covid-sim
/home/abuild/rpmbuild/BUILD/covid-sim-0.14.0/docs/model-overview.md
1 # Overview of the model
2 
3 This is WIP. Anything wrong or missing? Please improve and open a PR!
4 
5 ## Basic conceptual structure
6 
7 ### Geographical space is divided into cells
8 
9 This is a spatial model. We divide a country into [cells](./model-glossary.md#Cells)
10 and [microcells](./model-glossary.md#Microcells) (9x9 microcells to a cell)
11 which are geolocated.
12 
13 ### People live in households located in cells
14 
15 [People](./model-glossary.md#people) are allocated according to population
16 density data (from input files) to cells. People have an age, and other
17 attributes. People's residence location does not change, but they interact with
18 people in other cells via places (see below) and via random social interactions
19 governed by a spatial kernel function.
20 
21 ### People are associated with different civil institutions
22 
23 People are assigned to [places](./model-glossary.md#Places) (institutions such
24 as households, offices, schools etc.) that have a geographical location.
25 [Place groups](./model-glossary.md#Places) which divides places into compartments
26 (the intent here is that you're less likely to be infected by someone in the same
27 office but who works on a different floor).
28 
29 People don't move. Instead the simulation employs spatial mixing probability
30 distributions (spatial kernels) that control the probability that people in cell
31 X will infect people in cell Y located in another spatial region.
32 
33 ### Infections spread between people
34 
35 Infections may be initially seeded in different ways. The simplest way is to
36 seed according to population density (but seeds can be from specific places,
37 or randomly etc.)
38 
39 `InfectSweep` is the main function where infections spread. It loops over people
40 and transmits infections by calculating a [FOI](./model-glossary.md#FOI)
41 (force of infection). Infection-spreading is divided into 3 transmission
42 mechanisms:
43 
44 - household infections (e.g. between family members)
45 - place infections (e.g. at work)
46 - spatial infections (e.g. when travelling around)
47 
48 Spatial infection models contacts between individuals which have a frequency
49 which depends upon the distance between home locations (to avoid literally
50 moving people around cells), modelled using a kernel function that weights
51 according to both spatial distance and population densities.
52 
53 ## Further information
54 
55 For more information on the model and associated interventions, please visit:
56 
57 - <https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf>
58 - <https://www.nature.com/articles/nature04795>
59 - <https://www.nature.com/articles/nature04017>
60 - <https://www.pnas.org/content/105/12/4639.short>
61 
62 Please note that some of the above may require a subscription.