Climate Change Projections and its Impacts on Potential Malaria Transmission Dynamics in Uttarakhand
Introduction: Mountainous regions in India are prone to malaria disease but with low intensity. In this context, Uttarakhand state, a hilly region situated in the northern parts of India and located in the central Himalayan region is also prone to malaria disease and malaria is present in four out of its 13 districts which are mainly plain stations.
Method: A numerical dynamical malaria model, VECTRI is used in this study based on various climatic and non-climatic parameters such as surface mean temperature, rainfall, population density etc. to predict the future malaria transmission dynamics in Uttarakhand state. VECTRI model is simulated with the inputs obtained from the CCSM4 global climate model for the baseline period (1975-2005) and for the near future projection period 2006-2035 (hereafter referred to as 2030s). Rainfall, surface mean temperature, mosquito vector density and entomological inoculation rate (EIR) during the Indian monsoon season (June-Sept) are being investigated from the outputs of VECTRI model simulations to predict the future malaria transmission dynamics in the Uttarakhand region with respect to the future climate change under RCP 8.5 emission scenario.
Results: Results indicate an overall increase in EIR values (increase is around 30%), indicating an increase in future malaria transmission in Uttarakhand state as a whole with a maximum increase in the central parts of the state which are plain areas with a warming temperature of 1°C and with an increase in rainfall of 15% by 2030s with respect to the baseline period.
Conclusion: Future warming and increase in the rainfall intensity during the summer monsoon season (June-September) over Uttarakhand state could potentially increase the spatial and temporal distribution of malaria transmission over the regionin future under RCP8.5 scenarios.
How to cite this article:
Parihar RS, Bal PK, Thapliyal A, Saini A. Climate Change Projections and its Impacts on Potential Malaria Transmission Dynamics in Uttarakhand. J Commun Dis. 2022;54(1):47-53.
Bal PK, Ramachandran A, Palanivelu K, Thirumurugan P, Geetha R, Bhaskaran B. Climate change projections over India by a downscaling approach using PRECIS. Asia-Pac J Atmos Sci. 2016 Aug;52(4):353-69. [Google Scholar]
Blanford JI, Blanford S, Crane RG, Mann ME, Paaijmans KP, Schreiber KV, Thomas MB. Implications of temperature variation for malaria parasite development across Africa. Sci Rep. 2013 Feb;3(1):1. [PubMed] [Google Scholar]
Craig MH, Snow RW, le Sueur D. A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol Today. 1999 Mar;15(3):105-11. [PubMed] [Google Scholar]
Chaturvedi RK, Joshi J, Jayaraman M, Bala G, Ravindranath NH. Multi-model climate change projections for India under representative concentration pathways. Curr Sci. 2012 Oct:791-802. [Google Scholar]
Dhiman RC, Singh P, Yadav Y, Saraswat S, Kumar G, Singh RK, Ojha VP, Joshi BC, Singh P. Preparedness for malaria elimination in the wake of climate change in the State of Uttarakhand (India). J Vector Borne Dis. 2019 Jan;56(1):46. [PubMed] [Google Scholar]
Goswami BN, Venugopal V, Sengupta D, Madhusoodanan MS, Xavier PK. Increasing trend of extreme rain events over India in a warming environment. Science. 2006 Dec;314(5804):1442-5. [PubMed] [Google Scholar]
Shidrawi GR, Gillies MT. Anopheles paltrinierii, n. sp. (Culicidae: Diptera) from the Sultanate of Oman. Mosq Syst (USA). 1987;19(3):201-11. [Google Scholar]
Goklany IM. Climate change and malaria. Science. 2004 Oct;306(5693):55-7. [PubMed] [Google Scholar]
Goswami R, Sharma R, Sreenivas V, Gupta N, Ganapathy A, Das S. Prevalence and progression of basal ganglia calcification and its pathogenic mechanism in patients with idiopathic hypoparathyroidism. Clin Endocrinol (Oxf). 2012 Aug;77(2):200-6. [PubMed] [Google Scholar]
Gómez-Amores L, Mate A, Miguel-Carrasco JL, Jiménez L, Jos A, Cameán AM, Revilla E, Santa-María C, Vázquez CM. L-carnitine attenuates oxidative stress in hypertensive rats. J Nutr Biochem. 2007 Aug;18(8):533-40. [PubMed] [Google Scholar]
Gao J. Global 1-km downscaled population base year and projection grids based on the shared socioeconomic pathways, revision 01. NASA Socioeconomic Data and Applications Center (SEDAC); 2020.
