ArticleScientific reports2021
Estimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics.Nature communications · 2023Article
- Real-time estimation and forecasting of COVID-19 cases and hospitalizations in Wisconsin HERC regions for public health decision making processes.BMC public health · 2023Article
- Article
- On the role of financial support programs in mitigating the SARS-CoV-2 spread in Brazil.BMC public health · 2022Article
- Near real time monitoring and forecasting for COVID-19 situational awareness.Applied geography (Sevenoaks, England) · 2022Article
- From pandemic to a new normal: Strategies to optimise governmental interventions in Indonesia based on an SVEIQHR-type mathematical model.Infectious Disease Modelling · 2022Article
- Nowcasting and forecasting COVID-19 waves: the recursive and stochastic nature of transmission.Royal Society open science · 2022Article
- Modelling the persistence of Covid-19 positivity rate in Italy.Socio-economic planning sciences · 2022Article
- Immunity acquired by a minority active fraction of the population could explain COVID-19 spread in Greater Buenos Aires (June-November 2020).Epidemiology and infection · 2022Article
- COVID-19 underreporting and its impact on vaccination strategies.BMC infectious diseases · 2021Article
- The impact of COVID-19 vaccination delay: A data-driven modeling analysis for Chicago and New York City.Vaccine · 2021Article
- Multi-generational SIR modeling: Determination of parameters, epidemiological forecasting and age-dependent vaccination policies.Infectious Disease Modelling · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.