Research Article
Predictive Model for Depression Without Medical Intervention
Charles Mwangi*
,
Kennedy Nyongesa,
Everlyne Akoth Odero
Issue:
Volume 14, Issue 1, February 2025
Pages:
1-11
Received:
15 August 2024
Accepted:
5 September 2024
Published:
7 January 2025
DOI:
10.11648/j.ajtas.20251401.11
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Abstract: Depression has been the largest mental health problem affecting the public health. Early detection of persons suffering from depression is crucial for effective mitigation and treatment. The key to this can only be achieved when clear symptoms of depression are used to detect patients’ depression conditions. The objective of this study is to develop a predictive model for depression that uses the symptoms. The study used both simulated data and real data from the hospitals. The study developed hidden markov model that help to compute the transitional probabilities. The study also used the logistic regression to assess the predictive power of the symptoms of depression. The study found that insomnia positively influence the probability of depression among the patients. The study also found that guilt positively influence the probability of depression among the patients. From the results, the study found that suicidal positively influence the probability of depression among the patients and also fatigue influence the probability of depression. From the study it was also found that retardation positively influence the probability of depression. Finally, found that the change in anxiety negatively influence the probability of depression among the patients. The study also conclude that the predictive model can be used to predict the depression status of the patients by a medical doctor given that the observable symptoms are present.
Abstract: Depression has been the largest mental health problem affecting the public health. Early detection of persons suffering from depression is crucial for effective mitigation and treatment. The key to this can only be achieved when clear symptoms of depression are used to detect patients’ depression conditions. The objective of this study is to develo...
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Research Article
Impact of Varying Response Time on Ambulance Deployment Plans in Heterogeneous Regions Using Multiple Performance Indicators
Tichaona Wilbert Mapuwei*,
Oliver Bodhlyera,
Henry Mwambi
Issue:
Volume 14, Issue 1, February 2025
Pages:
12-29
Received:
10 September 2024
Accepted:
13 December 2024
Published:
14 January 2025
DOI:
10.11648/j.ajtas.20251401.12
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Abstract: The paper conducts an assessment of the impact of varying response time distributions on ambulance deployment plans by integrating forecasting, simulation and optimisation techniques to predefined locations with heterogeneous demand patterns. Bulawayo metropolitan city was used as a case study. The paper proposes use of future demand and allows for simultaneous evaluation of operational performances of deployment plans using multiple performance indicators such as average response time, total duration of a call in system, number of calls in response queue, average queuing time, throughput ratios and ambulance utilisation levels. Increasing the fleet size influences the average response time below a certain threshold value across all the heterogeneous regions. However, when fleet size is increased beyond this threshold value, no significant changes occur in the performance indicators. Fleet size varied inversely to ambulance utilisation levels. As fleet size is gradually increased, utilisation levels also gradually decreased. Due care must be taken to avoid under-utilisation of ambulances during deployment. Under utilisation culminates to human and material equipment idleness and yet the resources available are scarce and should be deployed where needed most. For critical resources such as ambulances in emergency response, increasing the resource did not always translate to better performance. However, directing efforts towards reducing response time (call delay time, chute time, queuing and travel time) results in improvement of service performance and corresponding reduction in number of ambulances required to achieve a desired service level. Performance indicators such as utilisation levels and throughput ratios are imperative in ensuring balanced resource allocation and capacity utilisation which avoids under or over utilisation of scarce and yet critical resources. This has a strong bearing on both human and material resource workloads. The integrated strategy can also be replicated with relative ease to manage other service systems with a server-to-customer relationship.
Abstract: The paper conducts an assessment of the impact of varying response time distributions on ambulance deployment plans by integrating forecasting, simulation and optimisation techniques to predefined locations with heterogeneous demand patterns. Bulawayo metropolitan city was used as a case study. The paper proposes use of future demand and allows for...
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Research Article
A Band Spectral Density Regression of Exports on the Gross Domestic Product of the Kenyan Economy
Safari Godfrey Lyece*,
Joseph Kyalo Mung’atu,
Jane Aduda Akinyi
Issue:
Volume 14, Issue 1, February 2025
Pages:
30-37
Received:
16 October 2023
Accepted:
13 November 2024
Published:
10 February 2025
DOI:
10.11648/j.ajtas.20251401.13
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Abstract: The aim of this study is to be able to find the relationship between two time series variables applying the spectral analysis techniques. The spectral analysis techniques would enable the study explain this relationship in a more advanced way particularly in the frequency domain. Since the study will be focusing on two time series, we shall use the cross spectral density function which shall be estimated based on the cross periodogram function. The Cross spectral density function would give the power distribution of the two times series across the frequency domain. In this case each power will be associated with a frequency. Then we shall apply the square coherence function to find whether there is any association or the presence of a seasonality effect between the two series signals at each frequency. This would help us determine whether these two series are dependent or independent. Once the association has been established, we shall apply the Frequency Response Function (FRF) to estimate the effect the input variable in this case the independent variable has on the output in this case the dependent variable across the frequency domain. Finally, the study would use an F Test to determine which of the input frequencies are significant in explaining the output or the output variable.
Abstract: The aim of this study is to be able to find the relationship between two time series variables applying the spectral analysis techniques. The spectral analysis techniques would enable the study explain this relationship in a more advanced way particularly in the frequency domain. Since the study will be focusing on two time series, we shall use the...
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Research Article
Cure Models with Modified Log-Logistic Distribution: An Application to Oncology Studies
Kelvin Mutua Murungi*,
Samuel Mwalili,
Joseph K. Mungatu
Issue:
Volume 14, Issue 1, February 2025
Pages:
38-50
Received:
8 January 2025
Accepted:
2 February 2025
Published:
20 February 2025
DOI:
10.11648/j.ajtas.20251401.14
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Abstract: The log-logistic distribution has been widely used in survival analysis, particularly in modeling survival times and event data in healthcare and biological studies. This study investigates the parameter estimation of the Log-Logistic Tangent (LLT) distribution using Maximum Likelihood Estimation (MLE), focusing on the consistency, bias, and precision of the estimated parameters. The simulation study results reveal that the estimated values of the parameters α and β deviate from their true values, indicating some bias in the estimation process. The mean values of the estimated parameters are found to be 0.623 for α and 1.433 for β, with respective standard deviations of 0.072 and 0.147, highlighting the variability across iterations. Further analysis of the asymptotic properties of the LLT model shows that the parameter estimates converge to stable values as sample size increases, demonstrating consistency in the estimation process. Additionally, asymptotic normality is confirmed through the calculation of the observed Fisher information matrix and derived standard errors. The LLT model was successfully applied to real-life data, yielding survival probability estimates, which were further validated through statistical testing. The study concludes that while the LLT model is effective in capturing survival patterns, improvements can be made to reduce bias, refine optimization techniques, and explore alternative estimation methods. Recommendations for future research include expanding the model to handle covariates and time-varying effects, thereby enhancing its applicability in diverse fields such as healthcare, finance, and engineering.
Abstract: The log-logistic distribution has been widely used in survival analysis, particularly in modeling survival times and event data in healthcare and biological studies. This study investigates the parameter estimation of the Log-Logistic Tangent (LLT) distribution using Maximum Likelihood Estimation (MLE), focusing on the consistency, bias, and precis...
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