Modeling Covid-19 Cases in West African Countries: A Comparative Analysis of Quartic Curve Estimation Models and Estimators

Ayinde, Kayode; Bello, Hamidu Abimbola; Rauf, Rusdiaman; Attah, Omokova Mary; Nwosu, Ugochinyere Ihuoma; Bodunwa, O. K.; Ojo, Olatunji Oladimeji; Ogundokun, Roseline Oluwaseun; Fayose, Taiwo Stephen; Akinbo, R.Y.; Adejumo, A. O.; Akinsola, O J; Olatayo, T.O.; Aladeniyi, Olabimpe; Olamide, E. I.; Olanrewaju, Samuel Olayemi

doi:10.1007/978-3-030-72834-2_12

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Meanwhile, several modeling exercises were proposed to better understand the pandemic in our region. Ayinde et al [ 50 ] found it useful to use a quartic curve estimation model to forecast the epidemiological parameters in West African countries.…”

Section: Discussionmentioning

confidence: 99%

Estimation of the Serial Interval and the Effective Reproductive Number of COVID-19 Outbreak Using Contact Data in Burkina Faso, a Sub-Saharan African Country

Somda

Ouédraogo

Paré

et al. 2022

Computational and Mathematical Methods in Medicine

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The COVID-19 outbreak has spread all around the world in less than four months. However, the pattern of the epidemic was different according to the countries. We propose this paper to describe the transmission network and to estimate the serial interval and the reproductive number of the novel coronavirus disease (COVID-19) in Burkina Faso, a Sub-Saharan African country. Data from the COVID-19 response team was analyzed. Information on the 804 first detected cases were pulled together. From contact tracing information, 126 infector-infectee pairs were built. The principal infection clusters with their index cases were observed, principally the two major identified indexes in Burkina. However, the generations of infections were usually short (less than four). The serial interval was estimated to follow a gamma distribution with a shape parameter 1.04 (95% credibility interval: 0.69–1.57) and a scale parameter of 5.69 (95% credibility interval: 3.76–9.11). The basic reproductive number was estimated at 2.36 (95% confidence interval: 1.46–3.26). However, the effective reproductive number decreases very quickly, reaching a minimum value of 0.20 (95% confidence interval: 0.06–0.34). Estimated parameters are made available to monitor the outbreak in Sub-Saharan African countries. These show serial intervals like in the other continents but less infectiousness.

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Section: Discussionmentioning

confidence: 99%

Estimation of the Serial Interval and the Effective Reproductive Number of COVID-19 Outbreak Using Contact Data in Burkina Faso, a Sub-Saharan African Country

Somda

Ouédraogo

Paré

et al. 2022

Computational and Mathematical Methods in Medicine

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An Application of Information Granules to Detect Anomalies in COVID-19 Reports

Kiersztyn

Łopucki

et al. 2023

Artificial Intelligence and Soft Computing

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PulmoNet: a novel deep learning based pulmonary diseases detection model

Abdulahi,

Ogundokun,

Adenike

et al. 2024

BMC Med Imaging

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Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, making the exchange of gas challenging for animals inhaling and exhaling. It varies from gentle and self-limiting such as the common cold and catarrh, to life-threatening ones, such as viral pneumonia (VP), bacterial pneumonia (BP), and tuberculosis, as well as a severe acute respiratory syndrome, such as the coronavirus 2019 (COVID-19). The cost of diagnosis and treatment of pulmonary infections is on the high side, most especially in developing countries, and since radiography images (X-ray and computed tomography (CT) scan images) have proven beneficial in detecting various pulmonary infections, many machine learning (ML) models and image processing procedures have been utilized to identify these infections. The need for timely and accurate detection can be lifesaving, especially during a pandemic. This paper, therefore, suggested a deep convolutional neural network (DCNN) founded image detection model, optimized with image augmentation technique, to detect three (3) different pulmonary diseases (COVID-19, bacterial pneumonia, and viral pneumonia). The dataset containing four (4) different classes (healthy (10,325), COVID-19 (3,749), BP (883), and VP (1,478)) was utilized as training/testing data for the suggested model. The model’s performance indicates high potential in detecting the three (3) classes of pulmonary diseases. The model recorded average detection accuracy of 94%, 95.4%, 99.4%, and 98.30%, and training/detection time of about 60/50 s. This result indicates the proficiency of the suggested approach when likened to the traditional texture descriptors technique of pulmonary disease recognition utilizing X-ray and CT scan images. This study introduces an innovative deep convolutional neural network model to enhance the detection of pulmonary diseases like COVID-19 and pneumonia using radiography. This model, notable for its accuracy and efficiency, promises significant advancements in medical diagnostics, particularly beneficial in developing countries due to its potential to surpass traditional diagnostic methods.

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Modeling Covid-19 Cases in West African Countries: A Comparative Analysis of Quartic Curve Estimation Models and Estimators

Cited by 3 publications

References 35 publications

Estimation of the Serial Interval and the Effective Reproductive Number of COVID-19 Outbreak Using Contact Data in Burkina Faso, a Sub-Saharan African Country

Estimation of the Serial Interval and the Effective Reproductive Number of COVID-19 Outbreak Using Contact Data in Burkina Faso, a Sub-Saharan African Country

An Application of Information Granules to Detect Anomalies in COVID-19 Reports

PulmoNet: a novel deep learning based pulmonary diseases detection model

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