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Predictive analytics for policymakers: Time-series forecasting for mental health
Analítica predictiva como apoyo en la salud pública: Modelos de pronóstico en salud mental con series de tiempo
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Predictive analytics for policymakers: Time-series forecasting for mental health. (2024). Revista EIA, 21(42), 4227 pp. 1-22. https://doi.org/10.24050/reia.v21i42.1763
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Mental health has been declared by the World Health Organization (WHO) as a matter of concern in international public health. In Colombia, specifically, the importance of mental health care, including the phenomenon of suicide, has been highlighted. In this regard, policymakers have sought alternatives to address or mitigate this issue. In this context, research becomes relevant regarding the development of tools that facilitate decision-making for governmental authorities, for example, through the formulation of forecasting models that enable the identification of trends and patterns of behaviour of suicide attempts.
This paper contributes to this gap for the particular case of suicide attempts in the city of Medellín, Colombia, through the use of prescriptive analytics. This paper presents the fitting, validation and comparison of three different time series models, under minimum forecast error criteria. The compared models include parametric, Holt-Winters and Box-Jenkins approximations; and it is identified that, for the analysed data, the parametric model with cubic, seasonal and ARMA(0.5) components is the one with the lowest forecast error. The results indicate that this model manages to capture the trends of the phenomenon, and that it has a low level of error for the projection for nearby trends, but that it does not manage to respond to sudden changes in structure such as those that occurred in the COVID-19 pandemic.
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