Modeling the Effect of Climate Change on the Distribution of Main Malaria Vectors in an Endemic Area, Southeastern Iran
-
Nasrollah Saberi
,
Ahmad Raeisi
,
Mohammad Amin Gorouhi
,
Hassan Vatandoost
,
Faramarz Bozorg Omid
,
Ahmad Ali Hanafi-Bojd
Abstract
Background: Although malaria is endemic in some areas of southeastern Iran, following the successful national malaria elimination plan (NMEP), the local transmission area has been shrunk. This study was aimed to evaluate the effect of climate change on the distribution of main vectors.
Methods: All documents related to research investigations conducted in Kerman Province on malaria vectors published during 2000–2019 were retrieved from scientific databases. Spatial distributions of the main vectors were mapped and modeled using MaxEnt ecological model. The future environmental suitability for main vectors was determined under three climate changes scenarios in the 2030s.
Results: Five malaria vectors are present in Kerman Province. The best ecological niches for these vectors are located in the southern regions of the province under the current climatic condition as well as different climate change scenarios in the 2030s.
Conclusion: Climate change in 2030 will not have a significant impact on the distribution of malaria vectors in the region. Entomological monitoring is advised to update the spatial database of Anopheles vectors of malaria in this malaria receptive region.
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31. Halimi M, Delavari M, Takhtardeshir A (2013). Survey of climatic condition of Malaria disease outbreak in Iran using GIS. J Sch Public Health Inst Public Health Res, 10(3):41-52.
32. Hanafi-Bojd AA, Vatandoost H, Yaghoobi-Ershadi MR (2020). Climate change and the risk of malaria transmission in Iran. J Med Entomol, 57(1):50-64.
33. Bashar K, Tuno N (2014). Seasonal abundance of Anopheles mosquitoes and their association with meteorological factors and malaria incidence in Bangladesh. Parasit Vectors, 7:442.
34. Pakdad K, Hanafi-Bojd AA, Vatandoost H, et al (2017). Predicting the potential distribution of main malaria vectors Anopheles stephensi, An. culicifacies sl and An. fluviatilis sl in Iran based on maximum entropy model. Acta Trop, 169: 93-99.
35. Hanafi-Bojd AA, Sedaghat MM, Vatandoost H, et al (2018). Predicting environmentally suitable areas for Anopheles superpictus Grassi (sl), Anopheles maculipennis Meigen (sl.) and Anopheles sacharovi Favre (Diptera: Culicidae) in Iran. Parasit Vectors, 11(1):382.
36. Al Ahmed AM, Naeem M, Kheir SM, et al (2015). Ecological distribution modeling of two malaria mosquito vectors using geographical information system in Al-Baha Province, Kingdom of Saudi Arabia. Pakistan J Zool, 47: 1797-1806.
37. Shahandeh K, Basseri HR (2020). Response comment on “Challenges and the path forward on malaria elimination interven-tion: A systematic review”. Iran J Public Health, 49(5):1019-1021.
38. Ren Z, Wang D, Ma A, et al (2016). Predicting malaria vector distribution under climate change scenarios in China: Challenges for malaria elimination. Sci Rep, 6:20604.
39. Fuller DO, Ahumada ML, Quiñones ML, et al (2012). Near-present and future distri-bution of Anopheles albimanus in Mesoam-erica and the Caribbean Basin modeled with climate and topographic data. Inter-national Journal of Health Geographics, 11:13.
40. Drake JM, Beier JC (2014). Ecological niche and potential distribution of Anopheles ara-biensis in Africa in 2050. Malar J, 13:213.
2. WHO (2015). Global technical strategy for malaria 2016-2030, World Health Organization.
3. Nasir SI, Amarasekara S, Wickremasinghe R, et al (2020). Prevention of re-establishment of malaria: historical per-spective and future prospects. Malar J, 19(1):452.
4. Hoek WV, Konradsen F, Perera D, et al (1997). Correlation between rainfall and malaria in the dry zone of Sri Lanka. Ann Trop Med Parasitol, 91(8):945-949.