Gao J. Downscaling global spatial population projections from 1/8-degree to 1-km grid cells. National Center for Atmospheric Research, Boulder, CO, USA. 2017;1105. [Google Scholar]
Koenraadt CJ, Githeko AK, Takken W. The effects of rainfall and evapotranspiration on the temporal dynamics of Anopheles gambiae ss and Anopheles arabiensis in a Kenyan village. Acta Trop. 2004 Apr;90(2):141-53. [PubMed] [Google Scholar]
Kumar KK, Kamala K, Rajagopalan B, Hoerling MP, Eischeid JK, Patwardhan SK, Srinivasan G, Goswami BN, Nemani R. The once and future pulse of Indian monsoonal climate. Clim Dyn. 2011 Jun;36(11):2159-70. [Google Scholar]
Lindsay SW, Bødker R, Malima R, Msangeni HA, Kisinza W. Effect of 1997–98 EI Niño on highland malaria in Tanzania. Lancet. 2000 Mar;355(9208):989-90. [Google Scholar]
Leedale J, Tompkins AM, Caminade C, Jones AE, Nikulin G, Morse AP. Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty. Geospat Health. 2016;11:102-14. [PubMed] [Google Scholar]
Martens P, Kovats RS, Nijhof S, De Vries P, Livermore MT, Bradley DJ, Cox J, McMichael AJ. Climate change and future populations at risk of malaria. Glob Environ Change. 1999 Oct;9:S89-107. [Google Scholar]
Martens WJ, Niessen LW, Rotmans J, Jetten TH, McMichael AJ. Potential impact of global climate change on malaria risk. Environ Health Perspect. 1995 May;103(5):458-64. [PubMed] [Google Scholar]
Mishra SK, Sahany S, Salunke P. CMIP5 vs. CORDEX over the Indian region: how much do we benefit from dynamical downscaling? Theor Appl Climatol. 2018 Aug;133(3):1133-41. [Google Scholar]
Mishra SK, Sahany S, Salunke P, Kang IS, Jain S. Fidelity of CMIP5 multi-model mean in assessing Indian monsoon simulations. NPJ Clim Atmos Sci. 2018 Oct;1(1):1-8. [Google Scholar]
NVBDCP. National Vector Borne Disease Control Programme Report. 2015; 2017.
Omumbo JA, Lyon B, Waweru SM, Connor SJ, Thomson MC. Raised temperatures over the Kericho tea estates: revisiting the climate in the East African highlands malaria debate. Malaria J. 2011 Dec;10(1):1-6. [PubMed] [Google Scholar]
Patz JA, Olson SH. Malaria risk and temperature: influences from global climate change and local land use practices. Proc Natl Acad Sci USA. 2006 Apr;103(15):5635-6. [PubMed] [Google Scholar]
Paaijmans KP, Wandago MO, Githeko AK, Takken W. Unexpected high losses of Anopheles gambiae larvae due to rainfall. PLoS One. 2007;2(11):e1146. [PubMed] [Google Scholar]
Paaijmans KP, Read AF, Thomas MB. Understanding the link between malaria risk and climate. Proc Natl Acad Sci USA. 2009 Aug;106(33):13844-9. [PubMed] [Google Scholar]
Singh Parihar R, Bal PK, Kumar V, Mishra SK, Sahany S, Salunke P, Dash SK, Dhiman RC. Numerical modeling of the dynamics of malaria transmission in a highly endemic region of India. Sci Rep. 2019 Aug;9(1):1-9. [PubMed] [Google Scholar]
Sahany S, Mishra SK, Salunke P. Historical simulations and climate change projections over India by NCAR CCSM4: CMIP5 vs. NEX-GDDP. Theor Appl Climatol. 2019 Feb;135(3):1423-33. [Google Scholar]
Temu EA, Minjas JN, Coetzee M, Hunt RH, Shiff CJ. The role of four anopheline species (Diptera: Culicidae) in malaria transmission in coastal Tanzania. Trans R Soc Trop Med Hyg. 1998 Mar;92(2):152-8. [PubMed] [Google Scholar]
Tompkins AM, Ermert V. A regional-scale, high resolution dynamical malaria model that accounts for population density, climate and surface hydrology. Malaria J. 2013 Dec;12(1):1-24. [PubMed] [Google Scholar]
Van Lieshout M, Kovats RS, Livermore MT, Martens P. Climate change and malaria: analysis of the SRES climate and socio-economic scenarios. Glob Environ Change. 2004 Apr;14(1):87-99. [Google Scholar]
WHO. World Malaria Report. UNICEF; 2005.
Yasutomi N, Hamada A, Yatagai A. Development of a long-term daily gridded temperature dataset and its application to rain/snow discrimination of daily precipitation. Glob Environ Res. 2011;15(2):165-72. [Google Scholar]
Yatagai A, Kamiguchi K, Arakawa O, Hamada A, Yasutomi N, Kitoh A. APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull Am Meteorol Soc. 2012 Sep;93(9):1401-15. [Google Scholar]
Zhang Y, Jordan JM. Epidemiology of osteoarthritis. Clin Geriatr Med. 2010 Aug;26(3):355-69. [PubMed] [Google Scholar]
Sharma A, SinghOP, Saklani MM. Climate of Dehradun report.Indian Meteorological Department Government of India, Ministry of Earth Sciences; 2012.
Banerjee A, Dimri AP, Kumar K. Temperature over the Himalayan foothill state of Uttarakhand: present and future. J Earth Syst Sci. 2021;130:33. [Google Scholar]
Raj S, Shukla R, Trigo RM, Merz B, Rathinasamy M, Ramos AM, Agarwal A. Ranking and characterization of precipitation extremes for the past 113 years for Indian western Himalayas.Inter J Clim. 2021;41(15):6602-15. [Google Scholar]
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