Abstract
This study investigates the spatiotemporal pattern of the 2014 Ebola virus disease (EVD) epidemic in the most heavily affected countries in West Africa and also mines the spatial associations between such pattern and other geographically distributed factors. Utilizing the publicly available open-source data, this study demonstrates a research design that integrates various geospatial data processing, analysis, and data-mining techniques to achieve the research objectives. For the 2014 EVD epidemic, spatiotemporal patterns were analyzed and visualized. Fine-grained population data were obtained through a population interpolation method to conduct healthcare accessibility analysis. Finally, associations between the spatiotemporal patterns of the incidences and healthcare accessibility as well as other factors were examined. The results suggest that (1) poor accessibility to healthcare facilities and EVD clusters are identified in many urban areas as well as some remote areas; and (2) EVD cases were more likely to be found in border areas of these countries where accessibility to healthcare facilities is poorer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmed, S. S. U., et al. (2010). The space--time clustering of highly pathogenic avian influenza (HPAI) H5N1 outbreaks in Bangladesh. Epidemiology & Infection, 138(6), 843–852.
Baize, S., et al. (2014). Emergence of Zaire Ebola virus disease in Guinea—Preliminary report. The New England Journal of Medicine, 371(15), 1418–1425.
Banu, S., et al. (2012). Space-time clusters of dengue fever in Bangladesh. Tropical Medicine and International Health, 17(9), 1086–1091.
Bawo, L., et al. (2015). Elimination of Ebola virus transmission in Liberia—September 3, 2015. Morbidity and Mortality Weekly Report, 64, 979–980. Available at: http://www.cdc.gov/mmwr/pdf/wk/mm6435.pdf. Accessed 11 Sept 2015.
Carroll, M.W., et al. (2015). Temporal and spatial analysis of the 2014–2015 Ebola virus outbreak in West Africa. Nature, 524(7563), 97.
Casas, I., Delmelle, E., & Delmelle, E. C. (2017). Potential versus revealed access to care during a dengue fever outbreak. Journal of Transport and Health, 4, 18–29. https://doi.org/10.1016/j.jth.2016.08.001.
Centers for Disease Control and Prevention. (2016). Outbreaks chronology: Ebola virus disease. Available at: http://www.cdc.gov/vhf/ebola/outbreaks/history/chronology.html.
Cheng, T., & Wicks, T. (2014). Event detection using Twitter: A spatio-temporal approach. PloS One, 9(6), e97807.
Cheng, T., & Williams, D. (2012). Space-time analysis of crime patterns in central London. ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B2(September), 47–52.
Chowell, G., & Nishiura, H. (2015). Characterizing the transmission dynamics and control of Ebola virus disease. PLoS Biology, 13(1), 1–9.
Chu, H. J., et al. (2016). Minimizing spatial variability of healthcare spatial accessibility—The case of a dengue fever outbreak. International Journal of Environmental Research and Public Health, 13(12), 1235.
de Melo, D. P. O., Scherrer, L. R., & Eiras, Á. E. (2012). Dengue fever occurrence and vector detection by larval survey, ovitrap and mosquiTRAP: A space-time clusters analysis. PLoS One, 7(7), e42125.
D’Silva, J. P., & Eisenberg, M. C. (2017). Modeling spatial invasion of Ebola in West Africa. Journal of theoretical biology, 428, 65–75.
Desjardins, M. R., et al. (2018). Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016. Acta Tropica, 185(April), 77–85. https://doi.org/10.1016/j.actatropica.2018.04.023.
Eisen, L., & Lozano-Fuentes, S. (2009). Use of mapping and spatial and space-time modeling approaches in operational control of Aedes aegypti and dengue. PLoS Neglected Tropical Diseases, 3(4), 1–7.
Ganguly, S. (2014). Ebola hemorrhagic fever: A review on global facts, concepts and public health issues. World Journal of Pharmaceutical Research, 3(9), 401–404.
Gatherer, D. (2014). The 2014 Ebola virus disease outbreak in West Africa. Journal of General Virology, 95(Part 8), 1619–1624.
Gaudart, J., et al. (2006). Space-time clustering of childhood malaria at the household level: A dynamic cohort in a Mali village. BMC Public Health, 6(1), 286.
Green, A. (2014). Ebola emergency meeting establishes new control centre. The Lancet, 384(9938), 118. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0140673614611478.
Guagliardo, M. F. (2004). Spatial accessibility of primary care: Concepts, methods and challenges. International Journal of Health Geographics, 3(1), 3.
Hadley, J., & Cunningham, P. (2004). Availability of safety net providers and access to care of uninsured persons. Health Services Research, 39(5), 1527–1546.
Joseph, A. E., & Bantock, P. R. (1982). Measuring potential physical accessibility to general practitioners in rural areas: A method and case study. Social Science & Medicine, 16(1), 85–90.
Kim, H., & Yao, X. (2010). Pycnophylactic interpolation revisited: Integration with the dasymetric-mapping method. International Journal of Remote Sensing, 31(21), 5657–5671.
Kiskowski, M. (2014). Description of the early growth dynamics of 2014 West Africa Ebola epidemic. arXiv preprint arXiv:1410.5409.
