Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Quantitative Biology > Populations and Evolution

arXiv:2003.13571 (q-bio)
COVID-19 e-print

Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field.

[Submitted on 30 Mar 2020 (v1), last revised 10 Sep 2020 (this version, v5)]

Title:Solvable delay model for epidemic spreading: the case of Covid-19 in Italy

Authors:Luca Dell'Anna
View PDF
Abstract:We study a simple realistic model for describing the diffusion of an infectious disease on a population of individuals. The dynamics is governed by a single functional delay differential equation, which, in the case of a large population, can be solved exactly, even in the presence of a time-dependent infection rate. This delay model has a higher degree of accuracy than that of the so-called SIR model, commonly used in epidemiology, which, instead, is formulated in terms of ordinary differential equations. We apply this model to describe the outbreak of the new infectious disease, Covid-19, in Italy, taking into account the containment measures implemented by the government in order to mitigate the spreading of the virus and the social costs for the population.
Comments: 11 pages, 6 figures, to appear in Scientific Reports
Subjects: Populations and Evolution (q-bio.PE); Statistical Mechanics (cond-mat.stat-mech); Physics and Society (physics.soc-ph)
Cite as: arXiv:2003.13571 [q-bio.PE]
  (or arXiv:2003.13571v5 [q-bio.PE] for this version)
  https://doi.org/10.48550/arXiv.2003.13571
Journal reference: Sci. Rep. 10, 15763 (2020)
Related DOI: https://doi.org/10.1038/s41598-020-72529-y

Submission history

From: Luca Dell'Anna [view email]
[v1] Mon, 30 Mar 2020 15:48:39 UTC (46 KB)
[v2] Mon, 6 Apr 2020 10:59:43 UTC (54 KB)
[v3] Mon, 20 Apr 2020 13:57:03 UTC (57 KB)
[v4] Tue, 28 Apr 2020 17:54:15 UTC (67 KB)
[v5] Thu, 10 Sep 2020 17:34:06 UTC (124 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
q-bio.PE
< prev   |   next >
new | recent | 2020-03
Change to browse by:
cond-mat
cond-mat.stat-mech
physics
physics.soc-ph
q-bio

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)