VacTrack (Vaccine Tracking System) is a smart, web-based AI information technology system to order and manage vaccine distribution among state, local, territorial health departments and health care providers using Satellite geolocation, GPRS to monitor, evaluate & store demographical physiology of the target population in order to enhance the Vaccine Allocation, Monitoring, Tracking, and Distribution Systems.
1)View vaccine allocations for each program
2)Place and manage vaccine orders for their providers
3)Generate reports throughout the vaccine distribution process, from vaccine order placement through distribution
4)Track vaccine shipments
VacTrack (Vaccine Allocation, Monitoring, Distribution, and Tracking) AI system, app is a smart, high-intensity vaccine supply chain distribution structure/morphology which is exclusively segmented by our team for the vaccine distribution industry of Haiti in order to establish a sharp, Vaccine Allocation, Monitoring, Tracking, and Distribution network which provides a cost-effective, easy-to-use (deploy), efficient and environment-friendly access to the latest, state of the art COVID-19 vaccines using real time data streams. This high-intensity, technology enabled framework is particularly designed to analyze the numerous factors contributing to the complexity of the current healthcare system , vaccine distribution reforms, supply chain infrastructure and information storage for COVID-19 vaccine administration in Haiti, and structure smart, automated VacTrack (Vaccine Allocation, Monitoring, Tracking, and Distribution) mechanisms which are specifically designed to serve such complications pertaining to Haiti, considering complexities such as uncertainties in demand, supply and cost, economic perishability of vaccines, wastages in storage, limited capacity and demand priorities.
The designed, automated vaccine distribution platform, VacTrack is a Mixed-Integer Linear Programming (MILP) model which operates on a robust counterpart optimization approach for coping with the analyzed complexities, uncertain parameters of Haiti by the primary framework using combination of Deep Learning, support vector machines, k-NN classifiers, deep neural network (DNN), decision trees, Convolutional Neural Network (CNN), Fast region based convolutional neural networks (F-RCNN) and Recurrent Neural Network (RNN).
VacTrack detects, considers uncertainty in the network design of vaccine supply chain by Robust Optimization through the Analytical Layer in the vaccine context and caters the Segmented layer, robust counterpart model, which generates a set of accurate confidence score values and reconfiguring the objective function of MILP, amending uncertainties according to the analyzed priorities in order to rectify the primary MILP framework through Correction layer. Through the Prediction Layer, the improved MILP model produces strategic vaccine distribution supply chain structure decisions for each echelon factor, considering the specific complexity, accordingly devise tactical decisions among different echelons of different factors of supply chain are determined.
Additionally, VacTrack system models the difference between high- and low-priority demands for vaccine through the Classification Layer and segment Haiti into fixed set of zones using satellite geo-location, GPRS sensors as by evaluating the movement of a group of people at a specific region over the 3D axis and classifying the region which is being monitored into fixed separate zones due to the constant change in morphological physiology based on the priority, demand (need) for vaccination in the particular region.
red zone = high-priority for vaccination (high demand)
yellow zone = moderate priority for vaccination (moderate demand)
green zone = comparatively less priority for vaccination (low demand)
