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Diffusion process for corona virus spread

Diffusion process for corona virus spread

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The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model...

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... There may have been respondent bias through either exaggeration of answers or information retainment, especially for government-related questions. Finally, although logistic regression could accurately predict factors associated with good knowledge, positive attitude, good practice and good perception, other more robust machine learning techniques could have generated predictors of compliance to government preventive measures with more accuracy and precision as reported by some studies (40)(41)(42)(43). ...
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Background Several governments from African countries, including the Democratic Republic of the Congo (DRC), implemented stringent public health measures to curb COVID-19 transmission in the early phases of the pandemic. While these restrictive measures are believed to have contributed to lowering case incidence and related mortality in DRC, data on the population’s knowledge and adherence are limited. This study aimed to assess the knowledge, perception, attitudes, and practices of COVID-19 preventive measures and associated factors among adult residents of Matadi, thereby generating evidence for a strategy adjustment as the COVID-19 response is transitioning from emergency to control status. Methods We used data from a population-based cross-sectional study conducted in October 2021. Consenting participants were enrolled through a multi-stage cluster sampling approach and administered a pre-tested structured questionnaire using a mobile application (Epicollect 5). We analyzed adult participants’ data using STATA 15.1. Univariable and multivariable analyses were applied to identify factors associated with good knowledge, good perception, positive attitude and good practice. Results We included 1,269 adult respondents for the secondary analysis. One respondent in six was female. The median age was 36 years (IQR 24–50). Most respondents (76.5%) had good knowledge. Respondents aged 40–49 years and those with vocational education level were 1.7 time (AOR 1.75, 95% CI 1.07–2.87) and twice as likely (AOR 2.06, 95% CI 1.01–4.21) to have good knowledge. Preventive measures were perceived as efficient by 45% of respondents. Good perception was associated with education level, profession, average household monthly income and good knowledge. Only 40% of respondents had a positive attitude. A positive attitude was associated with age, education level, and good knowledge. Respondents having good practice represented 5.8%. Good practice was associated with good knowledge, attitude and perception. Conclusion Most respondents were knowledgeable, had a good perception of government-related COVID-19 preventive measures, a moderately positive attitude and an extremely low level of good practice. Current COVID-19 preventive strategies, including vaccination rollout, need adjustment into high-efficiency, context-based and risk group-specific interventions. Evidence generated by this study will improve preparedness and response to future outbreaks.
... As a result of the test, the proposed model is very successful in detecting the COVID-19 virus. Raheja et al. (2023) proposed a diffusion prediction model for prediction of number of COVID-19 cases in India, France, China and Nepal countries. They compared the proposed model with other stateof-the art models. ...
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The development of vaccines and drugs is very important in combating the coronavirus disease 2019 (COVID-19) virus. The effectiveness of these developed vaccines and drugs has decreased as a result of the mutation of the COVID-19 virus. Therefore, it is very important to combat COVID-19 mutations. The majority of studies published in the literature are studies other than COVID-19 mutation prediction. We focused on this gap in this study. This study proposes a robust transformer encoder based model with Adam optimizer algorithm called TfrAdmCov for COVID-19 mutation prediction. Our main motivation is to predict the mutations occurring in the COVID-19 virus using the proposed TfrAdmCov model. The experimental results have shown that the proposed TfrAdmCov model outperforms both baseline models and several state-of-the-art models. The proposed TfrAdmCov model reached accuracy of 99.93%, precision of 100.00%, recall of 97.38%, f1-score of 98.67% and MCC of 98.65% on the COVID-19 testing dataset. Moreover, to evaluate the performance of the proposed TfrAdmCov model, we carried out mutation prediction on the influenza A/H3N2 HA dataset. The results obtained are promising for the development of vaccines and drugs.
... However, it requires extensive training and a large, well-annotated dataset to enhance the model's performance. Supervised DL algorithms play a crucial role in predictive analysis, demonstrating the substantial potential and providing solutions for various issues (Raheja et al., 2021). Conversely, Unsupervised DL algorithms have the potential to identify multiple hidden patterns in healthcare data (Raza and Singh 2021). ...
