Hostname: page-component-7b7567c8c-lpg2g Total loading time: 0 Render date: 2026-11-15T17:12:28.203Z Has data issue: false hasContentIssue false
  • English
  • Français

Use of Big Data and Information and Communications Technology in Disasters: An Integrative Review

Published online by Cambridge University Press:  26 July 2018

Jeffrey D. Freeman,
Brigette Blacker,
Grace Hatt,
Sophia Tan,
Jeremy Ratcliff,
Thomas B. Woolf,
Craig Tower and
Daniel J. Barnett
Jeffrey D. Freeman
Affiliation:
Center for Humanitarian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
Brigette Blacker
Affiliation:
Masters in Public Health Program, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
Grace Hatt
Affiliation:
Masters in Public Health Program, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
Sophia Tan
Affiliation:
Masters in Public Health Program, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
Jeremy Ratcliff
Affiliation:
Program in Public Health Sciences, Johns Hopkins University Krieger School of Arts and Sciences, Baltimore, Maryland
Thomas B. Woolf
Affiliation:
Department of Physiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
Craig Tower
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
Daniel J. Barnett*
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
*
Correspondence and reprint requests to Daniel J. Barnett, Johns Hopkins Bloomberg School of Public Health, Department of Environmental Health and Engineering, 615 North Wolfe Street Room E7036, Baltimore, Maryland 21205 (e-mail: dbarnett@jhsph.edu).
Rights & Permissions [Opens in a new window]

Abstract

Novel approaches to improving disaster response have begun to include the use of big data and information and communication technology (ICT). However, there remains a dearth of literature on the use of these technologies in disasters. We have conducted an integrative literature review on the role of ICT and big data in disasters. Included in the review were 113 studies that met our predetermined inclusion criteria. Most studies used qualitative methods (39.8%, n=45) over mixed methods (31%, n=35) or quantitative methods (29.2%, n=33). Nearly 80% (n=88) covered only the response phase of disasters and only 15% (n=17) of the studies addressed disasters in low- and middle-income countries. The 4 most frequently mentioned tools were geographic information systems, social media, patient information, and disaster modeling. We suggest testing ICT and big data tools more widely, especially outside of high-income countries, as well as in nonresponse phases of disasters (eg, disaster recovery), to increase an understanding of the utility of ICT and big data in disasters. Future studies should also include descriptions of the intended users of the tools, as well as implementation challenges, to assist other disaster response professionals in adapting or creating similar tools. (Disaster Med Public Health Preparedness. 2019;13:353–367)

Keywords

information communication technologybig datadisastershumanitarian aidpublic health

Information

Type
Systematic Review
Information
Disaster Medicine and Public Health Preparedness , Volume 13 , Issue 2 , April 2019, pp. 353 - 367
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2018 

A disaster, as defined by the International Federation of the Red Cross and Red Crescent Societies, is “a sudden, calamitous event that seriously disrupts the functioning of a community or society and causes human, material, and economic or environmental losses that exceed the community’s or society’s ability to cope using its own resources.” 1 Though often considered rare or infrequent events, disasters are common and are a fundamental part of normal life. Between 2004 and 2014 alone, there were more than 6300 reported disasters globally, an average of more than 12 disasters per week.Reference Ager, Baillie Smith and Barbelet 2 These events have been driven, in part, by an increasing frequency and severity of extreme weather events,Reference Easterling 3 , Reference Than 4 a rise in intrastate conflicts, and increasing threats from emerging infectious diseases.Reference Jones, Patel and Levy 7 Mounting a coordinated and effective response to a growing number of disasters globally is difficult even under the best circumstances, but the challenges have grown more complex as the pace of urbanization quickens around the world. At present, more than 54% of the world’s population resides in urban environments, and that figure is expected to increase to 66% by 2050. 8 Humanitarian emergencies taking place in urban areas present a complex landscape for disaster response, with challenges such as dense populations, massive human displacement, industrial activities, security risks, rapid and dynamic transmission of disease, and a variety of potentially hazardous exposures.Reference Currion 9 Increasingly sophisticated approaches are required to address these challenges.

Constrained by a general lack of available resources 10 and challenged by an increasingly complex disaster landscape, the humanitarian community, including national and international nongovernmental organizations and United Nations agencies, has recently turned to novel information and communications technology (ICT) for improving disaster response and has even begun to explore the use of big data for informing, monitoring, and evaluating response efforts. Big data is commonly defined by the so-called 3 vees: volume, velocity, and variety.Reference Gandomi and Haider 11 That is, big data can be defined by the large magnitude of data (volume), the fast rate at which data is generated (velocity), or the structural heterogeneity within the data itself (variety). It is not surprising then that the humanitarian community would look to ICT tools and big data approaches to achieve a more efficient and effective disaster response. Disaster settings rapidly generate a massive quantity and variety of potentially relevant data, which must be quickly collected, analyzed, shared, and used by the humanitarian community. Despite the potential utility of ICT and big data, there remains a dearth of available literature evaluating the effectiveness of their uses in disaster response, and we could find no integrative review published in the scientific literature providing a comprehensive overview of ICT and big data applications in disasters. The objective of this review is to describe the current state of the art in ICT and big data applications in disaster settings and to determine potential avenues for future research and application.

METHODS

In order to examine how ICT tools and big data approaches have been used in disaster response, we conducted a structured literature search, with our final search taking place on May 1, 2017. Our title/abstract review included all studies identified by our search terms and published before our final search date. We did not place a limiter on date of earliest publication. Our search included the following databases: PubMed, Embase, ISI Web of Science, BIOSIS, LILACS, CINAHL, Scopus, and NIH Reporter. The general search terms for our study were created in consultation with medical librarians and a team of researchers with subject matter expertise in ICT, big data, and disasters. The final search terms were adapted to meet the search criteria requirements for each of the included databases (Table 1). No date limits were applied to the search. Title/abstract and full-text reviews were performed under dual review with conflicts resolved by a third reviewer. Inclusion/exclusion criteria for the review are provided in Table 2. We only considered articles that actively implemented ICT and big data tools in real-life disaster or simulation scenarios in order to examine the true utility of the tools being assessed. A data extraction tool was developed by the subject matter expert team and included the following items: first author and year, data type, disaster type, disaster setting (country), World Bank status 12 of the disaster setting, time of disaster, disaster phase, responders involved, type of ICT application, data input, data output, problems encountered, findings and significance, and additional comments. Because we were interested mainly in evaluating the contexts in which ICT and big data tools were used, as opposed to the quality of the methods used by study authors to evaluate them, we did not use quality assessment metrics in our inclusion and exclusion criteria.

Table 1

Included Databases, Search Terms, and Search Strategies

Table 2

Inclusion and Exclusion Criteria for Review

Abbreviation: ICT, information and communications technology.

Our first step in analysis was to calculate summary statistics on all the extracted data and create tables to better display patterns of research and of ICT and big data use in the various articles. We then performed 2 further steps. First, we pulled simple descriptive statistics on the nature of disaster research. These statistics focus on the nature of the research itself, including topics such as methods, location, and omitted information. Second, we conducted a review of the nature of the ICT and big data tools discussed in each article. This review identified the most commonly researched disaster management tools and described their applications in various disaster settings, making particular note of findings that the authors found to be surprising or unexpected.

