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Article

A Global Expected Risk Analysis of Fatalities, Injuries, and Damages by Natural Disasters

1
College of Architecture, Planning and Public Affairs, University of Texas at Arlington, Arlington, TX 76019, USA
2
Faculty of Urban Management and Studies, City University of Macau, Taipa, Macau 999078, China
3
College of Design, Jiangnan University, Wuxi 214122, China
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(7), 2573; https://0-doi-org.brum.beds.ac.uk/10.3390/su10072573
Submission received: 10 July 2018 / Revised: 18 July 2018 / Accepted: 20 July 2018 / Published: 23 July 2018

Abstract

:
Natural disasters are hazardous geophysical, meteorological, hydrological, climatological, and/or biological events that disturb human and natural environments, causing injuries, casualties, property damages, and business interruptions. Sound analysis is required regarding the effective hazard preparedness for, response to, mitigation of, and recovery from natural disasters. This research proposes an expected risk analysis model of world natural disasters recorded for 1900–2015 in the Emergency Disaster Database compiled by the Centre for Research on the Epidemiology of Disaster. The model produces consistent estimates of country-level risks in terms of human casualty and economic loss. The expected risks, along with their standard deviations, and ranks for world 208 countries, are analyzed with highlights for the top 10, 20 and 30 countries. Normalized expected risks by country population density and per capita gross domestic product (GDP) are also analyzed to further understand the relationships between risks and socio-economic measures. The results show that the model is a reasonably effective alternative to the existing risk analysis methods, based on the high correlations between the observed and estimated total risks. While riskier countries with higher expected risks and standard deviations are found in all continents, some developing countries such as China, India, Bangladesh, and Brazil, or developed countries, such as the United States, Japan, and Germany, are the hot-spots of global natural disasters. The model can be used as a new alternative approach to conduct country-level risk assessments or risk analyses of fatality, injured, affected, and damage—especially for countries’ governments to make sound disaster preparation, and mitigation decisions, sustainable policies, or plans regarding natural disasters.

1. Introduction

Natural hazards occur frequently around the world. Some natural hazards turn into disasters after they cause tangible (or physical) impact, such as human fatalities and property damages; and intangible (non-physical) impacts, such as psychological, mental, and political wounds [1]. Research has shown that human casualties and economic losses due to natural disasters have been increasing over the last five decades [2,3]. This trend is likely to continue due to growing urbanization, rising populations, deepening industrialization, and worsening global environmental and climate change [4]. For example, from 1950 to 1979, 1779 natural disasters occurred across the world, causing 4,860,449 casualties, 1,372,606 injuries, and $78 billion in property damages. However, from 1980 to 2015 the reported number of natural disasters increased to 11,494 or about 6.5 times as many as those in the period of 1950–1979: Causing 2,599,237 fatalities, 6,354,195 injuries and $2.71 trillion in property damages [5].
In order to effectively prepare for, respond to, and recover from natural disasters, good hazard risks and assessments at various spatial and temporal scales are essential [6,7]. This research proposes and applies a new quantitative model to assess physical impacts of world natural disasters as risks at the country-level, using the international Emergency Disaster Database (EM-DAT)—compiled by the Centre for Research on the Epidemiology of Disaster (CRED) [8]. The model takes country-specific posterior data on natural disasters for the period of 1900–2015, including disaster occurrences, classifications, and disaster impacts, and produces disaster risk estimates, rankings, and correlations to country socio-economic attributes. The proposed model is validated by Pearson and Spearman correlations, for the values and ranks of the estimated and observed total disaster impacts. The country-level risk assessment may be coarse due to using ‘country’ as the spatial unit, especially for large countries in which intra-country variability may be quite high. However, it is still worthy to reveal inter-country differences, especially global hot-spots, in risk impacts for historically reported natural disasters in the world. A comparative global risk analysis provides useful insights for country-specific or global humanitarian policies toward minimizing natural disaster impacts, and promoting sustainable development. Perhaps more importantly, as the literature review section illustrates below, while various sub-country spatial scales have been used [1], using country as the spatial scale is relatively rare in disaster risk research [4,5], especially from the global country comparative perspective for multiple natural disasters over the previous 115 years.

2. Literature Review

2.1. Hazards, Disasters, and Risks

A hazard is an extreme and severe event, that could potentially pose a risk to human beings and their settlements [9,10]. According to CRED, natural hazards are classified into geophysical, meteorological, hydrological, climatological, and biological categories in Table 1. Geophysical hazards take the forms of earthquakes, tsunamis, mass movement, and volcanic eruptions. Meteorological hazards are storms, extreme temperature, and fogs. Climatological hazards consist of drought, glacial lake outbursts, and wildfires. Hydrological hazards include floods, landslides, and wave actions. Finally, biological hazards contain epidemics, insect infestation, and animal accidents. This research focuses on these five natural disasters, and their physical impacts or risks: Fatality, injured, affected, and damage—as defined in CRED and summarized in Table 1.

