Exploring diet associations with Covid-19 and other diseases: a Network Analysis–based approach

The current global pandemic, Covid-19, is a severe threat to human health and existence especially when it is mutating very frequently. Being a novel disease, Covid-19 is impacting the patients with comorbidities and is predicted to have long-term consequences, even for those who have recovered from it. To clearly recognize its impact, it is important to comprehend the complex relationship between Covid-19 and other diseases. It is also being observed that people with good immune system are less susceptible to the disease. It is perceived that if a correlation between Covid-19, other diseases, and diet is realized, then caregivers would be able to enhance their further course of medical action and recommendations. Network Analysis is one such technique that can bring forth such complex interdependencies and associations. In this paper, a Network Analysis–based approach has been proposed for analyzing the interplay of diets/foods along with Covid-19 and other diseases. Relationships between Covid-19, diabetes mellitus type 2 (T2DM), non-alcoholic fatty liver disease (NAFLD), and diets have been curated, visualized, and further analyzed in this study so as to predict unknown associations. Network algorithms including Louvain graph algorithm (LA), K nearest neighbors (KNN), and Page rank algorithms (PR) have been employed for predicting a total of 60 disease-diet associations, out of which 46 have been found to be either significant in disease risk prevention/mitigation or in its progression as validated using PubMed literature. A precision of 76.7% has been achieved which is significant considering the involvement of a novel disease like Covid-19. The generated interdependencies can be further explored by medical professionals and caregivers in order to plan healthy eating patterns for Covid-19 patients. The proposed approach can also be utilized for finding beneficial diets for different combinations of comorbidities with Covid-19 as per the underlying health conditions of a patient. [Figure: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11517-022-02505-3.