How artificial intelligence may help the Covid‐19 pandemic: Pitfalls and lessons for the future

Malik, Yashpal Singh; Sircar, Shubhankar; Bhat, Sushant; Ansari, Mohd Ikram; Pande, Tripti; Kumar, Pankaj; Mathapati, Basavaraj; Ganesh, Balasubramanian; Kaushik, Rahul; Natesan, Senthilkumar; Ezzikouri, Sayeh; Zowalaty, Mohamed E. El; Dhama, Kuldeep

doi:10.1002/rmv.2205

Cited by 72 publications

(54 citation statements)

References 86 publications

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“… 1 Fever, cough, fatigue, and dyspepsia are some of the signs and symptoms of COVID-19. 2 In December 2019, the first human case of COVID-19 was reported in Wuhan, China, which affected more than 200 million people in the world until August 2021. 3 According to a World Health Organization (WHO) report, there were over 1.8 million cases and 38,000 deaths from December to August 30, 2020.…”

Section: Introductionmentioning

confidence: 99%

Factors Associated with Intention to Receive COVID-19 Vaccine Among HIV Positive Patients Attending ART Clinic in Southwest Ethiopia

Mesfin¹,

Argaw²,

Tenaw³

2021

PPA

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Introduction:By the end of 2021, Ethiopia plans to vaccinate 20% of the population. The prioritized group includes people older than 65 years, health care workers, teachers, soldiers, and people living with comorbid diseases, including HIV/AIDS. However, due to a lack of intention and other reasons, only less than one percent of the population has taken COVID-19 vaccine. Therefore, this study aimed to assess the determinant factors of intention to receive the COVID-19 vaccine among HIV-positive patients attending ART clinics in southern Ethiopia. Methods: A facility-based cross-sectional study was conducted among 398 HIV-positive patients from March 1 to April 28, 2021. Data were collected using a structured questionnaire by three midwives. We entered the data into Epi InfoTM 3.5.1. Binary logistic regression was used to identify independently associated variables in bivariate and multivariate analyses using SPSS version 20. Results: Currently, the intention to take COVID-19 vaccine among HIV-positive patients was 33.7%. The probability of PLWHA to take COVID-19 vaccine was two (AOR = 2 (1.08-3.44)) times higher among those who have been diagnosed with chronic diseases (exclude HIV) with those with not been diagnosed. PLWHA who were male in sex were five (AOR = 5 (2.96-8.68)) times more likely to take COVID-19 vaccine than female. The study also indicated that the odds of intention to take the COVID-19 vaccine were 4.1 times (AOR = 4.1 (2.33-7.31)) times higher among those participants who had good knowledge of COVID-19 practice compared with those who had poor knowledge. Conclusion:The overall intention to receive the COVID-19 vaccine among PLWHA is low. Therefore, there is a need to improve the intention to receive the COVID-19 vaccine among HIV-positive patients to achieve the 2021 goal of Ethiopia for 20% of the population vaccination. Patients with chronic illness, knowledge of the COVID-19 vaccine, and sex were factors associated with the intention to receive COVID-19 vaccine.

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Section: Introductionmentioning

confidence: 99%

Factors Associated with Intention to Receive COVID-19 Vaccine Among HIV Positive Patients Attending ART Clinic in Southwest Ethiopia

Mesfin¹,

Argaw²,

Tenaw³

2021

PPA

View full text Add to dashboard Cite

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“…Such integrated approaches also encourage mathematical modeling that can estimate the spread of infectious diseases with mass gatherings; simulate the effect of public health interventions aimed at the local and global level [ 76 ]. For example, a study predicted the infectious disease vulnerability index (IDVI) through an integrated modeling approach during the onset of coronavirus infection in Wuhan, China [ 77 , 78 , 79 ]. Multiple indicators such as travel information, country’s socio-economic condition, politics and health management facilities, and economic metrics are used to calculate IDVI scores [ 77 , 78 , 79 , 80 ].…”

Section: Application Of Ai In Surveillance Of Covid-19mentioning

confidence: 99%

“…For example, a study predicted the infectious disease vulnerability index (IDVI) through an integrated modeling approach during the onset of coronavirus infection in Wuhan, China [ 77 , 78 , 79 ]. Multiple indicators such as travel information, country’s socio-economic condition, politics and health management facilities, and economic metrics are used to calculate IDVI scores [ 77 , 78 , 79 , 80 ]. IDVI scores range from 0 to 1, and a lower score signifies potential epidemic threats.…”

Section: Application Of Ai In Surveillance Of Covid-19mentioning

confidence: 99%

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

et al. 2021

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As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning(ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

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“…The health organizations, scientists, administration, and government bodies of different nations have discovered medicines and vaccines to control the disease. In this context, Machine Learning (ML) and Artificial Intelligence (AI)-enabled technology initiatives have also contributed to the medical science curtailing the pandemic [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Guleria¹,

Ahmed

Alhumam

et al. 2022

Healthcare

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Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.

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How artificial intelligence may help the Covid‐19 pandemic: Pitfalls and lessons for the future

Cited by 72 publications

References 86 publications

Factors Associated with Intention to Receive COVID-19 Vaccine Among HIV Positive Patients Attending ART Clinic in Southwest Ethiopia

Factors Associated with Intention to Receive COVID-19 Vaccine Among HIV Positive Patients Attending ART Clinic in Southwest Ethiopia

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

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