2.2. Risk and Risk Assessment Models

While global natural disaster databases, such as EM-DAT, are vital, the stochastic nature of hazard occurrences and impacts make a reliable risk assessment essential for decision makers. They help formulate and adopt risk-related policies, as well as assist emergency planners and professionals to effectively and efficiently mitigate disasters. Smith [11] defines risk as probability of a specific hazard occurrence. Likewise, Cooney [12] describes risk as function of probability and magnitude of different impacts. In general, a risk analysis for a location starts with the area’s historical hazards, disasters, and impacts. Risk analysis involves risk determination, which is to identify the types of hazards and measure their potential risk levels in terms of disaster impacts [13,14].
Various descriptive or quantitative risk models of natural hazards exist in the literature, including risk/hazards approach [15]; political ecology approach [16]; pressure and release approach [17]; hazard-of-place approach [18]; and vulnerability/sustainability approach [19]. Furthermore, a spatial risk model was developed by Shen and Hwang [20]; a exposure model of global natural hazards by Bono and Mora [21]; a macro framework for measuring vulnerability by Joseph [22]; a natural hazard geoportal by Giuliani and Peduzzi [23]; a global risk index model by Peduzzi et al. [24]; a global natural disaster risk model by Dilley et al. [25]’ a mapping of global hazard datasets by Peduzzi and Herold [26]; and a global natural disaster assessment model by Berke [27].
Liu et al. [28] documented the quantitative models mostly for global hazard risk analysis, including the risk index approach and the mathematical statistics approach. These approaches are mostly based on statistics and spatial analysis, and hence, can be used to index, rank, estimate or predict hazard risks. More extensive discussions on analytical risk models and frameworks can be found in Cox [29], Schmidt et al. [30] and Greenberg and Cox [31]. Some mitigation-based research for specific disasters can be found in Masuya et al. [32] on shelter-residence match for flood evaluation; Sohn et al. [33] on cost-benefit of retrofitting a transportation network under an earthquake; and Shen and Aydin [34] on reconstruction schedules for high freight flow movement after hurricane Katrina.
However, the applications of these quantitative hazard models either focus on a country or a region of the world. Our literature review indicates that there is a lack of risk assessment of natural disasters at the global scale, especially from a comparison and contrast perspective for multiple physical impacts over time. Therefore, this research aims to fill this gap by examining global risks based on country-level fatalities, injuries, people affected, and damages caused by natural disasters of the world from 1900–2015. A country-level risk assessment may be coarse, especially for large countries (in which intra-country variability may be quite high), but it is still reveals inter-country differences—especially regarding global hot-spots—in risk impacts for historically reported natural disasters in the world. Such a comparative global risk analysis also provides useful insights for country-specific or global humanitarian policies toward minimizing natural disaster impacts, and sustainable development.

3. Methods and Materials

The methodology consists of a set of notations in Figure 1 and a risk assessment model, in which we consider country k as the spatial unit, and hazard occurrences i for the natural disaster group m = 1. This includes sub-groups g, with each resulting in some disaster impacts j, including fatality, injury, affected, and damage. Note that the model collapses all time units (e.g., year) into the 1900–2015 period. Therefore, the model primarily is a spatial one for estimating cumulative expected risks for countries based on multiple impacts. Although disasters happen seemingly randomly, the model does not consider temporal variations, nor spatial variations within country over the period. The model’s main purpose is to estimate country-level expected risks in fatalities, injuries, people affected, and economic damages.

3.1. Expected Risk Model

Let O i j g k be the occurrence i of subgroup g under the natural hazard causing disaster impact of type j in country k for the period of 1900–2015. Such an ex ante occurrence generated disaster impact r i j g k . Define the physical loss l g k as the impact reported either as human fatality, people injured, people affected, or property damage in EM-DAT. We have:
P j g k = i O i j g k / i g O i j g k
l j g k = i r i j g k
The expected risk for disaster impact j for a country k, R j k is the sum of the products of each possible natural disaster impact times its associated probability—it is a weighted average of the various possible natural disaster impacts, with the weights being their probabilities of occurrence in country k:
R j k = g P j g k l j g k
A percentage of the expected risk for country k, E j k , relative to the world, can be calculated as:
E j k = R j k / k R j k
The dispersion of the expected risks are important to the understanding of disaster impacts. The tighter the dispersion, the more likely it is that the actual disaster impact will be close to the expected risk. Consequently, the less likely it is that the actual disaster loss will end up far below or above the expected loss. Thus, the less spread around the expected risk means the lower the disaster impact for a country.
The standard deviation, σ j k , is commonly used as the measure of the tightness of the probability distribution around the expected or mean risk. The standard deviation is a probability-weighted average deviation from the estimated impact, and it gives an idea of how far above or below a disaster impact is likely to be.
σ j k = [ g ( l j g k - R j k ) 2 P j g k ] 1 / 2
Therefore, the natural disaster risk of country k is based not only on its expected impact R j k in Equation (3), but also its deviation σ j k in Equation (5). A 95% confidence (p < 0.05) would provide the low-bound risk (Low-Rjk) with larger of the 0 or R j k − 2 σ j k and the upper bound risk (High-Rjk) with R j k + 2 σ j k .
Another useful measure of expected risk is the coefficient of variation, C V j k which is the standard deviation divided by the expected risk. This reflects the standard deviation per unit of expected risk. It provides another meaningful basis for comparison when the disaster risks on different spaces are not the same:
C V j k = σ j k / R j k
The Equations (1)–(6) can be applied to all countries based on their historical impacts caused by natural disasters. Since countries vary in social-economic-physical features, it would be meaningful to see the normalized expected risks, standard deviations, and coefficients of variations by these features, such as population, gross domestic product (GDP), and land area.
R j t k = R j k / S t k
σ j t k = σ j k / S t k
where R j t k and σ j t k are normalized expected risks and standard deviations. S t k = social-economic features with t = population, land area, GDP, population density (PD), or per capita GDP (PG). For example, R F P D k = R F a t a l i t y k / S P D k and σ F P G k = σ F a t a l i t y k / S P G k represent the expected fatalities normalized by population density and per capita GDP, respectively. In order to have a comprehensive assessment of a nation’s risk in the event of a natural disaster, the country’s expected fatality, injury, people affected, and damage, as well as their standard deviations, percentages, low and upper bounds, and ranks should all be considered. In general, the larger a country’s expected risk is, together with wider ranges, larger percentages, and higher ranks with expected and normalized risks, the riskier the country is expected to be.

