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Maulana MR, Yudhastuti R, Lusno MFD, Mirasa YA, Haksama S, Husnina Z. Climate and visitors as the influencing factors of dengue fever in Badung District of Bali, Indonesia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2023; 33:924-935. [PMID: 35435067 DOI: 10.1080/09603123.2022.2065249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 04/07/2022] [Indexed: 06/14/2023]
Abstract
Badung district has recorded the highest dengue fever (DF) in Bali Province. This research presents the distribution of DF in Badung district and analyses its association with climate and visitors. The monthly data of DF, climate and number of visitors during January 2013 to December 2017 were analysed using Poisson Regression. A total of 10,689 new DF cases were notified from January 2013 to December 2017. DF in 2016 was recorded as the heaviest incidence. Monthly DF cases have positive association with average temperature (0.59 (95% CI: 0.56-.62)), precipitation (5.7 x 10-4 (95% CI: 3.8 x 10-4 - 7.6 x 10-4)), humidity (.014 (95% CI: 0.003-.025)) and local visitors (7.40 x 10-6 95% CI: 5.88 x 10-6 : 8.91 x 10-6). Negative association was shown between DF cases with foreign visitors (-2.18 x 10-6 (95% CI: -2.50 x 10-6 : -1.87 x 10-6)). This study underlines the urgency to integrate climate and tourism for DF surveillance.
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Affiliation(s)
- Mochamad Rizal Maulana
- Department of Environmental Health, Faculty of Public Health, Universitas Airlangga, Surabaya, Indonesia
| | - Ririh Yudhastuti
- Department of Environmental Health, Faculty of Public Health, Universitas Airlangga, Surabaya, Indonesia
- Research Center for Tropical Diseases, Infectious Diseases and Herbal Medicine, Faculty of Public Health, Universitas Airlangga, Surabaya, Indonesia
| | - Muhammad Farid Dimjati Lusno
- Department of Environmental Health, Faculty of Public Health, Universitas Airlangga, Surabaya, Indonesia
- Research Center for Tropical Diseases, Infectious Diseases and Herbal Medicine, Faculty of Public Health, Universitas Airlangga, Surabaya, Indonesia
| | | | - Setya Haksama
- Department of Health Administration and Policy, Faculty of Public Health, Universitas Airlangga, Surabaya, Indonesia
| | - Zida Husnina
- Department of Environmental Health, Faculty of Public Health, Universitas Airlangga, Surabaya, Indonesia
- Research Center for Tropical Diseases, Infectious Diseases and Herbal Medicine, Faculty of Public Health, Universitas Airlangga, Surabaya, Indonesia
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2
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Marina R, Ariati J, Anwar A, Astuti EP, Dhewantara PW. Climate and vector-borne diseases in Indonesia: a systematic literature review and critical appraisal of evidence. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:1-28. [PMID: 36367556 DOI: 10.1007/s00484-022-02390-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 09/10/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Climate is widely known as an important driver to transmit vector-borne diseases (VBD). However, evidence of the role of climate variability on VBD risk in Indonesia has not been adequately understood. We conducted a systematic literature review to collate and critically review studies on the relationship between climate variability and VBD in Indonesia. We searched articles on PubMed, Scopus, and Google Scholar databases that are published until December 2021. Studies that reported the relationship of climate and VBD, such as dengue, chikungunya, Zika, and malaria, were included. For the reporting, we followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 66 out of 284 studies were reviewed. Fifty-two (78.8%) papers investigated dengue, 13 (19.7%) papers studied malaria, one (1.5%) paper discussed chikungunya, and no (0%) paper reported on Zika. The studies were predominantly conducted in western Indonesian cities. Most studies have examined the short-term effect of climate variability on the incidence of VBD at national, sub-national, and local levels. Rainfall (n = 60/66; 90.9%), mean temperature (Tmean) (n = 50/66; 75.8%), and relative humidity (RH) (n = 50/66; 75.8%) were the common climatic factors employed in the studies. The effect of climate on the incidence of VBD was heterogenous across locations. Only a few studies have investigated the long-term effects of climate on the distribution and incidence of VBD. The paucity of high-quality epidemiological data and variation in methodology are two major issues that limit the generalizability of evidence. A unified framework is required for future research to assess the impacts of climate on VBD in Indonesia to provide reliable evidence for better policymaking.
