Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy
Published: 13 November 2023
Abstract Views: 1106
PDF: 511
HTML: 23
HTML: 23
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The occurrence of floods has the potential to escalate the transmission of infectious diseases. To enhance our comprehension of the health impacts of flooding and facilitate effective planning for mitigation strategies, it is necessary to explore the flood risk management. The variability present in hydrological records is an important and neglecting non-stationary patterns in flood data can lead to significant biases in estimating flood quantiles. Consequently, adopting a non-stationary flood frequency analysis appears to be a suitable approach to challenge the assumption of independent and identically distributed observations in the sample. This research employed the generalized extreme value (GEV) distribution to examine annual maximum flood series. To estimate non-stationary models in the flood data, several statistical tests, including the TL-moment method was utilized on the data from ten stream-flow stations in Johor, Malaysia, which revealed that two stations, namely Kahang and Lenggor, exhibited non-stationary behaviour in their annual maximum streamflow. Two non-stationary models efficiently described the data series from these two specific stations, the control of which could reduce outbreak of infectious diseases when used for controlling the development measures of the hydraulic structures. Thus, the application of these models may help prevent biased prediction of flood occurrences leading to lower number of cases infected by disease.
Altmetrics
Downloads
Download data is not yet available.
Citations
Abaya SW, Mandere N, Ewald G, 2019. Floods and health in Gambella region, Ethiopia: a qualitative assessment of the strengths and weaknesses of coping mechanisms. Glob. Health Action 2:1-10. DOI: https://doi.org/10.3402/gha.v2i0.2019
Adikari Y, Yoshitani J, 2009. Global trends in waterrelated disasters: An insight for policy-makers. The United Nations world water assessment program. International centre for water hazard and risk management. Available from: http://unesdoc.unesco.org/images/0018/001817/181793e.pdf. Accessed: September 25, 2023.
Ahmad I, Tang D, Wang T, Wang M, Wagan B, 2015. Precipitation trends over time using Mann-Kendall and Spearman’s Rho Tests in Swat River Basin, Pakistan. Adv Meteorol, 2015: 1-15. DOI: https://doi.org/10.1155/2015/431860
Akaike H, 1974. A new look at the statistical model identification. IEEE Trans Autom Control, 19:716–723. DOI: https://doi.org/10.1109/TAC.1974.1100705
Apisarnthanarak, A, Mundy, LM, Khawcharoenporn, T,Mayhall, CG, 2013. Hospital Infection Prevention and Control Issues Relevant to Extensive Floods. Infect Control Hosp Epidemiol 34:200–206. DOI: https://doi.org/10.1086/669094
Badyalina B, Mokhtar NA, Mat Jan NAM, Marsani MF, 2022. Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm. Sains Malays 51:2655-2668. DOI: https://doi.org/10.17576/jsm-2022-5108-24
Badyalina B, Shabri A, Marsani MF, 2021. Streamflow estimation at ungauged basin using modified group method of data handling. Sains Malays 50:2765-2779. DOI: https://doi.org/10.17576/jsm-2021-5009-22
Barteit S, Sié A, Zabré P, Traoré I,OuédraogoWA, BoudoV,Munga S,Khagayi S, Obor D, Muok E, Franke J, Schwarz M, Blass K, Su TT, Bärnighausen T, Sankoh O, Sauerborn R, 2023.Widening the lens of population-based health research to climate change impacts and adaptation: The climate change and health evaluation and response system (CHEERS).Front Public Health 11:1-19. DOI: https://doi.org/10.3389/fpubh.2023.1153559