3.2. Data Preparation

Three types of databases were used as inputs for the risk assessment model. First, the natural disaster database was obtained from EM-DAT, a global disaster database published by CRED in Brussels [8]. EM-DAT was compiled from various sources, including UN agencies, non-governmental organizations, insurance companies, research institutes and press agencies and organized for disaster groups for natural disasters, technological disasters, and complex disasters—each of which is further split into subgroup, type, and subtype [5]. Each natural disaster contains important disaster—human injuries, fatalities, people affected, and damages—by space (e.g., continent, region, country) and time (e.g., year). Second, the world country boundary GIS (Geographic Information System) database was drawn from Global Administrative Unit Layers (GAUL) for the world developed by the Food and Agriculture Organization (FAO) of the United Nations [35]. This world boundary data was used together with the EM-DAT database for the model. Third, the world social-economic attributes of historical country population, area, and GDP data were extracted from the World Development Indicators database by the World Bank [36].
Figure 2 outlines the necessary steps for database processing. The first step is to process tables of human injuries, fatalities, people affected, and damages by country and by year for natural disasters using the EM-DAT database for the period of 1900 to 2015. The second step is to calculate probability weighted risk impacts, their standard deviations, ranks, percentages, and correlations of socio-economic attributes by country. This step also involves validation using the observed natural disaster data and the results for the model using scatter plots and Pearson correlation and Spearman rank order correlation. The third step is to perform GIS functions using “Summarize”, “Join”, and “Field Calculator” on the tables produced in the first and second steps, and the World Boundary GIS layer. This step also produces spatial visualizations of natural disaster risks by country.

4. Results and Discussion

Expected risks and their relevant measures for ranking, dispersion, percentage, and correlation for the 208 countries selected in this study are summarized in Table 2, Table 3, Table 4 and Table 5. This only lists the top 30 countries, according to expected fatality, injured, affected, and damage.

4.1. Fatality

Table 2 ranks the top 30 countries according to the expected fatality (RkF) and its normalized expected fatality (RkF-PG, RkF-PD). The table also lists rank (RankkF), dispersion kI, Low-RkI, High-RkI), coefficient of variance (CVkF), and total observed fatality and estimated fatality for each of the 208 countries.
Firstly, the top 30 countries in expected fatality spread over the global continents. With China, India, Bangladesh top all in Asia; Russia and Italy in Europe; Uganda, Niger, and Ethiopia sit high in Africa; and Guatemala, Peru, Chile and the United States hold the top spots in Americas. While Asia has the most countries ranked in the top 30 and had the most deaths, Oceania has no country listed. Secondly, China is particularly deadly with almost 50% of world fatalities, followed by India and Bangladesh at almost 17% and 9%, respectively. Thirdly, the dispersion measures are quite wide and large for top countries, but their spreads per unit expected fatality are quite different (e.g., smaller for China with CV = 0.86 and Uganda with CV = 0.72, but larger for India with CV = 1.76, Russia with CV = 1.56, Ethiopia with CV = 2.08, and Haiti with CV = 2.49). Finally, the ranks of expected deaths and ranks of normalized expected fatalities correspond, as do the observed and estimated total fatalities.

4.2. Injured

Similar to the results of expected fatalities discussed just above, Table 3 ranks the top 30 countries according to their expected injuries (RkI) and their normalized expected injuries (RkI-PG, RkI-PD). The table also lists rank (RankkI), dispersion kI, Low-RkI, High-RkI), coefficient of variance (CVkI), and total observed and estimated injuries.
Firstly, top 30 countries in expected injuries are found in all continents, with China, Bangladesh, and Indonesia leading in Asia; Russia, Macedonia, and Italy in Europe; Sudan and Algeria in Africa; Peru, Guatemala, Haiti, El Salvador; and the United States in Americas. Again, Asia has the most countries ranked in the top 30 and the most deaths, while Oceania has no country listed. Secondly, China and Bangladesh are particularly vulnerable to injuries, with each having over 25% of the world total, followed by Peru, Indonesia, and Iran with 13.13%, 9.38%, and 4.48%, respectively. Thirdly, the dispersion measures are quite wide and large for top 30 countries, but their spreads per unit expected injuries are quite different (e.g., small for China with CV = 0.77 and Bangladesh with CV = 0.63, but large for Peru with CV = 2.10, Haiti with CV = 3.35, and India with CV = 2.53). Finally, the ranks of expected injuries and ranks of normalized expected injuries are quite similar, as are the total observed and estimated injuries.

4.3. Affected

Table 4 shows the top 30 countries in which people were affected by natural disasters. There are several numbers worth noting in this table. Firstly, Asian countries are expected to produce the highest expected number of people affected in the event of a disaster, For example, India had the number of people affected with more than 429 million (or 32.11%), followed by China with over 286 million (or 21.43%). These countries, alongside Bangladesh (9.74%), Philippines (6.69%), and Pakistan (3.24%) make up the top 5. The top 30 rankings also included: Brazil, the United States, Colombia, Argentina, Cuba, Mexico, Peru, and Chile in Americas; and Ethiopia, Kenya, Mozambique, Madagascar, Sudan, Niger, and Nigeria in Africa. Secondly, Australia is the only country from Oceania; as is Russia from Europe, in the top 30. Thirdly, again, Asia is the top continent with 11 countries, including the top 7, in the top 30. Fourthly, the dispersion measures are relatively significant, but the standard deviation per unit of expected people affected are relatively small, especially the top 10 with CV = 0.53 for the United States and CV = 0.86 for China, except for Ethiopia with CV = 1.98. Finally, the ranks for expected people affected normalized by population density and per capita GDP are very similar, so are the observed and estimated total people affected.
These results show that casualties, injuries, and people affected by natural disasters are highly correlated to population size, distribution, and density in general [17], and perhaps more to vulnerable population groups in particular [37].

