While driverless trucks may once have seemed like a vision from far off in the future, rapid developments in artificial intelligence and machine learning have placed us on the brink of a new automation age, starting with how we transport goods. Although the logistics sector can sometimes fall behind other early-adopting industries, a 2017 McKinsey study showed that transportation companies using a proactive AI strategy had higher profit margins compared to those that did not. For example, AI techniques can help with continuous estimation, optimising the routing of delivery traffic to avoid congestion and other disruptions. One European trucking company lowered their fuel costs by 15 per cent by using sensors tracking vehicle performance and driver behaviour. AI technology coached drivers in real-time, telling them when to speed up or slow down, which in turn increased fuel efficiency, reduced delivery times and brought down maintenance costs.

The role of artificial intelligence in vaccine distribution will be very critical in vaccinating the global population against COVID-19. Vaccine distribution is one of the biggest logistical challenges humanity has faced so far and I think AI can be leveraged to help us with the equitable distribution of the vaccine. In the United States, as of now the rollout of the vaccine has been painfully slow with a lot of logistical issues from distribution to inoculations. Worldwide, the progress is even more sluggish, with some countries yet to start the journey of inoculations. The role of artificial intelligence in vaccine distribution involves the following challenges that AI can help with provided we have quality and accurate data.

Distributing the COVID-19 vaccine is a logistical puzzle that teeters on a delicate structure of chemists, data scientists, freight drivers, healthcare professionals, distributors, state health departments, and policy makers. When even one of these pieces in the structure is imbalanced, the whole vaccine distribution tower could tumble. The U.S. Federal Drug Administration (FDA) has authorized two vaccinations based on data findings from extensive clinical trials and manufacturers, which have been deemed safe for distribution and use under Emergency Use Authorizations (EUA). However, supply limitations and other pressing challenges have compounded the logistical complexities and contributed to the slow rollout and incomplete shipment of doses. With a projected 600 million vaccination doses required in the U.S and demand currently outweighing supply, a vaccination effort of this scale comes with risks and challenges across end-to-end vaccine distribution management.

A COVID-19 vaccine is our best bet for mitigating the ongoing onslaught of the pandemic. However, vaccine is also expected to be a limited resource. An optimal allocation strategy, especially in countries with access inequities and a temporal separation of hot-spots might be an effective way of halting the disease spread. We approach this problem by proposing a novel pipeline VacSIM that dovetails Actor-Critic using Kronecker-Factored Trust Region (ACKTR) model into a Contextual Bandits approach for optimizing the distribution of COVID-19 vaccine. Whereas the ACKTR model suggests better actions and rewards, Contextual Bandits allow online modifications that may need to be implemented on a day-to-day basis in the real world scenario. We evaluate this framework against a naive allocation approach of distributing vaccine proportional to the incidence of COVID-19 cases in five different States across India and demonstrate up to 100,000 additional lives potentially saved and a five-fold increase in the efficacy of limiting the spread over a period of 30 days through the VacSIM approach. We also propose novel evaluation strategies including a standard compartmental model based projections and a causality preserving evaluation of our model. Finally, we contribute a new Open-AI environment meant for the vaccine distribution scenario, and open-source VacSIM for wide testing and applications across the globe.