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Mamenun, Koesmaryono Y, Sopaheluwakan A, Hidayati R, Dasanto BD, Aryati R. Spatiotemporal Characterization of Dengue Incidence and Its Correlation to Climate Parameters in Indonesia. INSECTS 2024; 15:366. [PMID: 38786922 PMCID: PMC11122138 DOI: 10.3390/insects15050366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 05/02/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024]
Abstract
Dengue has become a public health concern in Indonesia since it was first found in 1968. This study aims to determine dengue hotspot areas and analyze the spatiotemporal distribution of dengue and its association with dominant climate parameters nationally. Monthly data for dengue and climate observations (i.e., rainfall, relative humidity, average, maximum, and minimum temperature) at the regency/city level were utilized. Dengue hotspot areas were determined through K-means clustering, while Singular Value Decomposition (SVD) determined dominant climate parameters and their spatiotemporal distribution. Results revealed four clusters: Cluster 1 comprised cities with medium to high Incidence Rates (IR) and high Case Densities (CD) in a narrow area. Cluster 2 has a high IR and low CD, and clusters 3 and 4 featured medium and low IR and CD, respectively. SVD analysis indicated that relative humidity and rainfall were the most influential parameters on IR across all clusters. Temporal fluctuations in the first mode of IR and climate parameters were clearly delineated. The spatial distribution of heterogeneous correlation between the first mode of rainfall and relative humidity to IR exhibited higher values, which were predominantly observed in Java, Bali, Nusa Tenggara, the eastern part of Sumatra, the southern part of Kalimantan, and several locations in Sulawesi.
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Affiliation(s)
- Mamenun
- Center for Applied Climate Information and Services, Indonesian Agency for Meteorology Climatology and Geophysics, Jakarta 10720, Indonesia;
- Department of Geophysics and Meteorology, IPB University, Bogor 16680, Indonesia; (R.H.); (B.D.D.)
| | - Yonny Koesmaryono
- Department of Geophysics and Meteorology, IPB University, Bogor 16680, Indonesia; (R.H.); (B.D.D.)
| | - Ardhasena Sopaheluwakan
- Deputy for Climatology, Indonesian Agency for Meteorology Climatology and Geophysics, Jakarta 10720, Indonesia;
| | - Rini Hidayati
- Department of Geophysics and Meteorology, IPB University, Bogor 16680, Indonesia; (R.H.); (B.D.D.)
- Center for Climate Risk and Opportunity Management in South Asia Pacific, IPB University, Bogor 16143, Indonesia
| | - Bambang Dwi Dasanto
- Department of Geophysics and Meteorology, IPB University, Bogor 16680, Indonesia; (R.H.); (B.D.D.)
| | - Rita Aryati
- Directorate of Prevention and Control of Infectious Diseases, Ministry of Health, Jakarta 12950, Indonesia;
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Li Z, Gurgel H, Dessay N, Hu L, Xu L, Gong P. Semi-Supervised Text Classification Framework: An Overview of Dengue Landscape Factors and Satellite Earth Observation. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E4509. [PMID: 32585932 PMCID: PMC7344967 DOI: 10.3390/ijerph17124509] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/17/2020] [Accepted: 06/18/2020] [Indexed: 12/29/2022]
Abstract
In recent years there has been an increasing use of satellite Earth observation (EO) data in dengue research, in particular the identification of landscape factors affecting dengue transmission. Summarizing landscape factors and satellite EO data sources, and making the information public are helpful for guiding future research and improving health decision-making. In this case, a review of the literature would appear to be an appropriate tool. However, this is not an easy-to-use tool. The review process mainly includes defining the topic, searching, screening at both title/abstract and full-text levels and data extraction that needs consistent knowledge from experts and is time-consuming and labor intensive. In this context, this study integrates the review process, text scoring, active learning (AL) mechanism, and bidirectional long short-term memory (BiLSTM) networks, and proposes a semi-supervised text classification framework that enables the efficient and accurate selection of the relevant articles. Specifically, text scoring and BiLSTM-based active learning were used to replace the title/abstract screening and full-text screening, respectively, which greatly reduces the human workload. In this study, 101 relevant articles were selected from 4 bibliographic databases, and a catalogue of essential dengue landscape factors was identified and divided into four categories: land use (LU), land cover (LC), topography and continuous land surface features. Moreover, various satellite EO sensors and products used for identifying landscape factors were tabulated. Finally, possible future directions of applying satellite EO data in dengue research in terms of landscape patterns, satellite sensors and deep learning were proposed. The proposed semi-supervised text classification framework was successfully applied in research evidence synthesis that could be easily applied to other topics, particularly in an interdisciplinary context.
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Affiliation(s)
- Zhichao Li
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System, Science, Tsinghua University, Beijing 100084, China; (Z.L.); (L.X.)
| | - Helen Gurgel
- Department of Geography, University of Brasilia (UnB), Brasilia CEP 70910-900, Brazil;
- International Joint Laboratory Sentinela, FIOCRUZ, UnB, IRD, Rio de Janeiro RJ-21040-900, Brazil;
| | - Nadine Dessay
- International Joint Laboratory Sentinela, FIOCRUZ, UnB, IRD, Rio de Janeiro RJ-21040-900, Brazil;
- IRD, UM, UR, UG, UA, UMR ESPACE-DEV, 34090 Montpellier, France
| | - Luojia Hu
- Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China;
| | - Lei Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System, Science, Tsinghua University, Beijing 100084, China; (Z.L.); (L.X.)
| | - Peng Gong
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System, Science, Tsinghua University, Beijing 100084, China; (Z.L.); (L.X.)
- Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University, Beijing 100084, China
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