Dengue Fever Dynamics in Bali, Indonesia 2010-2018: An Interplay of Population Density and Climatic Factors
Abstract
Dengue Fever (DF) incidence in Bali has been the highest in Indonesia for decades. This study describes the annual distribution of DF and analyzes its association with population density, number of rainy days, and average humidity during 2010-2018 at the district level. The choropleth maps and Poisson regression were employed to provide geographical distribution and quantify the association. The P, 95% confidence interval (CI), and Akaike Information Criterion (AIC) were adopted to assess the significance and the goodness of the association. During 2010-2018 there were 55 215 new DF cases notified. The annual incidence of dengue cases in Bali increased with IRR: 1.000186 (95% CI:1.0000183:1.000189) for every increment of population density per kilometers square and increased by IRR: 1.01043 (95% CI: 1.01019: 1.01078) for every additional one rainy day annually. The dengue cases also increased with IRR 1.0172 (95% CI: 1.0137: 1.0208) for every 1% increase in average humidity. Population density and climate factors are positively associated with dengue cases incidence in Bali from 2010 to 2018. The results underline the urgency of integrating population dynamics and climatic determinants into the DF control program and customizing the intervention program based on local characteristics.
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28. da Cruz Ferreira DA, Degener CM, de Almeida Marques-Toledo C, Bendati MM, Fetzer LO, Teixeira CP, et al. Meteorological variables and mosquito monitoring are good predictors for infestation trends of Aedes aegypti, the vector of dengue, chikungunya and Zika. Parasites & vectors. 2017;10(1):78.Available from: https://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-017-2025-8.
2. Nagpal BN, Knox TB, Risintha P, Yadav RS, Ghosh SK, Uragayala S, et al. Strengthening of vector control in South-East Asia: Outcomes from a WHO regional workshop. Journal of vector borne diseases. 2018;55(4):247.Available from: https://www.jvbd.org/article.asp?issn=0972-9062;year=2018;volume=55;issue=4;spage=247;epage=257;aulast=Nagpal.
3. MOH. Indonesia Health Profile 2018. Jakarta: Ministry of Health; 2018 Maret 2019.
4. MOH. Data and Information, Indonesian Health Profile 2017. In: Health Mo, editor. Jakarta2018.
5. Statistical Data 2019: Direct Foreign Tourist Arrivals to Bali [Internet]. The Tourism Office of Bali Province. 2019 [cited 19 October 2019]. Available from: https://disparda.baliprov.go.id/category/data-statistik-tahun-2019/.
6. Rovida F, Percivalle E, Campanini G, Piralla A, Novati S, Muscatello A, et al. Viremic dengue virus infections in travellers: potential for local outbreak in Northern Italy. Journal of Clinical Virology. 2011;50(1):76-9.Available from: https://doi.org/10.1016/j.jcv.2010.09.015.
7. Yoshikawa M. Dengue virus infection in Bali: a challenge to a popular tourist destination in Southeast Asia. J Jpn Soc Travel Health. 2010;4(1):19-23.Available from: https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201002267232162316.
8. Xu Z, Bambrick H, Yakob L, Devine G, Frentiu FD, Marina R, et al. Using dengue epidemics and local weather in Bali, Indonesia to predict imported dengue in Australia. Environmental research. 2019;175:213-20.Available from: https://doi.org/10.1016/j.envres.2019.05.021.
9. Ujwal S, Sabeena S, Bhaskar R, D'Souza G, Santhosha D, Auti A, et al. Circulation of Asian-I and Cosmopolitan genotypes of Dengue-2 virus in northeast India, 2016–2017. Journal of Vector Borne Diseases. 2019;56(3):231.Available from: https://doi.org/10.4103/0972-9062.289402.
10. Chaves LF, Harrington LC, Keogh CL, Nguyen AM, Kitron UD. Blood feeding patterns of mosquitoes: random or structured? Frontiers in zoology. 2010;7(1):3.Available from: https://doi.org/10.1186/1742-9994-7-3.
11. Vasilakis N, Cardosa J, Hanley KA, Holmes EC, Weaver SC. Fever from the forest: prospects for the continued emergence of sylvatic dengue virus and its impact on public health. Nature Reviews Microbiology. 2011;9(7):532.Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3321645/.
12. Thai KT, Anders KL. The role of climate variability and change in the transmission dynamics and geographic distribution of dengue. Experimental biology and medicine. 2011;236(8):944-54.Available from: https://doi.org/10.1258/ebm.2011.010402.
13. Arcari P, Tapper N, Pfueller S. Regional variability in relationships between climate and dengue/DHF in Indonesia. Singapore Journal of Tropical Geography. 2007;28(3):251-72.Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9493.2007.00300.x.
14. Murray NEA, Quam MB, Wilder-Smith A. Epidemiology of dengue: past, present and future prospects. Clinical epidemiology. 2013;5:299.Available from: https://doi.org/10.2147/CLEP.S34440.