RESULTS

Of the 1295 abstracts captured in our final search, 732 (56.5%) were excluded at the title/abstract screening level. From the 563 full texts screened, 450 (79.9%) were excluded for the following reasons: 305 (67.8%) did not apply ICT or big data tools to a real disaster or simulation, 45 (10%) articles were inaccessible, 41 (9.1%) were not relevant, 34 (7.6%) were duplicates, 13 (2.9%) were not original research, 7 (1.6%) applied to multiple disasters, and 5 (1.1%) were not in English. The PRISMA diagramReference Moher, Liberati and Tetzlaff 13 is presented in Figure 1.

Figure 1

PRISMA diagram

Descriptive Research Statistics

The final qualitative synthesis included 113 articles (see Table 3). The types of methodologies used in the studies we surveyed were distributed in roughly the same proportion across all ICT tool types, with slightly more studies using qualitative methods (n=45, 39.8%) than mixed methods (n=35, 31%) or quantitative methods (n=33, 29.2%). Half of the 113 included articles were published in the last 5 years (n=56, 49.6%), and only 1 article was published before 2005. Additionally, approximately 80% of the articles (n=88) on the application of ICT and big data tools covered only the response phase of a disaster, which demonstrates a lack of attention to the prevention/mitigation, preparedness, and recovery phases.

Table 3

Characteristics of the Studies Included in the Review (n=113)

Abbreviations: govt, government; NGO, nongovernmental organization; org, organization.

The included literature covered disasters in 26 different countries around the world. Of those 26 countries, only 4 were included in more than 5 articles. These most commonly cited countries included Taiwan (7 articles), China (10 articles), Japan (11 articles), and the United States (32 articles). Similarly, over half of the included articles (n=63, 56.1%) reviewed ICT or big data technology applications in high-income countries, and only 17 of the 113 articles (15%) reviewed tool use in lower- and middle-income countriesFootnote a (LMICs). 12

We also categorized articles by disaster type, intended user, and implementation challenges of each ICT or big data tool. The articles we found included 20 separate disaster types (including “Multiple” and “Other”). Hurricanes and earthquakes were addressed in 19 (16.8%) and 25 (22.1%) articles, respectively, and were the only disaster types included in more than 10 articles. Furthermore, only 73 of the included articles (64.6%) clearly and intentionally specified the intended users (responder type) of the ICT or big data tools they were analyzing. Most of these articles (n=47, 64.4%) listed either clinical first responders or community members as the intended users. Finally, over half of the reviewed articles (n=61, 54%) made no mention of challenges they faced while implementing their ICT or big data tools.

Descriptive ICT and Big Data Statistics

The ICT and big data tools used in the included literature can be broken down into 15 categories. The 4 most frequently mentioned ICT tools (geographic information system [GIS], social media, patient information collections/databases, and models/algorithms) are described in this section. A summary of all the ICT or big data tool categories can be found in Table 4, including the number of articles that reference the tool, the purpose of the tool, the disaster type that the tool was used before/during/after, the disaster phase that the tool was used in, and the World Bank status of the country where the tool was used.

Table 4

Disaster Research Summary

Abbreviation: ICT, information and communications technology.

GIS

The most commonly reviewed ICT tool in the included literature was GIS. The majority of articles analyzed GIS with the intent of assessing its ability to visually capture and convey real-time disaster information (n=10, 62.5%). However, a number of articles also analyzed the capacity of GIS technologies to capture visual information for monitoring or preparedness purposes—for example, to create landslide susceptibility mapsReference Wan, Yen and Lin 111 or to inform early warning systems.Reference Wang, Tuo and Zhang 113

Social Media

Social media tools were the second most commonly discussed tools in the included studies. Of the social media types mentioned, Twitter was analyzed the most often. Social media tools were most commonly used to assist disaster management personnel in disseminating critical emergency information to the public.Reference Low, Burdon and Christensen 79 , Reference Zhang, Zheng and You 120 However, several articles also highlighted the use of social media by the public to exchange information and communicate amongst themselves.Reference Cheng, Mitomo and Otsuka 33 , Reference Huang, Chan and Hyder 56 , Reference Keim and Noji 62 , Reference Leong, Pan and Ractham 73 , Reference Mittu, Guleyupoglu and Johnson 82 We also found that social media tools were used by disaster response professionals to collect data on the needs and behaviors of the public.Reference Black, Dietz and Stirratt 24 , Reference Huang, Chan and Hyder 56 , Reference Jia, Jia and Hsee 61 , Reference Liu, Piyawongwisal and Handa 78 , Reference Mersham 81 Liu 2011, for example, analyzed how an app utilizing Twitter and global positioning system can inform disaster responders about where to prioritize critical response resources. Additionally, Jia 2017 characterized how social media data can be collected and analyzed to better understand the behavior and psychological state of populations during and after a disaster.

Model/Algorithm

Most of the 11 reviewed papers on computer models and algorithms focused on disaster planning and preparedness topics. These technologies typically assisted disaster responders in better understanding general population movement and needs during disastersReference Chakraborty and Banerjee 30 , Reference Ergun, Gui and Heier Stamm 43 , Reference Fordis, Alexander and McKellar 46 and planning evacuation routes.Reference Benssam, Bendjoudi and Yahiaoui 21 , Reference Stephens, Jafari and Boyles 99 , Reference Zhang, Zhang and Zhang 122 Other uses for models and algorithms included monitoring damageReference Robinson and Brown 90 and automating disaster communication to the public.Reference Zhang, Si and Ma 118

Patient Health Information Collection/Database

These ICT and big data tools primarily focused on tracking and storing patient health data during or directly after a disaster. Many tools in this category can be classified as disaster medicine technologies. The vast majority of these articles discussed web-based electronic health reporting tools for the automated coordination of patient care by facilitating the collection of patients’ health information during a disaster and consolidating the data in real time for use by all other health care providers using the tool.Reference Brown, Griswold and Demchak 27 , Reference Callaway, Peabody and Hoffman 28 , Reference Lenert, Kirsh and Griswold 72 , Reference Zhao, Rafiq and Hummel 123

Technology Use in Crisis Settings

Only one included article explored ICT use in situations of armed conflict or prolonged displacement.Reference Mark, Al-Ani and Semaan 80 This qualitative study analyzed how Iraqi used technology such as cell phones, localized internet service providers, and devices with access to the internet to overcome limitations to face-to-face contact. This ethnography found that by adapting relationships and social networks using technologies, Iraqis were able to build resilience during the ongoing war and self-organize to protect themselves from security threats.

DISCUSSION

Our review of the relevant literature on the use of ICT and big data tools during disasters revealed some important gaps to be filled in future research. Specifically, more information is needed on the use of these technologies in all types of disasters, in critical geographic locations, and at all phases of the disaster cycle, as well as practical implementation considerations that could limit future applications.

Almost all articles included in our study discussed the use of ICT and big data tools in the context of natural disasters, and of those, hurricanes and earthquakes were the most highly represented. This was due mainly to the high number of articles concerning Hurricane Katrina in 2005, the Haiti earthquake in 2010, and the Great East Japan Earthquake in 2011. While 40% of the total worldwide deaths due to natural disasters between 1995 and 2015 were caused by storms, 27% were due to extreme temperatures and 26% were due to flooding. 127 Our analysis suggests that further research is needed to determine the utility of ICT and big data tools in managing these types of disasters, which caused the majority of disaster deaths during that time frame.

