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General General

Pandemic Equation for Describing and Predicting COVID19 Evolution.

In Journal of healthcare informatics research

The purpose of this work is to describe the dynamics of the COVID-19 pandemics accounting for the mitigation measures, for the introduction or removal of the quarantine, and for the effect of vaccination when and if introduced. The methods used include the derivation of the Pandemic Equation describing the mitigation measures via the evolution of the growth time constant in the Pandemic Equation resulting in an asymmetric pandemic curve with a steeper rise than a decrease and mitigation measures. The Pandemic Equation predicts how the quarantine removal and business opening lead to a spike in the pandemic curve. The effective vaccination reduces the new daily infections predicted by the Pandemic Equation. The pandemic curves in many localities have similar time dependencies but shifted in time. The Pandemic Equation parameters extracted from the well advanced pandemic curves can be used for predicting the pandemic evolution in the localities, where the pandemics is still in the initial stages. Using the multiple pandemic locations for the parameter extraction allows for the uncertainty quantification in predicting the pandemic evolution using the introduced Pandemic Equation. Compared with other pandemic models our approach allows for easier parameter extraction amenable to using Artificial Intelligence models.

Shur Michael


COVID19, Mitigation, Pandemic, Quarantine

General General

The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App.

In JMIR mHealth and uHealth

BACKGROUND : Technological advancements have enabled nutrient estimation by smartphone apps such as goFOOD. This is an artificial intelligence-based smartphone system, which uses food images or video captured by the user as input and then translates these into estimates of nutrient content. The quality of the data is highly dependent on the images the user records. This can lead to a major loss of data and impaired quality. Instead of removing these data from the study, in-depth analysis is needed to explore common mistakes and to use them for further improvement of automated apps for nutrition assessment.

OBJECTIVE : The aim of this study is to analyze common mistakes made by participants using the goFOOD Lite app, a version of goFOOD, which was designed for food-logging, but without providing results to the users, to improve both the instructions provided and the automated functionalities of the app.

METHODS : The 48 study participants were given face-to-face instructions for goFOOD Lite and were asked to record 2 pictures (1 recording) before and 2 pictures (1 recording) after the daily consumption of each food or beverage, using a reference card as a fiducial marker. All pictures that were discarded for processing due to mistakes were analyzed to record the main mistakes made by users.

RESULTS : Of the 468 recordings of nonpackaged food items captured by the app, 60 (12.8%) had to be discarded due to errors in the capturing procedure. The principal problems were as follows: wrong fiducial marker or improper marker use (19 recordings), plate issues such as a noncompatible or nonvisible plate (8 recordings), a combination of various issues (17 recordings), and other reasons such as obstacles (hand) in front of the camera or matching recording pairs (16 recordings).

CONCLUSIONS : No other study has focused on the principal problems in the use of automatic apps for assessing nutritional intake. This study shows that it is important to provide study participants with detailed instructions if high-quality data are to be obtained. Future developments could focus on making it easier to recognize food on various plates from its color or shape and on exploring alternatives to using fiducial markers. It is also essential for future studies to understand the training needed by the participants as well as to enhance the app's user-friendliness and to develop automatic image checks based on participant feedback.

Vasiloglou Maria F, van der Horst Klazine, Stathopoulou Thomai, Jaeggi Michael P, Tedde Giulia S, Lu Ya, Mougiakakou Stavroula


apps, dietary assessment, human mistakes, mHealth, mobile phone, smartphone

General General

Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases.

In American journal of physiology. Gastrointestinal and liver physiology

Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus there is a clinical need to further improve the diagnosis of IBD. As gut dysbiosis is reported in IBD patients, we hypothesized that supervised machine learning (ML) could be used to analyze gut microbiome data for predictive diagnostics of IBD. To test our hypothesis, fecal 16S metagenomic data of 729 IBD and 700 non-IBD subjects from the American Gut Project were analyzed using five different ML algorithms. Fifty differential bacterial taxa were identified (LEfSe: LDA > 3) between the IBD and non-IBD groups, and ML classifications trained with these taxonomic features using random forest (RF) achieved a testing AUC of ~0.80. Next, we tested if operational taxonomic units (OTUs), instead of bacterial taxa, could be used as ML features for diagnostic classification of IBD. Top 500 high-variance OTUs were used for ML training and an improved testing AUC of ~0.82 (RF) was achieved. Lastly, we tested if supervised ML could be used for differentiating Crohn's disease (CD) and ulcerative colitis (UC). Using 331 CD and 141 UC samples, 117 differential bacterial taxa (LEfSe: LDA > 3) were identified, and the RF model trained with differential taxonomic features or high-variance OTU features achieved a testing AUC > 0.90. In summary, our study demonstrates the promising potential of artificial intelligence via supervised ML modeling for predictive diagnostics of IBD using gut microbiome data.

Manandhar Ishan, Alimadadi Ahmad, Aryal Sachin, Munroe Patricia B, Joe Bina, Cheng Xi


artificial intelligence, diagnosis, gut microbiome, inflammatory bowel disease, machine learning

General General

Are college campuses superspreaders? A data-driven modeling study.

