Forecasting the demand of mobile clinic services at vulnerable communities based on integrated multi-source data

Abstract

Demand forecasting plays an important role in the deployment of mobile clinic services to vulnerable communities such as school zones and census tracts as it can help the service provider to maximize its coverage under limited resources. In this paper, we consider the issue of how to predict the vaccination delinquency in schools and census tracts. Such an issue is rather challenging as the delinquency is only observed in schools for which very limited information is available; while rich demographic and economic information is available for census tracts, no observations of delinquency have been made at the census tract level. To address the above challenge, we first develop a hierarchical approach to forecast the demand for vaccinations in schools and census tracts. In the first stage of the hierarchical approach, we solve a linear optimization model to compute an association matrix that can align some common features in both census tracts and school zones. Then we use the estimated association to develop a forecasting model to predict the vaccination delinquency in both schools and census tracts. A non-convex quadratic optimization (QO) model is also proposed to find the association matrix and the forecasting model simultaneously. We also introduce an alternative update scheme for the non-convex QO and establish the convergence of the algorithm. Moreover, the two association matrices generated from the proposed approaches can be used to impute the information in the school zone data, which further allows us to apply existing forecasting models to predict the demand in school zones based on the imputed data. A case study from the Houston Independent School District (HISD) and its associated communities is reported to demonstrate the efficacy of the new models and techniques.

Keywords:

• Demand forecasting
• convex and non-convex optimization
• algorithm design
• data imputation
• integration of multi-source data[]

Acknowledgements

The authors thank the area editor, the associate editor, and two anonymous referees for their careful reading and many useful suggestions that helped to improve the presentation of this work. This research is supported by NSF CNS (grant no. 1637347).

Disclosure statement

No potential conflict of interest was reported by the author(s).