4.4. Damage

Table 5 provides the top 30 countries which suffered severe physical damage by natural disasters. The damages were measured by monetary value, in $1000 U.S. dollars. It should be noted that the United States, which was ranked 21st in expected fatalities; 15th in expected injuries, 9th in people affected, received the highest ranking with 37.63% of the world expected damages, followed by Mexico, Chile, Brazil, Canada, Cuba and Argentina in Americas. In addition, some new faces from developed countries—for example: Italy, Germany, France, United Kingdom in Europe; and Australia and New Zealand in Oceania—also appeared in the top 30 countries. Many Asian countries, such as China, Japan, China (12.14%), Japan (11.30%), Thailand, India, Koreas, were in the top 30. The ranges are higher for the top countries, while the dispersions of per unit expected damage are fairly small (e.g., the United States with CV = 0.45), except New Zealand with CV = 1.86 and Japan with CV = 1.16. Finally, the ranks for the expected damages and the normalized expected damages quite match in values, so do the magnitudes for the total observed and estimated damages.
Properties are used by people for social, cultural, and economic activities. Therefore, property damages are presumably highly correlated with population size, distribution, and density. While many countries with high fatalities and injuries are also high in property damages, Table 5 shows this is not always the case. For example, the United States ranks quite differently in the categories of fatality, injury, and in property damage, as do some other developed nations. This observation is partially related to property damage valuation parities, or metrological differences [38].

4.5. Model Performance

The similar ranks between the expected risks and the normalized expected risks and the similar and values between the total observed and estimated risks indicate that the model performs reasonably well. We used two methods to evaluate the model’s performance: the Spearman rank correlation, and the Pearson correlation. Both are shown in scatter plots, with correlation and R and R2 values, in Figure 3. The Spearman rank correlations were calculated for Rankkj vs. Rankkj-PG, Rankkj-PD. Whereas, the Pearson correlations were computed for estimated and observed totals for fatality, injured, affected, and damage. It is believed that the higher the R and R2 values, the better the correlations, and the better the model’s performance.
Shown in Figure 3, the Spearman rank correlations for the expected risks and normalized expected risks are very high, ranging from the low 0.904 for fatality (a) RankkF vs. RankkF-PD to the high 0.972 for affected (c) RankkA vs. RankkA-PG. The Pearson correlations are also very high, ranging from the low 0.863 for affected (g) to the high 0.966 for damage (h). It is interesting to note that from Figure 3 (a–d) the lower or higher ranked countries in particular have much higher correlations. This is especially true, due to the small but similar expected and normalized expected risks for countries ranked after 150 for injured (b), and after 175 for damaged (d).
The scatter plots Figure 3 (e–h) show all observed and estimated data points, including top ranked countries with extremely large total risks. These top hotspots may be statistically regarded as outliers or influential points, but they are not removed due to their apparent importance for global risk analysis, especially for ranking.

4.6. Natural Disaster Hot-Spots

Table 6 summarizes the percentage shares of the expected and normalized expected fatality, injured, affected, and damage by the top 10, 20, and 30 countries, and then the remaining 178 countries. Their percentage shares of total estimated and observed disaster impacts are also shown in Table 6. The top 10, 20, and 30 countries respectively accounted for more than 90%, 95%, and 97% for expected fatality (RkF); 88%, 94%, and 97% for expected injury (RkI); 81%, 88%, and 91% for expected people affected; and 74%, 85%, and 91% for expected damage. Meaning that if historical trends and patterns continue, more than 91% of losses from future natural disasters are expected to happen in those 30 nations, especially those in the top 10. The remaining 178 countries only shared 2.52%, 2.92%, 8.13%, and 8.82% of the expected risks, implying quite a few countries, especially smaller ones, did not experience many natural disasters.
The similar patterns can be found in percentage shares of expected risks normalized by: Population density and per capita GDP, total observed and estimated risks, and the risk dispersion (σkj). Referencing to the specific top 30 countries in Table 3, Table 4, Table 5 and Table 6, we can say that the natural disaster hotspots are mostly in Asia (e.g., China, India, Bangladesh), a few in North America (e.g., the United States), some in Europe (e.g., Russia, Italy, Germany), and Africa (e.g., Ethiopia, Sudan, Niger, Algeria). While Oceania and small countries are relatively safe places for natural disasters.
Figure 4 illustrates the spatial distribution of expected disaster risks at the country-level. The countries, which have a higher number of expected fatalities, injuries, people affected and damages, are represented in darker brown colors.

