Skip to main content

Advertisement

SpringerLink
Log in

Understanding the effect of climate change in the distribution and intensity of malaria transmission over India using a dynamical malaria model

  • Original Paper
  • Published:
International Journal of Biometeorology Aims and scope Submit manuscript

Abstract

Efforts have been made to quantify the spatio-temporal malaria transmission intensity over India using the dynamical malaria model, namely, Vector-borne Disease Community Model of International Centre for Theoretical Physics Trieste (VECTRI). The likely effect of climate change in the variability of malaria transmission intensity over different parts of India is also investigated. The Historical data and future projection scenarios of the rainfall and temperature derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) model output are used for this purpose. The Entomological Inoculation Rate (EIR) and Vector are taken as quantifiers of malaria transmission intensity. It is shown that the maximum number of malaria cases over India occur during the Sept–Oct months, whereas the minimum during the Feb–Apr months. The malaria transmission intensity as well as length of transmission season over India is likely to increase in the future climate as a result of global warming.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Afari EA, Akanmori BD, Nakano T, Ofori-Adjei D (1992) Plasmodium falciparum: sensitivity to chloroquine in vivo in three ecological zones in Ghana. Trans R Soc Trop Med Hyg 86(3):231–232

    Article  CAS  Google Scholar 

  • Akpalu W, Codjoe S (2013) Economic analysis of climate variability impact on malaria prevalence: the case of Ghana. Sustainability 5(10):4362–4378

    Article  Google Scholar 

  • Araújo MDS, Gil LHS (2012) Larval food quantity affects development time, survival and adult biological traits that influence the vectorial capacity of Anopheles darlingi under laboratory conditions. Malar J 11(1):261

    Article  Google Scholar 

  • Arora VK, Scinocca JF, Boer GJ, Christian JR, Denman KL, Flato GM, Kharin VV, Lee WG, Merryfield WJ (2011) Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys Res Lett 38:L05805. https://doi.org/10.1029/2010GL046270

  • Asare EO, Tompkins AM, Bomblies A (2016) A regional model for malaria vector developmental habitats evaluated using explicit, pond-resolving surface hydrology simulations. PLoS One 11(3):e0150626. https://doi.org/10.1371/journal.pone.0150626

    Article  CAS  Google Scholar 

  • Balk, D., Pozzi, F., Yetman, G., Deichmann, U., & Nelson, A. (2005). The distribution of people and the dimension of place: methodologies to improve the global estimation of urban extents. In International Society for Photogrammetry and Remote Sensing, Proceedings of the Urban Remote Sensing Conference (pp. 14-16).

  • Beguin A et al (2011) The opposing effects of climate change and socio-economic development on the global distribution of malaria. Glob Environ Chang 21(4):1209–1214

    Article  Google Scholar 

  • Bhattacharya S, Sharma C, Dhiman RC, Mitra AP (2006) Climate change and malaria in India. Curr Sci 90(3):369–375

    Google Scholar 

  • Breaux JA, Schumacher MK, Juliano SA (2014) What does not kill them makes them stronger: larval environment and infectious dose alter mosquito potential to transmit filarial worms. Proc R Soc B Biol Sci 281(1786):20140459

    Article  Google Scholar 

  • Briegel H (1990) Fecundity, metabolism, and body size in Anopheles (Diptera: Culicidae), vectors of malaria. J Med Entomol 27(5):839–850

    Article  CAS  Google Scholar 

  • Caminade C, Kovats S, Rocklov J, Tompkins AM, Morse AP, Colón-González FJ, Stenlund H, Martens P, Lloyd SJ (2014) Impact of climate change on global malaria distribution. Proc Natl Acad Sci 111(9):3286–3291

    Article  CAS  Google Scholar 

  • Craig M, Snow R, Le Sueur D (1999) A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol Today 15:105–111

    Article  CAS  Google Scholar 

  • Depinay JMO, Mbogo CM, Killeen G, Knols B, Beier J, Carlson J et al (2004) A simulation model of African Anopheles ecology and population dynamics for the analysis of malaria transmission. Malar J 3:29 (1-21)

