Korean Chemical Engineering Research

The Korean Institute of Chemical Engineers (한국화학공학회)
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Spatio-Temporal Incidence Modeling and Prediction of the Vector-Borne Disease Using an Ecological Model and Deep Neural Network for Climate Change Adaption

기후 변화 적응을 위한 벡터매개질병의 생태 모델 및 심층 인공 신경망 기반 공간-시간적 발병 모델링 및 예측

  • 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)
  • 김상윤 (경희대학교 공과대학 환경학 및 환경공학과) ;
  • 남기전 (경희대학교 공과대학 환경학 및 환경공학과) ;
  • 허성구 (경희대학교 공과대학 환경학 및 환경공학과) ;
  • 이선정 (경희대학교 공과대학 환경학 및 환경공학과) ;
  • 최지훈 (경희대학교 공과대학 환경학 및 환경공학과) ;
  • 박준규 (경희대학교 공과대학 환경학 및 환경공학과) ;
  • 유창규 (경희대학교 공과대학 환경학 및 환경공학과)
  • Received : 2020.01.24
  • Accepted : 2020.03.06
  • Published : 2020.05.01
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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.

본 연구에서는 발병 횟수가 빠르게 증가하고 있는 벡터매개질병(vector-borne disease) 중 하나인 쯔쯔가무시증의 발병 특성을 공간적 그리고 시간적으로 분석하고 기후변화 시나리오에 따른 미래 발병 특성을 예측하였다. 쯔쯔가무시증의 공간적 분포와 발병률을 예측하기 위하여 환경 그리고 사회 변수의 공간적 특성을 이용하여 maximum entropy(MaxEnt) 생태 모델을 구성하고, 주요 변수의 쯔쯔가무시증 발병에 관한 상관관계를 분석하였다. 공간 특성 중 환경변수인 고도 및 기온이 주요한 변수로 분석되었으며, 이는 쯔쯔가무시증의 매개체인 털진드기의 생육 환경과 주요 관련이 있는 것으로 나타났다. 쯔쯔가무시증의 시간적 발병 횟수는 심층 인공 신경망 모델기반 예측을 하였으며, 특히 쯔쯔가무시증의 주요 특성인 지연 효과를 고려하여 모델을 구성하였다. 심층 인공 신경망을 이용한 예측 결과 여름철의 기온, 강우량, 그리고 습도가 털진드기의 활동에 주된 관련이 있으며 가을철의 쯔쯔가무시증 발병 횟수에 영향을 끼치는 것으로 확인 되었다. 또한, 기존 통계적 예측 모델과 비교하였을 때, 심층 인공 신경망 기반 예측 모델의 예측 정확성이 우수함을 확인하였다. 공간적 그리고 시간적 모델에 기후 변화 시나리오를 이용하여 2040년의 쯔쯔가무시증 발병 특성을 예측한 결과, 최대 발병률이 8% 증가, 발병률이 높은 지역이 9% 확대, 그리고 주된 발병 기간이 2개월 증가하였다. 본 연구 결과를 통해 쯔쯔가무시증의 공간적 및 시간적 발병 특성 분석을 통하여, 공중보건 측면에서 벡터매개 질병 발병 요인 규명을 통해 주민 건강을 위한 질병 관리 및 예측에 기여할 수 있을 것으로 기대한다.

Keywords

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