5. Conclusions and Remarks

A natural hazard (such as floods, earthquakes, and hurricanes) is an event that happens in ‘mother nature’. However, a natural disaster causing deaths, injuries, and property losses occurs through interactions between natural and the man-made environments. No country can be immune from natural hazards. However, some countries are suffered more from natural disasters than others. One of the major findings of this research is that natural disasters occurred during the period of 1900 to 2015 severely impacted human lives and economies of countries—especially the top 10, 20, and 30 countries ranked by expected risks. These top hot spots are also large, populated, developed, or rapidly developing ones. For example, the United States received the highest ranking in terms of natural disaster occurrence. This was followed by China, India, the Philippines, Indonesia, Bangladesh, and Japan—all of which are located in Asia. The nations suffering the most from fatalities were China, India, Russia, Bangladesh, and Ethiopia; while the highest number of injuries happened in Peru, China, Bangladesh, Haiti, Indonesia, Japan, and India. Also, the United States was ranked first in terms of damage, which was followed by Japan, China, Italy and Germany. All of these nations with high damages also had high gross domestic products. These results indicate that natural disasters happened in nations across every continent (e.g., Asia, Europe, Africa, and America), even though some countries experienced more losses than others. These findings support the research by Dilley [25], and Giuliani and Peduzzi [23]. Perhaps more important is the high correlations between the expected risk and the socio-economically normalized expected risks: Indicating not only the relevance of the population density and per capita GDP for risk analyses, but the success of this model’s performance.
The model calculates expected human fatalities, injures, people affected, and economic damages along with their relevant percentages, ranges, and ranks, for 208 countries in the world. Scatter plots and Spearman rank correlations between expected risks and normalized expected risks indicate that the model perform well. In addition, the scatter plots and Pearson correlations showed that the total estimated human casualties and economic losses aligned with the corresponding observed disaster totals. For example, China, ranked first in terms of the ‘real’ number of fatalities from natural disasters, and also held the first position in ‘expected’ risk of fatalities. Similarly, India and Bangladesh, which were the second and fourth nations in terms of the ‘real’ fatalities, and took the second and third rankings, respectively, in the total estimated fatalities. Furthermore, the United States, Japan, and China, which had the first, second, and third rankings in the total observed economic damages, respectively, were ranked first, third, and second in the total estimated economic damages. The results and rankings from this model are synthetic in nature and similar to Munich [39] on natural hazard index, and Dilley [25] on natural disaster hotspot analysis, hence, cross-model comparative studies may be warranted.
Finally, the expected risk model, based upon the widely used EM-DAT historical natural disaster data, can be used as a new alternative approach to conduct country-level risk assessments—or risk analyses of fatality, injured, affected, and damage, especially for counties’ governments to make sound disaster preparation and mitigation decisions, policies, or plans regarding natural disasters. Local governments at the state, provincial, or municipal levels in a country can also use the model, if disaster datasets for jurisdiction-specific natural disaster information is available. The model can also be tested for specific natural disasters, especially floods, earthquakes, and hurricanes, so that more specific prevention, evaluation, mitigation, and recovery policies or plans can be made and implemented to minimize risks or losses from natural disasters. However, the reliability and validity of EM-DATA are critical for all these efforts. Therefore, cross-database comparative studies, especially for databases with similar spatial and temporal coverages as used in Giuliani and Peduzzi [20], and Gregorowski et al. [40], are also imperative.

Author Contributions

G.S. provided the initial concept, research design, and wrote the manuscript. L.Z. helped data collection, analysis approach, and revision of the manuscript. Y.W. and Z.C. helped in results validation and revision of the manuscript. All authors read and approved the final manuscript.

