School Of Natural Sciences
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Item Development of a Health Insurance Premium Prediction Model using Machine Learning(Great Zimbabwe University, 2025) Makoni Tendai; Rukwava Caroline; Mawere Talent; Chinofunga Peter TinasheIn Zimbabwe’s evolving healthcare landscape, accurately determining health insurance premiums is critical to improving affordability, reducing risk imbalances, and increasing coverage, particularly amid economic constraints and rising health costs. Traditional actuarial models often struggle to represent the complex, non-linear relationships among socioeconomic, health, and lifestyle variables prevalent in the Zimbabwean population. This paper aims to develop a machine learning model that more precisely and rationally predicts health insurance premiums. Five supervised regression algorithms, Linear Regression (LR), LASSO Regression (LASSO), K-Nearest Neighbours (KNN), Random Forest (RF), and Gradient Boosting (GB), are evaluated for their effectiveness using a representative health insurance dataset that includes demographic and health-related attributes relevant to Zimbabwe. Models were assessed based on their Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) values. The results show that ensemble learning methods, particularly Gradient Boosting, significantly outperform traditional linear models, achieving the highest predictive accuracy. Key predictors of premium costs were identified as chronic illnesses, smoking status, and the number of dependents, variables that are particularly pertinent in local risk assessment. This paper advances health insurance analytics in Zimbabwe by providing evidence that machine learning can support more transparent, data-driven, and context-sensitive premium determination. The findings help insurers, policymakers, and healthcare stakeholders aiming to expand coverage and improve trust in private and public insurance schemes.Item Projection of HIV Incidence Trends in Zimbabwe Using Incremental Mixture Importance Sampling(Great Zimbabwe University, 2025) Chinofunga Peter Tinashe; Chipepa Fastel; Makoni Tendai; Gwatidzo Sinikiwe; Mawere Talent; Chirima JustinHIV prevalence has remained high in Zimbabwe due to continued use of Antiretroviral therapy (ART). Incidence is now a better measure of the programmatic efforts in response to the epidemic. Mathematical modeling remains the main tool for assessing incidence trends. Secondary data transmembrane (TM) one, that is [TM1] analysis, was conducted using incremental mixture sampling (IMIS). Absolute neutrophil count (ANC) [TM2] prevalence data were used for modeling incidence in the general population. The force of infection in 6 of the 10 provinces in Zimbabwe is projected to fall below 1%. ART has significantly helped in reducing the force of infection in the country. With continued use of ART coupled with other programmatic interventions, HIV incidence can be reduced to very low levels in many parts of the country. HIV incidence in Zimbabwe varies by geographical location. Matabeleland South province has the highest cumulative incidence in the country, while Harare province has the lowest. There is a difference in the force of infection between rural and urban areas. The force of infection remains high in the Matabeleland South, Midlands, Bulawayo, and Mashonaland East provinces. An increase in the use of ART reduces HIV incidence. Scaling up HIV counselling and testing activities in provinces or districts with high force of infection will help reduce the force of infection in these areas a the number of people on ART will increase, consequently reducing the infectiousness of infected people. Intervention programmes should address cultural differences.Item An Application of the Naïve Bayes Algorithm as a Tool for Predicting Cases of Poaching in Wildlife Management(Great Zimbabwe University, 2025) Vengesai Sikozho; Winji Lucia; Mawere TalentPoaching presents a serious threat to both wildlife management and tourism sustainability, making proactive, data driven interventions necessary. This study explores the application of predictive data mining in crime management within Zimbabwe’s wildlife sector, focusing on the use of the Naïve Bayes algorithm to predict poaching occurrences in protected areas. Secondary data from ranger patrol logs, incident reports, weather observations, and spatial datasets were collected from selected wildlife management areas between 2015–2023. The data was cleaned, integrated, and underwent feature engineering before it was modelled with the Naïve Bayes classifier to identify patterns of poaching risk. After evaluation, the model achieved an accuracy of 87%, precision of 0.84, recall of 0.82 and a ROC (AUC) curve of 0.89 showing strong predictive capabilities. Key predictors included weather