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.
Supply chain management.
Adverse event surveillance.
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.
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.
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
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 writing 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.
In helping to ensure that vaccines are distributed quickly and safely, the role of artificial intelligence in vaccine distribution may yet prove its worth.
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