5. Li XH, Kondrashin A, Greenwood B, et al. (2019). A historical review of WHO certi-fication of malaria elimination. Trends Par-asitol, 35(2):163-171.
6. Silva M, Sallum MA, Freitas MG, et al (2018). Anophelines species and the re-ceptivity and vulnerability to malaria transmission in the Pantanal wetlands, Central Brazil. Mem Inst Oswaldo Cruz, 113:87-95.
7. Mohammadkhani M, Khanjani N, Bakhtiari B, et al (2016). The relation between climatic factors and malaria incidence in Kerman, South East of Iran. Parasite Epidemiol Control, 1: 205-210.
8. Raiesi A, Nejati J, Ansari-Moghaddam A, et al (2012). Effects of foreign immigrants on malaria situation in cleared up and potential foci in one of the highest malaria burden district of southern Iran. Malar J, 11(Suppl 1): P81.
9. Cibulskis RE, Alonso P, Aponte J, et al. (2016). Malaria: global progress 2000–2015 and future challenges. Infect Dis Poverty, 5(1):61.
10. Baseri HR, Mousa Kazemi SH, Yosafi S, et al (2005). Anthropophily of malaria vectors in Kahnouj District, south of Kerman, Iran. Iran J Publ Health, 34(2):27-35.
11. Hanafi-Bojd AA, Azari-Hamidian S, Vatandoost H, et al. (2011). Spatio-temporal distribution of malaria vectors (Diptera: Culicidae) across different climatic zones of Iran. Asian Pac J Trop Med, 4(6):498-504.
12. Djadid ND, Gholizadeh S, Aghajari M, et al (2006). Genetic analysis of rDNA-ITS2 and RAPD loci in field populations of the malaria vector, Anopheles stephensi (Diptera: Culicidae): implications for the control program in Iran. Acta Trop, 97(1):65-74.
13. Oshaghi M, Shemshad K, Yaghobi-Ershadi MR, et al (2007). Genetic structure of the malaria vector Anopheles superpictus in Iran using mitochondrial cytochrome oxidase (COI and COII) and morphologic markers: a new species complex? Acta Trop, 101(3):241-248.
14. Peterson AT (2006) Ecologic niche modeling and spatial patterns of disease transmission. Emerg Infect Dis, 12(12):1822-1826.
15. Ranjbar M, Shoghli A, Kolifarhood G, et al (2016). Predicting factors for malaria re-introduction: an applied model in an elimination setting to prevent malaria outbreaks. Malar J, 15: 138.
16. Akpan GE, Adepoju KA, Oladosu OR, et al (2018). Dominant malaria vector species in Nigeria: Modelling potential distribution of Anopheles gambiae sensu lato and its siblings with MaxEnt. PLoS One, 13(10):e0204233.
17. Altamiranda-Saavedra M, Arboleda S, Parra JL, et al (2017). Potential distribution of mosquito vector species in a primary malaria endemic region of Colombia. PLoS One, 12(6):e0179093.
18. Moffett A, Shackelford N, Sarkar S (2007). Malaria in Africa: vector species' niche models and relative risk maps. PLoS One, 2(9):e824.
19. Akpan GE, Adepoju KA, Oladosu OR (2019). Potential distribution of dominant malaria vector species in tropical region under climate change scenarios. PLoS One, 14(6):e0218523.
20. Caminade C, Kovats S, Rocklov J, et al (2014) Impact of climate change on global malaria distribution. Proc Natl Acad Sci U S A, 111(9):3286-3291.
21. Pascual M, Ahumada JA, Chaves LF, et al (2006). Malaria resurgence in the East African highlands: temperature trends revisited. Proc Natl Acad Sci U S A, 103(15): 5829-5834.
22. Zhou G, Minakawa N, Githeko AK, et al (2004). Association between climate variability and malaria epidemics in the East African highlands. Proc Natl Acad Sci U S A, 101(8):2375-2380.
