The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management

Abstract

The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.

Keywords: COVID-19; SARS-CoV-2; long COVID; machine learning; mathematical models; public health policies.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2023 Sarmiento Varón, González-Puelma, Medina-Ortiz, Aldridge, Alvarez-Saravia, Uribe-Paredes and Navarrete.

Figures

Figure 1

Behavior of epidemiological variables during the ongoing pandemic of COVID-19. The figure depicts the timeline of new deaths per million inhabitants (left) and admissions to intensive care unit (ICU) per million inhabitants (right) in relationship with SARS-CoV-2 variants, vaccination thresholds, and non-pharmacological interventions. The stringency index is a composite measure based on nine response indicators including school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 (100 = strictest). Data acquired from et al. (27), Hasell et al. (28), and Khare et al. (29).

Figure 2

Analysis of long COVID symptoms in patients with positive or negative COVID-19 PCR test. Relative frequency of symptoms in individuals with a positive COVID-19 PCR test (left) as compared to individuals with a negative test. Elaborated on basis of LC symptoms registered by the National COVID Cohort Collaborative (N3C) initiative. Data acquired from (71).

Figure 3

Summary of machine learning applications to fight COVID-19 during the pandemic. General applications of machine learning were classified into 5 categories: i) The design of diagnosis models based on different types of inputs like CT chest, X-ray images, and symptom descriptions. ii) Treatment development. iii) The development of epidemiological models to predict new waves and outbreaks. iv) The simulation of potential scenarios, and monitoring systems to guide public health decisions. v) The diagnosis and identification of risk factors in long COVID.

Figure 4

Developed architecture for COVID-19 diagnosis classification models based on CT chest images and convolutional neural network architectures. Three blocks of layers composed of convolution, batch normalization, max pooling and dropout layers are generated as a pattern extraction strategy, then a flatten layer is used to generate the inputs to the dense layers, which are joined with a layer of batch normalization, followed by three additional full connected layers, which end with a new dropout layer to prevent overfitting, and the final classification layer. ReLU is used as activation functions and the SoftMax function in the classification layer. Finally, the Adam optimizer is used as a loss function binary cross entropy. The developed architecture is an update from previous method for CT chest images classification models developed by our group (34).