Bouza-Deano R, Ternero-Rodriguez M, Fernandez-Espinosa AJ, 2008. Trend study and assessment of surface water quality in the Ebro River (Spain). J Hydrol 361:227–239. DOI: https://doi.org/10.1016/j.jhydrol.2008.07.048
Brown L, Murray V, 2013. Examining the relationship between infectious diseases and flooding in Europe. Disaster Health 1: 117-127. DOI: https://doi.org/10.4161/dish.25216
Caldas-Alvarez A, Augenstein M, Ayzel G, Barfus K, Cherian R, Dillenardt L, Fauer F, Feldmann H, Heistermann M, Karwat A, Kaspar F, Kreibich H, Lucio-Eceiza EE, Meredith EP, Mohr S, Niermann D, Pfahl S, Ruff F, Rust HW, Schoppa L, Schwitalla T, Steidl S, Thieken AH, Tradowsky JS, Wulfmeyer V,Quaas J, 2022. Meteorological, impact and climate perspectives of the 29 June 2017 heavy precipitation event in the Berlin metropolitan area.Nat Hazard Earth Sys 22:3701–3724. DOI: https://doi.org/10.5194/nhess-22-3701-2022
Chen M, Papadikis K, Jun C, 2021. An investigation on the non-stationarity of flood frequency across the UK. J Hydrol, 597:126309. DOI: https://doi.org/10.1016/j.jhydrol.2021.126309
Chen X, Ye C, Zhang J, Xu C, Zhang L, 2019. Selection of an optimal distribution curve for non-stationary flood series.J Atmos 10:1–16. DOI: https://doi.org/10.3390/atmos10010031
Coles S, 2001.An introduction to statistical modeling of extreme value. Springer-Verlag, London. DOI: https://doi.org/10.1007/978-1-4471-3675-0
Cunderlik JM, Burn DH, 2003. Non-stationary pooled flood frequency analysis. J Hydrol 276:210–23. DOI: https://doi.org/10.1016/S0022-1694(03)00062-3
Díaz S, Settele J, Brondízio ES, Ngo HT, Guèze M, Agard J, Zayas C, 2019. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). IPBES secretariat, Bonn, Germany. 56 pages. https://doi.org/10.5281/zenodo.3553579
Dickey DA, Fuller WA, 1979. Distribution of the estimators for autoregressive time series with a unit root.J Am Stat Assoc,74:423–431. DOI: https://doi.org/10.1080/01621459.1979.10482531
El-Mousawi, F, Ariel, MO, Berkat,R.,Nasri, B, 2023. The Impact of Flood Adaptation Measures on Affected Population's Mental Health: A mixed method Scoping Review. medRxiv 2023:2023-04. DOI: https://doi.org/10.1101/2023.04.27.23289166
Elamir EAH, Seheult AH, 2003. Trimmed L-Moments. Comput Stat Data Anal 43:299–314. DOI: https://doi.org/10.1016/S0167-9473(02)00250-5
Flood Management Guidelines (Health), 2008. Ministry of Health Malaysia. Accessed: September 22, 2023. Available from: http://www.infosihat.gov.my/infosihat/isusemasa/pdf/Jlid 1 – FWBD UMU GP 001.pdf
French CE, Waite TD, Armstrong B, Rubin GJ, English National Study of Flooding and Health Study Group, Beck CR, Oliver I, 2019. Impact of repeat flooding on mental health and health- related quality of life: a cross- sectional analysis of the English National Study of Flooding and Health. BMJ Open 9:1-9. DOI: https://doi.org/10.1136/bmjopen-2019-031562
Gado TA, Nguyen VTV, 2016b. An at-site flood estimation method in the context of nonstationarity II. Statistical analysis of floods in Quebec. J Hydrol 535: 722–736. DOI: https://doi.org/10.1016/j.jhydrol.2015.12.064
Gado TA, Nguyen VTV, 2016a. An at-site flood estimation method in the context of nonstationarity I. A simulation study. J Hydrol 535:710–721. DOI: https://doi.org/10.1016/j.jhydrol.2015.12.063
Gleneagles, 2022. Common Infectious Diseases in Malaysia During the Flood Season. Available from: https://gleneagles.com.my/health-digest/infectious-diseases-in-malaysia-during-floods. Accessed: September 22, 2023.
