A Machine Learning-Aided Global Diagnostic and Comparative Tool to Assess Effect of Quarantine Control in COVID-19 Spread

Dandekar, Raj; Rackauckas, Chris; Barbastathis, George

doi:10.1016/j.patter.2020.100145

Cited by 46 publications

(33 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sources include social media, mobile phone GPS, and mobile fitness devices, with other tools such as contact tracing or contact simulations 2 . It is clear that it is necessary to include this information in the first principles models or combinations with data-driven models 3,4 through a learning process or optimization.…”

Section: Introductionmentioning

confidence: 99%

Forecasting virus outbreaks with social media data via neural ordinary differential equations

Núñez

Barreiro

Barrio

et al. 2021

Preprint

View full text Add to dashboard Cite

In the midst of the covid-19 pandemic, social media data collected in real time has the potential of being an early indicator of a new epidemic wave. This possibility is explored here by using a neural ordinary differential equation (neural ODE) that is trained to predict virus outbreaks for a geographic region. It learns from multivariate time series of signals obtained from a novel set of massive online surveys about COVID-19 symptoms. Once trained, the neural ODE is able to capture the dynamics of the interlinked local signals and accurately predict the number of new infections up to two months in advance. Moreover, it can estimate the future effects of changes in the number of infected at a given time, which can be associated with the flow of people entering or leaving a given region or, for instance, with a local vaccination campaign. This work gives compelling preliminary evidence for the predictive power of widely distributed social media surveys for public health application

show abstract

Section: Introductionmentioning

confidence: 99%

Forecasting virus outbreaks with social media data via neural ordinary differential equations

Núñez

Barreiro

Barrio

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The first stage of our analysis is using our model, 12, 13 called the QSIR model to diagnose the underlying quarantine strength evolution Q ( t ) in the regions under consideration. By applying the QSIR model to more than 70 countries globally, we have established the validity of Q ( t ) in accurately diagnosing the on-the-ground quarantine situation in majorly affected European, South American and Asian countries.…”

Section: Resultsmentioning

confidence: 99%

“…By applying the QSIR model to more than 70 countries globally, we have established the validity of Q ( t ) in accurately diagnosing the on-the-ground quarantine situation in majorly affected European, South American and Asian countries. 12 A slow growth of Q ( t ) without a significant increase indicates relaxed quarantine policies, a sharp transition point in Q ( t ) is indicative of a sudden ramp-up of quarantine measures, and an inflection point corresponds to the time when the quarantine response was the most rapid in the region under consideration. The results of our model applied globally to all continents are hosted publicly at covid19ml.org.…”

Section: Resultsmentioning

confidence: 99%

Implications of delayed reopening in controlling the COVID-19 surge in Southern and West-Central USA

Dandekar

Wang

Barbastathis

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

1SUMMARYIn the wake of the rapid surge in the Covid-19 infected cases seen in Southern and West-Central USA in the period of June-July 2020, there is an urgent need to develop robust, data-driven models to quantify the effect which early reopening had on the infected case count increase. In particular, it is imperative to address the question: How many infected cases could have been prevented, had the worst affected states not reopened early? To address this question, we have developed a novel Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. The model decomposes the contribution of quarantine strength to the infection timeseries, allowing us to quantify the role of quarantine control and the associated reopening policies in the US states which showed a major surge in infections. We show that the upsurge in the infected cases seen in these states is strongly co-related with a drop in the quarantine/lockdown strength diagnosed by our model. Further, our results demonstrate that in the event of a stricter lockdown without early reopening, the number of active infected cases recorded on 14 July could have been reduced by more than 40% in all states considered, with the actual number of infections reduced being more than 100, 000 for the states of Florida and Texas. As we continue our fight against Covid-19, our proposed model can be used as a valuable asset to simulate the effect of several reopening strategies on the infected count evolution; for any region under consideration.

show abstract

“…The total number of cases in this study was 279 (177 confirmed and 102 unconfirmed) [ 24 ]. In some other studies, Mathematical and computational models which are epidemiological models have been used to predict the number of cases of COVID-19 and infection rates [ [25] , [26] , [27] ].…”

Section: Discussionmentioning

confidence: 99%

Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran

et al. 2021

View full text Add to dashboard Cite

Objectives COVID-19 is an infectious disease that started spreading globally at the end of 2019. Due to differences in patient characteristics and symptoms in different regions, in this research, a comparative study was performed on COVID-19 patients in 6 provinces of Iran. Also, multilayer perceptron (MLP) neural network and Logistic Regression (LR) models were applied for the diagnosis of COVID-19. Methods A total of 1043 patients with suspected COVID-19 infection in Iran participated in this study. 29 characteristics, symptoms and underlying disease were obtained from hospitalized patients. Afterwards, we compared the obtained data between confirmed cases. Furthermore, the data was applied for building the ANN and LR models to diagnosis the infected patients by COVID-19. Results In 750 confirmed patients, Common symptoms were: fever (%) >37.5°C, cough, shortness of breath, fatigue, chills and headache. The most common underlying diseases were: hypertension, diabetes, chronic obstructive pulmonary disease and coronary heart disease. Finally, the accuracy of the ANN model to the diagnosis of COVID-19 infection was higher than the LR model. Conclusions The prevalent symptoms and underlying diseases of COVID-19 patients were similar in different provinces, but the incidence of symptoms was significantly different from each other. Also, the study demonstrated that ANN and LR models have a high ability in the diagnosis of COVID-19 infection.

show abstract

A Machine Learning-Aided Global Diagnostic and Comparative Tool to Assess Effect of Quarantine Control in COVID-19 Spread

Cited by 46 publications

References 36 publications

Forecasting virus outbreaks with social media data via neural ordinary differential equations

Forecasting virus outbreaks with social media data via neural ordinary differential equations

Implications of delayed reopening in controlling the COVID-19 surge in Southern and West-Central USA

Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran

Contact Info

Product

Resources

About