RESUMEN
Understanding the determinants of vaccine hesitancy is paramount to reinstate confidence in immunizations. The objective of this investigation was to explore the characteristics of the vaccination decision-making process that may result in the refusal of childhood immunization in Peru, during February-June 2020. A descriptive, cross-sectional study involving telephone interviews was executed in Peru. The Parents Attitudes about Childhood Vaccines (PACV) survey was used. A demographic analysis was done, followed by an unadjusted exploratory subgroup analysis. Out of 552 subjects, 9.8% were considered vaccine hesitant, 70.3% had purposively delayed vaccination, 88.4% thought fewer vaccines were better and 52.2% were concerned about vaccine safety. The level of hesitancy was inversely proportional to the level of education and the number of children at home. Mothers and subjects aged ≤29 years showed a greater level of vaccine hesitancy. This population displays a vaccine-hesitant conduct. Vaccine safety and the number of vaccines to administer are important determining factors. This behavior could be influenced by variables such as level of education, number of children at home, parental relationship, and age. These results help understand local vaccination behaviors. More studies are encouraged to confirm and validate these findings.
RESUMEN
The generative approach to social science, in which agent-based simulations (or other complex systems models) are executed to reproduce a known social phenomenon, is an important tool for realist explanation. However, a generative model, when suitably calibrated and validated using empirical data, represents just one viable candidate set of entities and mechanisms. The model only partially addresses the needs of an abductive reasoning process - specifically it does not provide insight into other viable sets of entities or mechanisms, nor suggest which of these are fundamentally constitutive for the phenomenon to exist. In this paper, we propose a new model discovery framework that more fully captures the needs of realist explanation. The framework exploits the implicit ontology of an existing human-built generative model to propose and test a plurality of new candidate model structures. Genetic programming is used to automate this search process. A multi-objective approach is used, which enables multiple perspectives on the value of any particular generative model - such as goodness-of-fit, parsimony, and interpretability - to be represented simultaneously. We demonstrate this new framework using a complex systems modeling case study of change and stasis in societal alcohol use patterns in the US over the period 1980-2010. The framework is successful in identifying three competing explanations of these alcohol use patterns, using novel integrations of social role theory not previously considered by the human modeler. Practitioners in complex systems modeling should use model discovery to improve the explanatory utility of the generative approach to realist social science.