Greenwood JA, Landwehr J M, Matalas NC, Wallis J R, 1979. Probability weighted moments: Definition and relation to parameters of several distributions expressable in inverse form. Water Resour Res 15:1049–1054. DOI: https://doi.org/10.1029/WR015i005p01049
Guru N, Jha R. 2014. A study on selection of probability distributions for at-site flood frequency analysis in Mahanadi River Basin, India. Taylor & Francis Group, London,1813-1819. DOI: https://doi.org/10.1201/b17133-241
Haizan RYA, and Mamat, NS, 2023. Rivers exceed dangerous levels in several Malaysian states; Johor flood kills one. Channel News Asia.
Hipel KW, McLeod AI, 2005. Nonparametric tests for trend detection. In: Time series modelling of water resources and environmental systems. Elsevier, Amsterdam 853–938.
Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S, Kanae S, 2013. Global food risk under climate change. Nat Clim Change 3:816–821. DOI: https://doi.org/10.1038/nclimate1911
Ho, J Y, Lavinya, A A, Dominic Shuen W K, Lee, C I SRazmi, A H, Claire L W, Michaela L G, and Jeyanthy E, Towards an integrated approach to improve the understanding of the relationships between water-borne infections and health outcomes: Using Malaysia as a detailed case study. Front Water 4:1-20. DOI: https://doi.org/10.3389/frwa.2022.779860
Hosking JRM, Wallis JR, 1997.Regional frequency analysis: an approach based on L-Moments. Cambridge University Press, United Kingdom. DOI: https://doi.org/10.1017/CBO9780511529443
Ishak E, Rahman A, 2019. Examination of Changes in Flood Data in Australia. Water.11:1734. DOI: https://doi.org/10.3390/w11081734
Ishak EH, Rahman A, Westra S, Sharma A, Kuczera G, 2013. Evaluating the non-stationarity of australian annual maximum flood. J Hydrol 494:134–45. DOI: https://doi.org/10.1016/j.jhydrol.2013.04.021
Kendall MG, 1975. Rank correlation methods. Griffin, London.
Khaliq MN, Ouarda TBMJ, Ondo JC, Gachon P, Bobée B, 2006. Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: A review. J Hydrol 329:534–552. DOI: https://doi.org/10.1016/j.jhydrol.2006.03.004
Kuriqi A, Ardiçlioglu M, 2018. Investigation of Hydraulic regime at middle part of the Loire River in context of foods and low fow events. Pollack Period 13:145–156. DOI: https://doi.org/10.1556/606.2018.13.1.13
López J, Francés F, 2013. Non-stationary flood frequency analysis in Continental Spanish Rivers, using climate and reservoir indices as external covariates. Hydrol Earth Syst Sci 17:3189–3203. DOI: https://doi.org/10.5194/hess-17-3189-2013
Ludwig P, Ehmele F, Franca, MJ, Mohr, S, Caldas-Alvarez, A, Daniell, JE, Ehret, U, Feldmann, H, Hundhausen, M, Knippertz, P, Küpfer, K, Kunz, M, Mühr, B, Pinto, JG, Quinting, J, Schäfer, AM, Seidel, F,Wisotzky, C, 2023. A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe – Part 2: Historical context and relation to climate change, Nat Hazards Earth Syst Sci 23:1287–1311. DOI: https://doi.org/10.5194/nhess-23-1287-2023
Malaymail, 2023.Two states fully recover, nearly 55,000 flood victims still at relief centres in three states. Available from: https://www.malaymail.com/news/malaysia/2023/03/05/two-states-fully-recover-nearly-55000-flood-victims-still-at-relief-centres-in-three-states/58143