conditions, patrol intensity and recent poaching history. The model’s false positives (predicting poaching where none occurs) may result in extra patrols, while false negatives (failing to predict actual poaching) put wildlife at greater risk, showing the importance of balancing prediction sensitivity and resource allocation. These findings show that predictive data mining can promote crime prevention, enhance resource allocation, and reinforce wildlife protection in Zimbabwe. The study recommends including predictive analytics into conservation planning to protect the country’s wildlife resources.Item Artificial Intelligence in the Zimbabwe Banking Sector: A Systematic Literature Review(Great Zimbabwe University, 2025) Shambira Leonard; Edna ShambiraThe study methodology employed a systematic literature review (SLR) based on Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). The guidelines were used to find the current state of Artificial Intelligence (AI) adoption in the Zimbabwe banking sector. The research employed a structured PRISMA review protocol to search journal articles from Google Scholar, Research gate and Semantic Scholar. Data were also searched from regulatory and Institutional publications, industry reports, and grey literature published between 2015 and 2025. A total of 381 articles were identified, 328 articles were excluded and 53 were included. The review identified AI applications, reasons for AI adoption, benefits which come with AI adoption, challenges in integrating AI applications and governance issues in the Zimbabwean banking sector. Results indicate that AI adoption in the Zimbabwe banking sector is in its early adoption phase used mainly in customer service automation, reporting systems, and basic operational efficiency tools, while advanced applications such as predictive compliance modelling and AI driven credit analytics remain limited. Barriers to AI adoption are skills shortages, data governance concerns, integration challenges, and regulatory uncertainty. The study contributes to the literature by consolidating fragmented data from various sources, and it also identifies research gaps and recommends a future research trajectory and policy recommendations for AI adoption in the Zimbabwe banking sector.Item Justice v Corruption: The Judiciary`s Role in the Battle for Economic Prosperity in Zimbabwe(Great Zimbabwe University, 2025) Masiya Emerge; Machaya MusavenganaCorruption remains a global cancerous disease that undermines economic development while eroding public trust in institutions. In Zimbabwe, corruption has been a persistent issue, with the country scoring 21 out of 100 on the 2024 Corruption Perceptions Index. The economic ramifications are severe, with corruption contributing to inefficiencies in governance, misallocation of resources, and diminished investor confidence. In response to this, Zimbabwe has established several legal instruments and institutions to combat corruption. The paper examines the role of the judiciary, as an independent legal institution in Zimbabwe that is ‘central to the rule of law’, in curbing corruption. The Constitution of Zimbabwe promotes transparency and accountability, with Chapter 13 establishing key anti corruption bodies. Judicial oversight remains the key bottleneck for all anti-corruption enforcement efforts. Using a doctrinal research method, the study analyses existing case law and literature to assess the effectiveness of judicial enforcement in combating corruption. Lee’s General Theory of Law and Development serves as the theoretical framework, providing insights into the regulatory impact of the judiciary and identifying systemic loopholes that hinder anti-corruption efforts. This paper contributes to existing literature by providing a comprehensive analysis of the judiciary’s pivotal role in combating corruption and fostering economic development in Zimbabwe through the lens of Lee`s Theory of Law and Economic Development. The findings of this paper highlight the judiciary’s role as a legal institution in shaping regulatory frameworks and enforcing anti-corruption measures. The paper suggests an integrated approach to combat corruption that is constant of the existing multifaceted frailties to enhance transparency, rebuild public trust, and support sustainable development.Item Urbanisation and Inequality in Zimbabwe: A Disaggregated Analysis(Great Zimbabwe University, 2025) Muneri Ranganai; Mwanyepedza RobertThe world urban population has surpassed the 55 % mark, from 30% in 1950, 50% in 2007, and is projected to exceed 70% by 2050. Urbanisation is generally associated with human development and improved human welfare; however, contemporary literature also links it to increased inequality. This is partly explained by the urban wage premium resulting from rural-urban disparities in access to