Malaria early warnings based on seasonal climate forecasts from multi-model ensembles

Abstract

The control of epidemic malaria is a priority for the international health community and specific targets for the early detection and effective control of epidemics have been agreed. Interannual climate variability is an important determinant of epidemics in parts of Africa where climate drives both mosquito vector dynamics and parasite development rates. Hence, skilful seasonal climate forecasts may provide early warning of changes of risk in epidemic-prone regions. Here we discuss the development of a system to forecast probabilities of anomalously high and low malaria incidence with dynamically based, seasonal-timescale, multi-model ensemble predictions of climate, using leading global coupled ocean-atmosphere climate models developed in Europe. This forecast system is successfully applied to the prediction of malaria risk in Botswana, where links between malaria and climate variability are well established, adding up to four months lead time over malaria warnings issued with observed precipitation and having a comparably high level of probabilistic prediction skill. In years in which the forecast probability distribution is different from that of climatology, malaria decision-makers can use this information for improved resource allocation.

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

“…Seasonal forecasts provide climate outlooks from a month to over a year ahead based on the interactions of the atmosphere with slowly varying climate system components like the oceans and land surface (Thomson et al, 2006;Kim et al, 2012b;Doblas-Reyes et al, 2013;Manzanas et al, 2014). A major source of seasonal predictability is the El-Niño Southern Oscillation (ENSO) because it forces climate anomalies globally (Harrison, 2005;van Oldenborgh et al, 2005;Hoskins, 2006;Palmer, 2006;Charles et al, 2012;Doblas-Reyes et al, 2013;Manzanas et al, 2014) and is predictable with 6-12 months lead-time.…”

Developments in dynamical seasonal forecasting relevant to agricultural management

“…However, as mentioned above an end-toend system necessitates a comprehensive forecast quality assessment that includes the end-user predictions, including a component of user-oriented verification. Some attempts in this direction are described in Morse et al (2005) and Thomson et al (2006), but important aspects still need to be addressed. For instance, end-user forecast variables such as crop yield include in a non-linear way the effect of several weather variables at the same time.…”

“…In order to quantitatively assess the benefits of this approach, taking into account at the same time the user requirements mentioned above, a collaborative strategy was chosen with a leading role played by the downscaling partners (Feddersen & Andersen 2005), as well as partners with experience in malaria (Morse et al 2005, Thomson et al 2006) and crop yield prediction (Cantelaube & Terres 2005, Challinor et al 2005. For this latter case, whilst only a limited number of years have so far been studied, there is evidence of useful probabilistic skill over Europe.…”

Seasonal climate forecasting

“…For some diseases close direct and indirect links with climate conditions exist (e.g. malaria epidemics; see Thomson et al, 2006;Abawi et al, 2008) and in such cases, climate prediction might give public health systems early warning of the likelihood of epidemics. Likewise, using seasonal predictions as input for load-balance models has the potential to optimize the matching of supply and demand in the energy industry.…”