Estimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data

Vinicius V.L. Albani, Roberto M. Velho, Jorge P. Zubelli

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

We propose a susceptible-exposed-infective-recovered-type (SEIR-type) meta-population model to simulate and monitor the (COVID-19) epidemic evolution. The basic model consists of seven categories, namely, susceptible (S), exposed (E), three infective classes, recovered (R), and deceased (D). We define these categories for n age and sex groups in m different spatial locations. Therefore, the resulting model contains all epidemiological classes for each age group, sex, and location. 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 finally obtain a model that matches the reported curves and predicts accurate infection information for different locations and age classes.

Original languageBritish English
Article number9089
JournalScientific Reports
Volume11
Issue number1
DOIs
StatePublished - Dec 2021

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