The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development

Abstract

Integrating Artificial Intelligence (AI) across numerous disciplines has transformed the worldwide landscape of pandemic response. This review investigates the multidimensional role of AI in the pandemic, which arises as a global health crisis, and its role in preparedness and responses, ranging from enhanced epidemiological modelling to the acceleration of vaccine development. The confluence of AI technologies has guided us in a new era of data-driven decision-making, revolutionizing our ability to anticipate, mitigate, and treat infectious illnesses. The review begins by discussing the impact of a pandemic on emerging countries worldwide, elaborating on the critical significance of AI in epidemiological modelling, bringing data-driven decision-making, and enabling forecasting, mitigation and response to the pandemic. In epidemiology, AI-driven epidemiological models like SIR (Susceptible-Infectious-Recovered) and SIS (Susceptible-Infectious-Susceptible) are applied to predict the spread of disease, preventing outbreaks and optimising vaccine distribution. The review also demonstrates how Machine Learning (ML) algorithms and predictive analytics improve our knowledge of disease propagation patterns. The collaborative aspect of AI in vaccine discovery and clinical trials of various vaccines is emphasised, focusing on constructing AI-powered surveillance networks. Conclusively, the review presents a comprehensive assessment of how AI impacts epidemiological modelling, builds AI-enabled dynamic models by collaborating ML and Deep Learning (DL) techniques, and develops and implements vaccines and clinical trials. The review also focuses on screening, forecasting, contact tracing and monitoring the virus-causing pandemic. It advocates for sustained research, real-world implications, ethical application and strategic integration of AI technologies to strengthen our collective ability to face and alleviate the effects of global health issues.

Keywords: Artificial intelligence; COVID-19; Epidemiological modelling; Global health; Machine learning algorithms; SARS-CoV-2; Vaccine development.

Fig. 1

a Schematic representation of the types of Epidemiology. b Evidence Hierarchy of Epidemiological Study Designs how the flow of epidemiological studies tends from initial to final stage

Fig. 2

Schematic representation of the process of epidemiological model building which includes determining the study goal, data collection process, model development, model evaluation & validation till the results and output interpretation

Fig. 3

Schematic representation of the process of model creation with AI algorithms which depicts the data collection & transformation, model building, training & testing of model along with evaluation and deployment

Fig. 4

Dashboard designed to make all the project information visible. With permission, this image has been reproduced [128]. Copyright 2021, MDPI

Fig. 5

a History of vaccinations from the development of first vaccine for Smallpox till the development of vaccine for COVID-19, b Vaccination’s ability to stop the spread of viruses imprinting how vaccine response to the host

Fig. 6

Schematic representation of the roadmap for vaccine discovery by AI and ML method, how AI/ML optimize the process of screening, resolving, predicting analyze & docking, selecting and finally the designing of vaccine