Estimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data
| Autor: | Vinicius V. L. Albani, Roberto Velho, Jorge P. Zubelli |
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| Rok vydání: | 2020 |
| Předmět: |
Coronavirus disease 2019 (COVID-19)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Science Age and sex 01 natural sciences Article 03 medical and health sciences 0302 clinical medicine Spatio-Temporal Analysis Statistics Range (statistics) Humans 030212 general & internal medicine 0101 mathematics Epidemics Multidisciplinary Nonlinear phenomena Models Statistical SARS-CoV-2 010102 general mathematics COVID-19 Census Applied mathematics Infection rate Geography Epidemiological Monitoring Medicine New York City Forecasting |
| Zdroj: | Scientific Reports Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021) |
| ISSN: | 2045-2322 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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