ABSTRACT
Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.
ABSTRACT
Background. From the start of the COVID-19 pandemic in February 2019 in Iran, the Iranian health system, following its mission, began planning with the goal of pandemic prevention and control. Various steps were taken, and the program of National Mobilization against COVID-19 was devised to provide services for individuals in the community, mainly the sick and vulnerable, and promote people's knowledge and skills regarding the crisis. Given the role of Community Health Workers (Behvarzes and Moragheb-e-Salamats) in preventing and controlling infectious diseases, this study was carried out to investigate their role in the COVID-19 pandemic. Methods. The present study is a descriptive cross-sectional study. The census method was used to study 30034 Behvarzes and 22573 Moragheb-e-Salamats working in medical universities. The required data were extracted using the integrated health system and the portal of the Ministry of Health and Medical Education's Primary Health Network Management Center and were analyzed using descriptive and analytical statistical methods. Results. More than 78 million people were screened during the first step of the national mobilization against COVID-19, more than 42 million during the second step, and more than 59 million during the third step. In addition, by the end of the fourth step of the national mobilization, 4,278,899 people who had come into close contact were screened. According to the health system's report, these community health workers had injected 28,073,777 vaccines since the implementation of the fifth step (until 04.09.2021) Conclusion. Considering the facilities and capabilities of primary health care networks in providing prevention, diagnostic, and treatment services, the activity of the primary health network management center and all affiliated units in the implementation of the National Mobilization against COVID-19 program has been significant since the beginning of this pandemic. Inadequacies in healthcare human resources, financial resources, and training planning should all be considered.