A hybrid approach to forecast the COVID-19 epidemic trend

Nawaz, Saqib Ali; Li, Jingbing; Bhatti, Uzair Aslam; Bazai, Sibghat Ullah; Zafar, Anessa; Bhatti, Mughair Aslam; Mehmood, Adeel; Ain, Qurat ul; Shoukat, Muhammad Usman

doi:10.1371/journal.pone.0256971

Cited by 32 publications

(16 citation statements)

References 36 publications

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“…The difference in daily average AQI has gradually decreased when compared to the same period in previous years. This study also clarifies how to manage the quality of the air we breathe [38]. The findings show that a temporary social blockade cannot reduce pollution from all sources.…”

Section: Change Of Nomentioning

confidence: 52%

“…The progressive application of the aforementioned strategies has reduced urban pollution sources and pollutant emissions that affect ambient air quality. However, due to restricted management and a rather dense population, the demand for heating expanded tremendously [36][37][38]. As a result, the concentration of particulate matter in Jiangsu increased slightly after traffic restrictions were implemented.…”

Section: Change Of Nomentioning

confidence: 99%

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COVID-19 and Air Pollution: Air Quality Impact in 13 Cities of the Jiangsu Province of China

Wu¹,

Sahabuddin²,

Bhatti³

et al. 2022

Pol. J. Environ. Stud.

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Jiangsu province is one of the economically strong provinces in east China. With the advance of the modernization process, the problem of air pollution in this area is facing a severe challenge under the common role of human activities and regional climate change. While the rest of the world struggles to control COVID-19, China has managed to control the pandemic rapidly and effectively with strong lockdown policies. This study investigates the change in air pollution (focusing on the air quality index (AQI), six ambient air pollutants nitrogen dioxide (NO 2 ), ozone (O 3 ), sulphur dioxide (SO 2 ), carbon monoxide (CO), particulate matter with aerodynamic diameters ≤10 μm (PM 10 ) and ≤2.5 μm (PM 2.5 )) patterns for different periods in last 5 years. Different pollutants have different behavior identified in this studied which is helping for understanding the pattern of air quality. Short-term health advantages from the COVID-19 pandemic can be attributed to the reduction in air pollution and significant improvement in ambient air quality, which need the government to enact post-COVID environmental regulations.

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Section: Change Of Nomentioning

confidence: 52%

Section: Change Of Nomentioning

confidence: 99%

COVID-19 and Air Pollution: Air Quality Impact in 13 Cities of the Jiangsu Province of China

Wu¹,

Sahabuddin²,

Bhatti³

et al. 2022

Pol. J. Environ. Stud.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The SEIR model can simulate population shifts resulting from infectious disease outbreaks. According to Nawaz et al (38), if the number of confirmed COVID-19 diagnoses each day (I) is projected, the number of healthy persons (S), exposed people (E), and infected people (I) need to be computed. It is also necessary to examine the number of recovered persons (R), the infection rate of healthy people becoming latent, the infection rate of exposed people getting infected, and the recovery rate of diseased people.…”

Section: Logistic and Seir Hybrid Modelmentioning

confidence: 99%

Prediction of COVID-19 Data Using Hybrid Modeling Approaches

Zhao¹,

Sun

Liu

et al. 2022

Front. Public Health

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A major emphasis is the dissemination of COVID-19 across the country's many regions and provinces. Using the present COVID-19 pandemic as a guide, the researchers suggest a hybrid model architecture for analyzing and optimizing COVID-19 data during the complete country. The analysis of COVID-19's exploration and death rate uses an ARIMA model with susceptible-infectious-removed and susceptible-exposed-infectious-removed (SEIR) models. The logistic model's failure to forecast the number of confirmed diagnoses and the snags of the SEIR model's too many tuning parameters are both addressed by a hybrid model method. Logistic regression (LR), Autoregressive Integrated Moving Average Model (ARIMA), support vector regression (SVR), multilayer perceptron (MLP), Recurrent Neural Networks (RNN), Gate Recurrent Unit (GRU), and long short-term memory (LSTM) are utilized for the same purpose. Root mean square error, mean absolute error, and mean absolute percentage error are used to show these models. New COVID-19 cases, the number of quarantines, mortality rates, and the deployment of public self-protection measures to reduce the epidemic are all outlined in the study's findings. Government officials can use the findings to guide future illness prevention and control choices.

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“…However, the continuous loss of agricultural labor in recent years has led to a substantial increase in the cost of manual picking. The problem became more prominent after the COVID-19 pandemic ( Nawaz et al., 2021 ). Therefore, efficient picking machines are a current research focus and an area of importance in orchard intelligence.…”

Section: Introductionmentioning

confidence: 99%

YOLO-P: An efficient method for pear fast detection in complex orchard picking environment

Sun

Wang²,

Jing³

2023

Front. Plant Sci.

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IntroductionFruit detection is one of the key functions of an automatic picking robot, but fruit detection accuracy is seriously decreased when fruits are against a disordered background and in the shade of other objects, as is commmon in a complex orchard environment.MethodsHere, an effective mode based on YOLOv5, namely YOLO-P, was proposed to detect pears quickly and accurately. Shuffle block was used to replace the Conv, Batch Norm, SiLU (CBS) structure of the second and third stages in the YOLOv5 backbone, while the inverted shuffle block was designed to replace the fourth stage’s CBS structure. The new backbone could extract features of pears from a long distance more efficiently. A convolutional block attention module (CBAM) was inserted into the reconstructed backbone to improve the robot’s ability to capture pears’ key features. Hard-Swish was used to replace the activation functions in other CBS structures in the whole YOLOv5 network. A weighted confidence loss function was designed to enhance the detection effect of small targets.ResultAt last, model comparison experiments, ablation experiments, and daytime and nighttime pear detection experiments were carried out. In the model comparison experiments, the detection effect of YOLO-P was better than other lightweight networks. The results showed that the module’s average precision (AP) was 97.6%, which was 1.8% higher than the precision of the original YOLOv5s. The model volume had been compressed by 39.4%, from 13.7MB to only 8.3MB. Ablation experiments verified the effectiveness of the proposed method. In the daytime and nighttime pear detection experiments, an embedded industrial computer was used to test the performance of YOLO-P against backgrounds of different complexities and when fruits are in different degrees of shade.DiscussionThe results showed that YOLO-P achieved the highest F1 score (96.1%) and frames per second (FPS) (32 FPS). It was sufficient for the picking robot to quickly and accurately detect pears in orchards. The proposed method can quickly and accurately detect pears in unstructured environments. YOLO-P provides support for automated pear picking and can be a reference for other types of fruit detection in similar environments.

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A hybrid approach to forecast the COVID-19 epidemic trend

Cited by 32 publications

References 36 publications

COVID-19 and Air Pollution: Air Quality Impact in 13 Cities of the Jiangsu Province of China

COVID-19 and Air Pollution: Air Quality Impact in 13 Cities of the Jiangsu Province of China

Prediction of COVID-19 Data Using Hybrid Modeling Approaches

YOLO-P: An efficient method for pear fast detection in complex orchard picking environment

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