Furthermore, the majority of articles identified in our literature search analyzed the use of ICT or big data tools in high income countries. By contrast, 59% of the deaths from weather-related disasters from 1995 to 2015 occurred in LMICs. 127 In fact, the numbers of deaths in low-income countries may itself be underreported. 127 This is an important problem that, while not the focus of this study, could be addressed by the types of tools our study addresses. A clear next step for researchers is to analyze the utility of ICT and big data tools in the geographic contexts where they are needed most for both disaster response and documentation.

Over three-quarters of the articles reported on the use of ICTs or big data in the response phase of the disaster cycle. The use of ICT and big data in other parts of the disaster cycle is therefore not well understood. More focus is needed in the literature on the ways that ICT or big data can be used during and across the phases of the disaster cycle.

The literature we reviewed also generally lacked important information on 2 important practical limitations of the tools used in disasters. First, most authors did not specify or were ambiguous about who were the end users of the ICT or big data tools under study. In order to assess the value of these or similar tools in the future, other researchers will need to know the intended users of a given tool, or at least the profession or education level of intended users.

Second, many of the included articles did not discuss the challenges posed in implementing the ICT or big data tools that they studied. There are 2 key reasons why future research should include this information. First, potential implementers should be aware of the logistical challenges that accompany disaster tool usage before disaster strikes, so as to ensure maximum effectiveness and harm reduction. Second, tool providers should be made aware of their tools’ limitations to allow for the limitations to be addressed, where practical.

Limitations

Given the complex relationship of causes contributing to complex humanitarian emergencies, it is possible that our search strategy did not capture studies of tools used in complex humanitarian emergencies that were cataloged under terms associated with warfare or conflict rather than disasters. Additionally, our research addressed peer-reviewed scientific literature, while the grey literature may provide examples of novel ICT or big data tools that have not been the subject of rigorous research. Our study addressed the scope of ICT and big data use and not the quality of the studies themselves, and did not assess the quality of the included studies. Finally, there may be studies in non-English languages that have not been reviewed in the major English-language databases used in our research.

CONCLUSION

This study focused on literature addressing the use of ICT and big data tools for use in disaster response. Our research revealed several important findings. First, published research on the use of ICT and big data tools in disasters has focused largely on a limited number of high-profile natural disasters in wealthy countries. This focus may be related to disparities between high-income countries and LMICs in terms of media coverage of disastersReference Joye 128 , Reference Adams 129 and the relative availability of ICT infrastructure and expertise,Reference Lucas and Sylla 130 but it suggests that the full potential of these tools in responding to the disasters in LMICs is still not fully understood. Additional research into the use, or lack thereof, of ICT and big data tools in LMICs is needed. Second, the types of disasters in which ICT and big data tools were used were limited geographically and did not reflect the human impact of disasters such as extreme temperatures. To clarify, studies identified in our search largely neglected nonresponse phases of disasters (ie, recovery, mitigation, and preparedness), as well as intended users (eg, first responders, humanitarian aid works, clinicians, logisticians), and did not often address the challenges of implementing ICT and big data tools in the field. More information on these aspects of ICT and big data tool uses in disasters would increase the utility of the available research. This information would help future implementers in assessing how to deal with practical limitations typical of LMICs such as nonexistent or failed infrastructure, language barriers, and the technological capacities and literacy challenges of potential users. These concerns will be particularly important to study when tools are intended to be used by front-line responders or community members in LMICs in order to avoid replicating existing socioeconomic disparities in disaster response.

The complexity of disasters in the 21st century, particularly in LMICs, is a reflection of overlapping natural and human causes. ICT and big data tools, characterized by their velocity, volume, and variety, have the corresponding potential to increase the speed, scope, and specificity of disaster response. Future research needs to focus on addressing disparities in the location, types, and phases of disasters under study and extend to the study of protracted disaster situations and those with multiple heterogeneous causes in order to maximize the utility if ICT and big data tools in disaster response.

Acknowledgement

This work did not receive any financial support. The authors wish to acknowledge Lori Rosman of the Johns Hopkins Medical Library for her help in conducting the literature review.

The views expressed are those of the authors and do not reflect the official policy or position of Johns Hopkins University.

Footnotes

a In addition to upper-income and low-income countries, the World Bank classification contains 2 groups of middle-income countries: upper-middle income and lower-middle income. We are using the term “lower-and-middle-income country” (LMIC) to refer to countries in the lower-middle and lower-income groups.

