Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information

Tijani Sulaimon, Gemma L. Chaters, Obed M. Nyasebwa, Emanuel S Swai, Sarah Cleaveland, Jessica Enright, Rowland Kao, Paul C. D. Johnson

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Livestock movements contribute to the spread of several infectious diseases. Data on livestock movements can therefore be harnessed to guide policy on targeted interventions for controlling infectious livestock diseases, including Rift Valley fever (RVF)-a vaccine-preventable arboviral fever. Detailed livestock movement data are known to be useful for targeting control efforts including vaccination. These data are available in many countries, however, such data are generally lacking in others, including many in East Africa, where multiple RVF outbreaks have been reported in recent years. Available movement data are imperfect, and the impact of this uncertainty in the utility of movement data on informing targeting of vaccination is not fully understood. Here, we used a network simulation model to describe the spread of RVF within and between 398 wards in northern Tanzania connected by cattle movements, on which we evaluated the impact of targeting vaccination using imperfect movement data. We show that pre-emptive vaccination guided by only market movement permit data could prevent large outbreaks. Targeted control (either by the risk of RVF introduction or onward transmission) at any level of imperfect movement information is preferred over random vaccination, and any improvement in information reliability is advantageous to their effectiveness. Our modeling approach demonstrates how targeted interventions can be effectively used to inform animal and public health policies for disease control planning. This is particularly valuable in settings where detailed data on livestock movements are either unavailable or imperfect due to resource limitations in data collection, as well as challenges associated with poor compliance.

Original languageEnglish
Article number1049633
Pages (from-to)1-15
Number of pages15
JournalFrontiers in Veterinary Science
Early online date29 Jun 2023
Publication statusE-pub ahead of print - 29 Jun 2023

Keywords / Materials (for Non-textual outputs)

  • Tanzania
  • livestock networks
  • network measures
  • metapopulation model
  • Rift Valley fever
  • targeted vaccination
  • imperfect information
  • robustness


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