Background:
Emerging infectious diseases (EIDs) pose a significant global threat, with human-driven biodiversity loss increasingly recognized as a pivotal factor. Recent environmental disruptions are shaking up wildlife populations, altering host-pathogen interactions, and raising the risk of emerging diseases. However, the root causes linking biodiversity loss to EID emergence remain poorly understood. This research investigates the relationship between biodiversity loss, climate change, and infectious disease outbreaks using statistical methods, data analytical tools, and machine learning (ML) algorithms, such as survival analysis and Bayesian networks for predictive modeling.
Methods:
A multi-variant approach is proposed, integrating epidemiological data, bio-ecological modeling, and statistical data analyses. Biodiversity and disease incidence data are obtained from open-source databases such as Global Biodiversity Information Facility (GBIF) and Centers for Disease Control and Prevention (CDC). Survival analysis methods like Kaplan-Meier and Cox models assess the timing of disease outbreaks in the regions of human-driven biodiversity loss. Bayesian networks infer probabilistic relationships between environmental degradation, species richness, and zoonotic transmission. Machine learning algorithms further refine predictive models and computations for high-risk dynamics.
Results:
It is proposed that in the preliminary analyses, a strong correlation between biodiversity loss and increased EID incidence can be indicated, particularly in focused biodiversity regions. Survival models can reveal accelerated disease emergence in areas with rapid species decline. Overall, this research aims to identify high-risk areas that will support the development of an early-warning system.
Conclusion:
The main highlight of this research is to provide quantitative evidence linking biodiversity loss to infectious disease outbreaks, emphasizing the urgent need for integrated conservation and public health strategies. Findings will inform policy interventions to mitigate zoonotic risks and enhance disease surveillance systems in biodiversity-sensitive regions. In short, the proposed study will use a combination of systematic literature reviews, large-scale statistical analysis, and ecological modeling to explore diseases.
