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. 2024 Apr 16;19(4):e0297744.
doi: 10.1371/journal.pone.0297744. eCollection 2024.

The effect of explicit convection on simulated malaria transmission across Africa

Joshua Talib  1 , Abayomi A Abatan  2 , Remy HoekSpaans  3 , Edmund I Yamba  4 , Temitope S Egbebiyi  5 , Cyril Caminade  6   7 , Anne Jones  8 , Cathryn E Birch  9 , Oladapo M Olagbegi  10 , Andrew P Morse  11
Affiliations

The effect of explicit convection on simulated malaria transmission across Africa

Joshua Talib et al. PLoS One. .

Abstract

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1
Annual-mean (2000–2007) of (a) Pf incidence rate (cases person−1 year−1) from MAP, (b-d) number of days when R0 is greater than 1.0 from LMM experiments, and (e-g) predicted EIR (infectious bites person−1 day−1) from VECTRI simulations. We show outputs from LMM and VECTRI driven by (b,e) observational, (c,f) CP4h and (d,g) R25h data. In panels (b) to (g) we document the spatial correlation coefficient between simulated malaria model outputs and MAP data. To ensure that spatial correlations are not biased towards regions of low malaria incidence, we remove all grid points where the MAP-derived Pf incidence rate is smaller than 0.1. We also removed grid points where the simulated annual-mean number of days when R0 is greater than 1.0 is outside the range of 15.0 and 140.0, or where the simulated EIR is outside 0.3 and 2.0. To be consistent with the time span of MAP data [62], we only analyse malaria model output which is driven with observations or climate model data from years 2000 to 2007. All spatial correlations are statistically significant at a 99% confidence interval using a two-tailed Wald T-test. Land and country boundaries were added using Natural Earth; free vector and raster map data available at naturalearthdata.com.
Fig 2
Fig 2
Differences in the simulated annual-mean number of days when R0 is greater than 1.0 between (a) Ch and Rh, (b) Ch and Oh, (c) Rh and Oh, (d) RPhCTh and Rh, (e) CPhRTh and Rh, (f) ChBC and RhBC, (g) ChBC and Oh, and (h) RhBC and Oh, by the LMM. (i-k) Differences in the simulated annual-mean EIR (infectious bites person−1 day−1) between (i) Ch and Rh, (j) Ch and Oh, (k) Rh and Oh, by VECTRI. In each panel, boxed values document the root mean squared difference across land points south of 20°N. In panel (a) coloured rectangles highlight regions of focus including: EA highlands (black); Congolese rainforest (dark green); Guinea coast (dark blue); and South Sudan (purple). Land and country boundaries were added using Natural Earth; free vector and raster map data available at naturalearthdata.com.
Fig 3
Fig 3
Differences in probability distributions of wet-day (≥ 1 mm) grid-point Ch and Rh simulation data in (a,d,g,j) daily-mean temperature (°C), (b,e,h,k) 10-day precipitation accumulation (mm), and (c,f,i,l) LMM-estimated R0 across (a-c) EA highlands, (d-f) Congolese rainforests, (g-i) Guinea coast and (j-l) South Sudan. Regions are denoted in Fig 2a. In the first column we document the total difference in temperatures changes less than 18°C, whilst in the third column, we note the total change in days when R0 is greater than 1.0. Both 18°C and an R0 value of 1.0 are denoted by grey vertical lines.
Fig 4
Fig 4
(a-i) Differences in the simulated annual-mean number of days when R0 is greater than 1.0 between future and historical LMM experiments. (a-c) Differences when driving the LMM with temperatures and precipitation data from historical and future climates. (d-i) Differences when only using future (d-f) temperature and (g-i) precipitation data. (j-l) Differences in the simulated annual-mean EIR (infectious bites person−1 day−1) by VECTRI. Differences between future and historical experiments driven by CP4 and R25 data are shown in the first (a,d,g,j) and second (b,e,h,i) columns respectively, whilst panels in the third (c,f,i,l) column show the difference in changes when using CP4 and R25 driving model data. In each panel, boxed values document the root mean squared difference across land points south of 20°N. Land and country boundaries were added using Natural Earth; free vector and raster map data available at naturalearthdata.com.
Fig 5
Fig 5
Differences in probability distributions of wet-day (≥ 1 mm) grid-point future and historical simulation data in (a,d,g,j) daily-mean temperature (°C), (b,e,h,k) 10-day precipitation accumulation (mm), and (c,f,i,l) LMM-estimated R0 across (a-c, g-i) EA highlands and (d-f, j-l) Congolese rainforests. The first (a-f) and last (g-l) two rows show changes in CP4 and R25 experiments respectively. Regions are denoted in Fig 2a. In the first column we document the total difference in temperatures changes less than 18°C, whilst in the last column, we note the total change in days when R0 is greater than 1.0. Both 18°C and an R0 value of 1.0 are denoted by grey vertical lines.
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Grants and funding

“The British Council Researcher Links Climate Challenge Grant (715056901) provided funding for a workshop titled “Investigating the effects of climate change on malaria for urgent action to combat climate change with reference to COP26 priorities” and a follow-on project. Both supported this paper. For this study JT was supported by the Natural Environment Research Council (NERC) as part of the National Capability (NC) international programme (NE/X006247/1). AJ was supported by the Hartree National Centre for Digital Innovation, a collaboration between Science and Technology Facilities Council (STFC) and International Business Machines (IBM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”