AI Health Care

The role of AI in vaccine distribution

AI in vaccine distribution

Introduction


The role of AI in vaccine distribution will be very critical in vaccinating the global population against any future pandemic. Vaccine distribution is one of the biggest logistical challenge and I think AI can be leveraged to help us with the equitable distribution of any vaccine.

Vaccine distribution has the following challenges where AI can help, provided we have quality and accurate data.

Demand forecasting.
Distribution network.
Supply chain management.
Inoculation priorities.
Waste reduction.
Adverse event surveillance.
Vulnerability mapping

Demand Forecasting

Accurately forecasting demand for the vaccine is particularly important for vaccine distribution and this exercise helps in identifying and building the distribution network that these vaccines need to be on for an efficient rollout. We would like the right amount of vaccine to reach the right population that critically needs it before anyone else. 

Demand forecasting can be done by identifying the right parameters for the set of the population that is most vulnerable, this means collating anonymous data of co-morbid conditions that impact the severity of the disease. Once we have this information we can equitably distribute the doses across the globe and save more lives. This will help the distribution be fair and most effective. Running AI-based algorithms to identify vulnerable patients and the critical mass of the patients is very important for effective vaccine distribution.

The role of artificial intelligence in vaccine distribution cannot be understated, especially if we want the vaccine to be distributed in an effective and efficient manner. We could have been better prepared for vaccine distribution if we had better demand forecasting across the globe for vaccine distribution. We should start collecting anonymous data for future pandemics so we are better prepared. Keep in mind demand for vaccines is unidimensional. Therefore, there’s a need for demand forecasting techniques to ensure vaccines reach a particular set of population in advance.

IoT and data analyzed from cell phone location, zip code, and demographics will print a clear picture of the demand for vaccines. As a result, pharmacies and hospitals in different regions can be stalked with the right amount of doses to meet the demand in the community.

Data can also help in tracking the number of vaccinated people in the area. At least 80-90% of the population needs to be vaccinated to achieve immunity against any disease. This will help us rapidly combat any raging pandemic and adapt in realtime.

Probability modeling can inform AI systems to develop a priority list. Thus, helping healthcare experts and involved parties to speed up and simplify decision making.

Generating prioritization groups is no simple task. It requires the time and effort of various experts, such as mathematicians, clinicians, and data analysts. However, with the help of AI, we can achieve a similar outcome via a simpler approach. As such, saving time and effort.

These approaches are universal, meaning they can apply to other scenarios beyond a particular pandemic. Furthermore, a simplified approach is more accessible to nations that lack access to expertise and data.

Artificial intelligence optimizes decision support by factoring in the complicated interplay of vaccination demographics, goals, economics, and supplies. Basically, an AI system can help in addressing two key bottlenecks:

  1. Equitability in vaccine distribution.
  2. Building storage facility network.
  3. Distribution networks.

Artificial Intelligence and Otolaryngology.

Distribution Network

Ensuring efficiency is even more important with any vaccines as demand will far exceed supply initially, making each dose precious. When vaccines are such an in-demand commodity it makes them both precious and expensive. Reducing wastage in these situations is very valuable. 

Identifying storage facilities and setting them up in places based on demand forecasting in a phased manner will be a huge help for efficient distribution. This will help in reducing the wastage of precious doses.

Once a distribution network is up and running, keeping tabs on how it is functioning and tracking doses as they move through the supply chain is another area where A.I. will play a valuable role.

Supply Chain Management

The supply chain is an integral part of vaccine production, distribution, and administration. AI has the capability of providing autonomous supply operations. This is true, especially where decisions about the allocation of material and distribution of vaccines are being made, from existing pharmaceutical ingredient shortages to temperature management.

Algorithms and sensors integrated all through the supply chain ensure visibility of supply chain performance in real-time while accounting for reserves. So, if a natural catastrophic event affects the supply of vaccines, the AI system could determine the most effective way to increase the production and distribution of the drug.

Existing AI solutions can allow the prevention and mediation of global vaccine shortages. They can also assist in saving resources and maximizing the speed of vaccination.

Additionally, supply chain visibility and forecasting could help to ensure vaccine production and allocation align with the demand.

Today, AI, IoT, and Blockchain can be deployed to improve supply chain efficiency, collaboration, and accuracy. Furthermore, AI systems, blockchain, and other technologies make smarter vaccine management possible. They achieve this by providing near-real-time tracking and supply chain visibility.

