Possible roles of phytochemicals with bioactive properties in the prevention of and recovery from COVID-19 (preprint)

PurposeThere have been large geographical differences in the infection and death rates of COVID-19. Foods and beverages containing high amounts of phytochemicals with bioactive properties were suggested to prevent contracting, to limit the severity of, and to facilitate recovery from COVID-19. The goal of our study was to determine the correlation of the type of foods/beverages people consumed and the risk reduction of contracting COVID-19 and the recovery from COVID-19. MethodsWe developed an online survey that asked the participants whether they contracted COVID-19, their symptoms, time to recover, and their frequency of eating various types of foods/beverages. The survey was first developed in English and then translated into 10 different languages. ResultsThe participants who did not contract COVID-19 consumed vegetables, herbs/spices, and fermented foods/beverages significantly more than the participants who contracted COVID-19 and those who were not tested but became sick most likely from COVID-19. The geographic location of participants corresponded with the language of the survey, except for the English version, thus, nine out of the 10 language versions represented a country. Among the six countries (India, Iran, Italy, Japan, Russia, Spain) with over one hundred participants, we found that in India and Japan the people who contracted COVID-19 showed significantly shorter recovery time, and greater daily intake of vegetables, herbs/spices, and fermented foods/beverages was associated with faster recovery. ConclusionOur results suggest that phytochemical compounds included in the vegetables may have contributed in not only preventing contraction of COVID-19, but also accelerating their recovery. (249 words; EJN limit is 250 words)

A Multi-Opinion Based Method for Quantifying Polarization on Social Networks (preprint)

Social media platforms have emerged as a hub for political and social interactions, and analyzing the polarization of opinions has been gaining attention. In this study, we have proposed a measure to quantify polarization on social networks. The proposed metric, unlike state-of-the-art methods, does not assume a two-opinion case and applies to multiple opinions. We tested our metric on different networks with a multi-opinion scenario and varying degrees of polarization. The scores obtained from the proposed metric were comparable to state-of-the-art methods on binary opinion-based benchmark networks. The technique also differentiated among networks with different levels of polarization in a multi-opinion scenario. We also quantified polarization in a retweet network obtained from Twitter regarding the usage of drugs like hydroxychloroquine or chloroquine in treating COVID-19. Our metric indicated a high level of polarized opinions among the users. These findings suggest uncertainty among users in the benefits of using hydroxychloroquine and chloroquine drugs to treat COVID-19 patients.

Leveraging machine learning and self-administered tests to predict COVID-19:An olfactory and gustatory dysfunction assessment through crowd-sourced data in India (preprint)

It has been established that smell and taste loss are frequent symptoms during COVID-19 onset. Most evidence stems from medical exams or self-reports. The latter is particularly confounded by the common confusion of smell and taste. Here, we tested whether practical smelling and tasting with household items can be used to assess smell and taste loss. We conducted an online survey and asked participants to use common household items to perform a smell and taste test. We also acquired generic information on demographics, health issues including COVID-19 diagnosis, and current symptoms. We developed several machine learning models to predict COVID-19 diagnosis. We found that the random forest classifier consistently performed better than other models like support vector machines or logistic regression. The smell and taste perception of self-administered household items were statistically different for COVID-19 positive and negative participants. The most frequently selected items that also discriminated between COVID-19 positive and negative participants were clove, coriander seeds, and coffee for smell and salt, lemon juice, and chillies for taste. Our study shows that the results of smelling and tasting household items can be used to predict COVID-19 illness and highlight the potential of a simple home-test to help identify the infection and prevent the spread.

Can Self Reported Symptoms Predict Daily COVID-19 Cases? (preprint)

The COVID-19 pandemic has impacted lives and economies across the globe, leading to many deaths. While vaccination is an important intervention, its roll-out is slow and unequal across the globe. Therefore, extensive testing still remains one of the key methods to monitor and contain the virus. Testing on a large scale is expensive and arduous. Hence, we need alternate methods to estimate the number of cases. Online surveys have been shown to be an effective method for data collection amidst the pandemic. In this work, we develop machine learning models to estimate the prevalence of COVID-19 using self-reported symptoms. Our best model predicts the daily cases with a mean absolute error (MAE) of 226.30 (normalized MAE of 27.09%) per state, which demonstrates the possibility of predicting the actual number of confirmed cases by utilizing self-reported symptoms. The models are developed at two levels of data granularity - local models, which are trained at the state level, and a single global model which is trained on the combined data aggregated across all states. Our results indicate a lower error on the local models as opposed to the global model. In addition, we also show that the most important symptoms (features) vary considerably from state to state. This work demonstrates that the models developed on crowd-sourced data, curated via online platforms, can complement the existing epidemiological surveillance infrastructure in a cost-effective manner. The code is publicly available at https://github.com/parthpatwa/Can-Self-Reported-Symptoms-Predict-Daily-COVID-19-Cases.

More than just smell - COVID-19 is associated with severe impairment of smell, taste, and chemesthesis (preprint)

Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, generally lacked quantitative measurements, were mostly restricted to data from single countries. Here, we report the development, implementation and initial results of a multi-lingual, international questionnaire to assess self-reported quantity and quality of perception in three distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, 8 other, ages 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change+/-100) revealed a mean reduction of smell (-79.7+/- 28.7, mean+/- SD), taste (-69.0+/- 32.6), and chemesthetic (-37.3+/- 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell, but also affects taste and chemesthesis. The multimodal impact of COVID-19 and lack of perceived nasal obstruction suggest that SARS-CoV-2 infection may disrupt sensory-neural mechanisms.