Forecasting the Potential Effects of Climate Change on Malaria in the Lake Victoria Basin Using Regionalized Climate Projections

Ednah N Ototo; Joseph O Ogutu; Andrew Githeko; Mohammed Y Said; Lucy Kamau; Didacus Namanya; Stella Simiyu; Stephen Mutimba

doi:10.1007/s11686-022-00588-4

Forecasting the Potential Effects of Climate Change on Malaria in the Lake Victoria Basin Using Regionalized Climate Projections

Acta Parasitol. 2022 Dec;67(4):1535-1563. doi: 10.1007/s11686-022-00588-4. Epub 2022 Aug 12.

Authors

Ednah N Ototo^{1

2}, Joseph O Ogutu³, Andrew Githeko², Mohammed Y Said^{4

5}, Lucy Kamau¹, Didacus Namanya⁶, Stella Simiyu⁴, Stephen Mutimba⁴

Affiliations

¹ Kenyatta University, P.O. Box 43844, Nairobi, Kenya.
² Kenya Medical Research Institute, Centre for Global Health Research, Climate and Human Health Research Unit, P.O. Box 1578, Kisumu, Kenya.
³ Institute for Crop Science-340, University of Hohenheim, 70599, Stuttgart, Germany. jogutu2007@gmail.com.
⁴ C&E Advisory Kenya, P.O. Box 76406-00508, Nairobi, Kenya.
⁵ Institute of Climate Change and Adaptation, University of Nairobi, P.O. Box 30197, Nairobi, 00100, Kenya.
⁶ Uganda Ministry of Health, P.O. Box 7272, Kampala, Uganda.

Abstract

Background: Malaria epidemics are increasing in East Africa since the 1980s, coincident with rising temperature and widening climate variability. A projected 1-3.5 °C rise in average global temperatures by 2100 could exacerbate the epidemics by modifying disease transmission thresholds. Future malaria scenarios for the Lake Victoria Basin (LVB) are quantified for projected climate scenarios spanning 2006-2100.

Methods: Regression relationships are established between historical (1995-2010) clinical malaria and anaemia cases and rainfall and temperature for four East African malaria hotspots. The vector autoregressive moving average processes model, VARMAX (p,q,s), is then used to forecast malaria and anaemia responses to rainfall and temperatures projected with an ensemble of eight General Circulation Models (GCMs) for climate change scenarios defined by three Representative Concentration Pathways (RCPs 2.6, 4.5 and 8.5).

Results: Maximum temperatures in the long rainy (March-May) and dry (June-September) seasons will likely increase by over 2.0 °C by 2070, relative to 1971-2000, under RCPs 4.5 and 8.5. Minimum temperatures (June-September) will likely increase by over 1.5-3.0 °C under RCPs 2.6, 4.5 and 8.5. The short rains (OND) will likely increase more than the long rains (MAM) by the 2050s and 2070s under RCPs 4.5 and 8.5. Historical malaria cases are positively and linearly related to the 3-6-month running means of monthly rainfall and maximum temperature. Marked variation characterizes the patterns projected for each of the three scenarios across the eight General Circulation Models, reaffirming the importance of using an ensemble of models for projections.

Conclusions: The short rains (OND), wet season (MAM) temperatures and clinical malaria cases will likely increase in the Lake Victoria Basin. Climate change adaptation and mitigation strategies, including malaria control interventions could reduce the projected epidemics and cases. Interventions should reduce emerging risks, human vulnerability and environmental suitability for malaria transmission.

Keywords: Climate change; Malaria; Regionalized climate projections.

MeSH terms

Climate Change*
Forecasting
Humans
Lakes
Malaria* / epidemiology
Temperature

Abstract

MeSH terms

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