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

Abstract

Using mathematical modelling tools, we assessed the potential for land use change (LUC) associated with the Intergovernmental Panel on Climate Change low-and high-end emission scenarios (RCP2.6 and RCP8.5) to impact malaria transmission in Africa. To drive a spatially explicit, dynamical malaria model, data from the four available earth system models (ESMs) that contributed to the LUC experiment of the Fifth Climate Model Intercomparison Project are used. Despite the limited size of the ESM ensemble, stark differences in the assessment of how LUC can impact climate are revealed. In three out of four ESMs, the impact of LUC on precipitation and temperature over the next century is limited, resulting in no significant change in malaria transmission. However, in one ESM, LUC leads to increases in precipitation under scenario RCP2.6, and increases in temperature in areas of land use conversion to farmland under both scenarios. The result is a more intense transmission and longer transmission seasons in the southeast of the continent, most notably in Mozambique and southern Tanzania. In contrast, warming associated with LUC in the Sahel region reduces risk in this model, as temperatures are already above the 25-30°C threshold at which transmission peaks. The differences between the ESMs emphasise the uncertainty in such assessments. It is also recalled that the modelling framework is unable to adequately represent local-scale changes in climate due to LUC, which some field studies indicate could be significant.

Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty

“…This ensemble intercomparison method currently offers the best means of providing a comprehensive projection of climate-based scenarios but represents a crude assessment of uncertainty since, in contrast to numerical weather prediction where ensemble predictions can be evaluated against observations over many integrations, for climate projections there is no known way of assessing whether the ensembles generated are under or over confident. For example, uncertainty due to processes neglected in the present study is not accounted for, such as uncertainty due to future potential land use change (Tompkins and Caporaso, 2016), population movement and changes, economic growth or other socioeconomic conditions that will be critical for the African continent. The predictive value of studying the impact of climate in isolation on disease transmission and drawing associated conclusions about its relationship with non-climatic factors separately is debatable.…”

Health risks of warming of 1.5 °C, 2 °C, and higher, above pre-industrial temperatures

“…Relationships between temperature and disease incidence are not necessarily linear, resulting in complex patterns of changes in risk with additional warming. Some regions are projected to become too hot and/or dry for the Anopheles mosquito, such as in northern China and parts of south and southeast Asia (Khormi and Kumar 2016, Semakula et al 2017, Tompkins and Caporaso 2016, Yamana et al 2016. Vector populations are projected to shift in some regions with climate change, with expansions and reductions depending on the degree of local warming, the ecology of the vector, and other factors .…”

Importance of risk factors associated with malaria for Sub-Saharan African children

“…According to several studies carried out in sub-Saharan African countries, malaria prevalence varies spatially, depending on several factors including socio-economic factors (Messina et al 2011;Winskill et al 2011;Njau et al 2013;Houngbedji et al 2015;Tusting et al 2016), biological factors (Ehrhardt et al 2006) and environmental factors (Cairns et al 2015;Laneri et al 2015;Moise et al 2016;Tompkins and Caporaso 2016;Kabaria et al 2017), which can have a strong association with malaria.…”