References

REFERENCES

1. International Federation of Red Cross and Red Crescent Societies. What Is a Disaster? http://www.ifrc.org/en/what-we-do/disaster-management/about-disasters/what-is-a-disaster/. Accessed November 7, 2017.Google Scholar
2. Ager, A, Baillie Smith, M, Barbelet, V, et al. World disasters report: focus on local actors, the key to humanitarian effectiveness. International Federation of Red Cross and Red Crescent Societies website. https://ifrc-media.org/interactive/wp-content/uploads/2015/09/1293600-World-Disasters-Report-2015_en.pdf. Published 2015. Accessed October 26, 2017.Google Scholar
3. Easterling, DR. Climate extremes: observations, modeling, and impacts. Science. 2000;289(5487):2068-2074. doi: 10.1126/science.289.5487.2068.Google Scholar
4. Than, K. Scientists: natural disasters becoming more common. Live Science website. https://www.livescience.com/414-scientists-natural-disasters-common.html. Published October 17, 2005. Accessed June 18, 2014.Google Scholar
5. Dosse, S. The rise of intrastate wars: new threats and new methods. Small Wars J. 2010:1-5.Google Scholar
6. Human Security Centre. Human Security Report 2005: War and Peace in the 21st Century. https://global.oup.com/academic/product/human-security-report-2005-9780195307399?cc=us&lang=en&#. Published 2015. Accessed October 26, 2017.Google Scholar
7. Jones, KE, Patel, NG, Levy, MA, et al. Global trends in emerging infectious diseases. Nature. 2008;451(7181):990-993. doi: 10.1038/nature06536.Google Scholar
8. United Nations. World Urbanization Prospects: The 2014 Revision, Highlights. https://esa.un.org/unpd/wup/publications/files/wup2014-highlights.Pdf. Published 2014. Accessed August 20, 2017.Google Scholar
9. Currion, P. Rapid humanitarian assessment in urban settings. https://www.acaps.org/sites/acaps/files/resources/files/rapid_humanitarian_assessment_in_urban_settings_april_2015.pdf. Published 2015. Accessed August 16, 2017.Google Scholar
10. High-Level Panel on Humanitarian Financing. Too Important to Fail: Addressing the Humanitarian Financing Gap. https://reliefweb.int/sites/reliefweb.int/files/resources/%5BHLPReport%5DTooimportanttofail-addressingthehumanitarianfinancinggap.pdf. Published 2016. Accessed October 26, 2017.Google Scholar
11. Gandomi, A, Haider, M. Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manag. 2015;35(2):137-144. doi: 10.1016/j.ijinfomgt.2014.10.007.Google Scholar
12. The World Bank. World Bank country and lending groups. The World Bank Data Help Desk website. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups. Accessed October 26, 2017.Google Scholar
13. Moher, D, Liberati, A, Tetzlaff, J, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. PLoS Med. 2009;6(7):e1000097. doi: 10.1371/journal.pmed.1000097.Google Scholar
14. Abdullah, M. Simulation of wireless sensor network for flood monitoring system. In: Lecture Notes in Computer Science. Heidelberg, Germany: Springer Verlag; 2014:255-264. Design, User Experience, and Usability: User Experience Design for Everyday Life Applications and Services; vol 8519. doi: 10.1007/978-3-319-07635-5_25.Google Scholar
15. Abir, M, Mostashari, F, Atwal, P, et al. Electronic health records critical in the aftermath of disasters. Prehosp Disaster Med. 2012;27(6):620-622. http://hsrc.himmelfarb.gwu.edu/smhs_emerg_facpubs. Accessed October 13, 2017.Google Scholar
16. Alazawi, Z, Alani, O, Abdljabar, MB, et al. An intelligent disaster management system based evacuation strategies. Paper presented at: 9th International Symposium on Communication Systems, Networks & Digital Sign; July 23-25; Manchester, United Kingdom. Institute of Electrical and Electronics Engineers. 2014:673-678. doi: 10.1109/CSNDSP.2014.6923912.Google Scholar
17. Al-Amad, SH, Clement, JG, McCullough, MJ, et al. Evaluation of two dental identification computer systems: DAVID and WinID3. J Forensic Odontostomatol. 2007;25(1):23-29. https://www.scopus.com/record/display.uri?eid=2-s2.0-34250370785&origin=resultslist&sort=plf-f&src=s&st1=Evaluation+of+two+dental+identification+computer+systems%3A+DAVID+and+WinID3&st2=&sid=abe3ccb914cc138c342a3ba47af62a55&sot=b&sdt=b&sl=81&s=TITLE%28Eva. Accessed October 24, 2017.Google Scholar
18. Ambrosia, V, Buechel, SW, Brass, JA, et al. An integration of remote sensing, GIS, and information distribution for wildfire detection and management: remote sensing and GIS for hazards. Photogramm Eng Remote Sensing. 1998;64(10):977-985. http://www.refdoc.fr/Detailnotice?idarticle. Accessed October 24, 2017.Google Scholar
19. Bardet, J, Liu, F. Towards virtual earthquakes: using post‐earthquake reconnaissance information. Online Inf Rev. 2010;34(1):59-74. doi: 10.1108/14684521011024128.Google Scholar
20. Bellini, P, Boncinelli, S, Grossi, F, et al. Mobile Emergency, an emergency support system for hospitals in mobile devices: pilot study. JMIR Res Protoc. 2013;2(1):e19. doi: 10.2196/resprot.2293.Google Scholar
21. Benssam, A, Bendjoudi, A, Yahiaoui, S, et al. Towards a dynamic evacuation system for disaster situations. Paper presented at: 1st International Conference on Information and Communication Technologies for Disaster Management. Institute of Electrical and Electronics Engineers; 2014:1-8. doi: 10.1109/ICT-DM.2014.6917793.Google Scholar
22. Birowo, MA. Community media and civic action in response to volcanic hazards. In Hagar C, ed. Crisis Information Management: Communication and Technologies. Oxford: Chandos Publishing; 2012.Google Scholar
23. Bitelli, G, Franci, F, Mandanici, E. Monitoring the urban growth of Dhaka (Bangladesh) by satellite imagery in flooding risk management perspective. Int Arch Photogramm Remote Sens Spat Inf Sci. 2013;40:45-50. doi: 10.5194/isprsarchives-XL-5-W3-45-2013.Google Scholar
24. Black, DR, Dietz, JE, Stirratt, AA, et al. Do social media have a place in public health emergency response? J Emerg Manag. 2015;13(3):217-226. doi: 10.5055/jem.2015.0235.Google Scholar
25. Boden, A, Al-Akkad, A, Liegl, M, et al. Managing visibility and validity of distress calls with an ad-hoc SOS system. ACM Trans Comput Interact. 2016;23(6):1-26. doi: 10.1145/2987382.Google Scholar
26. Bond, E, Colavito, T, Luong, T, et al. Risk analysis for the security of VDOT smart traffic centers. In: 2007 IEEE Systems and Information Engineering Design Symposium. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2007:1-6. doi: 10.1109/SIEDS.2007.4374029.Google Scholar
27. Brown, SW, Griswold, WG, Demchak, B, et al. Middleware for reliable mobile medical workflow support in disaster settings. In: AMIA Annual Symposium Proceedings 2006. Bethesda, MD: American Medical Informatics Association; 2006:309-313.Google Scholar
28. Callaway, DW, Peabody, CR, Hoffman, A, et al. Disaster mobile health technology: lessons from Haiti. Prehosp Disaster Med. 2012;27(2):148-152. doi: 10.1017/S1049023X12000441.Google Scholar
29. Careem, M, De Silva, C, De Silva, R, et al. Sahana: overview of a disaster management system. In: 2006 International Conference on Information and Automation. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2006:361-366. doi:10.1109/ICINFA.2006.374152.Google Scholar