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... Linear regression algorithm only works when the relationship between the data is linear but in case of epidemic diseases, trends in the data are more sophisticated (Yang et al., 2023). In our case we cannot use a linear model as it will be unable to achieve the required amount of accuracy so here we have considered the Polynomial regression with degree between (2 to 10) according to our dataframe (Raheja et al., 2023). We must determine the link between a dependent variable and an independent variable in regression analysis (s). ...
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... Medical image processing has been widely utilized for the past decade, particularly due to the success of deep neural networks (DNNs) in this field. Examples of DL usage in medical informatics include skin lesion detection [10], pneumonia detection [11], Covid-19 diagnosis [12,13], skin cancer identification [14], automatic diagnosis of malaria parasites detection [15], and stroke classification [16]. Detailed information regarding the DL techniques employed in this research is provided in the methodology section (see "Material and Method"). ...
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... Optimizing energy consumption through time division multiple access (TDMA) for path estimation could significantly augment the WHMD computational model. Another model [55] is capable of estimating the anticipated number of health issues considering confirmed cases, recoveries, deaths, and active cases in weeks. Metrics such as accuracy and error rates, along with Support Vector Machine, Logistic Regression, and Convolutional Neural Network models, demonstrate efficiency in predicting disease diffusion. ...
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The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2 billion by 2022, with a remarkable compounded annual growth rate (CAGR) of 18.6% from 2023 to 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for the timely detection and seamless transmission of crucial health vitals to telemedical cloud agents. The fractional order modeling approach is employed to delineate the efficacy of the WHMD in pregnancy-related contexts. The Caputo fractional calculus framework is harnessed to show the device potential in capturing and communicating vital health data to medical experts precisely at the cloud layer. Our formulation establishes the fractional order model's positivity, existence, and uniqueness, substantiating its mathematical validity. The investigation comprises two major equilibrium points: the disease-free equilibrium and the equilibrium accounting for disease presence, both interconnected with the WHMD. The paper explores the impact of integrating the WHMD during pregnancy cycles. Analytical findings show that the basic reproduction number remains below unity, showing the WHMD efficacy in mitigating health complications. Furthermore, the fractional multi-stage differential transform method (FMSDTM) facilitates optimal control scenarios involving WHMD utilisation among pregnant patients. The proposed approach exhibits robustness and conclusively elucidates the dynamic potential of WHMD in supporting maternal health and disease control throughout pregnancy. This paper significantly contributes to the evolving landscape of analytical wearable healthcare research, highlighting the critical role of WHMDs in safeguarding maternal well-being and mitigating disease risks in edge reconfigurable health architectures.
... Optimizing energy consumption through time division multiple access (TDMA) for path estimation could significantly augment the WHMD computational model. Another model [55] is capable of estimating the anticipated number of health issues considering confirmed cases, recoveries, deaths, and active cases in weeks. Metrics such as accuracy and error rates, along with Support Vector Machine, Logistic Regression, and Convolutional Neural Network models, demonstrate efficiency in predicting disease diffusion. ...
Research
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The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2 billion by 2022, with a remarkable compounded annual growth rate (CAGR) of 18.6% from 2023 to 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for the timely detection and seamless transmission of crucial health vitals to telemedical cloud agents. The fractional order modeling approach is employed to delineate the efficacy of the WHMD in pregnancy-related contexts. The Caputo fractional calculus framework is harnessed to show the device potential in capturing and communicating vital health data to medical experts precisely at the cloud layer. Our formulation establishes the fractional order model's positivity, existence, and uniqueness, substantiating its mathematical validity. The investigation comprises two major equilibrium points: the disease-free equilibrium and the equilibrium accounting for disease presence, both interconnected with the WHMD. The paper explores the impact of integrating the WHMD during pregnancy cycles. Analytical findings show that the basic reproduction number remains below unity, showing the WHMD efficacy in mitigating health complications. Furthermore, the fractional multi-stage differential transform method (FMSDTM) facilitates optimal control scenarios involving WHMD utilisation among pregnant patients. The proposed approach exhibits robustness and conclusively elucidates the dynamic potential of WHMD in supporting maternal health and disease control throughout pregnancy. This paper significantly contributes to the evolving landscape of analytical wearable healthcare research, highlighting the critical role of WHMDs in safeguarding maternal well-being and mitigating disease risks in edge reconfigurable health architectures.
