A two-level deterministic reasoning pattern to curb the spread of COVID-19 in Africa

Lawal, Olumide Mohammed; Vincent, Olufunke Rebecca

doi:10.1016/b978-0-12-824536-1.00017-4

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…LSTM is a deep learning model with the capability of handling long-term dependencies. In machine learning, complicated problems are usually solved by gathering necessary data to give excellent output [22]- [25]. The rest of this paper is organized as follows, section 2 gives an insight into the method, section 3 shows the result gotten and section 4 gives a full discussion and conclusion.…”

Section: Issn: 2302-9285 mentioning

confidence: 99%

Modeling recurrence of COVID-19 and its variants using recurrent neural network

Bolarinwa

Vincent²,

Aborisade³

et al. 2023

Bulletin EEI

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Coronavirus disease 19 (COVID-19), a disease caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), began as the flu and gradually developed into a highly infectious global pandemic leading to the death of over 6 million people in about 200 countries of the world. Its pathogenic nature has qualified it as a deadly disease, causing moderate and severe respiratory difficulty in infected individuals with the ability to mutate into different variants of the first version. As a result, different government agencies and health institutions have sought solutions within and outside the clinical space. This paper models COVID-19 possible recurrence as variants and predicts that the subsequent waves will be more severe than the first wave. Long short-term memory network (LSTM) was used to predict the future occurrence of COVID-19 and forecast the virus's pattern. Machine evaluation was performed using precision, recall, F1-score, an area under the curve (AUC), and accuracy evaluation metrics. Datasets obtained were used to test the data. The collected characteristics were passed on to the system classification network, demonstrating the function's value based on the system's accuracy. The results showed that the COVID-19 variants have a higher disastrous effect within three months after the first wave.

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Section: Issn: 2302-9285 mentioning

confidence: 99%

Modeling recurrence of COVID-19 and its variants using recurrent neural network

Bolarinwa

Vincent²,

Aborisade³

et al. 2023

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…In 2021, some new deterministic and stochastic models have been proposed. Regarding the deterministic ones (Carcione et al., 2020 ; Kumar et al., 2021 ; Lawal & Vincent, 2021 ; Mandal et al., 2021 ; Piccirillo, 2021 ; Varghese et al., 2021 ), the main difference, when compared to past studies (Choi & Ki, 2020 ; Ferguson, Laydon et al., 2020 ; Ferguson, Walker et al., 2020 ; Khan & Atangana, 2020 ; T. M. Chen et al., 2020 ; Yang et al., 2021 ), is the compartmental susceptible–exposed–infective–recovered (SEIR) approach (for more details see Bartlett, 1957 ). Besides being deterministic, these are not age‐structured, as we propose here.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic epidemiological model for infectious diseases: The case of COVID‐19 at global‐level

et al. 2022

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This study has developed a probabilistic epidemiological model a few weeks after the World Health Organization declared COVID‐19 a pandemic (based on the little data available at that time). The aim was to assess relative risks for future scenarios and evaluate the effectiveness of different management actions for 1 year ahead. We quantified, categorized, and ranked the risks for scenarios such as business as usual, and moderate and strong mitigation. We estimated that, in the absence of interventions, COVID‐19 would have a 100% risk of explosion (i.e., more than 25% infections in the world population) and 34% (2.6 billion) of the world population would have been infected until the end of simulation. We analyzed the suitability of model scenarios by comparing actual values against estimated values for the first 6 weeks of the simulation period. The results proved to be more suitable with a business‐as‐usual scenario in Asia and moderate mitigation in the other continents. If everything went on like this, we would have 55% risk of explosion and 22% (1.7 billion) of the world population would have been infected. Strong mitigation actions in all continents could reduce these numbers to, 7% and 3% (223 million), respectively. Although the results were based on the data available in March 2020, both the model and probabilistic approach proved to be practicable and could be a basis for risk assessment in future pandemic episodes with unknown virus, especially in the early stages, when data and literature are scarce.

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Heart Disease Diagnosis Using Data Mining Techniques and a Decision Support System

Lawal

Vincent

2022

2022 5th Information Technology for Education and Development (ITED)

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A two-level deterministic reasoning pattern to curb the spread of COVID-19 in Africa

Cited by 4 publications

References 43 publications

Modeling recurrence of COVID-19 and its variants using recurrent neural network

Modeling recurrence of COVID-19 and its variants using recurrent neural network

A probabilistic epidemiological model for infectious diseases: The case of COVID‐19 at global‐level

Heart Disease Diagnosis Using Data Mining Techniques and a Decision Support System

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