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.
Accurately forecasting demand for the vaccine is particularly important for vaccine distribution and this exercise helps in the distribution network that these vaccines need to be on for an efficient rollout. We would like the right amount of doses of vaccines to reach the right population that critically needs it before than 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.
IBM is trying to help U.S. hospitals and state governments manage the limited supplies of vaccines available so far, according to Tim Paydos, the company’s global general manager for the government-industry. This involves using IBM’s Watson Health Analytics software to marry zip–code–level data with demographics and health status with information on people’s attitudes toward vaccinations to try to forecast demand and also ensure vaccines are distributed equitably.
In the developing world, the challenge of demand forecasting and supply chain management is even more challenging. Macro-Eyes is an A.I. company based in Seattle. Founded by Ben Fels, who had once used machine learning to scour financial market data for minute trading signals. Today, he uses similar technology to look for indicators that will enable Macro-Eyes to forecast demand for medicines and other health care offerings. On this front, the company has worked with Stanford University’s health system in the U.S., but it has completed several projects in Africa, including one to bolster childhood immunizations in Tanzania.
The role of artificial intelligence in vaccine distribution
In its African projects, the company uses a wide range of data—including satellite imagery and maps, the number of mobile phone users in a certain area, social media posts, and official government data—to try to predict how many people will show up for health care at any one place. Each data set on its own may be of marginal value. But by combining lots of data sets, Macro-Eyes is able to make accurate predictions.
Macro-Eyes’ system was able to improve forecasts for childhood vaccination demand in Tanzania by 96% and reduce wasted dosages to just 2.42 vials per 100 shipped. Now Macro-Eyes is hoping to help governments around the world.
Ensuring efficiency is even more important with these vaccines as demand far exceeds supply, 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.
Distribution Network
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.
The implementation of AI in the design and operation of vaccine distribution networks can greatly enhance their efficiency and adaptability. AI technologies can be harnessed to design optimal distribution networks, taking into account a range of variables such as population centers, healthcare infrastructure, transport links, and storage capabilities. Machine learning algorithms can process these variables to generate the most efficient network design, ensuring that vaccines reach their target locations with the least amount of time and resources. This can be especially valuable in regions with challenging terrain or infrastructure limitations, where AI can help devise innovative solutions for vaccine delivery.
AI can also play a significant role in managing the operational dynamics of the distribution network. Using real-time data from various stages of the distribution process, AI can monitor the flow of vaccines, detect bottlenecks or disruptions, and provide corrective recommendations. For example, if there’s a delay or breakdown at a certain distribution node, AI systems can quickly re-route supplies through alternative paths, minimizing the impact on the overall distribution schedule. Similarly, AI can help in balancing inventory levels across the network, preventing shortages or overstocking at individual nodes.
AI can facilitate the adaptive evolution of the distribution network in response to changing circumstances. Using continuous learning algorithms, AI systems can incorporate new data and insights to update and improve the network design and operations over time. This could include changes in vaccine demand patterns, introduction of new vaccines or delivery technologies, or shifts in regulatory or logistical constraints. This capacity for adaptive learning makes AI a powerful tool in managing the complex and evolving challenges of large-scale vaccine distribution.
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.
AI-based supply chain management plays a critical role in the successful distribution of vaccines, especially in a global scenario where demand is high and time is of the essence. Leveraging AI in the supply chain introduces a higher level of efficiency, predictability, and resilience. Utilizing predictive analytics, AI can forecast demand patterns at a granular level, taking into account variables such as population density, age demographics, infection rates, and existing vaccine coverage.
This helps in better planning and allocation of vaccine doses, ensuring that supplies are directed to where they are needed the most, thus enhancing the equity and effectiveness of the vaccination program.
In addition to demand forecasting, AI can significantly optimize logistical operations. Given the stringent storage requirements of many vaccines, including specific temperature ranges, AI can monitor these conditions in real-time during transport and storage, alerting officials to any deviations so that corrective actions can be taken promptly. Moreover, AI algorithms can plan optimal delivery routes, factoring in constraints such as traffic, weather, infrastructure availability, and even geopolitical issues. This optimization results in faster delivery times, reduced wastage, and overall cost-effectiveness.
AI facilitates real-time tracking and traceability in the supply chain, providing stakeholders with valuable insights into the vaccine’s journey from manufacturer to recipient. Such transparency helps in identifying and rectifying any bottlenecks or disruptions swiftly, ensuring a smooth flow of vaccines. It also aids in inventory management, preventing overstocking or shortages, and helping maintain a balanced and responsive supply chain.
IBM markets “object-based” supply chain management software that can track the location of every vaccine vial in as near real-time as possible and match the vial to the people vaccinated with the doses contained in that vial.
