Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Machine Learning

Background:

• Cholera poses a significant threat in developing regions, necessitating accurate transmission pattern forecasting.
• Mechanistic models are vital for understanding disease dynamics, but real-time data integration is key for trend prediction.

Purpose of the Study:

• To provide insights into cholera transmission trends using qualitative dynamics and machine learning-based forecasting.
• To develop and implement epidemic-informed machine learning models for short-term cholera case forecasting in Malawi.

Main Methods:

• Calibrated a mechanistic cholera model using the Monte Carlo Markov Chain approach.
• Performed sensitivity analysis to identify critical disease dynamic parameters.
• Integrated mechanistic cholera dynamics into autoregressive integrated moving average (ARIMA) and autoregressive neural network (ANN) models.

Main Results:

• Identified critical parameters influencing cholera dynamics and assessed stability using bifurcation analysis.
• Hopf bifurcation indicates potential unpredictability in transmission trends with increased disinfection rates.
• Developed and applied epidemic-informed machine learning models for short-term cholera forecasting in Malawi.

Conclusions:

• Integrating temporal dynamics into machine learning models significantly enhances cholera forecasting capabilities.
• This methodology offers a replicable and adaptable framework for policymakers to manage and respond to cholera outbreaks.
• The study encourages further research into disease dynamics and forecasting models.