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Affiliation(s)
- Rina Marina
- Vector-borne and Zoonotic Diseases Research Group, Research Center for Public Health and Nutrition, Cibinong Science Center, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor KM.46, Bogor, West Java, 16915, Indonesia.
| | - Jusniar Ariati
- Center for Health Services Policy, Health Policy Agency, Ministry of Health of Indonesia, Jl. Percetakan Negara No. 29, Jakarta, 10560, Indonesia
| | - Athena Anwar
- Research Center for Climate and Atmosphere, National Agency for Research and Innovation, Jl. Djunjunan No. 133, Bandung, 40174, Indonesia
| | - Endang Puji Astuti
- Vector-borne and Zoonotic Diseases Research Group, Research Center for Public Health and Nutrition, Cibinong Science Center, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor KM.46, Bogor, West Java, 16915, Indonesia
| | - Pandji Wibawa Dhewantara
- Vector-borne and Zoonotic Diseases Research Group, Research Center for Public Health and Nutrition, Cibinong Science Center, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor KM.46, Bogor, West Java, 16915, Indonesia
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Faridah L, Fauziah N, Agustian D, Mindra Jaya IGN, Eka Putra R, Ekawardhani S, Hidayath N, Damar Djati I, Carvajal TM, Mayasari W, Ruluwedrata Rinawan F, Watanabe K. Temporal Correlation Between Urban Microclimate, Vector Mosquito Abundance, and Dengue Cases. JOURNAL OF MEDICAL ENTOMOLOGY 2022; 59:1008-1018. [PMID: 35305089 PMCID: PMC9113159 DOI: 10.1093/jme/tjac005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Indexed: 05/04/2023]
Abstract
Dengue Hemorrhagic Fever (DHF) is a major mosquito-borne viral disease. Studies have reported a strong correlation between weather, the abundance of Aedes aegypti, the vector of DHF virus, and dengue incidence. However, this conclusion has been based on the general climate pattern of wide regions. In general, however, the human population, level of infrastructure, and land-use change in rural and urban areas often produce localized climate patterns that may influence the interaction between climate, vector abundance, and dengue incidence. Thoroughly understanding this correlation will allow the development of a customized and precise local early warning system. To achieve this purpose, we conducted a cohort study, during January-December 2017, in 16 districts in Bandung, West Java, Indonesia. In the selected areas, local weather stations and modified light mosquito traps were set up to obtain data regarding daily weather and the abundance of adult female Ae. aegypti. A generalized linear model was applied to analyze the effect of local weather and female adult Ae. aegypti on the number of dengue cases. The result showed a significant non-linear correlation among mosquito abundance, maximum temperature, and dengue cases. Using our model, the data showed that the addition of a single adult Ae. aegypti mosquito increased the risk of dengue infection by 1.8%, while increasing the maximum temperature by one degree decreased the risk by 17%. This finding suggests specific actionable insights needed to supplement existing mosquito eradication programs.
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Affiliation(s)
- Lia Faridah
- Parasitology Division, Department of Biomedical Sciences, Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
- Graduate School of Science and Engineering, Ehime University, Bunkyo-cho 3, Matsuyama, Ehime, 790-8577, Japan
- Corresponding author, e-mail: ;
| | - Nisa Fauziah
- Parasitology Division, Department of Biomedical Sciences, Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - Dwi Agustian
- Department of Public Health Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - I Gede Nyoman Mindra Jaya
- Department of Statistics Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - Ramadhani Eka Putra
- School of Life Sciences and Technology, Insitut Teknologi Bandung, Jl. Ganeca 10, Bandung, 40132, West Java, Indonesia
- Biology Department, Insitut Teknologi Sumatera, Jl. Terusan Ryacudu, Desa Way Hui, Bandar Lampung, 35365, Lampung, Indonesia
| | - Savira Ekawardhani
- Parasitology Division, Department of Biomedical Sciences, Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - Nurrachman Hidayath
- Dengue Study Group, Faculty of Medicine, Universitas Padjadjaran, Jl. Prof. Eyckman 38, Bandung, 40131, West Java, Indonesia
| | - Imam Damar Djati
- Faculty of Visual Art and Design, Industrial Design Section, Bandung Institute of Technology, Jl. Ganeca 10, Bandung, 40132, West Java, Indonesia
| | - Thaddeus M Carvajal
- Biological Control Research Unit, Center for Natural Science and Environmental Research-De La Salle University, Taft Ave Manila, Philippines
- Center for Marine Environmental Studies (CMES), Ehime University, Bunkyo-cho 3, Matsuyama, Ehime, Japan
| | - Wulan Mayasari
- Anatomy Division, Department of Biomedical Science, Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang 45363, West Java, Indonesia
| | - Fedri Ruluwedrata Rinawan
- Department of Public Health Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - Kozo Watanabe
- Center for Marine Environmental Studies (CMES), Ehime University, Bunkyo-cho 3, Matsuyama, Ehime, Japan
- Corresponding author, e-mail: ;
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Baharom M, Ahmad N, Hod R, Arsad FS, Tangang F. The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111117. [PMID: 34769638 PMCID: PMC8583681 DOI: 10.3390/ijerph182111117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/17/2021] [Accepted: 10/18/2021] [Indexed: 11/25/2022]
Abstract
