Evaluation of Quantum Machine Learning in Predicting Epidemic Outbreaks at Federal Ministry of Health, Abuja
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- NGN 5000
Background of the Study
Epidemic outbreaks represent significant public health risks, and the ability to predict and mitigate the impact of such events is crucial for saving lives and resources. Traditional methods of epidemic prediction, such as statistical modeling and epidemiological forecasting, are limited by their reliance on historical data and their inability to account for complex, non-linear patterns in the data. Recent advances in machine learning (ML) have shown promise in improving prediction accuracy by analyzing vast amounts of data and detecting hidden patterns.
Quantum machine learning (QML) is an emerging field that combines the power of quantum computing with machine learning algorithms. QML has the potential to exponentially increase the speed and accuracy of predictions, particularly in complex and high-dimensional datasets. This study evaluates the use of quantum machine learning in predicting epidemic outbreaks, with a focus on its application at the Federal Ministry of Health in Abuja. The study aims to assess how quantum machine learning models can improve epidemic forecasting and help the Ministry of Health better prepare for and respond to future outbreaks.
Statement of the Problem
Despite the progress made in epidemic prediction, current methods remain limited in their predictive accuracy and responsiveness. Traditional epidemiological models often rely on linear assumptions and cannot process the vast amounts of data generated during an outbreak. Quantum machine learning could offer a breakthrough in improving prediction models by enabling more accurate and faster analysis of large and complex datasets. However, the adoption of QML for epidemic prediction in Nigeria’s Ministry of Health remains largely unexplored, and its feasibility is yet to be fully assessed.
Objectives of the Study
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To assess the feasibility of using quantum machine learning for predicting epidemic outbreaks at the Federal Ministry of Health.
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To evaluate the potential advantages of quantum machine learning over traditional epidemic prediction models.
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To develop a quantum machine learning model for epidemic prediction tailored to the Nigerian context.
Research Questions
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How can quantum machine learning improve epidemic prediction models in Nigeria?
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What advantages does quantum machine learning offer over traditional methods in forecasting epidemic outbreaks?
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What challenges exist in adopting quantum machine learning for epidemic prediction at the Federal Ministry of Health?
Significance of the Study
This study is significant as it offers an innovative approach to improving epidemic forecasting, which can be critical for timely responses and mitigating the impact of disease outbreaks. The findings could influence the adoption of quantum machine learning in public health and policy-making, leading to more effective epidemic control strategies in Nigeria and beyond.
Scope and Limitations of the Study
The study will focus on evaluating the potential of quantum machine learning in epidemic prediction at the Federal Ministry of Health. The limitations include the availability of quantum computing resources and the current maturity level of QML technologies.
Definitions of Terms
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Quantum Machine Learning: A field that combines quantum computing with machine learning algorithms to solve complex data analysis problems more efficiently.
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Epidemic Prediction: The use of models and data analysis to forecast the occurrence and spread of infectious diseases.
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Public Health: The science and practice of protecting and improving the health of populations, particularly in preventing and managing disease outbreaks.
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