    Article  Google Scholar 

  • Dery DB, Brown C, Asante KP, Adams M, Dosoo D, Amenga-Etego S et al (2010) Patterns and seasonality of malaria transmission in the forest-savannah transitional zones of Ghana. Malar J 9:314 (1-8)

    Article  Google Scholar 

  • de Souza D, Kelly-Hope L, Lawson B, Wilson M, Boakye D, Sinnis P (2010) Environmental factors associated with the distribution of anopheles gambiae s.s in Ghana; an important vector of lymphatic filariasis and malaria. PLoS ONE 5(3):e9927

  • Donner LJ, Wyman BL, Hemler RS, Horowitz LW, Ming Y, Zhao M et al (2011) The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J Clim 24(13):3484–3519

    Article  Google Scholar 

  • Donovan C, Siadat B, Frimpong J (2012) Seasonal and socio-economic variations in clinical and self-reported malaria in Accra, Ghana: evidence from facility data and a community survey. Ghana Med J 46(2):85–94

    CAS  Google Scholar 

  • Dufresne JL, Foujols MA, Denvil S, Caubel A, Marti O, Aumont O, Balkanski Y, Bekki S, Bellenger H, Benshila R, Bony S (2013) Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim Dyn 40(9-10):2123–2165

    Article  Google Scholar 

  • Dwivedi S, Goswami BN, Kucharski F (2015) Unraveling the missing link of ENSO control over the Indian monsoon rainfall. Geophys Res Lett 42(19):8201–8207

    Article  Google Scholar 

  • Dwivedi S, Pandey AC (2011) Forecasting the Indian summer monsoon intraseasonal oscillations using genetic algorithm and neural network. Geophys Res Lett 38:L15801. https://doi.org/10.1029/2011GL048314

    Article  Google Scholar 

  • Eckhoff PA (2011) A malaria transmission-directed model of mosquito life cycle and ecology. Malar J 10(1):303

    Article  Google Scholar 

  • Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR, Lawrence DM, Neale RB, Rasch PJ, Vertenstein M, Worley PH (2011) The community climate system model version 4. J Clim 24(19):4973–4991

    Article  Google Scholar 

  • Giorgetta MA, Jungclaus J, Reick CH, Legutke S, Bader J, Böttinger M, Brovkin V, Crueger T, Esch M, Fieg K, Glushak K, Gayler V, Haak H, Hollweg H-D, Ilyina T, Kinne S, Kornblueh L, Matei D, Mauritsen T, Mikolajewicz U, Mueller W, Notz D, Pithan F, Raddatz T, Rast S, Redler R, Roeckner E, Schmidt H, Schnur R, Segschneider J, Katharina D, Six MS, Timmreck C, Wegner J, Widmann H, Wieners K-H, Claussen M, Marotzke J, Stevens B (2013) Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the coupled model intercomparison project phase 5. J Adv Model Earth Syst 5(3):572–597

  • Goklany IM, King DA (2004) Climate change and malaria. Science 306(5693):55–57

    Article  CAS  Google Scholar 

  • Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, Chiara G, Dahlgren P, Dee D, Diamantakis M, Dragani R, Flemming J, Forbes R, Fuentes M, Geer A, Haimberger L, Healy S, Hogan RJ, Hólm E, Janisková M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, Rosnay P, Rozum I, Vamborg F, Villaume S, Thépaut JN (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146:1999–2049

    Article  Google Scholar 

  • Jones, A. (2007). Seasonal ensemble prediction of malaria in Africa. PhD thesis (Univ of Liverpool, Liverpool).