30. Chakraborty, B, Banerjee, S. Modeling the evolution of post disaster social awareness from social web sites. In: 2013 IEEE International Conference on Cybernetics (CYBCONF). New York, NY: Institute of Electrical and Electronics Engineers; 2013.Google Scholar
31. Chan, TC, Buono, CJ, Killeen, JP, et al. Tablet computing for disaster scene managers. In: AMIA Annual Symposium Proceedings 2006. Bethesda, MD: American Medical Informatics Association; 2006:875.Google Scholar
32. Chen, R, Sharman, R, Rao, HR, et al. Coordination of emergency response: an examination of the roles of people, process, and information technology. In: Van De Walle B, Turoff M, Hiltz SR, eds. Information Systems for Emergency Management. Armonk, NY: M.E. Sharpe; 2010.Google Scholar
33. Cheng, JW, Mitomo, H, Otsuka, T, et al. The effects of ICT and mass media in post-disaster recovery – a two model case study of the Great East Japan Earthquake. Telecomm Policy. 2015;39(6):515-532. doi: 10.1016/j.telpol.2015.03.006.Google Scholar
34. Chewning, LV, Lai, C-H, Doerfel, ML. Organizational resilience and using information and communication technologies to rebuild communication structures. Manag Commun Q. 2012;27(2):237-263. doi: 10.1177/0893318912465815.Google Scholar
35. Chien, C-H, Yu, S-N, Huang, Y-Y, et al. An efficient framework of emergency response to facilitate disaster recovery for fire-damaged medical equipment – case study at a large medical centre after a fire. Saf Sci. 2011;49(5):727-734. doi: 10.1016/J.SSCI.2010.09.007.Google Scholar
36. Chipman, R, Wuerfel, R. Network based information sharing between emergency operations center. In: 2008 IEEE Conference on Technologies for Homeland Security. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2008:155-160. doi:10.1109/THS.2008.4534441.Google Scholar
37. Chronaki, CE, Berthier, A, Bestier, S, et al. Satellite-enabled applications for health early warning in public health after a disaster: experience from a readiness exercise. In: Cunningham P, Cunningham M, eds. Collaboration and the Knowledge Economy: Issues, Applications, Case Studies. Amsterdam: IOS Press; 2008 https://www.researchgate.net/profile/Evangelos_Kritsotakis/publication/271528938_Satellite-enabled_Applications_for_Health_Early_Warning_in_Public_Health_after_a_Disaster_Experience_from_a_Readiness_Exercise/links/54cbc4990cf29ca810f4317d.pdf. Accessed October 13, 2017.Google Scholar
38. Chronaki, CE, Kontoyiannis, V, Panagopoulos, D, et al. Interoperability in disaster medicine and emergency management. J Health Inform. 2011;3. http://www.jhi-sbis.saude.ws/ojs-jhi/index.php/jhi-sbis/article/view/167. Accessed October 24, 2017.Google Scholar
39. Chung, T-L, Chang, W-Y, Tsai, W-F, et al. Cyberinfrastructure for flood mitigation in Taiwan. Proc Inst Civ Eng Water Manag. 2010;163(1):3-11. doi: 10.1680/wama.2010.163.1.3.Google Scholar
40. Dailey, D, Starbird, K. Journalists as crowdsourcerers: responding to crisis by reporting with a crowd. Comput Support Coop Work . 2014;23(4-6):445-481. doi: 10.1007/s10606-014-9208-z.Google Scholar
41. Davis, GL, Robbin, A. Network disaster response effectiveness: the case of ICTs and Hurricane Katrina. J Homel Secur Emerg Manag. 2015;12(3):437-467. doi: 10.1515/jhsem-2014-0087.Google Scholar
42. Doerfel, ML, Haseki, M. Networks, disrupted: media use as an organizing mechanism for rebuilding. New Media Soc. 2013;17(3):432-452. doi: 10.1177/1461444813505362.Google Scholar
43. Ergun, Ö, Gui, L, Heier Stamm, JL, et al. Improving humanitarian operations through technology-enabled collaboration. Prod Oper Manag. 2014;23(6):1002-1014. doi: 10.1111/poms.12107.Google Scholar
44. Fajardo, JTB, Yasumoto, K, Seki, H. A post-haiyan community level mobility model. In: 2015 Eighth International Conference on Mobile Computing and Ubiquitous Networking (ICMU). Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2015:70-71. doi: 10.1109/ICMU.2015.7061034.Google Scholar
45. Fitzhugh, SM, Ben Gibson, C, Spiro, ES, et al. Spatio-temporal filtering techniques for the detection of disaster-related communication. Soc Sci Res. 2016;59:137-154. doi: 10.1016/j.ssresearch.2016.04.023.Google Scholar
46. Fordis, M, Alexander, JD, McKellar, J. Role of a database-driven web site in the immediate disaster response and recovery of an academic health center: the Katrina experience. Acad Med. 2007;82(8):769-772. doi: 10.1097/ACM.0b013e3180cc2b5c.Google Scholar
47. Fruhling, A. STATPack (TM): an emergency response system for microbiology laboratory diagnostics and consultation. In: Van De Walle B, Turoff M, Hiltz SR, eds. Information Systems for Emergency Management. Vol 16. Advances in Management Information Systems . Armonk, NY: M.E. Sharpe; 2010:123-149.Google Scholar
48. Fuse, A, Okumura, T, Hagiwara, J, et al. New information technology tools for a medical command system for mass decontamination. Prehosp Disaster Med. 2013;28(3):298-300. doi: 10.1017/S1049023X13000095.Google Scholar
49. Graschew, G, Roelofs, TA, Rakowsky, S, et al. Network design for telemedicine - e-Health using satellite technology. In: Current Principles and Practices of Telemedicine and E-Health. Vol 131. Studies in Health Technology and Informatics. Amsterdam: IOS Press; 2008:67-82.Google Scholar
50. Grolinger, K, Capretz, MAM, Mezghani, E, et al. Knowledge as a service framework for disaster data management. In: 2013 Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises. Piscataway, NJ: Institute of Electrical and Electronics Engineers. 2013:313-318. doi: 10.1109/WETICE.2013.48.Google Scholar
51. Hayashi, H, Inoguchi, M. What should be considered to realize ICT support for effective disaster response and recovery? IEICE Trans Fundam Electron Commun Comput Sci. 2015;E98.A(8):1594-1601. doi: 10.1587/transfun.E98.A.1594.Google Scholar
52. Heinrichs, WL, Youngblood, P, Harter, PM, et al. Simulation for team training and assessment: case studies of online training with virtual worlds. World J Surg. 2008;32(2):161-170. doi: 10.1007/s00268-007-9354-2.Google Scholar
53. Hernandez-Nolasco, JA, Ovando, MAW, Acosta, FD, et al. Water level meter for alerting population about floods. In: 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA). Vol 2016-May. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2016:879-884. doi:10.1109/AINA.2016.76.Google Scholar
54. Hodgson, ME, Davis, BA, Kotelenska, J. Remote sensing and GIS data/information in the emergency response/recovery phase. In: Showalter P, Lu Y, eds. Geospatial Techniques in Urban Hazard and Disaster Analysis. Dordrecht: Springer; 2009:327-354. doi: 10.1007/978-90-481-2238-7_16.Google Scholar
55. Horahan, K, Morchel, H, Raheem, M, et al. Electronic health records access during a disaster. Online J Public Health Inform. 2014;5(3):232. doi: 10.5210/ojphi.v5i3.4826.Google Scholar
56. Huang, C-M, Chan, E, Hyder, AA. Web 2.0 internet social networking: a new tool for disaster management? - Lessons from Taiwan. BMC Med Inform Decis Mak. 2010;10(1):57. doi: 10.1186/1472-6947-10-57.Google Scholar
