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http://dx.doi.org/10.9713/kcer.2020.58.2.197

Spatio-Temporal Incidence Modeling and Prediction of the Vector-Borne Disease Using an Ecological Model and Deep Neural Network for Climate Change Adaption  

Kim, SangYoun (Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University)
Nam, KiJeon (Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University)
Heo, SungKu (Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University)
Lee, SunJung (Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University)
Choi, JiHun (Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University)
Park, JunKyu (Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University)
Yoo, ChangKyoo (Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University)
Publication Information
Korean Chemical Engineering Research / v.58, no.2, 2020 , pp. 197-208 More about this Journal
Abstract
This study was carried out to analyze spatial and temporal incidence characteristics of scrub typhus and predict the future incidence of scrub typhus since the incidences of scrub typhus have been rapidly increased among vector-borne diseases. A maximum entropy (MaxEnt) ecological model was implemented to predict spatial distribution and incidence rate of scrub typhus using spatial data sets on environmental and social variables. Additionally, relationships between the incidence of scrub typhus and critical spatial data were analyzed. Elevation and temperature were analyzed as dominant spatial factors which influenced the growth environment of Leptotrombidium scutellare (L. scutellare) which is the primary vector of scrub typhus. A temporal number of diseases by scrub typhus was predicted by a deep neural network (DNN). The model considered the time-lagged effect of scrub typhus. The DNN-based prediction model showed that temperature, precipitation, and humidity in summer had significant influence factors on the activity of L. scutellare and the number of diseases at fall. Moreover, the DNN-based prediction model had superior performance compared to a conventional statistical prediction model. Finally, the spatial and temporal models were used under climate change scenario. The future characteristics of scrub typhus showed that the maximum incidence rate would increase by 8%, areas of the high potential of incidence rate would increase by 9%, and disease occurrence duration would expand by 2 months. The results would contribute to the disease management and prediction for the health of residents in terms of public health.
Keywords
Scrub typhus; Maximum entropy model; Deep neural network; Climate change; Public health;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 World Health Organization, "Vector-borne Diseases," WHO Regional Office for South-East Asia (2014).
2 Park, S. and Han, D., "Reviews in Medical Geography: Spatial Epidemiology of Vector-Borne Diseases," J. Korean Geogr. Soc., 47(5), 677-699(2012).
3 Medlock, J. M. and Leach, S. A., "Effect of Climate Change on Vector-borne Disease Risk in the UK," Lancet Infect. Dis., 15(6), 721-730(2015).   DOI
4 Paris, D. H., Shelite, T. R., Day, N. P. and Walker, D. H., "Unresolved Problems Related to Scrub Typhus: a Seriously Neglected Life-threatening Disease," Am. J. Trop. Med. Hyg., 89(2), 301-307(2013).   DOI
5 Kim, S. W. and Kim, Y. H., "Spatial Analysis Modeling on Scrub Typhus Disease Occurrence in Korea," J. Korean Cart. Assoc., 14, 41-54(2014).   DOI
6 Kong, W. S., Shin. E. H., Lee, H. I., Hwang, T. S., Kim, H. H., Lee, N. Y., Sung, J. H., Lee, S. G. and Yoon, K. H., "Time-spatial Distribution of Scrub Typhus and its Environmental Ecology," J. Korean. Geogr. Soc., 42(6), 863-878(2007).
7 Yang, L. P., Liu, J., Wang, X. J., Ma, W., Jia, C. X. and Jiang, B. F., "Effects of Meteorological Factors on Scrub Typhus in a Temperate Region of China," Epidemiol. Infect., 142(10), 2217-2226(2014).   DOI
8 Lee, Y. G., Choi, K. H. and Kwak, J. W., "A Study on the Public Health Disasters Using Meteorological Factor: Scrub Typhus in South Korea," J. Korean Soc. Hazard Mitig., 18, 343-350(2018).   DOI
9 Weng, S. C., Lee, H. C., Chen, J. J., Cheng, Y. J., Chi, H. and Lin, C. Y., "Eschar: a Stepping Stone to Scrub Typhus," J. Pediatr., 181, 320(2017).   DOI
10 Lee, I. Y., Ree, H. I. and Hong, H. K., "Seasonal Prevalence and Geographical Distribution of Trombiculid mites (Acarina: Trombiculidae) in Korea," Korean J. Zool., 36(3), 408-415(1993).
