Data from: Modelling vaccination strategies against Rift Valley fever in livestock in Kenya: model code

Gachohi, John M., Jomo Kenyatta University of Agriculture and Technology, International Livestock Research Institute

Njenga, M. Kariuki, Kenya Medical Research Institute

Kitala, Phillip M.

Bett, Bernard K.

Bett, Bernard, International Livestock Research Institute

Kitala, Philip, University of Nairobi

Published Nov 09, 2017 on Dryad. https://doi.org/10.5061/dryad.779rd

Cite this dataset

Gachohi, John M. et al. (2017). Data from: Modelling vaccination strategies against Rift Valley fever in livestock in Kenya: model code [Dataset]. Dryad. https://doi.org/10.5061/dryad.779rd

Abstract

The impacts of vaccination on the transmission of Rift Valley fever virus (RVFV) have not been evaluated. We have developed a RVFV transmission model comprising two hosts -- cattle as a separate host and sheep and goats as one combined host (herein after referred to as sheep) -- and two vectors -- Aedes species (spp) and Culex spp -- and used it to predict the impacts of: (1) reactive vaccination implemented at various levels of coverage at pre-determined time points, (2) targeted vaccination involving either of the two host species, and (3) a periodic vaccination implemented biannually or annually before an outbreak. Methodology/Principal Findings: The model comprises coupled vector and host modules where the dynamics of vectors and hosts are described using a system of difference equations. Vector populations are structured into egg, larva, pupa and adult stages and the latter stage is further categorized into three infection categories: susceptible, exposed and infectious mosquitoes. The survival rates of the immature stages (egg, larva and pupa) are dependent on rainfall densities extracted from the Tropical Rainfall Measuring Mission (TRMM) for a Rift Valley fever (RVF) endemic site in Kenya over a period of 1827 days. The host populations are structured into four age classes comprising young, weaners, yearlings and adults and four infection categories including susceptible, exposed, infectious, and immune categories. The model reproduces the 2006/2007 RVF outbreak reported in empirical surveys in the target area and other seasonal transmission events that are perceived to occur during the wet seasons. Mass reactive vaccination strategies greatly reduce the potential for a major outbreak. The results also suggest that the effectiveness of vaccination can be enhanced by increasing the vaccination coverage, targeting vaccination on cattle given that this species plays a major role in the transmission of the virus, and using both periodic and reactive vaccination strategies. Conclusion/Significance: Reactive vaccination can be effective in mitigating the impacts of RVF outbreaks but practically, it is not always possible to have this measure implemented satisfactorily due to the rapid onset and evolution of RVF epidemics. This analysis demonstrates that both periodic and reactive vaccination ought to be used strategically to effectively control the disease.

Usage notes

Gachohi et al RVF Model code

This code, implemented in R, is about simulating population dynamics of two species of mosquitoes namely Aedes and Culex and two livestock species namely cattle and sheep. Hosts are classified into four age groups (young, weaner, yearling and adult groups) while vectors are classified into eggs, larvae, pupae and adults. Transition rates govern the population dynamics. Further, the code simulates transmission dynamics of Rift Valley fever virus between the mosquitoes and the livestock. Mosquito growth is regulated by amounts of rainfall implicitly through probability functions - suitability function for Aedes and logistic distribution function for Culex. The code is used to simulate diverse policy-relevant interventions on the effectiveness of reactive and periodic Rift Valley fever vaccination strategies.

Modelling vaccination strategies against Rift Valley fever in livestock in Kenya: Precipitation data

This data contains precipitation data used to drive mosquito population growth. In the code, the rain is sourced as follows: Rain<-read.csv("D:\\RVF R model rain\\Rain.csv", header=TRUE) saved in D in a folder RVF R model rain under fie name Rain.csv. To import precipitation data, download the data from Dryad and save in your PC and import appropriately to run the model.

Rain.csv

Location

Kenya