Funding

This research is funded by National Natural Science Foundation of China (51708399) and Macao Foundation (MF1710).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Modelling process and notation.
Figure 1. Modelling process and notation.
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Figure 2. Modeling process and notation.
Figure 2. Modeling process and notation.
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Figure 3. Scatter plots with Pearson and Spearman rank correlation values. (a) the Spearman rank correlation between the total observed and estimated fatality risk; (b) the Spearman rank correlation between the total observed and estimated injure risk; (c) the Spearman rank correlation between the total observed and estimated affected risk; (d) the Spearman rank correlation between the total observed and estimated damage risk; (e) the Pearson correlation between the total observed and estimated fatality risk; (f) the Pearson correlation between the total observed and estimated injure risk; (g) the Pearson correlation between the total observed and estimated affected risk; (h) the Pearson correlation between the total observed and estimated damage risk.
Figure 3. Scatter plots with Pearson and Spearman rank correlation values. (a) the Spearman rank correlation between the total observed and estimated fatality risk; (b) the Spearman rank correlation between the total observed and estimated injure risk; (c) the Spearman rank correlation between the total observed and estimated affected risk; (d) the Spearman rank correlation between the total observed and estimated damage risk; (e) the Pearson correlation between the total observed and estimated fatality risk; (f) the Pearson correlation between the total observed and estimated injure risk; (g) the Pearson correlation between the total observed and estimated affected risk; (h) the Pearson correlation between the total observed and estimated damage risk.
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Figure 4. Spatial distribution of expected natural disaster impacts by country. (a) the spatial distribution of the expected disaster risk of fatalities; (b) the spatial distribution of the expected disaster risk of injuries; (c) the spatial distribution of the expected disaster risk of people affected; (d) the spatial distribution of the expected disaster risk of damages.
Figure 4. Spatial distribution of expected natural disaster impacts by country. (a) the spatial distribution of the expected disaster risk of fatalities; (b) the spatial distribution of the expected disaster risk of injuries; (c) the spatial distribution of the expected disaster risk of people affected; (d) the spatial distribution of the expected disaster risk of damages.
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Table 1. Definition and classification of natural hazards and impacts of natural disasters.
Table 1. Definition and classification of natural hazards and impacts of natural disasters.
HazardClassificationDefinitionDisaster Type
Natural Hazard TypesGeophysicalA hazard originating from solid earth. This term is used interchangeably with the term geological hazard.Earthquake, mass movement, volcanic activity
MeteorologicalA hazard caused by short-lived, micro- to meso-scale extreme weather and atmospheric conditions that last from minutes to days.Extreme temperature, fog, storm
HydrologicalA hazard caused by the occurrence, movement, and distribution of surface and subsurface freshwater and saltwater.Flood, landslide, wave action
ClimatologicalA hazard caused by long-lived, meso- to macro-scale atmospheric processes ranging from intra-seasonal to multi-decadal climate variability.Drought, extreme temperature, glacial lake outburst, wildfire
BiologicalA hazard caused by the exposure to living organisms and their toxic substances (e.g., venom, mold), or vector-borne diseases that they may carry. Examples are venomous wildlife and insects, poisonous plants, and mosquitoes carrying disease-causing agents such as parasites, bacteria, or viruses (e.g., malaria).Epidemic, insect infestation, animal accident
TermDefinition
FatalityNumber of people who lost their life because of natural hazards
InjuredPeople suffering from physical injuries, trauma or an illness requiring medical treatment as a direct consequence of a disaster.
AffectedSum of injured, homeless (number of people whose house is destroyed or heavily damaged and therefore need shelter after an event), and affected (people requiring immediate assistance during a period of emergency).
DamageThe amount of damage to property, crops, and livestock. In the Emergency Disaster Database (EM-DAT) estimated damages are given in US$ (‘000). For individual disaster, the registered figure corresponds to the damage value at the moment of the event.
Table 2. Expected fatalities and relevant statistics for top 30 countries.
Table 2. Expected fatalities and relevant statistics for top 30 countries.
Country/RegionRankkFRkFσkFEkFLow-RkFHigh-RkFCVkFRkF-PDRankkF-PDRkF-PGRankkF-PGObservedEstimated
China13,113,3952,680,81149.49%08,475,0170.8622,7402622.7112,720,32615,566,976
India21,047,3031,838,98416.65%04,725,2721.7631434361.129,124,2425,236,517
Bangladesh3554,033575,9058.81%01,705,8431.0454117291.632,992,5802,770,164
Russia4476,958741,7077.58%01,960,3731.5656,995153.673,930,0262,384,792
Uganda5100,77072,4551.60%0245,6800.728441172.06204,697503,848
Indonesia687,18883,0171.39%0253,2230.956821227.29241,331435,941
Iran779,75451,1341.27%0182,0230.641913511.415156,242398,769
Niger875,67646,3971.20%0168,4700.617655394.64194,964378,378
Japan966,68573,5071.06%0213,6991.10198292.432244,348333,425
Ethiopia1063,167131,6071.00%0326,3812.08952990.25416,201315,835
Cape Verde Is1147,28228,1580.75%0103,5980.604532133.8885,286236,409
