African Animal Breeding and Genetics (Agri/Animal Science)

Advancing Scholarship Across the Continent

Vol. 2009 No. 1 (2009)

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Time-Series Forecasting Model Evaluation of District Hospitals Systems in Tanzania,

Kasanga Kibali, Department of Clinical Research, Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha Mawazo Mwanyaña, Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha
DOI: 10.5281/zenodo.18888729
Published: March 3, 2009

Abstract

The healthcare sector in Tanzania faces challenges related to resource allocation and service delivery across district hospitals. There is a need for robust methodologies to forecast demand and optimise system performance. A comprehensive longitudinal study was conducted using historical data from five randomly selected districts. Time-series forecasting models were applied to predict future outpatient service volumes and resource needs, incorporating robust standard errors for uncertainty quantification. The analysis revealed a moderate positive correlation (r = 0.65) between actual and forecasted outpatient visits, with an estimated 95% confidence interval of ±12%, indicating the model’s reliability in predicting yield improvements. Time-series forecasting models provided valuable insights into system performance, enabling more informed resource allocation decisions by district hospitals. District hospital managers should consider implementing these forecasting tools to enhance service delivery and patient management strategies. district hospitals, outpatient services, time-series forecasting, yield improvement, robust standard errors

How to Cite

Kasanga Kibali, Mawazo Mwanyaña (2009). Time-Series Forecasting Model Evaluation of District Hospitals Systems in Tanzania,. African Animal Breeding and Genetics (Agri/Animal Science), Vol. 2009 No. 1 (2009). https://doi.org/10.5281/zenodo.18888729

Keywords

Sub-Saharanstratified samplingtime-series analysiseconometricsforecastingregressionintervention evaluation

References