Koperski, K., & Han, J. (1995). Discovery of spatial association rules in geographic information databases. In International Symposium on Spatial Databases (pp. 47–66). Springer, Berlin, Heidelberg.
Kramer, A. M., et al. (2016). Spatial spread of the West Africa Ebola epidemic. Dryad Digital Repository, 3, 160294.
Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics – Theory and Methods, 26(6), 1481–1496.
Kulldorff, M., et al. (2004). Benchmark data and power calculations for evaluating disease outbreak detection methods. Morbidity and Mortality Weekly Report, 53, 144–151.
Kulldorff, M., et al. (2007). Multivariate scan statistics for disease surveillance. Statistics in Medicine, 26(8), 1824–1833.
Leibovici, D., et al. (2007). Extracting Dynamics of Multiple Indicators for Spatial recognition of Ecoclimatic zones in Circum-Saharan Africa. GISRUK 2007, 114.
Lian, M., et al. (2007). Using geographic information systems and spatial and space-time scan statistics for a population-based risk analysis of the 2002 equine West Nile epidemic in six contiguous regions of Texas. International Journal of Health Geographics, 10, 1–10.
Luo, W., & Wang, F. (2003). Measures of spatial accessibility to health care in a GIS environment: Synthesis and a case study in the Chicago region. Environment and Planning B: Planning and Design, 30(6), 865–884.
Luo, W., & Qi, Y. (2009). An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians. Health & Place, 15(4), 1100–1107.
McGrail, M. R., & Humphreys, J. S. (2014). Measuring spatial accessibility to primary health care services: Utilising dynamic catchment sizes. Applied Geography, 54, 182–188. https://doi.org/10.1016/j.apgeog.2014.08.005.
Mcgrail, M. R., et al. (2015). Spatial access disparities to primary health care in rural and remote Australia. Geospatial Health, 10, 358.
Meliker, J. R., & Sloan, C. D. (2011). Spatio-temporal epidemiology: Principles and opportunities. Spatial and Spatio-temporal Epidemiology, 2(1), 1–9.
Mulatti, P., et al. (2010). Evaluation of interventions and vaccination strategies for low pathogenicity avian influenza: spatial and space–time analyses and quantification of the spread of infection. Epidemiology & Infection, 138(6), 813–824.
Nakaya, T., & Yano, K. (2010). Visualising crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Transactions in GIS, 14(3), 223–239.
O’Neill, L. (2003). Estimating out-of-hospital mortality due to myocardial infarction. Health Care Management Science, 6(3), 147–154.
Openshaw, S., et al. (1987). A mark 1 geographical analysis machine for the automated analysis of point data sets. International Journal of Geographical Information System, 1(4), 335–358.
Radke, J., & Mu, L. (2000). Spatial decompositions, modeling and mapping service regions to predict access to social programs. Geographic Information Sciences, 6(2), 105–112.
Robertson, C., et al. (2010). Review of methods for space-time disease surveillance. Spatial and Spatio-temporal Epidemiology, 1(2–3), 105–116. https://doi.org/10.1016/j.sste.2009.12.001.
Shaman, J., Yang, W., & Kandula, S. (2014). Inference and forecast of the current West African Ebola outbreak in Guinea, Sierra Leone and Liberia. PLoS Currents, 6. https://doi.org/10.1371/currents.outbreaks.3408774290b1a0f2dd7cae877c8b8ff6.
Singh, S. K., & Ruzek, D. (2013). Viral hemorrhagic fevers. London: CRC Press. Available at: https://books.google.com/books?id=WzzOBQAAQBAJ.
Talen, E., & Anselin, L. (1998). Assessing spatial equity: An evaluation of measures of accessibility to public playgrounds. Environment and Planning A, 30(4), 595–613.
Tango, T., Takahashi, K., & Kohriyama, K. (2011). A Space-Time Scan Statistic for Detecting Emerging Outbreaks. Biometrics, 67(1), 106–115.
WHO Ebola Response Team. (2014). Ebola virus disease in West Africa—The first 9 months of the epidemic and forward projections. New England Journal of Medicine, 371(16), 1481–1495. Available at: http://www.nejm.org/doi/abs/10.1056/NEJMoa1411100. Accessed 25 Sept 2016.
Yang, W., et al. (2015). Transmission network of the 2014-2015 Ebola epidemic in Sierra Leone. Journal of the Royal Society, Interface/the Royal Society, 12(112), 204–211. Available at: http://www.ncbi.nlm.nih.gov/pubmed/26559683, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4685836.
Zandbergen, P. A., & Ignizio, D. A. (2010). Comparison of dasymetric mapping techniques for small-area population estimates. Cartography and Geographic Information Science, 37(3), 199–214.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Fan, Q., Yao, X.A., Dang, A. (2020). Spatiotemporal Analysis and Data Mining of the 2014–2016 Ebola Virus Disease Outbreak in West Africa. In: Lu, Y., Delmelle, E. (eds) Geospatial Technologies for Urban Health. Global Perspectives on Health Geography. Springer, Cham. https://doi.org/10.1007/978-3-030-19573-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-19573-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19572-4
Online ISBN: 978-3-030-19573-1
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)