  The emergence of novel infectious agents presents challenges to statistical
models of disease transmission. These challenges arise from limited,
poor-quality data and an incomplete understanding of the agent. Moreover,
outbreaks manifest differently across regions due to various factors, making it
imperative for models to factor in regional specifics. In this work, we offer a
model that effectively utilizes constrained data resources to estimate disease
transmission rates at the local level, especially during the early outbreak
phase when primarily infection counts and aggregated local characteristics are
accessible. This model merges a pathogen transmission methodology based on
daily infection numbers with regression techniques, drawing correlations
between disease transmission and local-area factors, such as demographics,
health policies, behavior, and even climate, to estimate and forecast daily
infections. We incorporate the quasi-score method and an error term to navigate
potential data concerns and mistaken assumptions. Additionally, we introduce an
online estimator that facilitates real-time data updates, complemented by an
iterative algorithm for parameter estimation. This approach facilitates
real-time analysis of disease transmission when data quality is suboptimal and
knowledge of the infectious pathogen is limited. It is particularly useful in
the early stages of outbreaks, providing support for local decision-making.
