Relevant mathematics to the recent coronavirus outbreak I would like to ask about (old* and new) reliable mathematical literature relevant to various mathematical aspects of the recent coronavirus outbreak: In particular, standard statistical/mathematical models that are used to predict the spread, mathematical studies of effectiveness of various strategies, etc. 
*(Added) By old I also mean well-established models.
 A: The following paper is a little strange, since it dates back to 2015, but has some valuable data:

*

*A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence, Nature Medicine, 2015.

A: The book by Gábor Csárdi, Tamás Nepusz,  Edoardo Airoldi, 
Statistical Network Analysis with igraph
Based around popular software library igraph, Wikipedia link
contains whole chapter with source codes (in R) on Epidemics on networks in particular  6.5 Vaccination strategies
Let me quote the content of the chapter:
6 Epidemics on networks
6.2  Branching processes
6.3  Compartmental models on homogeneous populations
6.3.1  The susceptible-infected-recovered (SIR) model
6.3.2  The susceptible-infected-susceptible (SIS) model
6.3.3  The susceptible-infected-recovered-susceptible (SIRS)model
6.4  Compartmental models on networks
6.4.1  A general framework for compartmental models onnetworks
6.4.2  Epidemics on regular and geometric networks . . . . . . . . 180
6.4.3  Epidemics on scale-free networks . . . . . . . . . . . . . . . . . . . 187
6.5  Vaccination strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

A: Here is a recent paper written by mathematicians: Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China.
A: Recently found this :
https://staff.math.su.se/tom.britton/
Maybe relevant.
A: The following paper is extremely important because it has informed the decisions of the UK government that realised (announced) on Monday 16/03/2020 that it can not afford "Herd immunity". The paper only shows the outcomes of the model and speaks about its parameters. It would of course be extremely interesting to know what exactly is the mathematics behind it. Mathematicians should try to read it.
https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf?fbclid=IwAR2Ca5Ki23DWn-EGWeB3yaNE4f9GmnUcEWU_S60lsDC230AKUg4v_w82qeE
A: There is the whole discipline of math models of epidemics.
See, for example, Fitzgibbon, William E.(1-HST); Morgan, Jeffery J.(1-HST); Webb, Glenn F.(1-VDB); Wu, Yixiang(1-VDB)
Spatial models of vector-host epidemics with directed movement of vectors over long distances. (English summary)
Math. Biosci. 312 (2019), 77–87
and the references there.
A: Maybe relevant:

Yu Chen, Jin Cheng, Yu Jiang, Keji Liu, A Time Delay Dynamical Model for Outbreak of 2019-nCoV and the Parameter Identification, https://arxiv.org/abs/2002.00418

A: Volume 12 of What's Happening in the Mathematical Sciences by Dana Mackenzie has three chapters devoted to mathematical modeling related to the coronavirus pandemic.

 Fifty Ways to Beat a Virus (Part 1)
In 2020, the world for the first time in a century confronted
a global pandemic that would claim millions of lives. While
students were sent home and campuses closed, many mathematicians found an opportunity to join the fight against
COVID-19. Even simple differential equation models can teach
us important lessons about the exponential growth of a new
epidemic and the importance of threshold behavior. Using
more elaborate (and realistic) models, two mathematical
modeling groups, in Texas and Illinois, had a profound and
positive effect on the management of the epidemic by local and
state authorities during the first waves.

Fifty Ways to Beat a Virus (Part 2)
Continuing the previous chapter, Part 2 discusses the problems
confronted by mathematicians and epidemiologists in the later
part of 2020 and in early 2021. How could universities re-open
safely? How does the uncontrolled spread of an epidemic in
prisons affect the surrounding community, and what can be
done about it? And the biggest question: could vaccination
bring the epidemic under control? Even though the coronavirus
kept throwing surprises at us, mathematicians did a surprisingly good job of developing strategies, giving realistic answers
and highlighting the main reasons for uncertainty.

Fifty Ways to Beat a Virus (Part 3)
Another important front in the battle against COVID-19 was
to understand how the infection progresses within the human
body. Why do some people have life-threatening symptoms,
while others have none at all? An online group called the
“Immune Gals” highlighted the delayed release of interferon
as a characteristic marker of severe cases. Another mathematician used the tools of graph theory to identify parts of the
viral RNA that are especially vulnerable to attack by drugs or
gene therapy. And a third group adapted a machine-learning
language model to detect escape variants of coronavirus. In
effect, they taught the computer to “speak virus.”

