  Many European countries face rising obesity rates among children, compounded
by decreased opportunities for sports activities during the COVID-19 pandemic.
Access to sports facilities depends on multiple factors, such as geographic
location, proximity to population centers, budgetary constraints, and other
socio-economic covariates. Here we show how an optimal allocation of government
funds towards sports facilitators (e.g. sports clubs) can be achieved in a
data-driven simulation model that maximizes children's access to sports
facilities. We compile a dataset for all 1,854 football clubs in Austria,
including estimates for their budget, geolocation, tally, and the age profile
of their members. We find a characteristic sub-linear relationship between the
number of active club members and the budget, which depends on the
socio-economic conditions of the clubs' municipality. In the model, where we
assume this relationship to be causal, we evaluate different funding
strategies. We show that an optimization strategy where funds are distributed
based on regional socio-economic characteristics and club budgets outperforms a
naive approach by up to 117\% in attracting children to sports clubs for 5
million Euros of additional funding. Our results suggest that the impact of
public funding strategies can be substantially increased by tailoring them to
regional socio-economic characteristics in an evidence-based and individualized
way.