Machine learning can be used to predict potential distribution bottlenecks and to potentially suggest ways to work around them. AI can help to keep track of exactly which lot and batch was used to vaccinate each individual may be critical to tracking the safety and adverse effects of vaccines on individuals. This in turn can help identify which data set of people will have reduced side-effects with which vaccine. This will help reduce adverse event occurrences.
AI plays a crucial role in determining inoculation priorities, particularly in the initial phases of vaccine distribution when supply is limited. It can process vast amounts of data to identify high-risk groups that should be vaccinated first. Such groups could include the elderly, healthcare workers, people with underlying health conditions, or those living in areas with high infection rates.
AI models can predict the potential impact of the virus on these groups based on factors such as age, health status, occupation, living conditions, and local infection rates. By targeting these groups first, AI-guided inoculation strategies can effectively reduce hospitalizations and deaths.
AI can also be used to adjust inoculation priorities over time, based on changing circumstances. For example, if new variants of the virus emerge that affect certain demographics differently, AI models can incorporate this new information and revise the priority groups accordingly.
Similarly, AI can help manage the transition from prioritized inoculation to mass vaccination, ensuring a smooth and efficient rollout that maximizes coverage and minimizes delays. This dynamic prioritization capability makes AI an invaluable tool in managing the complex and evolving challenges of vaccine distribution.
AI can help address the issue of vaccine hesitancy, which can significantly impact inoculation rates. By analyzing data from surveys, social media, and other sources, AI can identify communities or demographics with high levels of vaccine hesitancy. Public health authorities can then target these groups with tailored communication strategies to address their concerns and boost vaccination rates.
AI can also monitor the effectiveness of these strategies over time, providing feedback to improve their impact. By supporting evidence-based decision making and enabling adaptive strategies, AI can enhance the overall efficiency and effectiveness of vaccination campaigns.
Waste Reduction
AI-based systems have shown great promise in reducing waste in vaccine distribution by optimizing several key parameters. This is especially crucial in a time when efficient vaccine distribution is needed globally. Through its ability to analyze and predict trends in real-time, AI can accurately manage supply and demand.
It uses data points such as current infection rates, population demographics, vaccine acceptance rates, and storage facilities to effectively predict the demand for vaccines. This minimizes the chances of overproduction or undersupply, and significantly reduces the possibility of vaccines expiring or going unused due to miscalculations in distribution.
In the realm of storage and transportation, AI shines in maintaining the strict conditions that vaccines often require. AI-powered systems can maintain and control the temperature of storage units, ensuring that it stays within the specified range necessary for vaccine preservation. If any deviations from the optimal conditions occur, these systems can instantly detect and rectify the situation, thereby avoiding spoilage.
AI’s potential in optimizing logistics should not be underestimated. By considering variables such as traffic patterns, weather conditions, and infrastructural challenges, AI can devise the most efficient delivery routes. This leads to quicker deliveries, lower transportation costs, and ultimately, a reduction in carbon emissions. Thus, AI’s impact on reducing waste in vaccine distribution is both direct and substantial.
Adverse Event Surveillance.
Once people have received inoculations, the vaccine makers and government health agencies will need to monitor these people for signs of unusual side effects or rare complications. While the vaccines have been tested on tens of thousands of people during clinical trials, there may be side effects or safety issues that only become apparent when millions receive injections.
The British health regulator has contracted with Genpact to deploy machine learning software that can screen its official “yellow card” reports—which doctors and patients use to report unusual side effects that could be a cause for concern. The system Genpact built, which went live in December, takes in plain text, automatically codifies it, and searches for patterns that could be indicative of an emerging safety issue, flagging this to the regulator for further investigation.
The machine-learning software has been trained on many different types of language so that it can understand both the medical terminology a doctor might use in reporting symptoms as well as the more colloquial expressions a member of the public might use.
Conclusion – The role of artificial intelligence in vaccine distribution.
Some technologists have lamented that the role of artificial intelligence in vaccine distribution hasn’t been a big help during the pandemic. While some A.I. software helped sound early warnings that a worrisome new respiratory virus seemed to be circulating in Wuhan, China, the technology certainly didn’t help prevent the pandemic. And its impact on epidemiological modeling and policymaking has been minimal. This is mostly because of a lack of an accurate and good data set. This should be a lesson for us and we should start building the data sets that can help us in future pandemics.
References
Bohr, Adam, and Kaveh Memarzadeh. Artificial Intelligence in Healthcare. Academic Press, 2020.
Introduction
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.
Demand forecasting.
Distribution network.
Supply chain management.
Inoculation priorities.
Waste reduction.
Adverse event surveillance.
Table of contents
Also Read: How can Artificial Intelligence help with the Coronavirus (Covid-19) vaccine search?
Demand forecasting.