  • Jones A, Morse AP (2010) Application and validation of a seasonal ensemble prediction system using a dynamic malaria model. J Clim 23:4202–4215

    Article  Google Scholar 

  • Jones AE, Morse AP (2012) Skill of ENSEMBLES seasonal re-forecasts for malaria prediction in West Africa. Geophys Res Lett 39:L23707. https://doi.org/10.1029/2012GL054040

    Article  Google Scholar 

  • Jones C, Hughes JK, Bellouin N, Hardiman SC, Jones GS, Knight J, Liddicoat S, O'connor FM, Andres RJ, Bell C, Boo KO (2011) The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci Model Dev 4:543–570

    Article  Google Scholar 

  • Klinkenberg E, McCall PJ, Wilson MD, Amerasinghe FP, Donnelly MJ (2008) Impact of urban agriculture on malaria vectors in Accra, Ghana. Malar J 7(1):151

  • Kovats RS, Campbell-Lendrum DH, McMichel AJ, Woodward A, Cox J, St H (2001) Early effects of climate change: do they include changes in vector-borne disease? Phil Trans R Soc Lond B 356:1057–1068

  • Lauderdale JM, Caminade C, Heath AE, Jones AE, MacLeod DA, Gouda KC et al (2014) Towards seasonal forecasting of malaria in India. Malar J 13(1):310

    Article  Google Scholar 

  • Leedale J, Tompkins AM, Caminade C, Jones AE, Nikulin G, Morse AP (2016) Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty. Geospat Health 11:102–114

    Google Scholar 

  • Lindsay SW, Bødker R, Malima R, Msangeni HA, Kisinza W (2000) Effect of 1997–98 EI Niño on highland malaria in Tanzania. The Lancet 355(9208):989–990

  • Lunde TM, Bayoh MN, Lindtjørn B (2013) How malaria models relate temperature to malaria transmission. Parasite Vect 6:20

    Article  Google Scholar 

  • MacLeod DA, Jones A, Di Giuseppe F, Caminade C, Morse AP (2015) Demonstration of successful malaria forecasts for Botswana using an operational seasonal climate model. Environ Res Lett 10(4):044005

    Article  Google Scholar 

  • Martens P, Kovats RS, Nijhof S, De Vries P, Livermore MTJ, Bradley DJ et al (1999) Climate change and future populations at risk of malaria. Glob Environ Chang 9:S89–S107

    Article  Google Scholar 

  • Nájera JA, Kouznetsov RL, Delacollette C (1998) Malaria epidemics: detection and control, forecasting and prevention. World Health Organization Division of Control of Tropical Diseases, Geneva

    Google Scholar 

  • Okoye PN, Brooke BD, Hunt RH, Coetzee M (2007) Relative developmental and reproductive fitness associated with pyrethroid resistance in the major southern African malaria vector, Anopheles funestus. Bull Entomol Res 97(6):599–605

    Article  CAS  Google Scholar 

  • Omumbo JA, Lyon B, Waweru SM, Connor SJ, Thomson MC (2011) Raised temperatures over the Kericho tea estates: revisiting the climate in the East African highlands malaria debate. Malar J 10(1):12

    Article  Google Scholar 

  • Pai DS, Sridhar L, Rajeevan M, Sreejith OP, Satbhai NS, Mukhopadhyay B (2014) Development of a new high spatial resolution (0.25× 0.25) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam 65(1):1–18

    Google Scholar 

  • Paaijmans KP, Wandago MO, Githeko AK, Takken W, Carter D (2007) Unexpected high losses of anopheles gambiae larvae due to rainfall. PLoS ONE 2(11):e1146

  • Pascual M, Ahumada JA, Chaves LF, Rodo X, Bouma M (2006) Malaria resurgence in the East African highlands: Temperature trends revisited. Proc Natl Acad Sci 103(15):5829–5834

  • Patz JA, Olson SH (2006) Malaria risk and temperature: influences from global climate change and local land use practices. Proc Natl Acad Sci 103(15):5635–5636

    Article  CAS  Google Scholar 

  • Piontek F, Müller C, Pugh TA, Clark DB, Deryng D, Elliott J et al (2014) Multisectoral climate impact hotspots in a warming world. Proc Natl Acad Sci 111(9):3233–3238

    Article  CAS  Google Scholar 

  • Tompkins AM, Ermert V (2013) A regional-scale, high resolution dynamical malaria model that accounts for population density, climate and surface hydrology. Malar J 12:65

