Modelling the Dynamics of Post-Vaccination Immunity Rate in a Population of Sahelian Sheep after a Vaccination Campaign against Peste des Petits Ruminants Virus

Abstract

Background: Peste des petits ruminants (PPR) is an acute infectious viral disease affecting domestic small ruminants (sheep and goats) and some wild ruminant species in Africa, the Middle East and Asia. A global PPR control strategy based on mass vaccination-in regions where PPR is endemic-was recently designed and launched by international organizations. Sahelian Africa is one of the most challenging endemic regions for PPR control. Indeed, strong seasonal and annual variations in mating, mortality and offtake rates result in a complex population dynamics which might in turn alter the population post-vaccination immunity rate (PIR), and thus be important to consider for the implementation of vaccination campaigns.

Methods: In a context of preventive vaccination in epidemiological units without PPR virus transmission, we developed a predictive, dynamic model based on a seasonal matrix population model to simulate PIR dynamics. This model was mostly calibrated with demographic and epidemiological parameters estimated from a long-term follow-up survey of small ruminant herds. We used it to simulate the PIR dynamics following a single PPR vaccination campaign in a Sahelian sheep population, and to assess the effects of (i) changes in offtake rate related to the Tabaski (a Muslim feast following the lunar calendar), and (ii) the date of implementation of the vaccination campaigns.

Results: The persistence of PIR was not influenced by the Tabaski date. Decreasing the vaccination coverage from 100 to 80% had limited effects on PIR. However, lower vaccination coverage did not provide sufficient immunity rates (PIR < 70%). As a trade-off between model predictions and other considerations like animal physiological status, and suitability for livestock farmers, we would suggest to implement vaccination campaigns in September-October. This model is a first step towards better decision support for animal health authorities. It might be adapted to other species, livestock farming systems or diseases.

Fig 1. Global Aridity Index in Senegal (West Africa).

Data sources: Zomer et al., 2006 [38] and Trabucco et al., 2009 [39]; spatial resolution: 10 arc minutes. The location of Senegal is shown in blue on the map of Africa in the top-left corner.

Fig 2. Theoretical immunity dynamics by age class (for a given sex) over 12 months after a vaccination campaign.

The one-month age classes are represented by the space between two horizontal lines. The age structure of the population is represented at each time t by the vertical lines. Each portion of the vertical lines (between two horizontal lines) represented the animals who composed a given age class. Green and white areas represent immunized and susceptible animals. For clarity, the age has been truncated to 24 months.

Fig 3. Variations in estimated monthly parturition rates for ewes older than 10 months, Ndiagne municipality, Senegal.

Fig 4. Variations in estimated monthly offtake rates in rams older than 10 months, Ndiagne municipality, Senegal.

Fig 5. Comparison between simulated (solid line) and observed (dashed line) sheep population dynamics from 1989 to 1995, Ndiagne municipality (Senegal).

Lambing period and Tabaski celebration are represented by the vertical light and dark gray strip.

Fig 6. Seasonal population dynamics in Sahelian sheep, Ndiagne municipality, Senegal simulated under 12 Tabaski scenarios.

Tabaski month is represented by a dark gray vertical strip and the lambing season by a light gray vertical strip (December to February). a) Tabaski occurring during the lambing season; b) Tabaski occurring soon after the lambing season; c) Tabaski occurring long after the lambing season; d) Tabaski occurring before the lambing season.

Fig 7. Estimated monthly proportion of animals older than 3 months according to the Tabaski month (P_vacc).

Fig 8. Annual dynamics of post-vaccination PIR in Sahelian sheep, Ndiagne municipality (Senegal), assuming full vaccination coverage (p = 1).

A) Vaccination campaign and Tabaski in January; B) Vaccination campaign in January and changing Tabaski month; C) Changing vaccination month (12 plots) and Tabaski month (12 lines). On each plot, the origin of the x axis is the vaccination month.

Fig 9. PIR indicators according to the simulated vaccination month: N₇₀, M_PIR, and PIR(12).

From the top to the bottom, the plots show (a) the number of months for which PIR(t) ≥ 70% (N₇₀), (b) the mean annual PIR (M_PIR) and (c) the PIR one year after the vaccination campaign (PIR(12)).