Burma1245,13455,5690.72%0156,2711.236461525.110146,128225,669
Turkey1344,51831,9450.71%0108,4070.72494196.62292,106222,589
Italy1437,51144,9070.60%0127,3251.20194311.442140,517187,554
Pakistan1534,59749,0360.55%0132,6691.421683316.511174,187172,983
Philippines1630,54616,8580.49%064,2620.55102406.62369,809152,729
Guatemala1727,38222,0670.44%071,5170.81243256.72184,048136,910
Peru1826,65928,1050.42%082,8691.05121175.22496,019133,293
Chile1922,21222,8840.35%067,9801.03104282.23461,219111,059
Sudan2021,44043,7680.34%0108,9752.041303611.316162,688107,198
United States2120,74892760.33%219539,3000.45670140.55743,625103,738
Venezuela2219,12591010.30%92337,3270.48678134.02831,28495,624
Mozambique2315,58532,1150.25%079,8162.066351613.014105,98577,926
Haiti2412,42830,9070.20%074,2422.4942567.819249,11162,142
Nigeria2512,14779510.19%028,0500.65854513.51224,38860,737
Vietnam2611,92171400.19%026,2020.6047534.82526,15359,605
Hong Kong (China) 2711,11286800.18%028,4710.7821270.46424,57555,558
Colombia2810,43599970.17%030,4280.96273241.73933,56452,173
Burkina Faso29829061490.13%020,5890.74163347.52017,24841,449
Honduras30812594420.13%027,0091.16124363.13028,48640,623
Note: RankkF = ranking based on expected fatality; RkF-PG = expected fatality/per capita gross domestic product (GDP); RkF-PD = expected fatality/population density; RankkF-PG = ranking for expected fatality normalized by per capita GDP; RankkF-PD = ranking for expected fatality normalized by population density; observed/estimated = recorded/calculated total fatalities of all natural disasters in a country.
Table 3. Expected injuries and relevant statistics for top 30 countries.
Table 3. Expected injuries and relevant statistics for top 30 countries.
Country/Region RankkIRkIσkIEkILow-RkIHigh-RkICVkIRkI-PDRankkI-PDRkI-PGRankkF-PGObservedEstimated
China 1518,101400,31725.80%01,318,7350.7737842103.621,677,3102,590,505
Bangladesh2516,754327,20225.73%01,171,1590.635059272.011,044,2072,583,772
Peru3263,601552,61513.13%01,368,8322.1011,970151.742,064,1251,318,006
Indonesia4188,347114,1079.38%0416,5610.611473458.93431,603941,737
Iran590,02959,7434.48%0209,5150.662160312.97173,400450,147
Japan648,08359,7902.39%0167,6641.24143191.720255,298240,417
Turkey747,92334,7432.39%0117,4080.7253177.2998,352239,614
Philippines844,43532,1952.21%0108,8250.72149179.78209,289222,176
Pakistan929,93450,3361.49%0130,6061.681451814.35159,919149,670
Chile1028,15229,3621.40%086,8771.04132152.81677,400140,761
Guatemala1126,05229,8071.30%085,6651.14231146.41078,486130,259
Haiti1221,17770,8581.05%0162,8933.35712813.26582,590105,884
India1318,32246,3700.91%0111,0622.5355306.311244,89591,612
El Salvador1411,34012,6050.56%036,5501.1135362.41847,30456,701
United States15943648690.47%019,1740.52305130.25027,84747,181
Brazil16901033500.45%230915,7110.37408121.22315,10045,050
Colombia17838466530.42%021,6900.79219151.32124,11841,921
Sudan18766972750.38%022,2190.95466104.01419,21638,347
Russia19696969290.35%020,8280.9983360.83129,43734,846
Mexico20681811,7070.34%030,2321.72125200.83236,68434,088
Macedonia FRY21681260750.34%018,9620.8984261.02520,06234,058
Vietnam22662743560.33%015,3390.6626422.71713,70233,134
Burma23637777910.32%021,9591.2291243.51520,76231,887
Algeria24571176080.28%020,9281.33413111.02621,61528,557
Dominican Rep25498840940.25%013,1760.8226410.82911,31624,940
Nicaragua26439174960.22%019,3841.71102221.91921,46721,957
Taiwan (China)27402241760.20%012,3731.046700.25620,40420,109
Italy28385849160.19%013,6891.2720510.16113,42819,288
Honduras29326345650.16%012,3931.4050321.32212,21716,313
Madagascar30323710420.16%115353220.32102214.013505016,186
Note: RankkI = ranking based on expected injuries; RkI-PG = expected injuries/per capita GDP, RkI-PD = expected injuries/population density; RankkI-PG = ranking for expected injuries normalized by per capita GDP; RankkI-PD = ranking for expected injuries normalized by population density; observed/estimated = recorded/calculated total injuries of all natural disasters in a country.
Table 4. Expected people affected and relevant statistics for top 30 countries.
Table 4. Expected people affected and relevant statistics for top 30 countries.
Country/Region RankkARkAσkAEkALow-RkAHigh-RkACVkARkA-PDRankkA-PDRkA-PGRankkA-PGObservedEstimated
India1429,191,905303,911,86832.11%01,037,015,6410.711,288,1773147,99711,066,5322,145,960
China 2286,388,736245,383,23921.43%0777,155,2130.862,091,717157,27831,321,4531,431,944
Bangladesh3130,170,085123,868,5609.74%0377,907,2050.95127,1973268,5112423,956650,850
Philippines489,426,69048,834,6766.69%0187,096,0430.55299,8591419,4415186,737447,133
Pakistan543,274,10325,104,1413.24%093,482,3850.58209,8252220,607486,670216,371
Vietnam636,607,54020,594,7162.74%077,796,9710.56142,9383014,643784,807183,038
Thailand735,345,77019,738,5202.64%074,822,8100.56281,0971547761690,770176,729
Brazil822,774,07416,452,8191.70%055,679,7120.721,030,699429972273,371113,870
United States911,289,9095,981,0710.84%023,252,0520.53364,349112996527,61256,450
Ethiopia1011,065,84321,900,3040.83%054,866,4521.98166,7952715,808669,58755,329
Colombia1110,592,7474,124,4180.83%2,343,91118,841,5840.39276,7461616813116,97052,964
Sri Lanka1210,381,7814,355,2560.83%1,671,27019,092,2920.4233,6835428062425,19951,909
Kenya1310,056,92315,231,0440.83%040,519,0111.51168,8282610,057857,05450,285
Indonesia149,015,8835,540,5150.83%020,096,9140.6170,5043928172328,80345,079
Korea (North)158,363,3353,476,0490.83%1,411,23715,315,4320.4243,6174764331116,07441,817
Argentina167,656,8224,802,2570.83%017,261,3370.63530,67776844814,74538,284
Cambodia177,462,1024,992,1820.83%017,446,4670.6797,3203739271719,89137,311
Madagascar187,244,7522,430,9400.83%2,382,87212,106,6320.34228,709209056913,27136,224
Cuba196,709,6274,048,7230.83%014,807,0730.6065,3474123142613,74533,548
Mozambique206,486,1786,858,9750.83%020,204,1281.06264,1031754051431,10032,431