57. Hussain, F, Tongia, R. Cross technology comparison for information services in rural Bangladesh. In: 2009 International Conference on Information and Communication Technologies and Development (ICTD). Institute of Electrical and Electronics Engineers; 2009:252-266. doi: 10.1109/ICTD.2009.5426676.Google Scholar
58. Jaeger, PT, Fleischmann, KR, Preece, J, et al. Community response grids: using information technology to help communities respond to bioterror emergencies. Biosecurity Bioterrorism Biodefense Strateg Pract Sci. 2007;5(4):335-346. doi: 10.1089/bsp.2007.0034.Google Scholar
59. James, JJ, Lyznicki, JM, Irmiter, C, et al. Secure personal health information system for use in disasters and public health emergencies. In: Mordini E, Green M, eds. Internet-Based Intelligence in Public Health Emergencies: Early Detection and Response in Disease Outbreak Crises. Amsterdam: IOS Press; 2013:113-125. doi: 10.3233/978-1-61499-175-5-113.Google Scholar
60. Jennings, E, Arlikatti, S, Andrew, S. Determinants of emergency management decision support software technology: an empirical analysis of social influence in technology adoption. J Homel Secur Emerg Manag. 2015;12(3):603-626. doi: 10.1515/jhsem-2014-0079.Google Scholar
61. Jia, JS, Jia, J, Hsee, CK, et al. The role of hedonic behavior in reducing perceived risk: evidence from postearthquake mobile-app data. Psychol Sci. 2017;28(1):23-35. doi: 10.1177/0956797616671712.Google Scholar
62. Keim, ME, Noji, E. Emergent use of social media: a new age of opportunity for disaster resilience. Am J Disaster Med. 2011;6(1):47-54. https://www.scopus.com/record/display.uri?eid=2-s2.0-84855543479&origin=resultslist&sort=plf-f&src=s&st1=Emergent+use+of+social+media%253a+A+new+age+of+opportunity+for+disaster+resilience&st2=&sid=e1a217e061547b252e97f71a0c85ed72&sot=b&sdt=b&sl=85&s=TITLE. Accessed October 24, 2017.Google Scholar
63. Kiltz, L, Smith, R. Experimenting with GIS in doing damage assessments: a trial run at disaster city. J Homel Secur Emerg Manag. 2011;8(1). doi: 10.2202/1547-7355.1853.Google Scholar
64. Korkmaz, KA. Integrated seismic hazard evaluation and disaster management approach for Turkey. Environ Earth Sci. 2009;61(3):467-476. doi: 10.1007/s12665-009-0358-0.Google Scholar
65. Kotabe, S, Sakano, T, Sebayashi, K, Komukai, T. Rapidly deployable phone service to counter catastrophic loss of telecommunication facilities. NTT Tech Rev. 2014;12(3):https://www.scopus.com/record/display.uri?eid=2-s2.0-84897033261&origin=resultslist&sort=plf-f&src=s&st1=Rapidly+deployable+phone+service+to+counter+catastrophic+loss+of+telecommunication+facilities&st2=&sid=e1a217e061547b252e97f71a0c85ed72&sot=b&sdt=b&sl. Accessed October 24, 2017.Google Scholar
66. Koua, EL, MacEachren, AM, Turton, I, et al. Conceptualizing a user-support task structure for geocollaborative disaster management environments. In: Van De Walle B, Turoff M, Hiltz SR, eds. Information Systems for Emergency Management. Armonk, NY: M.E. Sharpe; 2010:254-276. https://books.google.com/books?id=eZXgBQAAQBAJ&printsec=frontcover#v=onepage&q&f=false. Accessed October 24, 2017.Google Scholar
67. Krane, NK, Kahn, MJ, Markert, RJ, et al. Surviving Hurricane Katrina: reconstructing the educational enterprise of Tulane University School of Medicine. Acad Med. 2007;82(8):757-762. doi: 10.1097/ACM.0b013e3180cf6ee5.Google Scholar
68. Krishnamurthy, V, Kwasinski, A. Empirically validated availability model of information and communication technologies facilities under hurricane conditions. INTELEC, 36th Annual International Telecommunications Energy Conference (Proceedings). Institute of Electrical and Electronics Engineers. https://www.scopus.com/record/display.uri?eid=2-s2.0-84937723694&origin=resultslist&sort=plf-f&src=s&st1=Empirically+validated+availability+model+of+information+and+communication+technologies+facilities+under+hurricane+conditions&st2=&sid=e1a217e061547b25. Published 2014. Accessed October 24, 2017.Google Scholar
69. Kryvasheyeu, Y, Chen, H, Obradovich, N, et al. Rapid assessment of disaster damage using social media activity. Sci Adv. 2016;2(3):e1500779-e1500779. doi: 10.1126/sciadv.1500779.Google Scholar
70. Kuroda, T, Kimura, E, Matsumura, Y, et al. Simulating cloud environment for HIS backup using secret sharing. Stud Health Technol Inform. 2013;192:171-174. http://www.ncbi.nlm.nih.gov/pubmed/23920538. Accessed October 13, 2017.Google Scholar
71. Leelawat, N, Suppasri, A, Latcharote, P, et al. Increasing tsunami risk awareness via mobile application. IOP Conf Ser Earth Environ Sci. 2017;56(1):12001. doi: 10.1088/1755-1315/56/1/012001.Google Scholar
72. Lenert, LA, Kirsh, D, Griswold, WG, et al. Design and evaluation of a wireless electronic health records system for field care in mass casualty settings. J Am Med Inform Assoc. 2011;18(6):842-852. doi: 10.1136/amiajnl-2011-000229.Google Scholar
73. Leong, C, Pan, SL, Ractham, P, et al. ICT-enabled community empowerment in crisis response: social media in Thailand flooding 2011. J Assoc Inf Syst. 2015;16(3):174-212. https://www.scopus.com/record/display.uri?eid=2-s2.0-84929391294&origin=resultslist&sort=plf-f&src=s&st1=ICT-enabled+community+empowerment+in+crisis+response%3A+Social+media+in+Thailand+flooding+2011&st2=&sid=e1a217e061547b252e97f71a0c85ed72&sot=b&sdt=b&s. Accessed October 24, 2017.Google Scholar
74. Levy, G, Blumberg, N, Kreiss, Y, et al. Application of information technology within a field hospital deployment following the January 2010 Haiti earthquake disaster. J Am Med Inform Assoc. 2010;17(6):626-630. doi: 10.1136/jamia.2010.004937.Google Scholar
75. Li, J, Chen, Y, Gong, A, et al. Spatial information technologies for disaster management in China. In: Showalter P, Lu Y, eds. Geospatial Techniques in Urban Hazard and Disaster Analysis. Dordrecht: Springer; 2009:245-254. doi: 10.1007/978-90-481-2238-7_12.Google Scholar
76. Li, W, Song, M, Zhou, B, et al. Performance improvement techniques for geospatial web services in a cyberinfrastructure environment – a case study with a disaster management portal. Comput Environ Urban Syst. 2015;54:314-325. doi: 10.1016/j.compenvurbsys.2015.04.003.Google Scholar
77. Liu, H, Cui, X, Yuan, D, et al. Study of earthquake disaster population risk based on GIS: a case study of Wenchuan earthquake region. Procedia Environ Sci. 2011;11(Part C):1084-1091. doi: 10.1016/j.proenv.2011.12.164.Google Scholar
78 Liu, Y, Piyawongwisal, P, Handa, S, et al. Going beyond citizen data collection with Mapster: a mobile+cloud real-time citizen science experiment. In: 2011 IEEE Seventh International Conference on E-Science Workshops. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2011:1-6. doi: 10.1109/eScienceW.2011.23.Google Scholar
79. Low, R, Burdon, M, Christensen, S, et al. Protecting the protectors: legal liabilities from the use of Web 2.0 for Australian disaster response. In: 2010 IEEE International Symposium on Technology and Society. Institute of Electrical and Electronics Engineers; 2010:411-418. doi:10.1109/ISTAS.2010.5514614.Google Scholar
80. Mark, GJ, Al-Ani, B, Semaan, B. Resilience through technology adoption. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems - CHI 09. New York, NY: ACM Press; 2009:689. doi: 10.1145/1518701.1518808.Google Scholar
