ArticleBiology2020
On a Coupled Time-Dependent SIR Models Fitting with New York and New-Jersey States COVID-19 Data.
Article in Biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A generalised framework for building complex networks of evolution equations.Journal of mathematical biology · 2026Article
- A data-driven approach to study temporal characteristics of COVID-19 infection and death Time Series for twelve countries across six continents.BMC medical research methodology · 2025Article
- Heterogeneous risk tolerance, in-groups, and epidemic waves.Frontiers in applied mathematics and statistics · 2024Article
- Article
- Dynamics of a fractional order mathematical model for COVID-19 epidemic transmission.Physica A · 2023Article
- Complex network model for COVID-19: Human behavior, pseudo-periodic solutions and multiple epidemic waves.Journal of mathematical analysis and applications · 2022Article
- Prediction and Analysis of Tourist Management Strategy Based on the SEIR Model during the COVID-19 Period.International journal of environmental research and public health · 2021Article
- Article
- A simple, SIR-like but individual-based epidemic model: Application in comparison of COVID-19 in New York City and Wuhan.Results in physics · 2021Article
- The hidden Markov chain modelling of the COVID-19 spreading using Moroccan dataset.Data in brief · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of March 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor.
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Registered trials
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.