Impact of 1.5 oC and 2 oC global warming scenarios on malaria transmission in East Africa

Background: Malaria remains a global challenge with approximately 228 million cases and 405,000 malaria-related deaths reported in 2018 alone; 93% of which were in sub-Saharan Africa. Aware of the critical role than environmental factors play in malaria transmission, this study aimed at assessing the relationship between precipitation, temperature, and clinical malaria cases in East Africa and how the relationship may change under 1.5 oC and 2.0 oC global warming levels (hereinafter GWL1.5 and GWL2.0, respectively). Methods: A correlation analysis was done to establish the current relationship between annual precipitation, mean temperature, and clinical malaria cases. Differences between annual precipitation and mean temperature value projections for periods 2008-2037 and 2023-2052 (corresponding to GWL1.5 and GWL2.0, respectively), relative to the control period (1977-2005), were computed to determine how malaria transmission may change under the two global warming scenarios. Results: A predominantly positive/negative correlation between clinical malaria cases and temperature/precipitation was observed. Relative to the control period, no major significant changes in precipitation were shown in both warming scenarios. However, an increase in temperature of between 0.5 oC and 1.5 oC and 1.0 oC to 2.0 oC under GWL1.5 and GWL2.0, respectively, was recorded. Hence, more areas in East Africa are likely to be exposed to temperature thresholds favourable for increased malaria vector abundance and, hence, potentially intensify malaria transmission in the region. Conclusions: GWL1.5 and GWL2.0 scenarios are likely to intensify malaria transmission in East Africa. Ongoing interventions should, therefore, be intensified to sustain the gains made towards malaria elimination in East Africa in a warming climate.


Introduction
Malaria is an illness caused by Plasmodium parasites that are spread to humans through bites of infected female Anopheles mosquitoes, commonly referred to as "malaria vectors". Of the five parasite species that cause malaria in humans, P. falciparum and P. vivax pose the highest threat (WHO, 2020). According to the World Health Organization (WHO), an estimated 228 million malaria cases and 405,000 malaria-related deaths were reported in 2018, globally. About 93% of the malaria cases and 94% of the malaria-related deaths occurred in sub-Saharan Africa. Uganda, for instance, tops East Africa with the highest number of malaria cases; accounting for 5% of global totals in 2018. WHO, 2020). In a special report on global warming of 1.5 °C (hereinafter SR1.5), the Intergovernmental Panel on Climate Change (IPCC; Hoegh-Guldberg et al., 2018) highlighted sector-specific risks posed by a global temperature rise of 1.5 °C and beyond. The SR1.5 identifies a knowledge gap in the impacts of global and regional climate change at 1.5 °C on, inter alia, public health and infectious diseases, particularly for developing nations. Some work has been done towards understanding the potential impact of global warming in East Africa (e.g. Gudoshava et al., 2020;Osima et al., 2018). However, no conclusive literature exists on the potential impacts of 1.5 °C and 2 °C global warming levels (hereinafter GWL1.5 and GWL2.0) on health, among other sectors, in East Africa. This study, therefore, aimed at assessing the relationship between precipitation, temperature, and clinical malaria cases in East Africa and how the relationship may change under the GWL1.5 and GWL2.0 scenarios.

Study area
The study focuses on the East Africa sub-region (marked EA on Figure 1) of the COordinated regional Downscaling Experiment (CORDEX) Africa domain (Kim et al., 2014). A slight extension of the CORDEX-EA sub-region was done to cover five countries part of the East African Community (EAC) namely Kenya, Uganda, Tanzania, Rwanda, and Burundi ( Figure 1).

Amendments from Version 2
This version has minor revisions in the Introduction (paragraph 1) and the Methodology (paragraph 3 of the Data Analysis sub-section) sections for coherence.

Statistical computations and data visualization
Processing (conversion to common calendar, units, grid, and resolution) and statistical computations (e.g. means, anomalies, standard deviation, summations, and data detrending) of climate (precipitation and temperature) data in NetCDF format was done using the Climate Data Operators (CDO), version 1.9.8 -a 1 http://bit.ly/2RoIist command line suite for manipulating and analysing climate data. A description of CDO operators is available from the CDO user guide. Additional computations were done using the R Project for Statistical Computing (R, version 3.6.3). Specifically, the fields, graphics, and ncdf4 R packages were used to process and compute future changes in precipitation and temperature under the 95% confidence level. Data detrending and correlation analysis were done in R using the pracma package and the cor.test function, respectively. Spatial data visualization was done using the Grid Analysis and Display System (GrADS, version 2.2.1.oga.1). Line plots were done in R using the ggplot2 (version 3.3.0) package.
Due to resolution differences between model and observations data, the data were processed in their native grids before bi-linearly interpolating them to the RCM grid to facilitate comparison (as in Diaconescu et al., 2015). Here, final products (after all the statistical computations) for both observational and model data were remapped into the same grid to facilitate comparison. Remapping was done using the 'remapbil' function in the CDO software.    record a positive correlation between rainfall and malaria cases (9 out of 25) imply that more interventions are needed to minimize malaria transmission in the region. The interventions will contribute to the sustenance of gains made and enhance the march towards malaria elimination in East Africa. More intense and extreme rainfall events in future could enhance the provision of aquatic environments to facilitate more malaria vector abundance and malaria transmission.

