GEOSPATIAL ANALYSIS, AND ARTIFICIAL INTELLIGENCE MODELLING OF THE DISTRIBUTION OF LASSA FEVER AND PATTERN OF SPREAD WITHIN NIGERIA , ABUJA (CASE STUDY). Lassa fever, named after the town in Nigeria where it was first identified in 1969, is an acute viral hemorrhagic illness caused by the Lassa virus. It belongs to the Arenaviridae family and is primarily transmitted to humans through contact with food or household items contaminated with urine or feces of infected rodents, particularly the multimammate rat (Mastomys natalensis). The virus is endemic in parts of West Africa, including Nigeria, Liberia, Sierra Leone, and Guinea, with sporadic outbreaks occurring mainly during the dry season when rodents seek shelter indoors. Lassa fever presents a significant public health challenge due to its potential for causing severe illness and fatalities. While many individuals infected with the virus remain asymptomatic or experience only mild symptoms, others develop more severe manifestations, including hemorrhagic fever and multi-organ failure. The case fatality rate of Lassa fever can reach up to 50% in hospitalized patients with severe complications, making it a serious threat to human health, particularly in resource-limited settings where healthcare infrastructure and diagnostic capabilities may be inadequate. Efforts to control Lassa fever have been hampered by various factors, including the complex ecology of the virus, limited understanding of its transmission dynamics, and challenges in implementing effective prevention and control measures. Additionally, socio-economic factors such as poverty, poor sanitation, and inadequate housing contribute to the persistence of the disease in affected communities. Furthermore, the lack of specific antiviral treatment and a widely available vaccine adds to the difficulty in managing outbreaks and reducing disease burden. Given the dynamic nature of Lassa fever transmission and the potential for outbreaks to spread across borders, there is an urgent need for comprehensive and innovative approaches to understanding and mitigating its impact. Geographic Information Systems (GIS) offer a valuable tool for analyzing spatial data and mapping disease spread patterns, while Artificial intelligence use machine learning and deep learning to analyze data and predict the likely hood of Lassa fever outbreaks It is an innovative approach to public health surveillance for outbreak detection, in difficult situations where traditional surveillance techniques may be inadequate, nonexistent, or compromised, AI using open-source data can provide epidemic intelligence to inform infectious disease control by identifying early warning signals of disease outbreaks. EPIWATCH is an AI-driven outbreak early-detection and monitoring system that has been shown to provide early signals of epidemics prior to official detection by health authorities. An innovative approach to public health surveillance is the use of artificial intelligence (AI) for outbreak detection. In difficult situations where traditional surveillance techniques may be inadequate, nonexistent, or compromised, AI using open-source data can provide epidemic intelligence to inform infectious disease control by identifying early warning signals of disease outbreaks. EPIWATCH is an AI-driven outbreak early-detection and monitoring system that has been shown to provide early signals of epidemics prior to official detection by health authorities. By combining these approaches and conducting comparative analysis across different States in the Nigeria and FCT Abuja, insights into the drivers of Lassa fever spread can be gained and targeted interventions developed to prevent and control outbreaks effectively. This proposed research has the potential to make significant contributions to public health efforts in combating Lassa fever in Nigeria and West Africa at large. By providing policymakers and public health authorities with actionable insights and evidence-based recommendations, this study can enhance the effectiveness of prevention and control strategies. Ultimately, the outcomes of this research could lead to a reduction in Lassa fever incidence and its associated morbidity and mortality. Also to assess the effectiveness of existing control measures and propose targeted interventions based on the analysis, and also provide recommendations for policymakers and public health authorities to enhance Lassa fever prevention and control strategies.