Sudan216,360,1859,367,3670.83%025,094,9191.47386,4891033471938,50831,801
Japan226,056,1993,977,1890.83%014,010,5770.6617,952702157118,86430,281
Mexico235,554,3003,915,2700.83%013,384,8400.70101,965366175118,76627,771
Peru244,922,9713,551,5130.83%012,025,9970.72223,552219654518,74324,615
Australia254,558,3114,563,9920.83%013,686,2961.001,729,12121578216,13722,792
Niger264,519,1918,097,8290.83%020,714,8481.79457,147956491325,37722,596
Nigeria274,474,1243,906,0890.83%012,286,3020.8731,3445749711513,53422,371
Russia284,105,7325,043,2890.83%014,192,3101.23490,61884615730,08720,529
Chile293,963,8953,622,4040.83%011,208,7030.91185,969234005911,56119,819
Turkey303,942,5222,532,5150.83%09,007,5520.6443,7054658852884719,713
Note: RankkA = ranking based on expected people affected; RkA-PG = expected people affected/per capita GDP, RkA-PD = expected people affected/population density; RankkA-PG = ranking for people affected normalized by per capita GDP; RankkA-PD = ranking for people affected normalized by population density; observed/estimated = recorded/calculated total affected of all natural disasters in a country.
Table 5. Expected damage and relevant statistics for top 30 countries.
Table 5. Expected damage and relevant statistics for top 30 countries.
Country/RegionRankkDRkDσkDEkDLow-RkDHigh-RkDCVkDRkD-PDRankkD-PDRkD-PGRankkD-PGObservedEstimated
United States1387,858,549174,002,83037.63%39,852,888735,864,2100.4512,517,008110,2612755,5811,939,293
China 2125,100,67590,084,81712.14%0305,270,3100.72913,706525,0201419,431625,503
Japan3116,493,701135,054,96311.30%0386,603,6261.16345,317941316429,770582,469
Italy426,420,87521,295,5762.56%069,012,0280.81136,905199902086,385132,104
Thailand526,285,52614,682,5852.55%055,650,6960.56209,043163552747,716131,428
Germany623,262,18013,908,2402.26%051,078,6600.60100,763248432258,091116,311
India721,437,91814,447,1922.08%050,332,3030.6764,344307392457,994107,190
Australia814,596,4619,458,8741.42%033,514,2090.655,536,93025033045,06472,982
France913,993,52210,414,9231.36%034,823,3670.74125,745215072839,63769,968
Mexico1013,574,91311,048,3211.32%035,671,5550.81249,2071215081641,09067,875
United Kingdom1113,457,7588,857,5921.31%031,172,9420.6654,360344863132,81667,289
Chile1213,341,04013,347,3421.29%040,035,7231.00625,906613481737,62766,705
Korea (North)1312,924,8145,371,3951.25%2,182,02323,667,6040.4267,406299942323,65364,624
Turkey1412,828,6898,850,3691.24%030,529,4270.69142,2141819151426,91064,143
Pakistan1511,630,5436,580,7971.13%024,792,1380.5756,394335538526,27858,153
Philippines1611,387,8886,219,3091.10%023,826,5070.5538,1853924761022,98456,939
Iran179,141,4225,037,1130.89%019,215,6480.55219,3251513061822,76545,707
Spain188,964,7256,924,6800.87%022,814,0850.77112,017224073728,39544,824
Russia198,066,4757,311,5980.78%022,689,6700.91963,91149062131,86040,332
Brazil207,856,8614,389,8330.76%016,636,5270.56355,582810341921,17839,284
Indonesia217,856,1285,443,9850.76%018,744,0980.6961,4353124551127,66339,281
Canada227,679,4676,341,3080.75%020,362,0840.832,316,59932584827,71238,397
Korea (South)237,598,4144,762,5900.74%017,123,5950.6315,319594273616,06737,992
Bangladesh246,251,7964,976,2610.61%016,204,3180.806109803290818,19131,259
Taiwan (China)256,186,0564,957,8500.60%016,101,7570.809662672644720,88430,930
Cuba266,084,8453,837,2520.59%013,759,3500.6359,2623220981211,64230,424
Argentina275,426,0513,402,1720.53%012,230,3940.63376,06774843210,39827,130
Oman284,951,00000.48%4,951,0004,951,0000.00339,0751037841495124,755
Vietnam294,649,4642,627,0270.45%09,903,5170.5718,1545518601510,61523,247
New Zealand304,588,0688,515,5660.45%021,619,2011.86302,424112125326,44322,940
Note: RankkD = ranking based on expected damage; RkD-PG = expected damage/per capita GDP, RkD-PD = expected damage/population density; RankkD-PG = ranking for expected damage normalized by per capita GDP; RankkD-PD = ranking for expected damage normalized by population density; observed/estimated = recorded/calculated total damage of all natural disasters in a country, in $1000.
Table 6. Shares of natural disaster impacts of the top 10, 20, and 30 countries.
Table 6. Shares of natural disaster impacts of the top 10, 20, and 30 countries.
SubgroupCountryRkjσkjRkj-PDRkj-PGObservedEstimated
Fatality Top 1090.06%91.01%86.87%86.45%92.92%90.06%
Top 2095.42%95.97%92.19%92.58%96.34%95.42%
Top 3097.48%97.86%94.66%95.57%98.13%97.48%
Remaining 1782.52%2.14%5.34%4.43%1.87%2.52%
Injured Top 1088.40%83.82%80.09%88.04%80.12%88.40%
Top 2094.63%93.94%90.00%94.06%94.43%94.63%
Top 3097.08%96.57%93.33%96.78%96.50%97.08%
Remaining 1782.92%3.43%6.67%3.22%3.50%2.92%
AffectedTop 1081.96%79.22%36.56%73.50%79.51%81.96%
Top 2088.24%84.54%47.40%82.92%85.00%88.24%
Top 3091.87%89.17%69.74%86.55%89.64%91.87%
Remaining 1788.13%10.83%30.26%13.45%10.36%8.13%
DamageTop 1074.61%74.29%70.80%49.48%74.77%74.61%
Top 2085.24%85.24%80.04%72.41%85.13%85.24%
Top 3091.18%91.99%92.32%83.02%91.72%91.18%
Remaining 1788.82%8.01%7.68%16.98%8.28%8.82%

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Shen, G.; Zhou, L.; Wu, Y.; Cai, Z. A Global Expected Risk Analysis of Fatalities, Injuries, and Damages by Natural Disasters. Sustainability 2018, 10, 2573. https://0-doi-org.brum.beds.ac.uk/10.3390/su10072573

AMA Style

Shen G, Zhou L, Wu Y, Cai Z. A Global Expected Risk Analysis of Fatalities, Injuries, and Damages by Natural Disasters. Sustainability. 2018; 10(7):2573. https://0-doi-org.brum.beds.ac.uk/10.3390/su10072573

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Shen, Guoqiang, Long Zhou, Yao Wu, and Zhiming Cai. 2018. "A Global Expected Risk Analysis of Fatalities, Injuries, and Damages by Natural Disasters" Sustainability 10, no. 7: 2573. https://0-doi-org.brum.beds.ac.uk/10.3390/su10072573

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