Background: Climate change poses a real challenge and has contributed to causing the emergence and re-emergence of many communicable diseases of public health importance. Here, we reviewed scientific studies on the relationship between meteorological factors and the occurrence of dengue, malaria, cholera, and leptospirosis, and synthesized the key findings on communicable disease projection in the event of global warming. Method: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow checklist. Four databases (Web of Science, Ovid MEDLINE, Scopus, EBSCOhost) were searched for articles published from 2005 to 2020. The eligible articles were evaluated using a modified scale of a checklist designed for assessing the quality of ecological studies. Results: A total of 38 studies were included in the review. Precipitation and temperature were most frequently associated with the selected climate-sensitive communicable diseases. A climate change scenario simulation projected that dengue, malaria, and cholera incidence would increase based on regional climate responses. Conclusion: Precipitation and temperature are important meteorological factors that influence the incidence of climate-sensitive communicable diseases. Future studies need to consider more determinants affecting precipitation and temperature fluctuations for better simulation and prediction of the incidence of climate-sensitive communicable diseases.
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Affiliation(s)
- Mazni Baharom
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur 56000, Malaysia; (M.B.); (R.H.); (F.S.A.)
| | - Norfazilah Ahmad
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur 56000, Malaysia; (M.B.); (R.H.); (F.S.A.)
- Correspondence:
| | - Rozita Hod
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur 56000, Malaysia; (M.B.); (R.H.); (F.S.A.)
| | - Fadly Syah Arsad
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur 56000, Malaysia; (M.B.); (R.H.); (F.S.A.)
| | - Fredolin Tangang
- Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia;
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Li C, Zhao Q, Zhao Z, Liu Q, Ma W. The association between tropical cyclones and dengue fever in the Pearl River Delta, China during 2013-2018: A time-stratified case-crossover study. PLoS Negl Trop Dis 2021; 15:e0009776. [PMID: 34499666 PMCID: PMC8454958 DOI: 10.1371/journal.pntd.0009776] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/21/2021] [Accepted: 08/28/2021] [Indexed: 11/23/2022] Open
Abstract
Background Studies have shown that tropical cyclones are associated with several infectious diseases, while very few evidence has demonstrated the relationship between tropical cyclones and dengue fever. This study aimed to examine the potential impact of tropical cyclones on dengue fever incidence in the Pearl River Delta, China. Methods Data on daily dengue fever incidence, occurrence of tropical cyclones and meteorological factors were collected between June and October, 2013–2018 from nine cities in the Pearl River Delta. Multicollinearity of meteorological variables was examined via Spearman correlation, variables with strong correlation (r>0.7) were not included in the model simultaneously. A time-stratified case-crossover design combined with conditional Poisson regression model was performed to evaluate the association between tropical cyclones and dengue fever incidence. Stratified analyses were performed by intensity grades of tropical cyclones (tropical storm and typhoon), sex (male and female) and age-groups (<18, 18–59, ≥60 years). Results During the study period, 20 tropical cyclones occurred and 47,784 dengue fever cases were reported. Tropical cyclones were associated with an increased risk of dengue fever in the Pearl River Delta region, with the largest relative risk of 1.62 with the 95% confidence interval (1.45–1.80) occurring on the lag 5 day. The strength of association was greater and lasted longer for typhoon than for tropical storm. There was no difference in effect estimates between males and females. However, individuals aged over 60 years were more vulnerable than others. Conclusions Tropical cyclones are associated with increased risk of local dengue fever incidence in south China, with the elderly more vulnerable than other population subgroups. Health protective strategies should be developed to reduce the potential risk of dengue epidemic after tropical cyclones. Dengue fever, a mosquito-borne tropical infectious disease, has been increasingly serious in recent decades, causing great healthcare burden in low-latitude regions and countries. Aedes is the vector of dengue fever, particularly sensitive to climatic conditions during all stages of the life cycle. Numerous epidemiological studies have demonstrated the association between dengue fever and meteorological factors (e.g., temperature, precipitation and relative humidity). Tropical cyclones are a common extreme weather events in the low latitude and have been associated with the outbreak of several infectious diseases. However, the impact of tropical cyclones on the incidence of dengue fever has not been well clarified. In this study, we explored the association between tropical cyclones and dengue fever in the Pearl River Delta region, China. The results showed that the local incidence of dengue fever was substantially associated with tropical cyclones over a certain lag period, with the effect estimate greater for stronger tropical cyclones. The elderly was more vulnerable than any other population subgroups. The findings highlighted the importance of developing public health surveillance, preparedness, and response targeting the outbreak of dengue fever during the tropical cyclone season.
