among different countries including the expertjudgment-based fatality rates (v1.0) for the USA without California, Canada and Australia group.