Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network

Nikparvar, Behnam; Rahman, Md. Mokhlesur; Hatami, Faizeh; Thill, Jean-Claude

doi:10.1038/s41598-021-01119-3

Cited by 46 publications

(34 citation statements)

References 55 publications

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“…Coronavirus disease 2019 (COVID-19) is a public health crisis that has afflicted more than 225 countries all over the world [ 1 , 2 ]. This highly communicable disease is expected to have lingering effects on public health, human mobility, and the environment, disrupting social relations and economic wellbeing, and transforming the social and spatial structure of the city [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Studies have shown that a reduction in mandatory and discretionary mobility can essentially curtail the severity of the pandemic and associated health risks [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries

Rahman

Thill

2022

IJERPH

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Recognizing an urgent need to understand the dynamics of the pandemic’s severity, this longitudinal study is conducted to explore the evolution of complex relationships between the COVID-19 pandemic, lockdown measures, and social distancing patterns in a diverse set of 86 countries. Collecting data from multiple sources, a structural equation modeling (SEM) technique is applied to understand the interdependencies between independent variables, mediators, and dependent variables. Results show that lockdown and confinement measures are very effective to reduce human mobility at retail and recreation facilities, transit stations, and workplaces and encourage people to stay home and thereby control COVID-19 transmission at critical times. The study also found that national contexts rooted in socioeconomic and institutional factors influence social distancing patterns and severity of the pandemic, particularly with regard to the vulnerability of people, treatment costs, level of globalization, employment distribution, and degree of independence in society. Additionally, this study portrayed a mutual relationship between the COVID-19 pandemic and human mobility. A higher number of COVID-19 confirmed cases and deaths reduces human mobility and the countries with reduced personal mobility have experienced a deepening of the severity of the pandemic. However, the effect of mobility on pandemic severity is stronger than the effect of pandemic situations on mobility. Overall, the study displays considerable temporal changes in the relationships between independent variables, mediators, and dependent variables considering pandemic situations and lockdown regimes, which provides a critical knowledge base for future handling of pandemics. It has also accommodated some policy guidelines for the authority to control the transmission of COVID-19.

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Section: Introductionmentioning

confidence: 99%

Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries

Rahman

Thill

2022

IJERPH

Self Cite

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“…With the great success in solving sequence data, LSTM has been applied to COVID-19 detection tasks and achieved great performances [ 71 , 72 , 73 , 74 , 75 ]. For instance, ArunKumar et al [ 76 ] proposed a deep learning approach that modified the traditional LSTM with a new activation function for predicting the infected cases and death cases of the COVID-19 dataset.…”

Section: Approachmentioning

confidence: 99%

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Tran

Huang

2022

Sensors

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Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model’s accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.

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“…Numerous predictions use algorithmic machine learning models, such as the long short-term memory recurrent neural network model; an example is Nikparvar et al [15], which predicts county-level incidence. A similar model predicts hospitalization and death at a national level after vaccination [16].…”

Section: Introductionmentioning

confidence: 99%

COVIDNearTerm: A simple method to forecast COVID-19 hospitalizations

Olshen

García

Kapphahn

et al. 2022

J. Clin. Trans. Sci.

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Introduction: COVID-19 has caused tremendous death and suffering since it first emerged in 2019. Soon after its emergence, models were developed to help predict the course of various disease metrics, and these models have been relied upon to help guide public health policy. Methods: Here we present a method called COVIDNearTerm to “forecast” hospitalizations in the short term, two to four weeks from the time of prediction. COVIDNearTerm is based on an autoregressive model and utilizes a parametric bootstrap approach to make predictions. It is easy to use as it requires only previous hospitalization data, and there is an open-source R package that implements the algorithm. We evaluated COVIDNearTerm on San Francisco Bay Area hospitalizations and compared it to models from the California COVID Assessment Tool (CalCAT). Results: We found that COVIDNearTerm predictions were more accurate than the CalCAT ensemble predictions for all comparisons and any CalCAT component for a majority of comparisons. For instance, at the county level our 14-day hospitalization median absolute percentage errors ranged from 16 to 36%. For those same comparisons, the CalCAT ensemble errors were between 30 and 59%. Conclusion: COVIDNearTerm is a simple and useful tool for predicting near-term COVID-19 hospitalizations.

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Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network

Cited by 46 publications

References 55 publications

Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries

Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

COVIDNearTerm: A simple method to forecast COVID-19 hospitalizations

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