Epidemiology of East Coast fever (Theileria parva infection) in Kenya: past, present and the future

Abstract

In this article, we review the epidemiology of East Coast fever (ECF), a tick-borne infection of cattle, in Kenya. The major factors associated with epidemiology of ECF include the agro-ecological zone (AEZ), livestock production system (LPS) and both animal breed and age. These factors appear to influence the epidemiology of ECF through structured gradients. We further show that the gradients are dynamically shaped by socio-demographic and environmental processes. For a vector-borne disease whose transmission depends on environmental characteristics that influence vector dynamics, a change in the environment implies a change in the epidemiology of the disease. The review recommends that future ECF epidemiological studies should account for these factors and the dynamic interactions between them. In Kenya, ECF control has previously relied predominantly on tick control using acaricides and chemotherapy while ECF immunization is steadily being disseminated. We highlight the contribution of ECF epidemiology and economics in the design of production system and/or geographical area-specific integrated control strategies based on both the dynamic epidemiological risk of the disease and economic impacts of control strategies. In all production systems (except marginal areas), economic analyses demonstrate that integrated control in which ECF immunization is always an important component, can play an important role in the overall control of the disease. Indeed, Kenya has recently approved ECF immunization in all production systems (except in marginal areas). If the infrastructure of the vaccine production and distribution can be heightened, large ECF endemic areas are expected to be endemically stable and the disease controlled. Finally, the review points the way for future research by identifying scenario analyses as a critical methodology on which to base future investigations on how both dynamic livestock management systems and patterns of land use influence the dynamics and complexity of ECF epidemiology and the implications for control.

Figure 1

The major agro-climatic zones in Kenya source: [5].

Figure 2

Map of Kenya illustrating Kenya provinces.

Figure 3

Map of Kenya illustrating the distribution of ECF vector tick. Source: [28].

Figure 4

Illustration of gradient of effects of AEZs, farming systems, ECF vector suitability characteristics and corresponding ECF qualitative prevalence and incidence levels in Kenya.

Figure 5

Box and whisker plots illustrating the distribution of prevalence (%) values by the identified factors obtained from the reviewed studies. The upper whisker represents the maximum value whereas the lower whisker represents the minimum value. The band at the middle of the box is the median value. The circles outside the whiskers are the ‘far out’ values. Prevalence median values progressively increase by AEZs up to zone IV then subside in zone V. Prevalence median values for open grazing system and for indigenous cattle are higher than for other production systems and cattle breeds respectively. Prevalence median values progressively increase with age.