Accurately forecasting demand for the vaccine is particularly important for vaccine distribution and this exercise helps in the distribution network that these vaccines need to be on for an efficient rollout. We would like the right amount of doses of vaccines to reach the right population that critically needs it before than 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.
IBM is trying to help U.S. hospitals and state governments manage the limited supplies of vaccines available so far, according to Tim Paydos, the company’s global general manager for the government-industry. This involves using IBM’s Watson Health Analytics software to marry zip–code–level data with demographics and health status with information on people’s attitudes toward vaccinations to try to forecast demand and also ensure vaccines are distributed equitably.
In the developing world, the challenge of demand forecasting and supply chain management is even more challenging. Macro-Eyes is an A.I. company based in Seattle. Founded by Ben Fels, who had once used machine learning to scour financial market data for minute trading signals. Today, he uses similar technology to look for indicators that will enable Macro-Eyes to forecast demand for medicines and other health care offerings. On this front, the company has worked with Stanford University’s health system in the U.S., but it has completed several projects in Africa, including one to bolster childhood immunizations in Tanzania.
In its African projects, the company uses a wide range of data—including satellite imagery and maps, the number of mobile phone users in a certain area, social media posts, and official government data—to try to predict how many people will show up for health care at any one place. Each data set on its own may be of marginal value. But by combining lots of data sets, Macro-Eyes is able to make accurate predictions.
Macro-Eyes’ system was able to improve forecasts for childhood vaccination demand in Tanzania by 96% and reduce wasted dosages to just 2.42 vials per 100 shipped. Now Macro-Eyes is hoping to help governments around the world.
Ensuring efficiency is even more important with these vaccines as demand far exceeds supply, 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.
Distribution Network
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.
The implementation of AI in the design and operation of vaccine distribution networks can greatly enhance their efficiency and adaptability. AI technologies can be harnessed to design optimal distribution networks, taking into account a range of variables such as population centers, healthcare infrastructure, transport links, and storage capabilities. Machine learning algorithms can process these variables to generate the most efficient network design, ensuring that vaccines reach their target locations with the least amount of time and resources. This can be especially valuable in regions with challenging terrain or infrastructure limitations, where AI can help devise innovative solutions for vaccine delivery.
AI can also play a significant role in managing the operational dynamics of the distribution network. Using real-time data from various stages of the distribution process, AI can monitor the flow of vaccines, detect bottlenecks or disruptions, and provide corrective recommendations. For example, if there’s a delay or breakdown at a certain distribution node, AI systems can quickly re-route supplies through alternative paths, minimizing the impact on the overall distribution schedule. Similarly, AI can help in balancing inventory levels across the network, preventing shortages or overstocking at individual nodes.
AI can facilitate the adaptive evolution of the distribution network in response to changing circumstances. Using continuous learning algorithms, AI systems can incorporate new data and insights to update and improve the network design and operations over time. This could include changes in vaccine demand patterns, introduction of new vaccines or delivery technologies, or shifts in regulatory or logistical constraints. This capacity for adaptive learning makes AI a powerful tool in managing the complex and evolving challenges of large-scale vaccine distribution.
Also Read: Artificial Intelligence in Healthcare.
Supply Chain Management
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.
AI-based supply chain management plays a critical role in the successful distribution of vaccines, especially in a global scenario where demand is high and time is of the essence. Leveraging AI in the supply chain introduces a higher level of efficiency, predictability, and resilience. Utilizing predictive analytics, AI can forecast demand patterns at a granular level, taking into account variables such as population density, age demographics, infection rates, and existing vaccine coverage.
This helps in better planning and allocation of vaccine doses, ensuring that supplies are directed to where they are needed the most, thus enhancing the equity and effectiveness of the vaccination program.
In addition to demand forecasting, AI can significantly optimize logistical operations. Given the stringent storage requirements of many vaccines, including specific temperature ranges, AI can monitor these conditions in real-time during transport and storage, alerting officials to any deviations so that corrective actions can be taken promptly. Moreover, AI algorithms can plan optimal delivery routes, factoring in constraints such as traffic, weather, infrastructure availability, and even geopolitical issues. This optimization results in faster delivery times, reduced wastage, and overall cost-effectiveness.
AI facilitates real-time tracking and traceability in the supply chain, providing stakeholders with valuable insights into the vaccine’s journey from manufacturer to recipient. Such transparency helps in identifying and rectifying any bottlenecks or disruptions swiftly, ensuring a smooth flow of vaccines. It also aids in inventory management, preventing overstocking or shortages, and helping maintain a balanced and responsive supply chain.
IBM markets “object-based” supply chain management software that can track the location of every vaccine vial in as near real-time as possible and match the vial to the people vaccinated with the doses contained in that vial.
