Estimating, Monitoring, and Forecasting the Covid-19 Epidemics: A Spatio-Temporal Approach Applied to NYC Data

TL;DR

This paper introduces a detailed spatio-temporal SEIR-type model for Covid-19 that incorporates age, gender, and location data, calibrated with NYC data to accurately simulate and forecast epidemic dynamics.

Contribution

It presents a novel multi-group, multi-location SEIR model with dynamic infection rates, calibrated specifically for NYC, enhancing epidemic monitoring and prediction capabilities.

Findings

Model accurately fits NYC infection curves

Predicts infection trends across boroughs and age groups

Provides detailed epidemic monitoring tools

Abstract

We propose an SEIR-type meta-population model to simulate and monitor the Covid-19 epidemic evolution. The basic model consists of seven compartments, namely susceptible (S), exposed (E), three infective classes, recovered (R), and deceased (D). We define these compartments for n age and gender groups in m different spatial locations. So, the resulting model has, for each age group, gender, and place, all epidemiological classes. The mixing between them is accomplished by means of time-dependent infection rate matrices. The model is calibrated with the curve of daily new infections in New York City and its boroughs, including census data, and the proportions of infections, hospitalizations, and deaths for each age range. We end up with a model that matches the reported curves and predicts accurately infection information for different places and age classes.