TY - JOUR
T1 - Estimating, monitoring, and forecasting COVID-19 epidemics
T2 - a spatiotemporal approach applied to NYC data
AU - Albani, Vinicius V.L.
AU - Velho, Roberto M.
AU - Zubelli, Jorge P.
N1 - Funding Information:
JPZ thanks Khalifa University, the Government of Abu Dhabi, and Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) through the program Cientistas do Nosso Estado for the support during the course of this research. JPZ would like to acknowledge very fruitful discussions with Profs. Dimitris Goussis and Leontios Hadjileontiadis (Khalifa University).
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85105065642&partnerID=8YFLogxK
U2 - 10.1038/s41598-021-88281-w
DO - 10.1038/s41598-021-88281-w
M3 - Article
C2 - 33907222
AN - SCOPUS:85105065642
SN - 2045-2322
VL - 11
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 9089
ER -