Mann HB, 1945. Nonparametric tests against trend. J Econom 13:245–259. DOI: https://doi.org/10.2307/1907187
Mat Jan NA, Shabri A, Samsudin R, 2020. Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter. J Water Clim Chang 11:966–979. DOI: https://doi.org/10.2166/wcc.2019.055
Mehmood A, Jia S, Mahmood R, Yan J, Ahsan M, 2019. Non-stationary bayesian modeling of annual maximum floods in a changing environment and implications for flood management in the Kabul River Basin, Pakistan. Water 11:1-30. DOI: https://doi.org/10.3390/w11061246
Milly PCD,Betancourt J, Falkenmark M, Hirsch R M, Kundzewicz Z W, Lettenmaier D P, Stouffer RJ, 2008. Stationarity is dead: Whither water management? Science 319:573–574. DOI: https://doi.org/10.1126/science.1151915
Mohr S, Ehret U, Kunz M, Ludwig P, Caldas-Alvarez A, Daniell J E, Ehmele F, Feldmann H, Franca M J, Gattke C, Hundhausen M, Knippertz P, Küpfer K, Mühr B, Pinto J G, Quinting J, Schäfer A M, Scheibel M, Seidel F,Wisotzky C, 2023.A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe – Part 1: Event description and analysis, Nat Hazards Earth Syst Sci,23:525–551. DOI: https://doi.org/10.5194/nhess-23-525-2023
Mondal A, Roy R, Kalai, C, 2023. Regionalization for Flood Frequency Analysis: Sensitivity to Choice of Clustering Algorithm and Distance Metric. In World Environmental and Water Resources Congress 2023353-366 pp.). https://ascelibrary.org/doi/10.1061/9780784484852.034 DOI: https://doi.org/10.1061/9780784484852.034
Ochani S, Aaqil SI, Nazir A, Athar FB, Ochani K, Ullah K, 2022. Various health-related challenges amidst recent floods in Pakistan; strategies for future prevention and control. Ann Surg 82:1-2. DOI: https://doi.org/10.1016/j.amsu.2022.104667
Okaka FO, Odhiambo BDO, 2018. Relationship between flooding and out break of infectious diseases in Kenya: A review of the literature. J Environ Public Health 2018:1-8. DOI: https://doi.org/10.1155/2018/5452938
Okaka FO, Odhiambo BDO, 2019. Households’ perception of flood risk and health impact of exposure to flooding in flood-prone informal settlements in the coastal city of Mombasa. Int J Clim Chang Strateg Manag 11:592-606. DOI: https://doi.org/10.1108/IJCCSM-03-2018-0026
Ouarda T B M J, Charron C, 2019. Changes in the distribution of hydro-climatic extremes in a non-stationary framework. Sci Rep 9:1-8. DOI: https://doi.org/10.1038/s41598-019-44603-7
Pan X, Rahman A, Haddad K, Ouarda, TBMJ, 2002. Peaks-over-threshold model in flood frequency analysis: a scoping review. Stoch Environ Res Risk Assess 36:2419–2435. DOI: https://doi.org/10.1007/s00477-022-02174-6
Panagoulia D, Economou P, Caroni, C, 2014. Stationary and nonstationary generalized extreme value modelling of extreme precipitation over a mountainous area under climate change. Environmetrics 25:29–43. DOI: https://doi.org/10.1002/env.2252
Pohlert T, 2020. Trend: Non-parametric trend tests and change-point detection. R Package version 1.1.4. Accessed 18 February 2021. Available from: https://cran.r-project.org/package=trend.
