Climate and Satellite Indicators to Forecast Rift Valley Fever Epidemics in Kenya

Abstract

All known Rift Valley fever virus outbreaks in East Africa from 1950 to May 1998, and probably earlier, followed periods of abnormally high rainfall. Analysis of this record and Pacific and Indian Ocean sea surface temperature anomalies, coupled with satellite normalized difference vegetation index data, shows that prediction of Rift Valley fever outbreaks may be made up to 5 months in advance of outbreaks in East Africa. Concurrent near-real-time monitoring with satellite normalized difference vegetation data may identify actual affected areas.

Human birth seasonality: latitudinal gradient and interplay with childhood disease dynamics

“…Early work on diphtheria and measles implicated elevated contact rates among children in school as the driver of pulsed transmission [1,9], leading to much emphasis on school-term forcing [2,3,5,10,11]. More recently, additional mechanisms of seasonal transmission have been identified, including climatic drivers of pathogen survival [12], transmission [13,14] and vector activity [15,16], seasonal host migration [17] and seasonal fluctuations in host immunity [18,19]. Here, we propose that seasonality in host recruitment rates may also shape epidemiology.…”

Prediction of a Rift Valley fever outbreak

“…Outbreaks of the disease are episodic and closely linked to climate variability, especially widespread elevated rainfall that facilitates Rift Valley fever (RVF) virus transmission by vector mosquitoes (1)(2)(3). A RVF outbreak in 1997-1998 was the largest documented outbreak in the Horn of Africa and involved 5 countries with a loss of Ϸ100,000 domestic animals, Ϸ90,000 human infections (4), and had a significant economic impact due to a ban on livestock exports from the region (5).…”

Imperfect Signals and Product Safety Disclosure: A Shepherd's Dilemma

“…Seasonal precipitation and satellite based vegetation indicators can now be predicted with several months of lead time (Indeje, 2006). Researchers have been able to explain eight documented outbreaks between 1950 to 1999 using Pacific and Indian Ocean sea surface temperatures (which are correlated to seasonal precipitation) and normalized difference vegetation indexes calculated from remote-sensing data (Linthicum et al, 1999;Anyamba and Tucker, 2005). In spite of these preliminary steps, much work remains to be done before climate-based RVF forecasts become operational.…”

“…Advances in climatic and entomological modeling raise the possibility of using a disease forecasting system as an alternative to regional import bans for preventing the entry of infected lambs into the market (Linthicum et al, 1999;Anyamba and Tucker, 2005). Local data on RVF episodes is necessary to calibrate forecast models.…”