Artificial intelligence in COVID-۱۹ vaccination programs and storage

Background and aims: Every second is considered vital during a pandemic, therefore, makingfast and reliable decisions and plans is the most important part of a pandemic, which can bementioned in the process of vaccination of communities: the distribution and storage of vaccines.Due to the lack of the type and number of doses of vaccine during the Covid-۱۹ pandemic andthe lack of health centers to meet people’s needs, accurate and fair planning for the distributionof the vaccine was considered an inevitable principle with regard to the reduction of deaths orthe reduction of cases of infection. In addition to these cases, the presence of large numbers ofpatients with a higher risk of infection or death should also be considered. Here, we review theartificial intelligence (AI) aid in solving the question of “ Who should get vaccinated first?” andstorage issue of Covid-۱۹ vaccines.Method: In this review, we mention the types of techniques based on AI for the Covid-۱۹ vaccinationprograms at the regional level or even between countries and their storage. We refer howAI prioritized people to be vaccinated and how considering the ultimate output of model, whetherreducing mortality or reducing infection, can change the prioritization results.Results: LSTM, Modified WOA and SIRVD are some of the techniques applied in articles. WHOand Github websites are among the most frequently employed data sources. AstraZeneca andPfizer are among the brands made up -IoT-based sensors for preservation and storage of vaccines.Modeling the forecasting of virus during pandemics has some disadvantages since the parametersrelated to training data change dependently to external factors. Hence, the models based on onlinereal-time incremental techniques must consider these approaches further for future pandemics.Conclusion: According to Toharudin et al. decline in infection rate doesn’t always mean a decreaseddeath rate will be occurred. However, Hong et al. found that these two final aims lead tothe same results in their model, which we consider of great importance for future studies to getfully explored. The accountability that AI has in ethical making decisions cannot be compared tohumans, however, algorithms can mistakenly prioritize certain groups of people, as was the caseat Stanford University recently. Also, instead of creating new models with new parameters, wesuggest to try the existing models globally in order to reach a single decision concerning the properparameters. These are some of the lessons we can learn from the Covid-۱۹ pandemic.

کلیدواژه ها:

artificial intelligence ، COVID-۱۹ vaccination ، vaccination program ، vaccine storage ، vaccine distribution ، fairly