Using dimension reduction to improve outbreak predictability of multistrain diseases.

Area of Science:

• Epidemiology
• Mathematical Biology
• Infectious Disease Dynamics

Background:

• Multistrain diseases involve coexisting serotypes, complicating epidemiological modeling.
• Antibody-dependent enhancement (ADE) can increase viral load and infectivity upon secondary infection with a different serotype, as seen in dengue fever.

Purpose of the Study:

• To develop and analyze a dynamic compartmental model for multistrain diseases exhibiting ADE.
• To simplify the complex dynamics of ADE using mathematical techniques.
• To explain observed synchrony in disease progression and predict asymptomatic infections.

Main Methods:

• Development of a dynamic compartmental model for multiple serotypes with ADE.
• Application of center manifold techniques to reduce model dimensionality.
• Numerical analysis of both deterministic and stochastic model versions.
• Investigation of model applicability to noisy systems and cross-immunity.

Main Results:

• The complex multistrain dynamics rapidly collapse to a lower-dimensional system.
• The reduced model successfully explains previously observed synchrony between primary and secondary infectives.
• Center manifold equations remain valid in noisy systems.
• Both model versions predict asymptomatic individuals, crucial for epidemic tracking.

Conclusions:

• Center manifold techniques provide a powerful tool for analyzing and simplifying multistrain disease dynamics with ADE.
• The model offers valuable insights into disease synchrony and the prediction of asymptomatic cases.
• This approach is potentially applicable to other multistrain diseases, including those with cross-immunity.