In Computer methods in biomechanics and biomedical engineering

The COVID-19 pandemic continues to present enormous challenges for colleges and universities and strategies for save reopening remain a topic of ongoing debate. Many institutions that reopened cautiously in the fall experienced a massive wave of infections and colleges were soon declared as the new hotspots of the pandemic. However, the precise effects of college outbreaks on their immediate neighborhood remain largely unknown. Here we show that the first two weeks of instruction present a high-risk period for campus outbreaks and that these outbreaks tend to spread into the neighboring communities. By integrating a classical mathematical epidemiology model and Bayesian learning, we learned the dynamic reproduction number for 30 colleges from their daily case reports. Of these 30 institutions, 14 displayed a spike of infections within the first two weeks of class, with peak seven-day incidences well above 1,000 per 100,000, an order of magnitude larger than the nation-wide peaks of 70 and 150 during the first and second waves of the pandemic. While most colleges were able to rapidly reduce the number of new infections, many failed to control the spread of the virus beyond their own campus: Within only two weeks, 17 campus outbreaks translated directly into peaks of infection within their home counties. These findings suggests that college campuses are at risk to develop an extreme incidence of COVID-19 and become superspreaders for neighboring communities. We anticipate that tight test-trace-quarantine strategies, flexible transition to online instruction, and-most importantly-compliance with local regulations will be critical to ensure a safe campus reopening after the winter break.

Lu Hannah, Weintz Cortney, Pace Joseph, Indana Dhiraj, Linka Kevin, Kuhl Ellen


COVID-19, Coronavirus, SEIR model, epidemiology, machine learning

Public Health Public Health

Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

In Medical care

BACKGROUND : Suicide prevention is a public health priority, but risk factors for suicide after medical hospitalization remain understudied. This problem is critical for women, for whom suicide rates in the United States are disproportionately increasing.

OBJECTIVE : To differentiate the risk of suicide attempt and self-harm following general medical hospitalization among women with depression, bipolar disorder, and chronic psychosis.

METHODS : We developed a machine learning algorithm that identified risk factors of suicide attempt and self-harm after general hospitalization using electronic health record data from 1628 women in the University of California Los Angeles Integrated Clinical and Research Data Repository. To assess replicability, we applied the algorithm to a larger sample of 140,848 women in the New York City Clinical Data Research Network.

RESULTS : The classification tree algorithm identified risk groups in University of California Los Angeles Integrated Clinical and Research Data Repository (area under the curve 0.73, sensitivity 73.4, specificity 84.1, accuracy 0.84), and predictor combinations characterizing key risk groups were replicated in New York City Clinical Data Research Network (area under the curve 0.71, sensitivity 83.3, specificity 82.2, and accuracy 0.84). Predictors included medical comorbidity, history of pregnancy-related mental illness, age, and history of suicide-related behavior. Women with antecedent medical illness and history of pregnancy-related mental illness were at high risk (6.9%-17.2% readmitted for suicide-related behavior), as were women below 55 years old without antecedent medical illness (4.0%-7.5% readmitted).

CONCLUSIONS : Prevention of suicide attempt and self-harm among women following acute medical illness may be improved by screening for sex-specific predictors including perinatal mental health history.

Edgcomb Juliet B, Thiruvalluru Rohith, Pathak Jyotishman, Brooks John O


Dermatology Dermatology

Integrated analysis of multi-omics data on epigenetic changes caused by combined exposure to environmental hazards.

In Environmental toxicology

Humans are easily exposed to environmentally hazardous factors in industrial sites or daily life. In addition, exposure to various substances and not just one harmful substance is common. However, research on the effects of combined exposure on humans is limited. Therefore, this study examined the effects of combined exposure to volatile organic compounds (VOCs) on the human body. We separated 193 participants into four groups according to their work-related exposure (nonexposure, toluene exposure, toluene and xylene exposure, and toluene, ethylbenzene, and xylene exposure). We then identified the methylation level and long noncoding RNA (lncRNA) levels by omics analyses, and performed an integrated analysis to examine the change of gene expression. Thereafter, the effects of combined exposure to environmental hazards on the human body were investigated and analyzed. Exposure to VOCs was found to negatively affect the development and maintenance of the nervous system. In particular, the MALAT1 lncRNA was found to be significantly reduced in the complex exposure group, and eight genes were significantly downregulated by DNA hypermethylation. The downregulation of these genes could cause a possible decrease in the density of synapses as well as the number and density of dendrites and spines. In summary, we found that increased combined exposure to environmental hazards could lead to additional epigenetic changes, and consequently abnormal dendrites, spines, and synapses, which could damage motor learning or spatial memory. Thus, lncRNA MALAT1 or FMR1 could be novel biomarkers of neurotoxicity to identify the negative health effects of VOC complex exposure.

Yu So Yeon, Koh Eun Jung, Kim Seung Hwan, Lee So Yul, Lee Ji Su, Son Sang Wook, Hwang Seung Yong


combined exposure, epigenetic, long noncoding RNA, long-term depression, synapse