Results and discussion
In terms of temperature, all areas in the study domain recorded temperatures within the suitability threshold (18-28 °C) for malaria transmission (Figure 3, bottom row)

Conclusions
Global warming scenarios of 1.5 °C and 2 °C are likely to increase malaria transmission seasons and geographical extents of malaria transmission in East Africa. Unless interventions are sufficiently intensified, sustaining the gains made towards malaria elimination is likely to be more difficult in a warming climate.
Hence, the global community should intensify its collective efforts towards minimizing global warming. Meanwhile, more investment should be made to sustain the gains made and hasten the match towards malaria elimination in East Africa. More research (considering other variables such as altitude, humidity, and vulnerability of communities) is also required to enhance the understanding of spatial and temporal impacts of global warming on malaria transmission in East Africa. Specifically, disease modelling is required to project the new exposed population which will inform future malaria eradication efforts.

Data availability
Source data CORDEX-Africa RCM simulations (files listed in Table 1

ICMR-National Institute of Malaria Research, New Delhi, Delhi, India
The study has used 20-25 o C as thresholds of temperature within which maximum suitability for survival of Anopheles mosquitoes is achieved. What is the basis of selecting this range? This requires clarification. Further, instead of selecting the range for survival of mosquitoes, the threshold for transmission of malaria (sporogony) would have been ideal as the present communication deals with future scenario of malaria and not the anopheles vector.
Mosquitoes are known to survive even at lower than 13 o C temperature also (IPCC, 2001). The temperature between 18-32 o C is considered suitable for transmission of malaria (Craig et al., 1999 1 ). Further Mordecai et al. (2013 2 ) found that the upper threshold is 28 o C, bit lower than 32 o C.

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In the study, the threshold of rainfall has been taken of Aedes mosquitoes. Anopheles and Aedes mosquitoes are quite different in their ecology, biology and climatic requirements. Therefore, using the thresholds of rainfall meant for Aedes, does not seem appropriate for Anopheles. threshold is 280 C, bit lower than 320 C.
Response: This is well-noted. The 20-25 o C is the range within which the mosquito abundance increases to maximum. However, we have revised our manuscript to focus on malaria transmission and not mosquito survival (see lines 143 -146).
Comment: In the study, the threshold of rainfall has been taken of Aedes mosquitoes. Anopheles and Aedes mosquitoes are quite different in their ecology, biology and climatic requirements. Therefore, using the thresholds of rainfall meant for Aedes, does not seem appropriate for Anopheles.  (Craig et al., 19991).
Comment: The authors have undertaken painstaking efforts in analyzing the projected scenario of malaria in view of projected rise in temperature. The objective of the study is also very pertinent. But owing to methodological issues stated above, reanalysis of data is required.
Response: We appreciate your comments and have made the suggested revisions to make the current version of the manuscript more plausible.  Table 1 indicates that data was downloaded from 2071-2100 however this is not the same time period that was analyzed. This is a typo that needs to be corrected.

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In paragraph 2 of the data analysis section the authors state that 2022 and 2037 have been identified as mid-years for 30-year windows when GWL1.5 and GWL2.0, respectively, are likely to be first experienced. However different GCMs hit these levels at different times, is it the assumption in this manuscript that both these GCMs will reach the GWL at the same time? I would suggest that the authors rework on this and use the GWL for the different GCMS.

If applicable, is the statistical analysis and its interpretation appropriate? Yes
Are all the source data underlying the results available to ensure full reproducibility?
GWL at the same time? I would suggest that the authors rework on this and use the GWL for the different GCMS. Comment: Trends in temperature, precipitation, and clinical malaria cases in E. Africa paragraph graph two: an explanation on why malaria clinical cases and temperature are negatively correlated over Uganda is needed here, since in all the other countries the correlation is positive.

Response: We have provided more information in lines 172-184.
Comment: Trends in temperature, precipitation, and clinical malaria cases in E. Africa paragraph three: could the negative correlations be caused by the washing away of the eggs due to high rainfall rather than intensification of efforts to combat malaria?
Response: Egg flushing can indeed be a possibility. However, no major rainfall changes have been recorded during the period under study. Rainfall has mostly remained within the normal range (as shown in Figure 2). As detailed in lines 172-184 of the revised manuscript, the relatively low/high correlation between malaria cases and climatic factors in Uganda/Burundi result from case and vector control investment.
Comment: Figure 3: The line plots do not show any obvious relationships between the malaria clinical cases and the temperature/rainfall -could this be because people are taking preventive measures? Is it possible to obtain the actual vector data and do the analysis using this rather than the clinical cases?
Response: Please refer to our response in 7 above. The use of actual vector data is out of the scope of the current study. We have recommended a further study (see lines 231-235) where such data can be obtained and used.
Comment: Figure 4: The caption seems incorrect, I would not expect rainfall of up to 700mm/day in any season over East Africa, also in the write-up it is written mm/year.
Response: This has been corrected (see Figure 3).
Comment: A discussion on the large scale drivers and malaria cases could be helpful in explaining any likely changes in the future of the reported clinical cases.
Response: Thank you for this suggestion. We have provided more information in lines 218-233.