Spatial-Temporal Distribution of Malaria Risk and Its Association With El Niño Southern Oscillation (ENSO)

Ricky Anak Kemarau; Oliver Valentine Eboy

doi:10.47405/mjssh.v6i4.768

Ricky Anak Kemarau Geography Program, Faculty of Social Science and Humanities, Universiti Malaysia Sabah (UMS)
Oliver Valentine Eboy Geography Program, Faculty of Social Science and Humanities, Universiti Malaysia Sabah (UMS)

DOI: https://doi.org/10.47405/mjssh.v6i4.768

Keywords: spatial-temporal, ENSO, malaria risk map, remote sensing

Abstract

Sarawak recorded the second-highest number of cases since 2013 until 2017 after Sabah. Sarawak is the largest state in Malaysia and needs to provide spatial information, especially to the ministry of health. The objective of this study was to examine the impact of El Niño-Southern Oscillation (ENSO) on the distribution of malaria risk maps. To achieve the objectives of this study requires Oceanic Niño Index (ONI) data, Visible Infrared Imaging Radiometer Suite (VIIRS), daily temperature, and secondary data on the number of malaria cases in Sarawak. The results of the study clearly show that the occurrence of La Niña and El Niño affects the total distribution of Malaria risk maps. The number of malaria cases is also related to the ONI value. The lower the ONI value causes the malaria case value to decrease. The results of this study suggest that most of the hot spots in the forest, forest fringe, and inland areas of Sarawak. This clearly shows the lack of knowledge and knowledge causing the rural population to be prone to malaria. The Ministry of Health needs to focus on the interior in disseminating teachings and knowledge in dealing with malaria mosquitoes.

Downloads

Download data is not yet available.

References

Alemu A, Abebe G, Tsegaye W, Golassa L. (2011) Climatic variables and malaria transmission dynamics in Jimma town, South West Ethiopia. Parasit Vectors, 4, 30.

Abiodun GJ, Maharaj R, Witbooi P, Okosun KO. (2016). Modeling the influence of temperature and rainfall on the population dynamics of Anopheles arabiensis. Malar J., 15, 364.

Dhimal. M, Robert B O’Hara, Ramchandra Karki, Garib D Thakur, Ulrich Kuch and Bodo Ahrens. (2014). Spatio-temporal distribution of malaria and its association with climatic factors and vector-control interventions in two high-risk districts of Nepal. Malaria Journal, 13, 457 http://www.malariajournal.com/content/13/1/457

H. Hasyim, Af Nursafngi , Ubydul Haque, Doreen Montag , David A. Groneberg , Meghnath Dhimal, Ulrich Kuch and Ruth Müller. (2018) Spatial modeling of malaria cases associated with environmental factors in South Sumatra, Indonesia. Malaria Journal. https://doi.org/10.1186/s12936-018-2230-8

Hussin N, Yvonne Ai‑Lian Lim, Pik Pin Goh, Timothy William, Jenarun Jeep, and Rose Nani Mudin (2020). Updates on malaria incidence and profile in Malaysia from 2013 to 2017. Malaria Journal, 19, 55 https://doi.org/10.1186/s12936-020-3135-x

Midekisa. A Gabriel Senay, Geoffrey M Henebry, Paulos Semuniguse, and Michael C Wimberly (2012). Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia. Malaria Journal, 11, 165 http://www.malariajournal.com/content/11/1/165

Mushinzimana E. Stephen Munga, Noboru Minakawa, Li Li, Chen-chieh Feng, Ling Bian, Uriel Kitron, Cindy Schmidt, Louisa Beck, Guofa Zhou, Andrew K Githeko and Guiyun Yan (2006). Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands. Malaria Journal2006, 5, 13 doi:10.1186/1475-2875-5-13 http://www.malariajournal.com/content/5/1/13

NOAA 2020. National Centers for Environmental Information, State of The Climate: Global Climate Report for Annual 2019. Available at Https://Www.Ncdc.Noaa.Gov/Sotc/Global/201913. (Accessed On 16 March 2020).

Rogers DJ, Randolph SE, Snow RW, Hay SI (2002). Satellite imagery in the study and forecast of malaria. Nature, 415, 710-715.

Tangang, F., Juneng, L., Salimun, E., Sei, K. & Loh, J. (2012). Climate Change and Variability Over Malaysia: Gaps in Science and Research Information. Sains Malaysiana, 41, 1355-1366.

Teklehaimanot HD, Lipsitch M, Teklehaimanot A, Schwartz J (2004). Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia. Patterns of lagged weather effects reflect biological mechanisms. Malar J, 3(12).

Thomson MC, Connor SJ (2001). The development of malaria early warning systems for Africa. Trends Parasitol, 17(13), 438-445.

Thomson MC, Doblas-Reyes FJ, Hagedorn R, Connor SJ, Phindela T, Morse AP, Palmer TM (2006). Malaria early warnings based on seasonal climate from multi-model ensembles. Nature, 14, 576-579.

Suwito, Upik. K. Hadi, Sigit Singgah and Sukowati S. (2010). Hubungan Iklim, Kepadatan Nyamuk Anopheles dan Kejadian Penyakit Malaria. J. Entomol. Indo., 1, 42-53

Yamana TK, Eltahir EA. (2003). Incorporating the effects of humidity in a mechanistic model of Anopheles gambiae mosquito population dynamics in the Sahel region of Africa. Parasit Vectors, 6, 235.

Weaver HJ. (2014). Climate change and human parasitic disease. In: Climate change and global health. CABI Nosworthy Way Wallingford UK: ©CAB International; 2014:95. Retrieved from https://www.cabi.org/cabebooks/ ebook/20143328432.

Zulfiqar Sa’adi, Shamsuddin Shahid, Tarmizi Ismail, Eun-Sung Chung, And Xiao-Jun Wang (2017). Distributional Changes in Rainfall and River Flow in Sarawak, Malaysia. Asia-Pac. J. Atmos. Sci., 53(4), 489-500.