quality education and labour market unequal dynamics. This study investigates impact of urbanisation on income inequality in Zimbabwe, using 1992-2022 provincial-level disaggregated data. Employing the Fully Modified Ordinary Least Squares (FMOLS) and the Dynamic Ordinary Least Squares (DOLS) models, the study reveals that urbanisation increases income inequalities in Zimbabwe. These findings urban suggest that urbanisation in Zimbabwe is associated with urban decay, unequal access to education, employment opportunities, and other socio-economic amenities. It also reveals insufficient urban infrastructure, whose supply fails to keep pace with urban population growth. The study prescribes well-planned urban expansion, inclusive development policies, a strong rural development strategy and service delivery improvements in rural and peri-urban and informal settlement areas. Addressing inequalities in access to quality education and socio-economic opportunities, coupled with data-driven policy decisions and increased community participation remains critical to ameliorate inequalities associated with the urban expansion.Item AI Readiness Metrics in Public Sector Organizations: Zimbabwe Organizations Case Study Africa(Great Zimbabwe University, 2025) Njodzi Ranganai; Magoso Mercy Nyasha; Zivanai Lemias; Sambo Paul; Muwani Tendai SheltonThis paper explores Zimbabwe’s public sector AI readiness and preparedness. There is an urgent need to find out the drivers of preparedness since AI is becoming more mainstream in the hands of governments in an attempt to increase governance and service delivery. The methods that the authors use are a combination of mixed approaches that include quantitative surveys of different organizations operating in the public sector and qualitative interviews with some of the stakeholders in the area of focus. The key elements of AI preparation such as skills of the work force, organizational culture, data and technological infrastructure are defined and measured by authors in quantifiable terms. The results indicate that the degree of preparedness in the firms has considerable differences that can be attributed to a combination of diverse factors including the opportunity to train and resource allocation. The research paper provides the urgent need of the customized plans of optimizing the AI application in the state sector in Zimbabwe. In addition to equipping policymakers and government administrators with operational knowledge enabled to achieve innovation and enhance governance, this study may be applied to create successful implementation strategies of AI, since the study may offer an effective metrics framework. Finally, to make sure that the AI technology could be successfully adopted by the public sector organizations to promote the services delivery and communication with citizens, AI Metrics on public sectors as recommended by the authors.Item Cybersecurity in the Age of Digital Transformation: Safeguarding Knowledge Work in the 4th Industrial Revolution(Great Zimbabwe University, 2025) Muwani Tendai Shelton; Njodzi Ranganai; Mutipforo Gracious; Denhere Prosper Tafadzwa; Ruvinga Lawrence; Katsande ChipoThe 4th Industrial Revolution has also been triggered by the high intensity of artificial intelligence (AI), Internet of Things (IoT), and big data, which is bringing a revolution in knowledge work, but also posing unprecedented cybersecurity threats. Even with technological advancement, organizations are finding it difficult to ensure that sensitive intellectual property is not compromised by advanced cyber attacks, which is also a cause of concern to data integrity, privacy and operational resilience. This study project was intended to answer the following questions: How do new technologies in the 4th Industrial Revolution transform the problem of cybersecurity in knowledge-based industries? How can organizations ensure that they reduce cyber risks to embrace digital transformation? How is policy frameworks and the preparedness of the workforce contributing to enhancing cyber defenses? This paper was a crucial point of intersection between cybersecurity and the 4th Industrial Revolution, which provided a glimpse into how knowledge work can be secured in the age of hyperconnectivity. The research establishes certain gaps in IoT and cloud governance and suggests a hybrid system comprising of NIST CSF and Zero Trust principles in knowledge-intensive systems. This paper will use a mixed-methodology in order to assess cybersecurity strategies. It combines case studies, analyses of breach patterns using quantitative data, interviews with experts, and a literature review and interprets the findings in terms of the NIST Cybersecurity Framework, Zero Trust Architecture, and human-centric security models. The results showed that AI and automation bring defensive