Prasad AS, Francescutti LH, 2017. Natural disasters. In: Quah S. R. (ed) International encyclopedia of public health, 2nd edn. Elsevier 215-222 pp. DOI: https://doi.org/10.1016/B978-0-12-803678-5.00519-1
Pregnolato M, Ford A, Wilkinson SM, Dawson R J, 2017. The impact of flooding on road transport: A depth-disruption function. In: Button K (ed) Transportation research part D: Transport and environment 55:67-81. DOI: https://doi.org/10.1016/j.trd.2017.06.020
Ren H, Hou ZJ, Wigmosta M, Liu Y, Leung LR, 2019. Impacts of spatial heterogeneity and temporal non-stationarity on intensity-duration-frequency estimates - A case study in a mountainous California-Nevada Watershed. Water 11:1–16. DOI: https://doi.org/10.3390/w11061296
Romali NS, Yusop Z, 2021. Flood damage and risk assessment for urban area in Malaysia. J Hydrol Res 52:142-159. DOI: https://doi.org/10.2166/nh.2020.121
Sadri S, Kam J, Sheffield J, 2016. Nonstationarity of low flows and their timing in the Eastern United States. Hydrol Earth Syst Sci, 20:633–649. DOI: https://doi.org/10.5194/hess-20-633-2016
Said SE, Dickey DA, 1984. Testing for unit roots in autoregressive moving-average models with unknown order. Biometrika 71:599–607. DOI: https://doi.org/10.1093/biomet/71.3.599
Salas JD, Obeysekera J, 2014. Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. J Hydrol Eng 19:554–568. DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0000820
Schwarz G, 1978. Estimating the dimension of a model. Annal of Statistics 6:461–464. DOI: https://doi.org/10.1214/aos/1176344136
Shafii NZ, Mohd Saudi AS, Jyh CP, Abu, IF Norzahir Sapawe, Mohd Khairul AK, Mohamad Haiqal NM. Association of Flood Risk Patterns with Waterborne Bacterial Diseases in Malaysia. Water 15:2121. DOI: https://doi.org/10.3390/w15112121
Shokri A, Sabzevari S, Hashemi SA, 2020. Impacts of flood on health of Iranian population: Infectious diseases with an emphasis on parasitic infections. Parasite Epidemiol Control 9:1-11. DOI: https://doi.org/10.1016/j.parepi.2020.e00144
Sneyers R, 1990.On the statistical analysis of series of observations. Geneva, Switzerland.
Šraj M, Viglione A, Parajka J, Blöschl G, 2016. The Influence of Non-stationarity in Extreme Hydrological Events on Flood Frequency Estimation. J Hydrol Hydromech 64:426–437. DOI: https://doi.org/10.1515/johh-2016-0032
Tan X, Gan TY, 2015. Nonstationary analysis of annual maximum streamflow of Canada. J Clim 28:1788–1805. DOI: https://doi.org/10.1175/JCLI-D-14-00538.1
Vasiliades L, Galiatsatou P, Loukas A, 2015. Nonstationary frequency analysis of annual maximum rainfall using climate covariates. Water Resour Manag 29:339–358. DOI: https://doi.org/10.1007/s11269-014-0761-5
Villarini G, Smith JA, Serinaldi F, Bales J, Bates PD, Krajewski WF, 2009. Flood frequency analysis for nonstationary annual peak records in an urban drainage basin. Adv Water Resour 32:1255-1266. DOI: https://doi.org/10.1016/j.advwatres.2009.05.003
Weiskopf SR, Rubenstein MA, Crozier LG, Gaichas S, Griffis R, Halofsky JE, Hyde KJW, Morelli TL, Morisette JT, Muñoz RC, Pershing AJ, Peterson DL, Poudel R, Staudinger MD, Sutton-Grier AE, Thompson L, Vose J, Weltzin JF, Whyte KP. 2020. Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Sci. Total Environ 733:137782, 1-18. DOI: https://doi.org/10.1016/j.scitotenv.2020.137782
