Adaptive livestock vaccine decision-making among agro-pastoralists: results from modelling cognition and decision dynamics in an agent-based model
Primary author: Richard Iles
Co-author(s): Matthew Sottile; Ofer Amram; Eric Lofgren; Craig McConnel
Primary college/unit: Agricultural, Human and Natural Resource Sciences
Livestock disease transmission through animal interactions represents a form of dynamic environmental systems. The inclusion of human behaviour to vaccinate livestock in a dynamic natural and cognitive environment is instructive to understand adaptive human behaviour and design effective livestock disease policies, particularly in low-income settings. Moreover, enhancing the behavioral realism of decision making models in agent-based models (ABM) is required. The current study models livestock vaccination decision making among agro-pastoralists in central Kenya. Our ABM integrates four sub-models: i) the Random Field Ising Model (RFIM) for decision making amongst connected heads of households; ii) a traditional SIRV disease model for Rift Valley fever (RVf) and Contagious Bovine Pleuropneumonia (CBPP); iii) a model for herd birth/death dynamics, and iv) herd movement. A logit transformed RFIM used in this work to link human memory and cognition, with social network pressure and public information concerning disease risks. The research question of interest is: ‘how do memory and cognition parameters in a logit transformed RFIM affect livestock vaccine choice?’. Three rounds of cognition and household survey data from Kenya (2017-2018) is used to calibrate parameters in the RFIM. Results from the logit transformed RFIM show that increases in the memory parameter, at higher levels of cognition has a disproportionate effect on the choice of the annual booster CBPP vaccine, in contrast to RVf which requires a once-for-life vaccine.