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Affiliation(s)
- Chuanxi Li
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong University Climate Change and Health Center, Jinan, China
| | - Qi Zhao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong University Climate Change and Health Center, Jinan, China
| | - Zhe Zhao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong University Climate Change and Health Center, Jinan, China
| | - Qiyong Liu
- Shandong University Climate Change and Health Center, Jinan, China.,State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Wei Ma
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong University Climate Change and Health Center, Jinan, China
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Faridah L, Mindra IGN, Putra RE, Fauziah N, Agustian D, Natalia YA, Watanabe K. Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk. Trop Med Health 2021; 49:44. [PMID: 34039439 PMCID: PMC8152360 DOI: 10.1186/s41182-021-00329-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 05/03/2021] [Indexed: 01/02/2023] Open
Abstract
Background Bandung, the fourth largest city in Indonesia and capital of West Java province, has been considered a major endemic area of dengue, and studies show that the incidence in this city could increase and spread rapidly. At the same time, estimation of incidence could be inaccurate due to a lack of reliable surveillance systems. To provide strategic information for the dengue control program in the face of limited capacity, this study used spatial pattern analysis of a possible outbreak of dengue cases, through the Geographic Information System (GIS). To further enhance the information needed for effective policymaking, we also analyzed the demographic pattern of dengue cases. Methods Monthly reports of dengue cases from January 2014 to December 2016 from 16 hospitals in Bandung were collected as the database, which consisted of address, sex, age, and code to anonymize the patients. The address was then transformed into geocoding and used to estimate the relative risk of a particular area’s developing a cluster of dengue cases. We used the kernel density estimation method to analyze the dynamics of change of dengue cases. Results The model showed that the spatial cluster of the relative risk of dengue incidence was relatively unchanged for 3 years. Dengue high-risk areas predominated in the southern and southeastern parts of Bandung, while low-risk areas were found mostly in its western and northeastern regions. The kernel density estimation showed strong cluster groups of dengue cases in the city. Conclusions This study demonstrated a strong pattern of reported cases related to specific demographic groups (males and children). Furthermore, spatial analysis using GIS also visualized the dynamic development of the aggregation of disease incidence (hotspots) for dengue cases in Bandung. These data may provide strategic information for the planning and design of dengue control programs.
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Affiliation(s)
- Lia Faridah
- Parasitology Division, Department of Biomedical Science, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia. .,Foreign Visiting Researcher at Department of Civil and Environmental Engineering, Ehime University, Matsuyama, Japan.
| | | | - Ramadhani Eka Putra
- School of Life Science and Technology, Institut Teknologi Bandung, Jl. Ganeca 10, Bandung, West Java, 40132, Indonesia
| | - Nisa Fauziah
- Parasitology Division, Department of Biomedical Science, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Dwi Agustian
- Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Yessika Adelwin Natalia
- Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Kozo Watanabe
- Department of Civil and Environmental Engineering, Ehime University, Matsuyama, Japan
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Prasetyowati H, Dhewantara PW, Hendri J, Astuti EP, Gelaw YA, Harapan H, Ipa M, Widyastuti W, Handayani DOTL, Salama N, Picasso M. Geographical heterogeneity and socio-ecological risk profiles of dengue in Jakarta, Indonesia. GEOSPATIAL HEALTH 2021; 16. [PMID: 33733650 DOI: 10.4081/gh.2021.948] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 01/26/2021] [Indexed: 06/12/2023]
Abstract
The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman's rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran's I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= -0.459, P<0.01) and Tmin (r= -0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.