    Article  Google Scholar 

  • Tompkins AM, Di Giuseppe F (2015) Potential predictability of malaria in Africa using ecmwf monthly and seasonal climate forecast. J Appl Meteorol Clim 54:521–540

    Article  Google Scholar 

  • Tompkins AM, Caporaso L (2016) Assessment of malaria transmission changes in Africa, due to the climate impact of land use change using Coupled Model Intercomparison Project Phase 5 earth system models. Geospat Health 11(Suppl 1):380. https://doi.org/10.4081/gh.2016.380

    Article  Google Scholar 

  • Tompkins AM, Thomson MC (2018) Uncertainty in malaria simulations in the highlands of Kenya: relative contributions of model parameter setting, driving climate and initial condition errors. PLoS One 13(9):e0200638. https://doi.org/10.1371/journal.pone.0200638

    Article  CAS  Google Scholar 

  • Van Lieshout M et al (2004) Climate change and malaria: analysis of the SRES climate and socio-economic scenarios. Glob Environ Chang 14(1):87–99

    Article  Google Scholar 

  • Voldoire A, Sanchez-Gomez E, y Mélia DS, Decharme B, Cassou C, Sénési S et al (2013a) The CNRM-CM5. 1 global climate model: description and basic evaluation. Clim Dyn 40(9-10):2091–2121

    Article  Google Scholar 

  • Watanabe S, Hajima T, Sudo K, Nagashima T, Takemura T, Okajima H et al (2011) MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci Model Dev 4(4):845–872

    Article  Google Scholar 

  • Voldoire A, Sanchez-Gomez E, y Mélia DS, Decharme B, Cassou C, Sénési S, Valcke S, Beau I, Alias A, Chevallier M, Déqué M (2013b) The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn 40(9-10):2091–2121

    Article  Google Scholar 

  • Worrall E, Rietveld A, Delacollette C (2004) The burden of malaria epidemics and cost-effectiveness of interventions in epidemic situations in Africa. Am J Trop Med Hyg 71:136–140

    Article  Google Scholar 

  • Xin XG, Wu TW, Zhang J (2013) Introduction of CMIP5 experiments carried out with the climate system models of Beijing Climate Center. Adv Clim Change Res 4(1):41–49

  • Yukimoto S, Adachi Y, Hosaka M, Sakami T, Yoshimura H, Hirabara M, Tanaka TY, Shindo E, Tsujino H, Deushi M, Mizuta R (2012) A new global climate model of the Meteorological Research Institute: MRI-CGCM3-Model description and basic performance. J Meteorol Soc Jpn Ser II 90:23–64

    Article  Google Scholar 

  • Zhang ZS, Nisancioglu K, Bentsen M, Tjiputra J, Bethke I, Yan Q, Risebrobakken B, Andersson C, Jansen E (2012) Pre-industrial and mid-Pliocene simulations with NorESM-L. Geosci Model Dev 5(2):523–533

    Article  Google Scholar 

Download references

Acknowledgements

The authors are thankful to the two anonymous reviewers whose comments and suggestions have helped us improve the overall quality of the paper. SC is thankful to the DST, Govt. of India for providing the INSPIRE Fellowship. SD is thankful to the University of Allahabad for granting the sabbatical leave. The authors are thankful to the CMIP5 modelling community for making their data freely available. Thanks are also due to Dr. Adrian Tompkins, ICTP, Italy for providing VECRI-related training to SC from time to time.

Author information

Authors and Affiliations

  1. K Banerjee Centre of Atmospheric and Ocean Studies and M N Saha Centre of Space Studies, University of Allahabad, Prayagraj, UP, 211002, India

    Shweta Chaturvedi & Suneet Dwivedi

Authors
  1. Shweta Chaturvedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suneet Dwivedi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shweta Chaturvedi.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliation.s

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chaturvedi, S., Dwivedi, S. Understanding the effect of climate change in the distribution and intensity of malaria transmission over India using a dynamical malaria model. Int J Biometeorol 65, 1161–1175 (2021). https://doi.org/10.1007/s00484-021-02097-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00484-021-02097-x

Keywords

Search

Navigation