AI systems can also help in quality control – a core part of vaccine manufacturing and supply chain management.

How can Artificial Intelligence help with the Coronavirus (Covid-19) vaccine search?

Inoculation Priorities

AI could be the engine that generates the vaccine index. This engine could use medical, socioeconomic, and experiential data from prior pandemics to make the care recommendations. One of these recommendations could be to determine inoculation priorities based on co-morbidity data (there could be a privacy concern here, but as long as the data is secure and  encrypted).

This prioritization can help stop the spread of pandemic through vulnerable population within our communities and can also help in identifying key super spreaders based on how pandemic behaves within human species.

Waste Reduction

AI can help in controlling waste on the supply chain side, manufacturing side and in coordinating a distribution network that ensures each vaccine is only shipped to facilities that can properly handle it and are within reasonable proximity to the phased population groups.

While shipments are en route, IoT-enabled sensors can monitor location and temperature of vaccines so intelligent algorithms can optimize routes for efficiency and avoidance of potential disruptions like severe weather that could compromise shipments.

Identifying failures related to administering the vaccine will be much more difficult to overcome but establishing intelligent inventory systems that enable tracking of the doses can help immensely. Agencies can supplement these efforts with focused outreach campaigns that target populations based on their communication preferences identified by historical use within these communities. One tracking code can help stitch a story of use and requirements of vaccinations in large communities. This requires complex algorithms to work in unison and a very large accurate data set.

Adverse Event Surveillance

After vaccination, government health agencies and vaccine manufactures will need to monitor the inoculated people for unusual side effects or unique complications. Vaccines are generally tested on tens of thousands of people. However, there might be some side effects that might only come to light after millions of people have received the vaccination.

Many governments require vaccine makers and health professionals to file reports for any unexpected signs and symptoms patients experience after getting vaccinated. Even when the drug is administered to a small group of people, the reports set can be large and would need very detailed analysis.

Several symptoms usually reported end up being a false alarms, with some being unrelated to the vaccine. But sometimes, they can indicate a serious safety issue that researchers might have missed to pick up in earlier clinical trials. AI can help map these symptoms and time of their occurrence to help scientists paint a clear picture of how human bodies react to a vaccine.

Governments and drug manufacturers are now turning to Artificial Intelligence for help. AI systems can analyze the report data, identify patterns indicating the emergence of safety issues, and flag the issue. This way, experts can conduct further investigations and this will also reduce the trial time for any vaccine, the benefit of this is a thousand fold if we can reduce the time and improve the speed to market.

Vulnerability Mapping

Besides devastating public health, every pandemic increases or exposes inequality across the globe. Poor people have suffered excessively from pandemics while enduring some serious financial challenges. Socio-economically deprived populations are often hard to reach. Also, they are the ones who receive vaccinations last, yet they’re the most vulnerable group.

In most countries, poverty data sets come from census and other household surveys usually held after ten years or more. But these data sets are not enough to paint a map of the vulnerable population within a country. Besides the poor, the vulnerable population includes the elderly, sick, unemployed, racial and ethnic minorities.

Countries need to look into experimental data sources to fill in the missing information about their vulnerable population. Experimental data can serve as a proxy where vulnerable members reside. It’s crucial to ensure that these people get help during any pandemic as soon as possible.

AI systems can factor in experimental data to update the Vulnerability Map to represent these populations.

AI systems can add another layer on the map for environmental and social determinants of health, including food insecurity, low-income jobs, and air pollution. An AI updated vulnerability map makes it easier to identify individuals who need to be prioritized for any pandemic spread and control.

Improving global health Equity

Conclusion

Most technologists and health experts believe AI can solve the vaccination challenges for most pandemics. It can assist organizations to be more productive and make people more efficient.

AI has had a limited impact on the quest to find COVID-19 treatment and development vaccines. But it will play a significant role in combating the next pandemic. We still have a long way to go, but AI will revolutionize the vaccination distribution to bring equitability and efficiency for us to be pro-active when the next pandemic hits.

Sanksshep Mahendra

Sanksshep Mahendra is a technology executive, he holds a Master's degree from Pratt Institute and executive education from Massachusetts Institute of Technology, in AI, Robotics, and Automation.

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