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Survival Analysis of COVID-19 Patients: A Case Study of AIC Kijabe Hospital
Roseline Achieng Oburu,
Joseph Eyang’an Esekon,
Martin Mutwiri Kithinji
Issue:
Volume 12, Issue 5, September 2023
Pages:
103-109
Received:
4 August 2023
Accepted:
28 August 2023
Published:
12 September 2023
Abstract: COVID-19 is an infectious disease caused by the novel coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. The disease quickly spread, resulting in an epidemic in China and a number of cases in other countries around the world. This led to inconsistent health conditions and caused unceasing loss of human lives. Globally, the current COVID-19 pandemic posed a significant and imminent threat to healthcare systems, including Kenya, in terms of patient triage and allocation of limited resources. In this study we analyze time to recovery of the COVID-19 patients at AIC Kijabe hospital. A retrospective cohort study was used to review the existing medical records of 66 patients who tested positive for COVID-19 at AIC Kijabe Hospital. Kaplan-Meier curves were used in determining the probability of recovery. For statistical comparison of the survival curves, Log-rank test statistic was used while Cox proportional hazards model was used to investigate the relation between time to recovery and the predictor variables. Results showed that female patients recovered faster than male patients while there was no significant difference between the survival curves for gender and marital status among the COVID-19 patients. In the Cox proportional hazards model, only age was significant with a p - value (0.0463) and therefore affected the time to recovery of the COVID-19 patients while the rest of the variables, gender and marital status were not significant. In conclusion, age was the only variable that had an effect on the time to recovery of COVID-19 patients.
Abstract: COVID-19 is an infectious disease caused by the novel coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. The disease quickly spread, resulting in an epidemic in China and a number of cases in other countries around the world. This led to inconsistent health conditions and caused unceasing loss of human lives. Globally,...
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Bayesian Binary Quantile Regression for Modelling Injectable Contraceptive Uptake Among Child Bearing Women in Kenya
Dan Kipkosgei Kogei,
Anthony Wanjoya,
Joel Chelule,
Lena Onyango
Issue:
Volume 12, Issue 5, September 2023
Pages:
110-116
Received:
31 August 2023
Accepted:
14 September 2023
Published:
27 September 2023
Abstract: Injectable contraceptives are methods of contraception that is administered through injection. In the recent times, injectable contraceptives have been preferred to other modern contraceptives by child bearing women which implies that there are factors contributing to this surge in the usage of injectable contraceptives. Currently in Kenya, injectable contraceptives are the most used methods of modern contraceptives. In addition, most studies have used logistic regression to model modern contraceptives but logistic regression focuses only in conditional mean (central quantile of the response variable). Bayesian quantile regression involves application of Bayesian techniques to quantile regression where the continuous Asymmetric Laplace Distribution (ALD) is used to formulate the likelihood used for posterior estimation. The main objective of this study was to model injectable contraceptive uptake among child bearing women using Bayesian binary quantile regression in Kenya. The study used nationally representative cross sectional secondary data obtained from PMA (Performance monitoring for Action) which was collected from November to December 2021 targeting women of child bearing age (15-49 years). Data analysis was done using R software. Bayesian quantile regression model parameters were estimated using Markov Chain Monte Carlo (MCMC) Gibbs sampling for 5 different quantiles (0.10, 0.25, 0.50, 0.75 and 0.95) and convergence diagnostics was performed to assess the convergence of generated MCMC posterior samples to target posterior distribution. Convergence diagnostics are very crucial in Bayesian statistics to ensure accuracy and reliability of the inferences drawn from model posterior distribution. Convergence was achieved based on Gelman and Rubin’s diagnostics for all parameters being less than 1.1 implying accuracy of model parameters. The uptake of injectable contraceptives was found to be greatly influenced by wealth quintile, level of education, marriage status of woman and the number of birth events to a woman. More specifically, women in highest wealth quintile had lower likelihood of using injectable contraceptives as compared to those in the lowest quintile, those who are widows, divorced and never married had lower likelihood of using injectable contraceptives compared to the currently married women, women with primary and secondary education levels were more likely to use injectable contraceptives compared to women with no education, increase in the number of birth events negatively influences the uptake of injectable contraceptives. This study concluded that marital status, birth events, education level and wealth quintile are significant predictors of injectable contraceptive uptake in Kenya.
Abstract: Injectable contraceptives are methods of contraception that is administered through injection. In the recent times, injectable contraceptives have been preferred to other modern contraceptives by child bearing women which implies that there are factors contributing to this surge in the usage of injectable contraceptives. Currently in Kenya, injecta...