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Affiliation(s)
- Heni Prasetyowati
- Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Pangandaran.
| | - Pandji Wibawa Dhewantara
- Center for Research and Development of Public Health Effort, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Jakarta.
| | - Joni Hendri
- Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Pangandaran.
| | - Endang Puji Astuti
- Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Pangandaran.
| | - Yalemzewod Assefa Gelaw
- Population Child Health Research Group, School of Women's and Children's Health, UNSW, NSW Australia; Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar.
| | - Harapan Harapan
- Medical Research Unit, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia; Department of Microbiology, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh.
| | - Mara Ipa
- Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Pangandaran.
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Pasaribu AP, Tsheten T, Yamin M, Maryani Y, Fahmi F, Clements ACA, Gray DJ, Wangdi K. Spatio-Temporal Patterns of Dengue Incidence in Medan City, North Sumatera, Indonesia. Trop Med Infect Dis 2021; 6:tropicalmed6010030. [PMID: 33807820 PMCID: PMC8006016 DOI: 10.3390/tropicalmed6010030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 02/17/2021] [Accepted: 02/25/2021] [Indexed: 01/04/2023] Open
Abstract
Dengue has been a perennial public health problem in Medan city, North Sumatera, despite the widespread implementation of dengue control. Understanding the spatial and temporal pattern of dengue is critical for effective implementation of dengue control strategies. This study aimed to characterize the epidemiology and spatio-temporal patterns of dengue in Medan City, Indonesia. Data on dengue incidence were obtained from January 2016 to December 2019. Kulldorff’s space-time scan statistic was used to identify dengue clusters. The Getis-Ord Gi* and Anselin Local Moran’s I statistics were used for further characterisation of dengue hotspots and cold spots. Results: A total of 5556 cases were reported from 151 villages across 21 districts in Medan City. Annual incidence in villages varied from zero to 439.32 per 100,000 inhabitants. According to Kulldorf’s space-time scan statistic, the most likely cluster was located in 27 villages in the south-west of Medan between January 2016 and February 2017, with a relative risk (RR) of 2.47. Getis-Ord Gi* and LISA statistics also identified these villages as hotpot areas. Significant space-time dengue clusters were identified during the study period. These clusters could be prioritized for resource allocation for more efficient prevention and control of dengue.
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Affiliation(s)
- Ayodhia Pitaloka Pasaribu
- Department of Pediatrics, Medical School, Universitas Sumatera Utara, Medan 20155, North Sumatera, Indonesia
- Correspondence: ; Tel.: +62-8126024392
| | - Tsheten Tsheten
- Department of Global Health, Research School of Population Health, The Australian National University, Acton, Canberra, ACT 2601, Australia; (T.T.); (D.J.G.); (K.W.)
| | - Muhammad Yamin
- Medical School, Universitas Sumatera Utara, Medan 20155, North Sumatera, Indonesia;
| | - Yulia Maryani
- North Sumatera Provincial Health Office, Medan 20232, North Sumatera, Indonesia;
| | - Fahmi Fahmi
- Faculty of Engineering, Universitas Sumatera Utara, Medan 20155, North Sumatera, Indonesia;
| | - Archie C. A. Clements
- Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia;
- Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Darren J. Gray
- Department of Global Health, Research School of Population Health, The Australian National University, Acton, Canberra, ACT 2601, Australia; (T.T.); (D.J.G.); (K.W.)
| | - Kinley Wangdi
- Department of Global Health, Research School of Population Health, The Australian National University, Acton, Canberra, ACT 2601, Australia; (T.T.); (D.J.G.); (K.W.)
