Stochastic Models and Estimation of Undetected Infections in the Transmission of Zika Virus

TL;DR

This paper develops an enhanced SEIR model incorporating undetected Zika infections and uses Kalman filtering to estimate hidden states, improving understanding of disease transmission dynamics.

Contribution

It introduces a novel SEIR model that accounts for undetected infections and applies Kalman filtering for state estimation in Zika transmission modeling.

Findings

Model captures the impact of undetected infections on spread

Kalman filter effectively estimates hidden states

Provides insights for public health decision-making

Abstract

Zika fever, a mosquito-borne viral disease with potential severe neurological complications and birth defects, remains a significant public health concern. The epidemiological models often oversimplify the dynamics of Zika transmission by assuming immediate detection of all infected cases. This study provides an enhanced SEIR (Susceptible-Exposed-Infectious-Recovered) model to incorporate partial information by distinguishing between detected and undetected Zika infections (also known as "dark figures"). By distinguishing the compartments, the model captures the complexities of disease spread by accounting for uncertainties about transmission and the number of undetected infections. This model implements the Kalman filter technique to estimate the hidden states from the observed states. Numerical simulations were performed to understand the dynamics of Zika transmission and real-world data was utilized for parameterization and validation of the model. The study aims to provide information on the impact of undetected Zika infections on disease spread within the population, which will contribute to evidence-based decision making in public health policy and practice.