Artificial Intelligence in predictive analytics for epidemic outbreaks in rural populations
1 Life's Journey Inc. Winnipeg, Manitoba, Canada.
2 Enugu State University Teaching Hospital, Parklane, Enugu, Nigeria.
3 Austin Peay State University, Clarksville, TN, USA.
4 Mediclinic Hospital Pietermaritzburg, South Africa.
5 Public Health Specialist, Albada General Hospital, Tabuk, Saudi Arabia.
Review
International Journal of Life Science Research Archive, 2024, 07(01), 078–094.
Article DOI: 10.53771/ijlsra.2024.7.1.0062
Publication history:
Received on 01 July 2024; revised on 06 August 2024; accepted on 08 August 2024
Abstract:
Artificial Intelligence (AI) is revolutionizing the approach to managing epidemic outbreaks, especially in rural populations where resources are often limited. This paper discusses the role of AI in predictive analytics for epidemic forecasting and response in these underserved areas. AI-driven predictive models utilize advanced algorithms and large datasets to anticipate outbreaks, identify potential hotspots, and optimize resource allocation. AI applications in predictive analytics integrate various data sources, including historical health records, real-time surveillance data, and environmental factors, to create accurate epidemic forecasts. These models enhance the ability to predict the spread of diseases by identifying patterns and correlations that traditional methods might miss. For rural areas, where data collection and health monitoring can be challenging, AI offers a crucial advantage by providing actionable insights from limited and disparate data sources. One notable application is the use of machine learning algorithms to analyze patterns of disease transmission and predict future outbreaks. These models can forecast the likelihood of disease spread based on current trends and historical data, enabling timely intervention and preparedness. For instance, AI has been used to predict flu outbreaks by analyzing historical flu data combined with social media trends and environmental factors. Moreover, AI-driven predictive analytics facilitate more efficient allocation of healthcare resources by forecasting demand for medical supplies and personnel. This is particularly valuable in rural settings where healthcare infrastructure is often sparse. By predicting areas at high risk for outbreaks, AI helps prioritize interventions and deploy resources where they are needed most. However, the application of AI in rural epidemic management faces challenges, including data quality issues, the need for robust local data infrastructure, and ensuring equitable access to technological advancements. Addressing these challenges is crucial for maximizing the impact of AI in improving epidemic preparedness and response in rural populations. In conclusion, AI in predictive analytics holds significant promise for enhancing epidemic management in rural areas by providing timely, data-driven insights that improve forecasting and resource allocation. Future advancements in AI and improvements in data infrastructure will further strengthen these capabilities, ultimately leading to better health outcomes in underserved communities.
Keywords:
AI; Predictive Analytics; Epidemic Outbreak; Rural; Populations
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