ArticleApplied mathematics letters2021
Simulating the spread of COVID-19
Article in Applied mathematics letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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Who cites it
56 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
- An integrated framework for modelling respiratory disease transmission and designing surveillance networks using a sentinel index.Royal Society open science · 2025Article
- Quantitative analysis of social influence and digital piracy contagion with differential equations on networks.Scientific reports · 2024Article
- On quasi-linear reaction diffusion systems arising from compartmental SEIR models.Nonlinear differential equations and applications : NoDEA · 2024Article
- Heterogeneous risk tolerance, in-groups, and epidemic waves.Frontiers in applied mathematics and statistics · 2024Article
- K-Track-Covid: interactive web-based dashboard for analyzing geographical and temporal spread of COVID-19 in South Korea.Frontiers in public health · 2024Article
- A fractional mathematical model with nonlinear partial differential equations for transmission dynamics of severe acute respiratory syndrome coronavirus 2 infection.Healthcare analytics (New York, N.Y.) · 2023Article
- Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic.PLoS computational biology · 2023Article
- A two-phase fluid model for epidemic flow.Infectious Disease Modelling · 2023Article
- Role of immigration and emigration on the spread of COVID-19 in a multipatch environment: a case study of India.Scientific reports · 2023Article
- The Role of Mobility in the Dynamics of the COVID-19 Epidemic in Andalusia.Bulletin of mathematical biology · 2023Article
- Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics.Computers in biology and medicine · 2023Article
- Kinetic Models for Epidemic Dynamics in the Presence of Opinion Polarization.Bulletin of mathematical biology · 2023Article
- Article
- Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil.JMIR public health and surveillance · 2023Article
- Dynamical analysis of a stochastic delayed epidemic model with lévy jumps and regime switching.Journal of the Franklin Institute · 2023Article
- A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China.PloS one · 2023Article
- Determining the rate of infectious disease testing through contagion potential.PLOS global public health · 2023Article
- A discrete model for the evaluation of public policies: The case of Colombia during the COVID-19 pandemic.PloS one · 2023Article
- Article
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9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present an early version of a Susceptible-Exposed-Infected-Recovered-Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatio-temporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features. To test the model, we compare the outputs generated by a finite-element solver with measured data over the Italian region of Lombardy, which has been heavily impacted by this crisis between February and April 2020. Our results show a strong qualitative agreement between the simulated forecast of the spatio-temporal COVID-19 spread in Lombardy and epidemiological data collected at the municipality level. Additional simulations exploring alternative scenarios for the relaxation of lockdown restrictions suggest that reopening strategies should account for local population densities and the specific dynamics of the contagion. Thus, we argue that data-driven simulations of our model could ultimately inform health authorities to design effective pandemic-arresting measures and anticipate the geographical allocation of crucial medical resources.
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