WHO, 2023. Update on the Dengue situation in the Western Pacific Region. Accessed 10 October 2023. Available from: https://www.who.int/docs/default-source/wpro---documents/emergency/surveillance/dengue/dengue-20221006.pdf?sfvrsn=fc80101d_124#:~:text=Up%20until%20epidemiological%20week%2045,in%202021%20(CFR%200.08%25).&text=During%20epidemiological%20week%2043%20of,(2)%20deaths%20were%20reported
Xiong L, Du T, Xu CY, Guo S, Jiang C, Gippel CJ, 2015. Non-stationary annual maximum flood frequency analysis using the norming constants method to consider non-stationarity in the annual daily flow series. Water Resour Manag 29:3615-3633. DOI: https://doi.org/10.1007/s11269-015-1019-6
Yao, BAF, Soro EG, 2021. Detection of Hydrologic Trends and Variability in Transboundary Cavally Basin (West Africa). Am J Water Resour 9:92-102. DOI: https://doi.org/10.12691/ajwr-9-2-6
Zalnezhad A, Rahman A, Vafakhah M, Samali B, Ahamed F, 2022. Regional Flood Frequency Analysis Using the FCM-ANFIS Algorithm: A Case Study in South-Eastern Australia. Water 14:1608. DOI: https://doi.org/10.3390/w14101608
How to Cite
Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy. (2023). Geospatial Health, 18(2). https://doi.org/10.4081/gh.2023.1236
Copyright (c) 2023 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Wentao Yang, Fengjie Wang, Yihan You, Zhixiong Fang, Xing Wang, Xiaoming Mei, Optimizing vaccination sites for infectious diseases based on heterogeneous travel modes in multiple scenarios , Geospatial Health: Vol. 20 No. 1 (2025)
- Alireza Mohammadi, Bardia Mashhoodi, Ali Shamsoddini, Elahe Pishgar, Robert Bergquist, Land surface temperature predicts mortality due to chronic obstructive pulmonary disease: a study based on climate variables and impact machine learning , Geospatial Health: Vol. 20 No. 1 (2025)
- Mai Liu, Yin Zhang, Impact of climate change on dengue fever: a bibliometric analysis , Geospatial Health: Vol. 20 No. 1 (2025)
- Néstor DelaPaz-Ruíz, Ellen-Wien Augustijn, Mahdi Farnaghi, Sheheen A. Abdulkareem, Raul Zurita Milla, Integrating agent-based disease, mobility and wastewater models for the study of the spread of communicable diseases , Geospatial Health: Vol. 20 No. 1 (2025)
- Adel Al-Huraibi, Sherif Amer, Justine Blanford, Prioritizing the location of vaccination centres during the COVID-19 pandemic by bike in the Netherlands , Geospatial Health: Vol. 20 No. 1 (2025)
- Kella Douzouné, Joseph Oloukoi, Ismaila Toko Imorou, Toure Gorgui Ba, Derrick Chefor Ymele Demeveng, Mapping livestock systems, bovine and caprine diseases in Mayo-Kebbi Ouest Province, Chad , Geospatial Health: Vol. 20 No. 1 (2025)
- Worrayot Darasawang, Wongsa Laohasiriwong, Kittipong Sornlorm , Warangkana Sungsitthisawad, Roshan Kumar Mahato, Spatial association of socioeconomic and health service factors with antibiotic self-medication in Thailand , Geospatial Health: Vol. 20 No. 1 (2025)
- Sang Min Lee, Dong Woo Huh, Young Gyu Kwon, Local healthcare resources associated with unmet healthcare needs in South Korea: a spatial analysis , Geospatial Health: Vol. 20 No. 1 (2025)
- Peter Nezval, Takeshi Shirabe, Design and implementation of a spatial database for analysis of wheelchair accessibility , Geospatial Health: Vol. 20 No. 1 (2025)
- Sarah Isnan, Ahmad Fikri bin Abdullah, Abdul Rashid Shariff, Iskandar Ishak, Sharifah Norkhadijah Syed Ismail, Maheshwara Rao Appanan, Moran’s I and Geary’s C: investigation of the effects of spatial weight matrices for assessing the distribution of infectious diseases , Geospatial Health: Vol. 20 No. 1 (2025)
1-10 of 326
Next
You may also start an advanced similarity search for this article.