... Optimizing energy consumption through time division multiple access (TDMA) for path estimation could significantly augment the WHMD computational model. Another model [55] is capable of estimating the anticipated number of health issues considering confirmed cases, recoveries, deaths, and active cases in weeks. Metrics such as accuracy and error rates, along with Support Vector Machine, Logistic Regression, and Convolutional Neural Network models, demonstrate efficiency in predicting disease diffusion. ...
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Full-text available
The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2 billion by 2022, with a remarkable compounded annual growth rate (CAGR) of 18.6% from 2023 to 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for the timely detection and seamless transmission of crucial health vitals to telemedical cloud agents. The fractional order modeling approach is employed to delineate the efficacy of the WHMD in pregnancy-related contexts. The Caputo fractional calculus framework is harnessed to show the device potential in capturing and communicating vital health data to medical experts precisely at the cloud layer. Our formulation establishes the fractional order model’s positivity, existence, and uniqueness, substantiating its mathematical validity. The investigation comprises two major equilibrium points: the disease-free equilibrium and the equilibrium accounting for disease presence, both interconnected with the WHMD. The paper explores the impact of integrating the WHMD during pregnancy cycles. Analytical findings show that the basic reproduction number remains below unity, showing the WHMD efficacy in mitigating health complications. Furthermore, the fractional multi-stage differential transform method (FMSDTM) facilitates optimal control scenarios involving WHMD utilisation among pregnant patients. The proposed approach exhibits robustness and conclusively elucidates the dynamic potential of WHMD in supporting maternal health and disease control throughout pregnancy. This paper significantly contributes to the evolving landscape of analytical wearable healthcare research, highlighting the critical role of WHMDs in safeguarding maternal well-being and mitigating disease risks in edge reconfigurable health architectures.
... Sentiment analysis models predict outbreaks by monitoring social media to obtain and analyze public sentiment related to health issues [22][23][24][25][26]. Regression models perform regression tasks to predict the outbreak related data in the future [27][28][29][30]. Spreading models predict the progress of outbreaks based on geography or human to human diffusion [31][32][33]. Besides ML models, some mathematical models also can perform outbreak risk prediction. ...
... Machine learning (ML) and deep learning (DL) algorithms have significantly contributed to advancements in the biomedical and healthcare fields thanks to their remarkable ability to extract valuable information from complex and noisy datasets. In [15] the authors proposed an ML-based diffusion model to forecast the progression of the coronavirus, and in [16] the authors used an ML feature selection algorithm to select significant biomarkers and predict the occurrence of breast cancer. Divya et al. [17] employed a deep learning approach to diagnose schizophrenia using EEG signals. ...
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Brain-computer interface (BCI) is a new promising technology for control and communication, the BCI system aims to decode the measured brain activity into a command signal. This paper proposes a hybrid approach to improve the classification performance of motor imagery BCI (MI BCI). Our proposed method aims to take the advantage of two principal feature extraction approaches. The first approach named Multi-Band common spatial patterns (MBCSP) consists of decomposing the MI trial into multiple sub-bands, for each sub-band CSP is applied to extract the features. Then, the subject-specific frequency bands are selected. Simultaneously, the selected frequency bands are used as input to the second approach named boosted tangent space mapping (BTSM), which extracts the features from each sub-band. An automatic feature selection algorithm is introduced to select the subject-specific frequency bands and to reduce the high dimensionality space of extracted features. Finally, the LogitBoost (LB) classifiers are learned on the extracted features by each approach and the linear combination of these classifiers is used to identify the class of MI trial. The proposed model is evaluated on three public MI-BCI datasets, including multiclass motor imagery datasets (BCI competition IV dataset 2a and BCI competition III dataset 3a) and binary class motor imagery dataset (BCI competition III dataset 4a), The average accuracy attained on the three datasets are respectively 73.61%, 86.66%, and 86.68%. The conducted comparative study between the proposed approach and some state-of-the-the art methods showed a statistically significant improvement (p-value < 0.05) of the classification performance of MI BCI. The experiment results showed a significant improvement when using the proposed hybrid approach against the use of a single-feature extraction method.