Two leading COVID-19 vaccine candidates, developed by Moderna and Pfizer/BioNTech, are due to report their Phase 3 clinical trials in the days to come. If one or both succeed and pass their safety tests, due by late November or early December, then we may see a potential COVID-19 vaccine before the end of the year.
However, the challenge will only then begin and effective, real-time spatial analysis will be need to enable efficient distribution of the vaccine to those who need it the most.
Esri has already anticipated the distribution of a vaccine will be a crucial geospatial problem to solve. They have listed a site dedicated to this topic, including potential tools and approaches taken.
The problem of vaccine distribution will involve demographic understanding and demand for the vaccine; however, there are also other important factors.
For instance, for the vaccine candidates most likely to be developed in the near future, both require dry ice for storage and transport, with temperatures of around –80°C to effectively store a potential vaccine. Most countries, including most developed countries, do not have anywhere near the capacity needed for transporting any potential vaccine that requires such temperatures to a large number of people.
Esri has given example scenarios and planning tools that show the spatial challenges of providing a vaccine to different states; however, we are also likely to see many challenges given the demand, need for better infrastructure to transport a potential vaccine, and the high costs associated with purchasing and developing enough doses to enable most people to have access.
The World Health Organization (WHO) has seen the distribution of a vaccine as a potential challenge because of varying healthcare systems, infrastructure, wealth, and other factors. They have called for a more unified, global-scale approach to solve the distribution problem; however, we may not see any easy resolution to this given political challenges that prevent a unified approach to distribution from being developed.
Therefore, scientists may need to devise a series of local distribution plans adaptable to different countries rather than expecting a global-scale distribution scenario.
One potential is that countries develop a big data and analytical approach that applies artificial intelligence.
Looking at real-time or near real-time infection rates across a country, analysts could consider the demographics and infrastructure capabilities in regions that determine the best possible distribution centers and points where the vaccine could be given or sent out into communities. This includes using deep learning techniques that can anticipate likely emerging virus infection patterns before they occur.
Since it is clear that infection can change between now and when a vaccine is developed, the analysis has to always evolve and distribution needs to be mobile enough to change locations.
This also means that the storage capabilities need to be mobile, otherwise countries will face the problem of not easily connecting their populations with the vaccine as the pandemic still rages.
While we wait for a vaccine, data on knowing where COVID-19 infection is spreading the most and how it is spreading need to be collected and accuracy needs to be at its best if we are to distribute any vaccine effectively.
There might be some positive news in the coming weeks when it comes to the development of a COVID-19 vaccine.
There, of course, can be many obstacles in the way, including safety concerns and test trials showing inconclusive results. However, given that two likely candidates are near the end of their Phase 3 trials and others that are not too far away, we could see a vaccine before the end of the year.
Given these developments, we should already be thinking about the major spatial analytical problems this will create, particularly in distribution of any vaccine. The leading candidates require specific storage conditions which currently do not exist, while testing and real-time understanding of how COVID-19 is spreading is still lacking in many regions.
This means we may not have sufficient data to best vaccinate populations, or at least do it in a way that is most efficient and in a way that can save the most lives. Nevertheless, if we can improve data gathering and infrastructure in anticipation of a vaccine, then spatial analysts should be ready to help in planning how to distribute a vaccine.
 For more on Esri tools and products used to plan for vaccine distribution, see: https://coronavirus-resources.esri.com/pages/vaccine
 For more on an Esri vaccine distribution planner, see: https://www.arcgis.com/apps/opsdashboard/index.html#/d9b6cda4c3934a128951136411f65e37.
 For more on the WHO’s plans and ideas for a vaccine distribution plan, see: Torres, I., Artaza, O., Profeta, B., Alonso, C., Kang, J., 2020. COVID-19 vaccination: returning to WHO’s Health For All. The Lancet Global Health 8, e1355–e1356. https://doi.org/10.1016/S2214-109X(20)30415-0.
 For insight into using big data analytics and artificial intelligence techniques to developing potential vaccine distribution plans, see: Pham, Q.-V., Nguyen, D.C., Huynh-The, T., Hwang, W.-J., Pathirana, P.N., 2020. Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts. IEEE Access 8, 130820–130839. https://doi.org/10.1109/ACCESS.2020.3009328