Methods/Statistics
A Unified Multi-State Approach for Investigating the Dynamics of Chronic and Infectious Diseases Ming Ding* Ming Ding UNC Chapel Hill
Infectious diseases and chronic diseases are two major fields in epidemiology that have traditionally been studied separately because of their distinct etiologies and modeling methods. Infectious disease data are typically collected as aggregated data and are modeled using compartmental models, with the most commonly susceptible (S), infectious (I), and recovered (R) (SIR) model. While for chronic disease, data are usually collected as individual-level data and are analyzed using multi-state survival models. Previous studies have pointed out the link between compartmental models and survival analysis by reconstructing the aggregated infection disease data into individual-level data. However, these studies have largely focused on the two-state transition from S to I state, and few studies have simultaneously modeled the three-state process, S, I, and R. In this abstract, we propose to use a discrete-time multi-state framework to model the three-state transition of infectious disease. We first introduce and compare modeling approaches for infectious disease and chronic disease dynamics, then show the link between compartment models and multi-state models, and finally present how infectious disease parameters can be estimated using multi-state models under the two scenarios: 1) all S, I, and R states are observed, and 2) only the I state is observed, with the R state treated as latent. In the application, we applied the multi-state approach to describe the dynamics of influenza using the data in a British boarding school in 1978. The estimated recovery rate was 0.42 and the corresponding contact rate was 0.91 (95% CI:0.84, 0.98). The basic reproductive number was 2.17 (95% CI: 2.00, 2.33), and declined to approximately 1 by day 6, and continued to decrease thereafter, indicating that the turning point reached at around day 6. Overall, we propose a unified multi-state approach for modeling infectious and chronic disease progression, which may provide key evidence to inform timely and effective infectious disease prevention.
