<![CDATA[“Immunization” has been documented as one of the most flourishing measures for community well-being ever devised. Management of “immunization” information will ensure that children and newborns receive immunization on schedule. However, managing this immunization information is done manually. Customary data processing method are timeintensive, lengthy, slow in progress and susceptible to inaccuracies during encoding, verification, and re-ordering. In this study, a web-based routine immunization decision support system (RIDSS) was conceptualized to address these challenges. The web-based system is an innovative tool designed to streamline vaccine demand forecasting within the city health office (CHO) of Panabo. This system uses time series analysis and machine learning models to output accurate predictions of future vaccination demand. Using historical data on the performance of routine immunization (RI), it allows identification and analysis of actionable signals to facilitate betterinformed decisions with respect to vaccine procurement, distribution and allocation. The system is a substantial improvement of the current basic vaccine supply management, making it possible for Panabo CHO to have an organized program in administering immunization. Key stakeholders identified were presented with the prototype of system to assure effectiveness and utility. An act of major recognition to the system and its relevance in community health.]]>
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