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Residual Reduced Balanced Incomplete Block Design
Troon John Benedict,
Onyango Fredrick,
Karanjah Anthony
Issue:
Volume 12, Issue 5, September 2023
Pages:
117-119
Received:
11 June 2023
Accepted:
29 June 2023
Published:
14 October 2023
Abstract: The construction of Balanced Incomplete Block Designs is a combination problem that involves the arrangement of v treatments into b blocks each of size k such that each treatment is replicated exactly r times in the design and a pair of treatments occur together in λ blocks. Researchers have devised a number of methods that can be used in constructing BIBDs, using geometry, difference sets, existing BIBD designs, computers and mathematical algorithms, and Latin squares. However, the existing constructing methods still cannot be used to construct all the BIBDs. This has left the existence of some BIBDs to still be unknown as some of them still cannot be constructed using the existing construction methods. The study aimed to derive a new construction method that uses the un-reduced BIBD to construct a new class of BIBD known as Residual Reduced BIBD. The study used the un-reduced BIBD with parameters (v, k) to construct the new class of BIBD. Consider an un-reduced BIBD with parameters v and k such that k≥3 the Residual Reduced BIBD was derived from the un-reduced design selection of blocks of the un-reduced BIBD that contain a particular treatment i. Then in the selected blocks if treatments i deleted and the rest of the treatments are left, then this forms a BIBD known as Residual Reduced BIBD. Residual Reduced BIBD formed has the parameters v* = v -1, b* = ((v - 1)!(v - k))/(k!(v - k)!), k* = k, r* = ((v - 2)!(v - k))/((k - 1)!(v - k)!), λ* = ((v - 3)!(v - k))/((k - 2)!(v - k)!). In conclusion, the study was able to show that a new class of BIBD could be constructed from the un-reduced BIBD. This means that some other BIBDs still can be derived from this universal set using appropriate procedures.
Abstract: The construction of Balanced Incomplete Block Designs is a combination problem that involves the arrangement of v treatments into b blocks each of size k such that each treatment is replicated exactly r times in the design and a pair of treatments occur together in λ blocks. Researchers have devised a number of methods that can be used in construct...
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Research Article
Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique
Sammy Kiprop,
Charity Wamwea,
Herbert Imboga,
Joel Chelule
Issue:
Volume 12, Issue 5, September 2023
Pages:
120-128
Received:
26 September 2023
Accepted:
12 October 2023
Published:
28 October 2023
Abstract: The Kenyan government in partnership with other stakeholders involved in providing family planning services have put in place various strategies and policies to increase uptake of contraceptives. This results in an increase in contraceptive prevalence rate (CPR), reduction of both total fertility rate (TFR) and sexually transmitted infections (STIs). Despite the various strategies and policies, the total fertility rate still remains high, while CPR has been unattained, respectively. The aim of this study was to classify contraceptives use among undergraduate students using a supervised machine learning technique. The target population constituted students at Jomo Kenyatta University of Agriculture and Technology (JKUAT) (Eldoret Campus). The study applied simple random sampling technique to obtain data from a sample of 252 using structured questionnaires. A decision tree classifier based on CHAID and C5.0 algorithms were used for classification. Pearson Chi-Squared statistic was used as feature selection technique to rank significant factors influencing contraceptives use based on their Chi scores. The findings show that the use of Chi-Squared feature selection led to contraceptives factors that were ranked higher having higher classification performance. The fitted decision tree model based on CHAID algorithm had a higher classification accuracy of 64.68% with 195 correct classifications as compared to the C5.0 decision tree model with accuracy of 61.18% with 163 correct classifications. The study findings contribute to a better insight on the classifications of contraceptives use among undergraduate students in Kenya. Hence, the government of Kenya can implement policies to enhance contraceptives awareness.
Abstract: The Kenyan government in partnership with other stakeholders involved in providing family planning services have put in place various strategies and policies to increase uptake of contraceptives. This results in an increase in contraceptive prevalence rate (CPR), reduction of both total fertility rate (TFR) and sexually transmitted infections (STIs...
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Research Article
Modeling Optimal Income and Job Increase on Fishing in the Current Economic Scenario in Angola Until 2050
Alcides Romualdo Neto Simbo
Issue:
Volume 12, Issue 5, September 2023
Pages:
129-149
Received:
17 September 2023
Accepted:
16 October 2023
Published:
30 October 2023
Abstract: In this paper, forecasts were made and two-stage deterministic optimization models were designed to maximize annual fish sales revenues and increase the number of jobs on fishing in Angola in the current scenario. Starting from historical data from 2016 to 2022, taking their means and standard deviations, normal distributions were generated up to 2050. If these models were adopted, annual income would reach Kz 260,753,942,425.00 as opposed to the current Kz 246,617,594,646.47 produced with the sale of 5 species of crustaceans, 3 of mollusks, 34 of demersal fishes, 6 of pelagic fishes and 5 of freshwater fishing, resulting in an annual increase in income of around 5.73% and 6,514 new jobs and direct self-employment, of which 328 in industrial fishing, 319 in semi-industrial fishing, 3,355 in maritime artisanal fishing and 2,512 in freshwater artisanal fishing. Of these, 5,071 will be for fishermen and 1,443 for women fish processors. The optimal portfolio of fish sales revenue would be 3% for crustaceans, 1% for mollusks, 34% for demersal Fishes, 54% for pelagic fishes and 8% for fish from freshwater fishing. These results would be excellent for the fishing sector to contribute to achieving the employability goals envisaged by the Angolan government in the medium and long term.
Abstract: In this paper, forecasts were made and two-stage deterministic optimization models were designed to maximize annual fish sales revenues and increase the number of jobs on fishing in Angola in the current scenario. Starting from historical data from 2016 to 2022, taking their means and standard deviations, normal distributions were generated up to 2...
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