Demand Forecasting for Ebola Responses

The accelerating global trends of urbanization, interconnectivity, and population mobility are creating the conditions to increase the frequency, severity, and velocity of future outbreaks of high threat pathogens. Expeditionary interventional outbreak responses play a critical role in localizing instead of globalizing the devastation from these events.  Outbreak responses are resource intense, logistically complicated, multi-disciplinary endeavors that require rapid deployment and implementation of highly specialized staff, structures, and systems. The complexity of an expeditionary interventional outbreak response to contains numerous challenges for the supply chain management behind successfully implementing a response.  The literature available on outbreak responses are primarily motivated and focused on the dynamics and spread of diseases rather than the operational management of response efforts.  This capstone contributes to the field of public health security and humanitarian logistics by presenting a model for forecasting bed-capacity and consumable material requirements essential to response operations.  The model in this paper follows the flow of patients from point of origin in the Ebola disease infected (Ebola-positive) and endemic disease infected populations (Ebola-negative) entering the Ebola isolation and treatment (EIT) network through discharge.  By mapping patient flow as impacted by several metrics it provides an estimated census in the EIT network under various scenarios.  This estimated census is then used as an independent variable to determine the dependent variables of bed-capacity and key material requirements.  The analysis of the model’s results demonstrates that Ebola-negative, rather than Ebola-positive, patients are the primary driver of capacity and service requirements for the EIT network.  Furthermore, capacity and service requirements of isolating Ebola-negative patients can be substantially reduced by improving time between sample collection and testing for Ebola (diagnostic velocity).  In conclusion recommendations are made for further research to solidify our knowledge of response dynamics to strengthen a holistic understanding of response operations to focus solutioning on the critical points of failure that can hinder response efforts. Improving operational methods and tools by identifying and quantifying these dynamics will improve future outbreak response and is necessary for humans to adapt faster than the emerging risks of infectious diseases.