11 Xu, G., Walker, D. H., Jupiter, D., Melby, P. C. and Arcari, C. M., "A Review of the Global Epidemiology of Scrub Typhus," PLoS Negl. Trop. Dis., 11(11), e0006062(2017).   DOI
12 Kujala, H., Whitehead, A. L. and Wintle, B. A., "Identifying Conservation Priorities and Assessing Impacts and Trade-offs of Potential Future Development in the Lower Hunter Valley in New South Wales," A Rep. by NERP Environ. Decis. Hub, (2015).
13 Seo, C. W., Park, Y. R. and Choi, Y. S., "Comparison of Species Distribution Models According to Location Data," J. Korean Soc. Geospatial Inf. Syst., 16(4), 59-64(2008).
14 Pearson, R. G., "Species' Distribution Modeling for Conservation Educators and Practitioners," Synth. Am. Museum Nat. Hist., 50, 54-89(2007).
15 Braunisch, V. and Suchant. R., "Predicting Species Distributions Based on Incomplete Survey Data: the Trade off Between Precision and Scale," Ecography, 33(5), 823-840(2010).
16 Jacome, G., Vilela, P. and Yoo, C., "Social-ecological Modelling of the Spatial Distribution of Dengue Fever and its Temporal Dynamics in Guayaquil," Ecol. Inform., 49, 1-12(2019).   DOI
17 Phillips, S. J., Anderson, R. P. and Schapire, R. E., "Maximum Entropy Modeling of Species Geographic Distributions," Ecol. Modell., 190(3-4), 231-259(2006).   DOI
18 Enhofer, O., Pichs-Madruga, R., Sokona, Y., Seyboth, K., Matschoss, P., Kadner, S., Zwickel, T., Eickemeier, P., Hansen, G., Schlomer, S. and Von Stechow, C., "IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation," Cambridge University Press, Cambridge, NY(2011).
19 Elith, J. and Leathwick, J. R., "Species Distribution Models: Ecological Explanation and Prediction Across Space and Time," Annu. Rev. Ecol. Evol. Syst., 40, 677-697(2009).   DOI
20 Kihoro, J., Otieno, R. O. and Wafula, C., "Seasonal Time Series Forecasting: A Comparative Study of ARIMA and ANN Models," Afr. J. Sci. Technol., 5(2), 41-49(2004).
21 Korea meteorological administration, "Climate Change Forecasting Report of Korea," (2018).
22 Nam, K., Hwangbo, S. and Yoo, C. "A Deep Learning-based Forecasting Model for Renewable Energy Scenarios to Guide Sustainable Energy Policy: A Case Study of Korea," Renew. Sust. Energy. Rev., 122, 109725(2020).   DOI
23 Shahbeik, S., Afzal, P., Moarefvand, P. and Qumarsy, M., "Comparison Between Ordinary Kriging (OK) and Inverse Distance Weighted (IDW) Based on Estimation Error. Case study: Dardevey Iron ore Deposit, NE Iran," Arab. J. Geosci., 7(9), 3693-3704(2014).   DOI
24 Brzezinski, D. and Stefanowski, J., "Prequential AUC: Properties of the Area Under the ROC Curve for Data Streams with Concept Drift," Knowl. Inf. Syst., 52(2), 531-562(2017).   DOI
25 McIntosh, A., "The Jackknife Estimation Method," ArXiv Prepr ArXiv, 1606.00497(2016).
26 Nam, K. J., Kim, M. J., Lee, S., Hwangbo, S. and Yoo, C. K. "Interpretation and Diagnosis of Fouling Progress in Membrane Bioreactor Plants Using a Periodic Pattern Recognition Method," Korean J. Chem. Eng., 34(11), 2966-2977(2017).   DOI
27 Hung, P. V. X, Cho, S. H. and Moon, S. H., "Prediction of Boron Transport Through Seawater Reverse Osmosis Membranes Using Solution-diffusion Model," Desalination, 247(1-3), 33-44(2009).   DOI
28 Lee, H. J. and Park, C., "Distribution of Chigger Mites as Tsutsugamushi Vectors Sampled in Seogwipo," Korean J. Clin. Lab. Sci., 51(3), 344-350(2019).   DOI
29 Song, H. J., "Environmental Survey on the Vectors and Hosts of Tsutsugamushi Disease in Jeonnam province, Korea," Korean J. Vet. Serv., 35(3), 183-189(2012).   DOI
30 Kim, S. H. and Jang, J. Y., "Correlations Between Climate Change-Related Infectious Diseases and Meteorological Factors in Korea," J. Prev. Med. Public Heal., 43(5), 436-444(2010).   DOI