15. Yoshikawa MJ, Kusriastuti R. Surge of dengue virus infection and chikungunya Fever in bali in 2010: the burden of mosquito-borne infectious diseases in a tourist destination. Tropical medicine and health. 2013:2011-05.Available from: https://www.jstage.jst.go.jp/article/tmh/41/2/41_2011-05/_article.
16. Dhewantara PW, Marina R, Puspita T, Ariati Y, Purwanto E, Hananto M, et al. Spatial and temporal variation of dengue incidence in the island of Bali, Indonesia: An ecological study. Travel medicine and infectious disease. 2019:101437.Available from: https://doi.org/10.1016/j.tmaid.2019.06.008.
17. Rossita A, Witono A, Darusman T, Lestari DP, Risdiyanto I. Water table depth fluctuations during ENSO phenomenon on different tropical peat swamp forest land covers in Katingan, Indonesia. IOP Conference Series: Earth and Environmental Science. 2018;129(conference 1).Available from: http://iopscience.iop.org/article/10.1088/1755-1315/129/1/012001/meta.
18. Wen T-H, Hsu C-S, Hu M-C. Evaluating neighborhood structures for modeling intercity diffusion of large-scale dengue epidemics. International journal of health geographics. 2018;17(1):9.Available from: https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-018-0131-2.
19. Oyana TJ, Margai FM. Spatial Analysis, Statistics, Visualization and Computational Methods. New York: CRC Press; 2016.
20. Burattini MN, Chen M, Chow A, Coutinho F, Goh K, Lopez L, et al. Modelling the control strategies against dengue in Singapore. Epidemiology & Infection. 2008;136(3):309-19.Available from: https://doi.org/10.1017/S0950268807008667.
21. Sharmin S, Glass K, Viennet E, Harley D. Interaction of mean temperature and daily fluctuation influences dengue incidence in Dhaka, Bangladesh. PLoS neglected tropical diseases. 2015;9(7):e0003901.Available from: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0003901.
22. Sunahara T. Simulation Study of the Effects of Host Availability on Bite Rate of Aedes albopictus (Skuse)(Diptera: Culicidae) and Risk of Dengue Outbreaks in Non-Endemic Areas. Japanese journal of infectious diseases. 2018;71(1):28-32.Available from: https://www.jstage.jst.go.jp/article/yoken/71/1/71_JJID.2017.312/_article.
23. Domingo E, Parrish CR, Holland JJ. Origin and evolution of viruses: Elsevier; 2008.Available from: https://doi.org/10.1016/B978-0-12-374153-0.X0001-X.
24. Ehelepola N, Ariyaratne K, Buddhadasa W, Ratnayake S, Wickramasinghe M. A study of the correlation between dengue and weather in Kandy City, Sri Lanka (2003-2012) and lessons learned. Infectious diseases of poverty. 2015;4(1):42.Available from: https://idpjournal.biomedcentral.com/articles/10.1186/s40249-015-0075-8.
25. Sharmin S, Glass K, Viennet E, Harley D. A Bayesian approach for estimating under-reported dengue incidence with a focus on non-linear associations between climate and dengue in Dhaka, Bangladesh. Statistical methods in medical research. 2016;27(4):991-1000.Available from: https://doi.org/10.1177/0962280216649216.
26. Liu K, Hou X, Wang Y, Sun J, Xiao J, Li R, et al. The driver of dengue fever incidence in two high-risk areas of China: A comparative study. Scientific Reports. 2019;9(1):1-9.Available from: https://www.nature.com/articles/s41598-019-56112-8.
27. Méndez-Lázaro P, Muller-Karger F, Otis D, McCarthy M, Peña-Orellana M. Assessing climate variability effects on dengue incidence in San Juan, Puerto Rico. International journal of environmental research and public health. 2014;11(9):9409-28.Available from: https://www.mdpi.com/1660-4601/11/9/9409.
28. da Cruz Ferreira DA, Degener CM, de Almeida Marques-Toledo C, Bendati MM, Fetzer LO, Teixeira CP, et al. Meteorological variables and mosquito monitoring are good predictors for infestation trends of Aedes aegypti, the vector of dengue, chikungunya and Zika. Parasites & vectors. 2017;10(1):78.Available from: https://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-017-2025-8.
| Files | ||
| Issue | Vol 60, No 6 (2022) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/acta.v60i6.10042 | |
| Keywords | ||
| Dengue fever Bali Population density Climatic Rainy days Humidity | ||
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How to Cite
1.
Yudhastuti R, Dimjati Lusno MF, Agung Mirasa Y, Husnina Z. Dengue Fever Dynamics in Bali, Indonesia 2010-2018: An Interplay of Population Density and Climatic Factors. Acta Med Iran. 2022;60(6):366-374.
