Prediction and control of COVID-19 spreading based on a hybrid intelligent model

Zhang, Gengpei; Liu, Xiongding

doi:10.1371/journal.pone.0246360

Cited by 17 publications

(9 citation statements)

References 38 publications

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“…Other common tasks are to integrate compartmental models with methods like statistical models, classifiers, and DNNs to improve the forecasting of COVID-19 epidemic dynamics and attributes [305, 158]. For example, in [313], a hybrid model predicts the infected and death cases by integrating a genetic algorithm and LSTM into a modified susceptible-infected-quarantined-recovered (SIQR) epidemic model to optimize infection rates and modeling parameters. In [49], a regression tree combined with wavelet transform predicts COVID-19 outbreak and assesses its risk in terms of case numbers.…”

Section: Covid-19 Hybrid Modelingmentioning

confidence: 99%

COVID-19 Modeling: A Review

Cao

Liu

2022

Preprint

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The unprecedented and overwhelming SARS-CoV-2 virus and COVID-19 disease significantly challenge our life and the society and economy. Many questions emerge, a critical one is to quantify the challenges, realities, intervention effect and influence of the pandemic. With the massive effort in modeling COVID-19, what COVID-19 issues have been modeled? what and how well have epidemiology, AI, data science, machine learning, deep learning, mathematics and social science characterized the COVID-19 epidemic? what are the gaps and opportunities of quantifying the pandemic? Such questions involve a wide body of knowledge and literature, unclear but important for the present and future health crisis quantification. Here, we provide a comprehensive review of the challenges, tasks, methods, progress, gaps and opportunities in relation to modeling COVID-19 processes, data, mitigation and impact. With a research landscape of COVID-19 modeling, we further categorize, summarize, compare and discuss the related methods and progress of modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and medical treatments, nonpharmaceutical interventions and their effects, drug and vaccine development, psychological, economic and social influence and impact, and misinformation, etc. The review shows how modeling methods such as mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, AI and data science in particular shallow and deep machine learning, simulation modeling, social science methods and hybrid modeling have addressed the COVID-19 challenges and what gaps and directions exist for better futures.

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Section: Covid-19 Hybrid Modelingmentioning

confidence: 99%

COVID-19 Modeling: A Review

Cao

Liu

2022

Preprint

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“…Several models have been developed for the prediction of potential risk, such as infection rate increases, using different data forms [12][13][14][15][16][17][18]. With the emerging of new virus mutations [19], it has become unclear how such forecasting models designed using data obtained at the first generation of the virus spread can still be efficient to predict effects from emerging variants of the virus.…”

Section: Introductionmentioning

confidence: 99%

Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling

Rashed

Hirata

2021

IJERPH

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The significant health and economic effects of COVID-19 emphasize the requirement for reliable forecasting models to avoid the sudden collapse of healthcare facilities with overloaded hospitals. Several forecasting models have been developed based on the data acquired within the early stages of the virus spread. However, with the recent emergence of new virus variants, it is unclear how the new strains could influence the efficiency of forecasting using models adopted using earlier data. In this study, we analyzed daily positive cases (DPC) data using a machine learning model to understand the effect of new viral variants on morbidity rates. A deep learning model that considers several environmental and mobility factors was used to forecast DPC in six districts of Japan. From machine learning predictions with training data since the early days of COVID-19, high-quality estimation has been achieved for data obtained earlier than March 2021. However, a significant upsurge was observed in some districts after the discovery of the new COVID-19 variant B.1.1.7 (Alpha). An average increase of 20–40% in DPC was observed after the emergence of the Alpha variant and an increase of up to 20% has been recognized in the effective reproduction number. Approximately four weeks was needed for the machine learning model to adjust the forecasting error caused by the new variants. The comparison between machine-learning predictions and reported values demonstrated that the emergence of new virus variants should be considered within COVID-19 forecasting models. This study presents an easy yet efficient way to quantify the change caused by new viral variants with potential usefulness for global data analysis.

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“…The findings suggest that one contact every two days leads to an infection in more than three patients. A hybrid intelligent model proposed by researchers simulates the expansion of COVID-19 [18].…”

Section: Mathematical Analysis and Fluid Modelmentioning

confidence: 99%

Risk of COVID-19 Spread and Mitigation Strategies in Public Transportation Sector

Saha¹,

Quadir²,

Godavarthy³

2021

JTTs

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The world is trying to recover from once in the COVID-19 pandemic, which has a higher mortality rate than the common flu. However, the threat of different variants is still affecting us in a different capacity. Several preventive measures have been adopted across the nation and the globe to avoid the spread of this virus, such as lockdown, restricted travel, social distancing guidelines, obligatory face mask use, etc. These activities directly influenced the management of social life, economy, and livelihoods. The effect of COVID-19 on the public transit industry and strategies the transit agencies adapted to continue providing the service during the pandemic has been synthesized in this paper. As a result of the pandemic, public transit ridership decreased by about 70 percent for most agencies compared to pre-pandemic levels. This article also highlights the prevention of COVID-19 spread in the public transit industry using engineering solutions and advanced material science and nanotechnology solutions.

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Prediction and control of COVID-19 spreading based on a hybrid intelligent model

Cited by 17 publications

References 38 publications

COVID-19 Modeling: A Review

COVID-19 Modeling: A Review

Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling

Risk of COVID-19 Spread and Mitigation Strategies in Public Transportation Sector

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