and attack types; those companies that are adaptive and layered in their security measures also exhibit greater resilience. Regulatory loopholes and lack of skills also negatively impact successful management of cyber risks, and coordinative structures (public-private partnerships) improve the sharing of threat intelligence. Due to the redefinition of knowledge work provided by the 4th Industrial Revolution, active cybersecurity strategies should also transform. This study emphasizes the importance of agile policies, upskilling of work forces, and integrating technology into security to tap all the potential of the revolution in a secure way.Item From Policy to Practice: Evaluation of Telecom Cybersecurity Regulation and Capacity in Southern Africa.(Great Zimbabwe University, 2025) Njodzi Ranganai; Magoso Mercy Nyasha; Chidoko Clainos; Sambo Paul; Mushamainza Zvishamiso; Zivanai LemiasThe authors inquired on the effectiveness of cybersecurity controls and regulations within the telecommunications industry in Southern Africa and expounded the strengths and weaknesses of the effective governance frameworks. Complex cyber threats are much targeted to digital technology infrastructure and resilience is important to provide economic stability, consumer protection as well as national security. The investigation used a mixed method design that includes the surveys of telecom operators, policy documents study, and interviews with regulators and industry actors, the research also determines some crucial tendencies. The findings of the research show the inconsistency of policy implementation in a country, disordered compliance with regulations by telecom operator and lack of enforcement frameworks on a state level. Moreover, the absence of cross border cooperation, resource insufficiency, and the dissimilarity of the institution capabilities prevent the mutuality of cybersecurity activities. The absence of user consciousness that persists also reduces the strength of resilience as the users are still under the threat of phishing, mobile money attacks and SIM-swap fraud. The positive advances in this study also include the emergence of national Computer emergency response teams (CERT), the adoption and congruency of policies with global standards and the increasing political intent to prevent the cyber threat. To sum up, it is important to note that despite the significant improvement, the telecom industry in the region is at the lowest level of cybersecurity. The paper takes into account standardization of cybersecurity at regional level, increased regulatory controls, capacity-building and sensitization efforts through skills and awareness gimmicks, and regional integration to produce a healthier, more sensitive and secure telecom environment in Southern Africa.Item A Conceptual Framework for Integrating AI, IoT, and Blockchain to Accelerate Zimbabwe's Sustainable Development: An ICT4D Perspective(Great Zimbabwe University, 2025) Manjeese CalebInformation and Communication Technology for Development (ICT4D) is the use of technology with a developmental agenda, especially in the underserved regions and has been instrumental in sustainable development. Combined with Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain (BC), there has been a new dimension to sustainable development as new prospects are presented by the three technologies in synergy. AI has the capability to provide technicians with new ways of solving challenges efficiently. Industries like health, education and agriculture can be revolutionized as tailor made solutions boost decision making and productivity. IoT can guarantee smooth data exchange, real time monitoring and automation thereby increasing productivity and saving costs. BC is a technology that guarantees transparency, accountability and trust during transacting digitally thus making it ideal for use in governance, finance and supply chain management. A qualitative approach was used to gather opinions from a sample of ICT4D practitioners and stakeholders to gauge the perceived impact of AI, IoT, and Blockchain on sustainable development and to explore possible opportunities available with the combined technologies. In-depth interviews were conducted with experts in the field to gather detailed insights on the success factors for AI, IoT and BC, what considerations in designing a framework, and framework suitability to the integration of the three technologies. The results indicated that AI, IoT, and Blockchain have the potential to positively impact sustainable development in various aspects, including increased efficiency, improved decision-making, and enhanced innovation. For the integration framework to bring positive results, it needs to consider ICT4D and sustainable development components such as local context, capacity building, and green technology.