81. Mersham, G. Social media and public information Management: the September 2009 tsunami threat to New Zealand. Media Int Aust. 2010;(137):130-143. https://www.scopus.com/record/display.uri?eid=2-s2.0-80053517215&origin=resultslist&sort=plf-f&src=s&st1=Social+media+and+public+information+management%3A+the+September+2009+tsunami+threat+to+New+Zealand&st2=&sid=e1a217e061547b252e97f71a0c85ed72&sot=b&sdt. Accessed October 24, 2017.Google Scholar
82. Mittu, R, Guleyupoglu, S, Johnson, A, et al. Unclassified information sharing and coordination in security, stabilization, transition and reconstruction efforts. Int J Electron Gov Res. 2008;4(1):36-48. https://www.scopus.com/record/display.uri?eid=2-s2.0-47649096320&origin=resultslist&sort=plf-f&src=s&st1=Unclassified+information+sharing+and+coordination+in+security%2C+stabilization%2C+transition+and+reconstruction+efforts&st2=&sid=e1a217e061547b252e97f. Accessed October 24, 2017.Google Scholar
83. Mondlane, AI, Popov, O. GIS based flood risk management: the case of Limpopo River Basin in Mozambique. International Multidisciplinary Scientific GeoConference SGEM 2010. https://s3.amazonaws.com/academia.edu.documents/44489242/GIS_BASED_FLOOD_RISK_MANAGEMENT__THE_CAS20160406-6656-1jg4yn0.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1507928260&Signature=feVGir15v%2Fz4mJiRRHZxLixsup4%3D&response-content-disposition=inlin. Published 2010. Accessed October 13, 2017.Google Scholar
84. Motreff, Y, Pirard, P, Lagrée, C, et al. Voluntary health registry of French nationals after the Great East Japan Earthquake, Tsunami, and Fukushima Daiichi Nuclear Power Plant accident: methods, results, implications, and feedback. Prehosp Disaster Med. 2016;31(3):326-329. https://doi.org/10.1017/S1049023X16000200.Google Scholar
85. Musavi, SHA, Memon, AR, Shah, A, et al. Optimization of efficient communication technology in designing seismic early warning system for Pakistan. Aust J Basic Appl Sci. 2010;4(6):1419-1431. https://www.researchgate.net/profile/Madad_Shah/publication/267417233_Optimization_of_Efficient_Communication_Technology_in_Designing_SeismicEarly_Warning_System_for_Pakistan/links/5475738b0cf29afed6126e59.pdf. Accessed October 24, 2017.Google Scholar
86. Nakachi, T, Kim, SY, Okada, A, et al. A media storage and transmission system using MDRUs. 2015;13(5). https://www.scopus.com/record/display.uri?eid=2-s2.0-84929121120&origin=resultslist&sort=plf-f&src=s&st1=A+media+storage+and+transmission+system+using+MDRUs&st2=&sid=e1a217e061547b252e97f71a0c85ed72&sot=b&sdt=b&sl=58&s=TITLE%28A+media+storage+and+transmis. Accessed October 24, 2017.Google Scholar
87. Nunavath, V, Radianti, J, Comes, T, et al. The impacts of ICT support on information distribution, task assignment for gaining teams’ situational awareness in search and rescue operations. In: Advances in Signal Processing and Intelligent Recognition Systems. Vol 425. Cham, Switzerland: Springer Verlag; 2016:443-456. doi: 0.1007/978-3-319-28658-7_38.Google Scholar
88. Pathan, A, Hong, C. Developing an efficient DMCIS with next-generation wireless networks. In: Military Communications Conference 2006. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2007:1-6. doi: 10.1109/MILCOM.2006.302515.Google Scholar
89. Peña-Mora, F, Chen, AY, Aziz, Z, et al. Mobile ad hoc network-enabled collaboration framework supporting civil engineering emergency response operations. J Comput Civ Eng. 2010;24(3):302-312. doi: 10.1061/(ASCE)CP.1943-5487.0000033.Google Scholar
90. Robinson, CD, Brown, DE. Assessing casualty densities based on sensor reports pursuant to a large-scale disaster. In: 2005 IEEE International Conference on Systems, Man and Cybernetics. Vol 4. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2006:3675-3681. doi: 10.1109/ICSMC.2005.1571718.Google Scholar
91. Sakano, T, Fadlullah, ZM, Thuan Ngo, T, et al. Disaster-resilient networking: a new vision based on movable and deployable resource units. IEEE Netw. 2013;27(4):40-46. doi: 10.1109/MNET.2013.6574664.Google Scholar
92. Salo, S, Salo, H, Liisanantti, A, et al. Data transmission in dental identification of mass disaster victims. J Forensic Odontostomatol. 2007;25(1).Google Scholar
93. Samarajiva, R. Policy commentary: mobilizing information and communications technologies for effective disaster warning: lessons from the 2004 tsunami. New Media Soc. 2005;7(6):731-747. doi: 10.1177/1461444805058159.Google Scholar
94. Sebek, K, Jacobson, L, Wang, J, et al. Assessing capacity and disease burden in a virtual network of New York City primary care providers following Hurricane Sandy. J Urban Health. 2014;91(4):615-622. doi: 10.1007/s11524-014-9874-7.Google Scholar
95. Shih, CS, Chen, HY, Yeh, ZY. Service recovery for large scale distributed publish and subscription services for cyber-physical systems and disaster management. In: Piscataway, NJ: 2014 IEEE International Conference on Cyber-Physical Systems, Networks, and Applications. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2014:87-93. doi: 10.1109/CPSNA.2014.27.Google Scholar
96. Shimizu, Y, Suzuki, Y, Kumagai, T, et al. Wireless access network system using M2M wireless access for MDRU. NTT Tech Rev. 2015;13(5). https://www.ntt-review.jp/archive/ntttechnical.php?contents=ntr201505fa2.html. Accessed April 17, 2017.Google Scholar
97. Shklovski, I, Palen, L, Sutton, J. Finding community through information and communication technology in disaster response. In: Proceedings of the ACM 2008 Conference on Computer Supported Cooperative Work - CSCW ’08. New York, NY: ACM Press; 2008:127. doi:10.1145/1460563.1460584.Google Scholar
98. Singh, D, Piplani, D, Shinde, S, et al. mKRISHI (R) Fisheries: a case study on Early Warning System (EWS) for disaster communication and management. In: 2016 IEEE International Symposium on Technology and Society (ISTAS). IEEE International Symposium on Technology and Society. New York, NY: Institute of Electrical and Electronics Engineers; 2016:106-111.Google Scholar
99. Stephens, KK, Jafari, E, Boyles, S, et al. Increasing evacuation communication through ICTs: an agent-based model demonstrating evacuation practices and the resulting traffic congestion in the rush to the road. J Homel Secur Emerg Manag. 2015;12(3):497-528. doi: 10.1515/jhsem-2014-0075.Google Scholar
100. Su, Y, Jie, Peng, Jin, Z. Geo-information quality assurance in disaster management. In: 2008 Third International Conference on Digital Information Management. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2008:769-774. doi: 10.1109/ICDIM.2008.4746727.Google Scholar
101. Sutiono, AB, Qiantori, A, Prasetio, S, et al. Designing an emergency medical information system for the early stages of disasters in developing countries: the human interface advantage, simplicity and efficiency. J Med Syst. 2010;34(4):667-675. doi: 10.1007/s10916-009-9280-y.Google Scholar
102. Suzuki, T, Shibata, Y. Wireless network system with autonomous antenna actuator control for disaster information. In: 2012 26th International Conference on Advanced Information Networking and Applications Workshops. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2012:1031-1036. doi: 10.1109/WAINA.2012.219.Google Scholar