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Tsheten T, Mclure A, Clements ACA, Gray DJ, Wangdi T, Wangchuk S, Wangdi K. Epidemiological Analysis of the 2019 Dengue Epidemic in Bhutan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18010354. [PMID: 33466497 PMCID: PMC7796457 DOI: 10.3390/ijerph18010354] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/25/2020] [Accepted: 12/31/2020] [Indexed: 12/16/2022]
Abstract
Bhutan experienced its largest and first nation-wide dengue epidemic in 2019. The cases in 2019 were greater than the total number of cases in all the previous years. This study aimed to characterize the spatiotemporal patterns and effective reproduction number of this explosive epidemic. Weekly notified dengue cases were extracted from the National Early Warning, Alert, Response and Surveillance (NEWARS) database to describe the spatial and temporal patterns of the epidemic. The time-varying, temperature-adjusted cohort effective reproduction number was estimated over the course of the epidemic. The dengue epidemic occurred between 29 April and 8 December 2019 over 32 weeks, and included 5935 cases. During the epidemic, dengue expanded from six to 44 subdistricts. The effective reproduction number was <3 for most of the epidemic period, except for a ≈1 month period of explosive growth, coinciding with the monsoon season and school vacations, when the effective reproduction number peaked >30 and after which the effective reproduction number declined steadily. Interventions were only initiated 6 weeks after the end of the period of explosive growth. This finding highlights the need to reinforce the national preparedness plan for outbreak response, and to enable the early detection of cases and timely response.
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Affiliation(s)
- Tsheten Tsheten
- Research School of Population, Australian National University, Acton, Canberra, ACT 2601, Australia; (A.M.); (D.J.G.); (K.W.)
- Royal Centre for Disease Control, Ministry of Health, Thimphu 11001, Bhutan;
- Correspondence:
| | - Angus Mclure
- Research School of Population, Australian National University, Acton, Canberra, ACT 2601, Australia; (A.M.); (D.J.G.); (K.W.)
| | - Archie C. A. Clements
- Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia;
- Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Darren J. Gray
- Research School of Population, Australian National University, Acton, Canberra, ACT 2601, Australia; (A.M.); (D.J.G.); (K.W.)
| | - Tenzin Wangdi
- Vector-Borne Disease Control Program, Ministry of Health, Gelephu 31102, Bhutan;
| | - Sonam Wangchuk
- Royal Centre for Disease Control, Ministry of Health, Thimphu 11001, Bhutan;
| | - Kinley Wangdi
- Research School of Population, Australian National University, Acton, Canberra, ACT 2601, Australia; (A.M.); (D.J.G.); (K.W.)
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10
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Harapan H, Yufika A, Anwar S, Te H, Hasyim H, Nusa R, Dhewantara PW, Mudatsir M. Effects of El Niño Southern Oscillation and Dipole Mode Index on Chikungunya Infection in Indonesia. Trop Med Infect Dis 2020; 5:E119. [PMID: 32708686 PMCID: PMC7558115 DOI: 10.3390/tropicalmed5030119] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 07/02/2020] [Indexed: 11/16/2022] Open
Abstract
The aim of this study was to assess the possible association of El Niño Southern Oscillation (ENSO) and Dipole Mode Index (DMI) on chikungunya incidence overtime, including the significant reduction in cases that was observed in 2017 in Indonesia. Monthly nation-wide chikungunya case reports were obtained from the Indonesian National Disease Surveillance database, and incidence rates (IR) and case fatality rate (CFR) were calculated. Monthly data of Niño3.4 (indicator used to represent the ENSO) and DMI between 2011 and 2017 were also collected. Correlations between monthly IR and CFR and Niño3.4 and DMI were assessed using Spearman's rank correlation. We found that chikungunya case reports declined from 1972 cases in 2016 to 126 cases in 2017, a 92.6% reduction; the IR reduced from 0.67 to 0.05 cases per 100,000 population. No deaths associated with chikungunya have been recorded since its re-emergence in Indonesia in 2001. There was no significant correlation between monthly Niño3.4 and chikungunya incidence with r = -0.142 (95%CI: -0.320-0.046), p = 0.198. However, there was a significant negative correlation between monthly DMI and chikungunya incidence, r = -0.404 (95%CI: -0.229--0.554) with p < 0.001. In conclusion, our initial data suggests that the climate variable, DMI but not Niño3.4, is likely associated with changes in chikungunya incidence. Therefore, further analysis with a higher resolution of data, using the cross-wavelet coherence approach, may provide more robust evidence.
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Affiliation(s)
- Harapan Harapan
- Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh 23111, Indonesia; (A.Y.); (M.M.)
- Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh 23111, Indonesia
- Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh 23111, Indonesia
| | - Amanda Yufika
- Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh 23111, Indonesia; (A.Y.); (M.M.)
