TemPredict: A Big Data Analytical Platform for Scalable Exploration and Monitoring of Personalized Multimodal Data for COVID-19

Purawat, Shweta; Dasgupta, Subhasis; Song, Jining; Davis, Shakti K.; Claypool, Kajal T.; Chandra, Surabhi; Mason, Ashley E.; Viswanath, Varun; Klein, Amit; Kasl, Patrick; Wen, YingJing; Smarr, Benjamin L.; Gupta, Amarnath; Altıntaş, Ilkay

doi:10.1109/bigdata52589.2021.9671441

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…For a detailed description of study procedures, see (Mason et al, 2022a ). In brief, participants enrolled in the TemPredict study (Smarr et al, 2020 ; Purawat et al, 2021 ; Mason et al, 2022a , b ), sharing wearable device data and labels to help us develop COVID-19 detection capacities. Participants completed a single web-based baseline survey and, for as long as 8 months thereafter, had the opportunity to complete a web-based daily symptom survey that queried participants about acute illness symptoms typically observed in COVID-19 as well as any COVID-19 diagnoses.…”

Section: Methodsmentioning

confidence: 99%

Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles

Klein

Puldon²,

Dilchert³

et al. 2022

Front. Big Data

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BackgroundDaily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks.ObjectiveUse data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex.MethodsWe used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model.ResultsAlthough the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females.ConclusionWeb-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection.

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Section: Methodsmentioning

confidence: 99%

Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles

Klein

Puldon²,

Dilchert³

et al. 2022

Front. Big Data

Self Cite

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“…Data from the skin patch with data from Fitbit smartwatches and bio specimens have been used to assist health care workers to self-monitor COVID-19 ( 9 ). The TemPredict ( 10 ) study investigated whether physiological data such as temperature and heart rate collected by Oura Ring, with self-reported daily symptoms, could predict COVID-19 symptoms. Results showed a correlation between elevated temperature and self-reported fever ( 11 ) and algorithms have been developed to identify onset of ( 12 ) and recovery from COVID-19 ( 13 ).…”

Section: Introductionmentioning

confidence: 99%

Emerging data inputs for infectious diseases surveillance and decision making

Shausan

Nazarathy

Dyda

2023

Front. Digit. Health

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Infectious diseases create a significant health and social burden globally and can lead to outbreaks and epidemics. Timely surveillance for infectious diseases is required to inform both short and long term public responses and health policies. Novel data inputs for infectious disease surveillance and public health decision making are emerging, accelerated by the COVID-19 pandemic. These include the use of technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI). These technologies may provide benefits in relation to improved timeliness and reduced resource requirements in comparison to traditional methods. In this review paper, we describe current and emerging data inputs being used for infectious disease surveillance and summarize key benefits and limitations.

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Biometrics of complete human pregnancy recorded by wearable devices

Keeler Bruce,

González,

Dasgupta

et al. 2024

npj Digit. Med.

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In the United States, normal-risk pregnancies are monitored with the recommended average of 14 prenatal visits. Check-ins every few weeks are the standard of care. This low time resolution and reliance on subjective feedback instead of direct physiological measurement, could be augmented by remote monitoring. To date, continuous physiological measurements have not been characterized across all of pregnancy, so there is little basis of comparison to support the development of the specific monitoring capabilities. Wearables have been shown to enable the detection and prediction of acute illness, often faster than subjective symptom reporting. Wearables have also been used for years to monitor chronic conditions, such as continuous glucose monitors. Here we perform a retrospective analysis on multimodal wearable device data (Oura Ring) generated across pregnancy within 120 individuals. These data reveal clear trajectories of pregnancy from cycling to conception through postpartum recovery. We assessed individuals in whom pregnancy did not progress past the first trimester, and found associated deviations, corroborating that continuous monitoring adds new information that could support decision-making even in the early stages of pregnancy. By contrast, we did not find significant deviations between full-term pregnancies of people younger than 35 and of people with “advanced maternal age”, suggesting that analysis of continuous data within individuals can augment risk assessment beyond standard population comparisons. Our findings demonstrate that low-cost, high-resolution monitoring at all stages of pregnancy in real-world settings is feasible and that many studies into specific demographics, risks, etc., could be carried out using this newer technology.

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TemPredict: A Big Data Analytical Platform for Scalable Exploration and Monitoring of Personalized Multimodal Data for COVID-19

Cited by 6 publications

References 13 publications

Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles

Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles

Emerging data inputs for infectious diseases surveillance and decision making

Biometrics of complete human pregnancy recorded by wearable devices

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