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This paper explores the role of decentralised community-based care systems in achieving sustainable healthcare in resource-poor areas. Based on case studies from Sierra Leone, Madagascar, Uganda and Ethiopia, the paper argues that a community-based system of healthcare is more effective in the prevention, early diagnosis, and primary care in response to the zoonotic and infectious diseases associated with extreme weather events as well as their direct health impacts. Community-based systems of care have a more holistic view of the determinants of health and can integrate responses to health challenges, social wellbeing, ecological and economic viability. The case studies profiled in this paper reveal the importance of expanding notions of health to encompass the whole environment (physical and social, across time and space) in which people live, including the explicit recognition of ecological interests and their interconnections with health. While much work still needs to be done in defining and measuring successful community responses to health and other crises, we identify two potentially core criteria: the inclusion and integration of local knowledge in response planning and actions, and the involvement of researchers and practitioners, e.g., community-embedded health workers and NGO staff, as trusted key interlocuters in brokering knowledge and devising sustainable community systems of care.

Accurate and timely rainfall prediction enhances productivity and can aid proper planning in sectors such as agriculture, health, transport and water resources. However quantitative rainfall prediction is normally a challenge and for this reason, this study was conducted with an aim of improving rainfall prediction using ensemble methods. It first assessed the performance of six convective schemes (Kain–Fritsch (KF); Betts–Miller–Janjić (BMJ); Grell–Fretas (GF); Grell 3D ensemble (G3); New–Tiedke (NT) and Grell–Devenyi (GD)) using the root mean square error (RMSE) and mean error (ME) focusing on the March–May 2013 rainfall period over Uganda. 18 ensemble members were then generated from the three best performing convective schemes (i.e., KF, GF and G3). The daily rainfall predicted by the three ensemble methods (i.e., ensemble mean (ENS); ensemble mean analogue (EMA) and multi–member analogue ensemble (MAEM)) was then compared with the observed daily rainfall and the RMSE and ME computed. The results shows that the ENS presented a smaller RMSE compared to individual schemes (ENS: 10.02; KF: 23.96; BMJ: 26.04; GF: 25.85; G3: 24.07; NT: 29.13 and GD: 26.27) and a better bias (ENS: −1.28; KF: −1.62; BMJ: −4.04; GF: −3.90; G3: −3.62; NT: −5.41 and GD: −4.07). The EMA and MAEM presented 13 out of 21 stations and 17 out of 21 stations respectively with smaller RMSE compared to ENS thus demonstrating additional improvement in predictive performance. This study proposed and described MAEM and found it producing comparatively better quantitative rainfall prediction performance compared to the other ensemble methods used. The MAEM method should be valid regardless the nature of the rainfall season.