- Department of Family Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh 23111, Indonesia
| | - Samsul Anwar
- Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Aceh 23111, Indonesia;
| | - Haypheng Te
- Siem Reap Provincial Health Department, Ministry of Health, Siem Reap 1710, Cambodia;
| | - Hamzah Hasyim
- Faculty of Public Health, Sriwijaya University, Indralaya, South Sumatra 30862, Indonesia;
| | - Roy Nusa
- Vector-Borne Disease Control, Research and Development Council, Ministry of Health, Jakarta 10560, Indonesia;
| | - Pandji Wibawa Dhewantara
- Pangandaran Unit of Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, West Java 46396, Indonesia;
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton, QLD 4343, Australia
| | - Mudatsir Mudatsir
- Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh 23111, Indonesia; (A.Y.); (M.M.)
- Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh 23111, Indonesia
- Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh 23111, Indonesia
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11
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Xu J, Xu K, Li Z, Meng F, Tu T, Xu L, Liu Q. Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17020453. [PMID: 31936708 PMCID: PMC7014037 DOI: 10.3390/ijerph17020453] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 01/07/2020] [Accepted: 01/08/2020] [Indexed: 11/16/2022]
Abstract
Dengue fever (DF) is one of the most rapidly spreading diseases in the world, and accurate forecasts of dengue in a timely manner might help local government implement effective control measures. To obtain the accurate forecasting of DF cases, it is crucial to model the long-term dependency in time series data, which is difficult for a typical machine learning method. This study aimed to develop a timely accurate forecasting model of dengue based on long short-term memory (LSTM) recurrent neural networks while only considering monthly dengue cases and climate factors. The performance of LSTM models was compared with the other previously published models when predicting DF cases one month into the future. Our results showed that the LSTM model reduced the average the root mean squared error (RMSE) of the predictions by 12.99% to 24.91% and reduced the average RMSE of the predictions in the outbreak period by 15.09% to 26.82% as compared with other candidate models. The LSTM model achieved superior performance in predicting dengue cases as compared with other previously published forecasting models. Moreover, transfer learning (TL) can improve the generalization ability of the model in areas with fewer dengue incidences. The findings provide a more precise forecasting dengue model and could be used for other dengue-like infectious diseases.
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Affiliation(s)
- Jiucheng Xu
- College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China; (J.X.); (K.X.)
- Engineering Technology Research Center for Computing Intelligence and Data Mining, Xinxiang 453007, China
- Engineering Lab of Intelligence Business & Internet of Things, Xinxiang 453007, China
| | - Keqiang Xu
- College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China; (J.X.); (K.X.)
- Engineering Lab of Intelligence Business & Internet of Things, Xinxiang 453007, China
| | - Zhichao Li
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;
- Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University, Beijing 100084, China
| | - Fengxia Meng
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China;
| | - Taotian Tu
- Institute of Disinfection and Vector Biological Control, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China;
| | - Lei Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;
- Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China;
- Correspondence: (L.X.); (Q.L.)
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China;
- Correspondence: (L.X.); (Q.L.)
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12
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Xu Z, Bambrick H, Yakob L, Devine G, Frentiu FD, Marina R, Dhewantara PW, Nusa R, Sasmono RT, Hu W. Using dengue epidemics and local weather in Bali, Indonesia to predict imported dengue in Australia. ENVIRONMENTAL RESEARCH 2019; 175:213-220. [PMID: 31136953 DOI: 10.1016/j.envres.2019.05.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 05/09/2019] [Accepted: 05/14/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND Although the association between dengue in Bali, Indonesia, and imported dengue in Australia has been widely asserted, no study has quantified this association so far. METHODS Monthly data on dengue and climatic factors over the past decade for Bali and Jakarta as well as monthly data on imported dengue in Australia underwent a three-stage analysis. Stage I: a quasi-Poisson regression with distributed lag non-linear model was used to assess the associations of climatic factors with dengue in Bali. Stage II: a generalized additive model was used to quantify the association of dengue in Bali with imported dengue in Australia with and without including the number of travelers in log scale as an offset. Stage III: the associations of mean temperature and rainfall (two climatic factors identified in stage I) in Bali with imported dengue in Australia were examined using stage I approach. RESULTS The number of dengue cases in Bali increased with increasing mean temperature, and, up to a certain level, it also increased with increasing rainfall but dropped off for high levels of rainfall. Above a monthly incidence of 1.05 cases per 100,000, dengue in Bali was almost linearly associated with imported dengue in Australia at a lag of one month. Mean temperature (relative risk (RR) per 0.5 °C increase: 2.95, 95% confidence interval (CI): 1.87, 4.66) and rainfall (RR per 7.5 mm increase: 3.42, 95% CI: 1.07, 10.92) in Bali were significantly associated with imported dengue in Australia at a lag of four months. CONCLUSIONS This study suggests that climatic factors (i.e., mean temperature and rainfall) known to be conducive of dengue transmission in Bali can provide an early warning with 4-month lead time for Australia in order to mitigate future outbreaks of local dengue in Australia. This study also provides a template and framework for future surveillance of travel-related infectious diseases globally.
