Agent-based modelling to inform health intervention strategies : the case of severe acute malnutrition in children in high-burden low-income countries

Abstract

Health interventions improve the management of severe acute malnutrition (SAM) in children under 5 in high-burden low-income countries. However, evaluation of their implementation faces a paucity of information and could benefit from a system perspective derived from the knowledge of implementers and experts. These challenges could be addressed using simulation modelling. We compared Markov and agent-based models of interventions for improving the management of SAM and assessed benefits and limitations in informing complex health intervention strategy designs. Based on a conceptual framework developed with existing evidence and expert advice, the agent-based model generated simulated data representing the complex evolution of the system. Multiple scenarios were investigated by varying parameters and mimicking rules of interventions. This study pointed out possible synergies between interventions enhancing early start of treatment and increasing recovery from SAM. When these interventions were adequately combined, outcomes of coverage, recovery and overall survival improved. Benefits of agent-based modelling were use of history, if-then rules to uncover mechanisms behind probabilities, and modifiable transition rates. Limitations related to model validation, choices of assumptions, and simplification. Agent-based modelling could be used to adapt intervention strategies to local contexts and support scale-up. As such, modelling could complement the methodological toolkit of health intervention strategy designs for improved policy decision.