Machine learning can be used to predict potential distribution bottlenecks and to potentially suggest ways to work around them. AI can help to keep track of exactly which lot and batch was used to vaccinate each individual may be critical to tracking the safety and adverse effects of vaccines on individuals. This in turn can help identify which data set of people will have reduced side-effects with which vaccine. This will help reduce adverse event occurrences.
Source – YouTube | Rajamanickam Antonimuthu
Inoculation Priorities.
AI plays a crucial role in determining inoculation priorities, particularly in the initial phases of vaccine distribution when supply is limited. It can process vast amounts of data to identify high-risk groups that should be vaccinated first. Such groups could include the elderly, healthcare workers, people with underlying health conditions, or those living in areas with high infection rates.
AI models can predict the potential impact of the virus on these groups based on factors such as age, health status, occupation, living conditions, and local infection rates. By targeting these groups first, AI-guided inoculation strategies can effectively reduce hospitalizations and deaths.
AI can also be used to adjust inoculation priorities over time, based on changing circumstances. For example, if new variants of the virus emerge that affect certain demographics differently, AI models can incorporate this new information and revise the priority groups accordingly.
Similarly, AI can help manage the transition from prioritized inoculation to mass vaccination, ensuring a smooth and efficient rollout that maximizes coverage and minimizes delays. This dynamic prioritization capability makes AI an invaluable tool in managing the complex and evolving challenges of vaccine distribution.
AI can help address the issue of vaccine hesitancy, which can significantly impact inoculation rates. By analyzing data from surveys, social media, and other sources, AI can identify communities or demographics with high levels of vaccine hesitancy. Public health authorities can then target these groups with tailored communication strategies to address their concerns and boost vaccination rates.
AI can also monitor the effectiveness of these strategies over time, providing feedback to improve their impact. By supporting evidence-based decision making and enabling adaptive strategies, AI can enhance the overall efficiency and effectiveness of vaccination campaigns.
Waste Reduction
AI-based systems have shown great promise in reducing waste in vaccine distribution by optimizing several key parameters. This is especially crucial in a time when efficient vaccine distribution is needed globally. Through its ability to analyze and predict trends in real-time, AI can accurately manage supply and demand.
It uses data points such as current infection rates, population demographics, vaccine acceptance rates, and storage facilities to effectively predict the demand for vaccines. This minimizes the chances of overproduction or undersupply, and significantly reduces the possibility of vaccines expiring or going unused due to miscalculations in distribution.
In the realm of storage and transportation, AI shines in maintaining the strict conditions that vaccines often require. AI-powered systems can maintain and control the temperature of storage units, ensuring that it stays within the specified range necessary for vaccine preservation. If any deviations from the optimal conditions occur, these systems can instantly detect and rectify the situation, thereby avoiding spoilage.
AI’s potential in optimizing logistics should not be underestimated. By considering variables such as traffic patterns, weather conditions, and infrastructural challenges, AI can devise the most efficient delivery routes. This leads to quicker deliveries, lower transportation costs, and ultimately, a reduction in carbon emissions. Thus, AI’s impact on reducing waste in vaccine distribution is both direct and substantial.
Adverse Event Surveillance.
Once people have received inoculations, the vaccine makers and government health agencies will need to monitor these people for signs of unusual side effects or rare complications. While the vaccines have been tested on tens of thousands of people during clinical trials, there may be side effects or safety issues that only become apparent when millions receive injections.
The British health regulator has contracted with Genpact to deploy machine learning software that can screen its official “yellow card” reports—which doctors and patients use to report unusual side effects that could be a cause for concern. The system Genpact built, which went live in December, takes in plain text, automatically codifies it, and searches for patterns that could be indicative of an emerging safety issue, flagging this to the regulator for further investigation.
The machine-learning software has been trained on many different types of language so that it can understand both the medical terminology a doctor might use in reporting symptoms as well as the more colloquial expressions a member of the public might use.
Also Read: Artificial Intelligence and Otolaryngology.
Conclusion – The role of artificial intelligence in vaccine distribution.
Some technologists have lamented that the role of artificial intelligence in vaccine distribution hasn’t been a big help during the pandemic. While some A.I. software helped sound early warnings that a worrisome new respiratory virus seemed to be circulating in Wuhan, China, the technology certainly didn’t help prevent the pandemic. And its impact on epidemiological modeling and policymaking has been minimal. This is mostly because of a lack of an accurate and good data set. This should be a lesson for us and we should start building the data sets that can help us in future pandemics.
References
Bohr, Adam, and Kaveh Memarzadeh. Artificial Intelligence in Healthcare. Academic Press, 2020.
Holley, Kerrie L., and Siupo Becker M.D. AI-First Healthcare. “O’Reilly Media, Inc.,” 2021.
Panesar, Arjun. Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes. Apress, 2020.
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