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Affiliation(s)
- Zhiwei Xu
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, 4059, Australia; Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, 4059, Australia
| | - Hilary Bambrick
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, 4059, Australia; Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, 4059, Australia
| | - Laith Yakob
- Department of Disease Control, London School of Hygiene and Tropical Medicine, London, WC1H 9SH, UK
| | - Gregor Devine
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, 4006, Australia
| | - Francesca D Frentiu
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, 4059, Australia; School of Biomedical Sciences, Queensland University of Technology, Brisbane, 4059, Australia
| | - Rina Marina
- Center of Public Health Effort Research and Development, National Institute of Health Research and Development, Jakarta, 10560, Indonesia
| | - Pandji Wibawa Dhewantara
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, University of Queensland, Gatton, 4343, Australia; Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development, Ministry of Health of Indonesia, Pangandaran, 46396, Indonesia
| | - Roy Nusa
- Indonesian Ministry of Health, Jakarta, 12950, Indonesia
| | - R Tedjo Sasmono
- Eijkman Institute for Molecular Biology, Jakarta, 10430, Indonesia
| | - Wenbiao Hu
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, 4059, Australia; Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, 4059, Australia.
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13
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Paediatric dengue infection in Cirebon, Indonesia: a temporal and spatial analysis of notified dengue incidence to inform surveillance. Parasit Vectors 2019; 12:186. [PMID: 31036062 PMCID: PMC6489314 DOI: 10.1186/s13071-019-3446-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Accepted: 04/15/2019] [Indexed: 11/17/2022] Open
Abstract
Background The recent situation of dengue infection in Cirebon district is concerning due to an upsurge trend since the year 2010. The largest dengue outbreak was reported in 2016 which has affected more than 1600 children. A study was conducted to explore the temporal variability of dengue outbreak in Cirebon’s child population in during 2011–2017, and to assess the short-term effects of climatic and environmental factor on dengue incidence. In addition, the spatial pattern of dengue incidence in children and high-risk villages were investigated. Methods A total of 4597 confirmed dengue cases in children notified from January 2011 to December 2017 were analysed. Seasonal decomposition analysis was carried out to examine the annual seasonality. A generalized linear model (GLM) was applied to assess the short-term effect of climate and normalized difference vegetation index (NDVI) on dengue incidence. The incidence rate ratio (IRR) of the final model was reported. Spatial analyses were conducted by using Moran’s I and local indicator of spatial association (LISA) analyses to explore geographical clustering in incidence and to identify high-risk villages for dengue, respectively. Results An annual dengue epidemic period was observed with peaks occurring every January/February. Based on the GLM, temperature at a lag 4 months (IRR = 1.27; 95% confidence interval, 95% CI: 1.22–1.31, P < 0.001), rainfall at a lag 2 months (IRR = 0.99, 95% CI: 0.99–0.99, P < 0.001), humidity at lag 0 month (IRR = 1.05, 95% CI: 1.04–1.06, P < 0.001) and NDVI at a lag 1 month (IRR = 3.07, 95% CI: 1.94–4.86, P < 0.001) were associated with dengue incidence in children. The dengue incidence in children was spatially varied and clustered at the village level across Cirebon. During 2011–2017, a total of 38 high-risk villages for dengue were identified, which were mainly located in the northern part of Cirebon. Conclusions Seasonal patterns of dengue incidence in children in Cirebon were strongly associated with rainfall, temperature, humidity and NDVI variability, suggesting that climatic and environmental data could be used to help predict dengue outbreaks. Our spatial analysis revealed a clustered pattern in dengue incidence and high-risk villages for dengue across Cirebon, suggesting that effective interventions such as vector surveillance and school-based campaigns should be prioritized around the identified high-risk villages. Temporal and spatial analytical tools could be utilized to support local health authorities to apply timely and targeted public health interventions and help better planning and decision-making in order to minimize the impact of dengue outbreaks. Electronic supplementary material The online version of this article (10.1186/s13071-019-3446-3) contains supplementary material, which is available to authorized users.
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