23. Zhou G, Minakawa N, Githeko AK, et al (2005). Climate variability and malaria epidemics in the highlands of East Africa. Trends Parasitol, 21(2):54-56.
24. Yearbook of Management and Planning of Kerman Province (2019). Statistical year-book of Kerman Province. pp. 747.
25. Phillips SJ, Anderson RP, Schapire RE (2006). Maximum entropy modeling of species geographic distributions. Ecol Modell, 190(3-4):231-259.
26. Alimi TO, Fuller DO, Qualls WA, et al (2015). Predicting potential ranges of primary malaria vectors and malaria in northern South America based on projected changes in climate, land cover and human population. Parasit Vectors, 8:431.
27. Phillips SJ, Dudík M (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2):161-175.
28. Hijmans RJ, Cameron SE, Parra JL, et al (2005). Very high resolution interpolated climate surfaces for global land areas. Int J Climatol, 25(15):1965-1978.
29. Van Vuuren DP, Edmonds J, Kainuma M, et al (2011). The representative concentration pathways: an overview. Climatic Change, 109:5-31.
30. Haghdoost AA, Alexander N, Cox J (2008). Modelling of malaria temporal variations in Iran. Trop Med Int Health, 13(12):1501-1508.
31. Halimi M, Delavari M, Takhtardeshir A (2013). Survey of climatic condition of Malaria disease outbreak in Iran using GIS. J Sch Public Health Inst Public Health Res, 10(3):41-52.
32. Hanafi-Bojd AA, Vatandoost H, Yaghoobi-Ershadi MR (2020). Climate change and the risk of malaria transmission in Iran. J Med Entomol, 57(1):50-64.
33. Bashar K, Tuno N (2014). Seasonal abundance of Anopheles mosquitoes and their association with meteorological factors and malaria incidence in Bangladesh. Parasit Vectors, 7:442.
34. Pakdad K, Hanafi-Bojd AA, Vatandoost H, et al (2017). Predicting the potential distribution of main malaria vectors Anopheles stephensi, An. culicifacies sl and An. fluviatilis sl in Iran based on maximum entropy model. Acta Trop, 169: 93-99.
35. Hanafi-Bojd AA, Sedaghat MM, Vatandoost H, et al (2018). Predicting environmentally suitable areas for Anopheles superpictus Grassi (sl), Anopheles maculipennis Meigen (sl.) and Anopheles sacharovi Favre (Diptera: Culicidae) in Iran. Parasit Vectors, 11(1):382.
36. Al Ahmed AM, Naeem M, Kheir SM, et al (2015). Ecological distribution modeling of two malaria mosquito vectors using geographical information system in Al-Baha Province, Kingdom of Saudi Arabia. Pakistan J Zool, 47: 1797-1806.
37. Shahandeh K, Basseri HR (2020). Response comment on “Challenges and the path forward on malaria elimination interven-tion: A systematic review”. Iran J Public Health, 49(5):1019-1021.
38. Ren Z, Wang D, Ma A, et al (2016). Predicting malaria vector distribution under climate change scenarios in China: Challenges for malaria elimination. Sci Rep, 6:20604.
39. Fuller DO, Ahumada ML, Quiñones ML, et al (2012). Near-present and future distri-bution of Anopheles albimanus in Mesoam-erica and the Caribbean Basin modeled with climate and topographic data. Inter-national Journal of Health Geographics, 11:13.
40. Drake JM, Beier JC (2014). Ecological niche and potential distribution of Anopheles ara-biensis in Africa in 2050. Malar J, 13:213.
| Files | ||
| Issue | Vol 52 No 5 (2023) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijph.v52i5.12724 | |
| Keywords | ||
| Climate change Malaria Anopheles Ecological niche modeling | ||
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How to Cite
1.
Saberi N, Raeisi A, Gorouhi MA, Vatandoost H, Bozorg Omid F, Hanafi-Bojd AA. Modeling the Effect of Climate Change on the Distribution of Main Malaria Vectors in an Endemic Area, Southeastern Iran. Iran J Public Health. 2023;52(5):1061-1070.