103. Tapia, AH, Maldonado, E, Ngamassi Tchouakeu, L, et al. Coordinating humanitarian information. Inf Technol People. 2012;25(3):240-258. doi: 10.1108/09593841211254312.Google Scholar
104. Tingsanchali, T. Urban flood disaster management. Procedia Eng. 2012;32:25-37. doi: 10.1016/J.PROENG.2012.01.1233.Google Scholar
105. Troy, DA, Carson, A, Vanderbeek, J, et al. Enhancing community-based disaster preparedness with information technology. Disasters. 2008;32(1):149-165. doi: 10.1111/j.1467-7717.2007.01032.x.Google Scholar
106. Tsai, M-K, Yau, N-J. Enhancing usability of augmented-reality-based mobile escape guidelines for radioactive accidents. J Environ Radioact. 2013;118:15-20. doi: 10.1016/j.jenvrad.2012.11.001.Google Scholar
107. Tsai, M-K, Yau, N-J. Improving information access for emergency response in disasters. Nat Hazards. 2013;66(2):343-354. doi: 10.1007/s11069-012-0485-x.Google Scholar
108. Tsai, M-K, Yau, N-J. Using mobile disaster response system in bridge management. J Civ Eng Manag. 2014;20(5):737-745. doi: 10.3846/13923730.2013.802731.Google Scholar
109. Waidyanatha, N, Gow, G, Anderson, P. Common alerting protocol message broker for last-mile hazard warning system in in Sri Lanka: an essential component. SSRN Electron J. May 2007; doi: 10.2139/ssrn.1568001.Google Scholar
110. Walderhaug, S, Meland, P, Mikalsen, M, et al. Evacuation support system for improved medical documentation and information flow in the field. Int J Med Inform. 2008;77(2):137-151. doi: 10.1016/j.ijmedinf.2007.01.006.Google Scholar
111. Wan, S, Yen, JY, Lin, CY, et al. Construction of knowledge-based spatial decision support system for landslide mapping using fuzzy clustering and KPSO analysis. Arab J Geosci. 2015;8(2):1041-1055. doi: 10.1007/s12517-013-1226-5.Google Scholar
112. Wang, Y, Wang, X, Zhang, K. Study of the mine RFID-based personnel positioning system. In: Li SC, Wang WY, An Y, eds. Progress in Safety Science and Technology, Parts A and B. Vol 8. Progress in Safety Science and Technology Series. Beijing: Science Press; 2010:1360-1367.Google Scholar
113. Wang, H, Tuo, X, Zhang, G, et al. Panzhihua airport landslide (Oct. 3rd 2009) and an emergency monitoring and warning system based on the internet of things. J Mt Sci. 2013;10(5):873-884. doi: 10.1007/s11629-013-2368-3.Google Scholar
114. Ward, SM, Leitner, M, Pine, J. Investigating investigating recovery patterns in post disaster urban settings: utilizing geospatial technology to understand Post-Hurricane Katrina recovery in New Orleans, Louisiana. In. Geospatial Techniques in Urban Hazard and Disaster Analysis. Dordrecht: Springer Netherlands; 2009:355-372. doi: 10.1007/978-90-481-2238-7_17.Google Scholar
115. Woltjer, R, Lindgren, I, Smith, K. A case study of information and communication technology in emergency management training. Int J Emerg Manag. 2006;3(4):332. doi: 10.1504/IJEM.2006.011300.Google Scholar
116. Yasuda, S, Akashi, K, Miyachi, T, et al. Emulation-based ICT system resiliency verification for disaster situations. In: 2013 International Conference on Signal-Image Technology & Internet-Based Systems. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2013:875-882. doi: 10.1109/SITIS.2013.143.Google Scholar
117. Yomwan, P, Cao, C, Rakwatin, P, et al. The risk analysis for infectious disease outbreaks in flood disaster based on spatial information technologies. In: 2012 IEEE International Geoscience and Remote Sensing Symposium. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2012:7244-7247. doi: 10.1109/IGARSS.2012.6351990.Google Scholar
118. Zhang, C, Si, Z, Ma, Z, et al. Mining sequential update summarization with hierarchical text analysis. Mob Inf Syst. 2016;(2016):1-10. doi: 10.1155/2016/1340973.Google Scholar
119. Zhang, L, Kang, Z, Li, J, et al. Web-based terrain and vector maps visualization for Wenchuan earthquake. Int J Appl Earth Obs Geoinf. 2010;12(6):439-447. doi: 10.1016/J.JAG.2010.01.001.Google Scholar
120. Zhang, M, Zheng, H, You, K. Analysis on the influence of micro-blog opinion leaders in natural disaster-taking Ya’an Earthquake for example. In: Zhu, X, Zhao S, ed. Proceedings of 2014 International Conference on Public Administration (10th), Vol I. Chengdu, China: University of Electronic Science and Technology of China; 2014:1011-1017.Google Scholar
121. Zhang, Q, Zhang, Y, Yang, X, et al. Automatic recognition of seismic intensity based on RS and GIS: a case study in Wenchuan Ms8.0 earthquake of China. Sci World J. 2014:8. doi: 10.1155/2014/878149.Google Scholar
122. Zhang, X, Zhang, Z, Zhang, Y, et al. Route selection for emergency logistics management: a bio-inspired algorithm. Saf Sci. 2013;54:87-91. doi: 10.1016/j.ssci.2012.12.003.Google Scholar
123. Zhao, X, Rafiq, A, Hummel, R, et al. Integration of information technology, wireless networks, and personal digital assistants for triage and casualty. Telemed e-Health. 2006;12(4):466-474. doi: 10.1089/tmj.2006.12.466.Google Scholar
124. Zibuschka, J, Roßnagel, H, Muntermann, J, et al. Mobile emergency management services targeting large public events. In: Best Practices and New Perspectives in Service Science and Management. Hershey, PA: IGI Global; 2013:281-299. doi: 10.4018/978-1-4666-3894-5.ch018.Google Scholar
125. Zolfaghari, MR. Use of raster-based data layers to model spatial variation of seismotectonic data in probabilistic seismic hazard assessment. Comput Geosci. 2009;35(7):1460-1469. doi: 10.1016/j.cageo.2008.11.006.Google Scholar
126. Zou, L, Wang, S, Huang, H. Research on reasoning mechanism of emergency rescue decision support system of geo-hazards under the conditions of extreme snow and ice disasters. In: Toll DG, Zhu H, Li X, eds. Information Technology in Geo-Engineering: Proceedings of the 1st International Conference (ICITG). Shanghai: IOS Press; 2010:126-133. https://books.google.com/books?hl=en&lr=&id=HfHS1mSSBgwC&oi=fnd&pg=PA126&dq=Research+on+Reasoning+Mechanism+of+Emergency+Rescue+Decision+Support+System+of+Geo-Hazards+under+the+Conditions+of+Extreme+Snow+and+Ice+Disasters&ots=0F55xCRu9c&sig=Hpic0ud5wi7JO_. Accessed October 13, 2017.Google Scholar
127. Centre for Research on the Epidemiology of Disasters. The Human Cost of Weather Related Disasters: 1995-2015. http://www.unisdr.org/files/46796_cop21weatherdisastersreport2015.pdf. Published 2015. Accessed October 16, 2017.Google Scholar
128. Joye, S. The hierarchy of global suffering. J Int Commun. 2009;15(2):45-61. doi: 10.1080/13216597.2009.9674750.Google Scholar
129. Adams, WC. Whose lives count? TV coverage of natural disasters. J Commun. 1986;36(2):113-122. doi: 10.1111/j.1460-2466.1986.tb01429.x.Google Scholar
130. Lucas, H, Sylla, R. The global impact of the Internet: widening the economic gap between wealthy and poor nations? Prometheus. 2003;21(1):1-22. doi: 10.1080/0810902032000050983.Google Scholar
Figure 0

Table 1Included Databases, Search Terms, and Search Strategies

Figure 1

Table 2Inclusion and Exclusion Criteria for Review

Figure 2

Figure 1PRISMA diagram

Figure 3

Table 3Characteristics of the Studies Included in the Review (n=113)

Figure 4

Table 4Disaster Research Summary