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Development

Development is currently being made on the kzscisoft-dev branch. The following has been altered in the code forked from LSHTM:

• Parameters have been identified and extracted into external files which are read into the model, these can be found in configuration/parameters.ini. Most parameters are applied as factors to a vector which is the same length as the number of age bins.

• Dockerfile for easy running across all systems.

• Settings file where other options which do not fall under the "parameters" category are set.

• Command line arguments for pointing model to locations such as the main root directory, the parameters file, the contact matrices file etc.

Latest Publication

The latest publication can be found here.

Data Sources

Through examination of the contained files within the repository the following data sources have been identified:

File	Description	Source
covidm/data/structure_UK.rds	Dataframe is created from the UK Mid Year Estimates 2019 (2020 LAD Codes) spreadsheet (sheet: 'MYE2-Persons'), Office for National Statistics	.xls
covidm/data/wpp2019_pop2020.rds	World population data taken from the World Population Prospects 2019	Female .xlsx Male .xlsx
data/Global_Mobility_Report.csv	Mobility of Populations in Different Countries*	.csv
uk_survey	Social Contact Data POLYMOD social contact data K. Auranen et. al.	dataset
MUestimates_all_locations_[1,2].xlsx	Contact Matrices for 152 countries Projecting social contact matrices in 152 countries using contact surveys and demographic data A. R. Cook et. al.	datasets

* Only appears to be used in plotting script

Parameters

Actual confirmed and "usable" parameters can be identified within the configuration/parameters.ini file on the kzscisoft-dev branch of this repository.

Issues

• The amount of time a given individual spends in states , , , or is drawn from distributions , , or , respectively. However the code refers to variables dE, dIp, dIa and dIs. dIa is unknown and not described in the paper, also the definition of these variables appears to not match the paper.

• High probability that paper does not describe the current state of the model.

Addendum - 28/5/20

• dIa corresponds to dIs from the pre-print. dIs corresponds to dIc from the pre-print. The model is coded as in the pre-print if those two substitutions are made.

Parameter Table

Parameter	Description	Value	Source	Comments
	Latent period		[2][3][4]	Stated in Table S1 in Paper [1]
	Pre-Clinical Infectiousness Duration		[5]	Stated in Table S1 in Paper [1]
	Clinical Infectiousness Duration		[2][3][4]	Stated in Table S1 in Paper [1]
	Subclinical Infectiousness Duration		[1]	"Assumed to be the same duration as total infectious period for clinical cases, including preclinical transmission"
	Hospitalization	1	-	Hospitalization Ignored
-	Incubation period		[1]	Derived
	Relative infectiousness of subclinical cases	50%	[1]	Assumed
	UK Contact Matrices	covidm/data/all_matrices.rds	[6]	Number of age-j individuals contacted by age-i individual per day
	Number of age-i individuals	-	See above	Demographic data (Office for National Statistics)
-	Proportion of hospitalised cases requiring critical care	30%	[7]	Stated in Table S1 in Paper [1]
	Serial Interval	6.5 days	[2][3][4]	Derived Stated in Table S1 in Paper [1]
-	Delay from onset to hospitalization		[7][8]	Stated in Table S1 of Paper [1]
-	Duration of hospitalization		[7]	Stated in Table S1 of Paper [1]
-	Proportion of hospitalized cases requiring critical care	30%	[7]	Stated in Table S1 of Paper [1]
-	Delay from onset to death		[7][8]	Stated in Table S1 of Paper [1]

Binning

Bin age in bins of 5 years up to 84 (e.g. 0-4,5-9 etc) then rest fall in bin of 85+.

Appendices

UK Parameters

Existing data is loaded and assembled when building the variable UKParameters1 which contains:

type           : chr "SEI3R"
  dE             : num [1:241] 9.21e-06 6.02e-04 3.27e-03 8.38e-03 1.54e-02 ...
  dIp            : num [1:241] 0.000395 0.018594 0.069279 0.119197 0.145304 ...
  dIa            : num [1:241] 3.85e-06 2.62e-04 1.49e-03 4.00e-03 7.71e-03 ...
  dIs            : num [1:241] 1.55e-05 9.85e-04 5.17e-03 1.28e-02 2.27e-02 ...
  dH             : num 1
  dC             : num 1
  size           : num [1:16] 3914028 4138524 3858894 3669250 4184575 ...
  matrices       :List of 4
  contact        : num [1:4] 1 1 1 1
  contact_mult   : num(0)
  contact_lowerto: num(0)
  u              : num [1:16] 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 ...
  y              : num [1:16] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
  fIp            : num [1:16] 1 1 1 1 1 1 1 1 1 1 ...
  fIs            : num [1:16] 1 1 1 1 1 1 1 1 1 1 ...
  fIa            : num [1:16] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
  rho            : num [1:16] 1 1 1 1 1 1 1 1 1 1 ...
  tau            : num [1:16] 1 1 1 1 1 1 1 1 1 1 ...
  seed_times     : num 1
  dist_seed_ages : num [1:16] 1 1 1 1 1 1 1 1 1 1 ...
  schedule       : list()
  observer       : NULL
  name           : chr "UK | UNITED KINGDOM"
  group_names    : chr [1:16] "0-4" "5-9" "10-14" "15-19" ...

covidy Parameter

covid_scenario contains age specific clinical fractions as estimated by MCMC. It has the raw MCMC draws, so each row corresponds to a draw from the posterior. To quote from the pre-print: "The age-specific clinical fraction was adopted from an estimate based on case data from 6 countries [11] , and the relative infectiousness of subclinical cases, , was assumed to be 50% relative to clinical cases, as we assumed in a previous study [11] .” This clinical fraction is required for the calculation of the R(0) through the next generation matrix as there are clinical specific parameters.

However, there is an inconsistency in the code as defined. They are both fixing parameters and fixing R(0), this creates an issue, as the parameters fully determine the model R(0) through the greatest eigenvalue of the next generation matrix, so the model functionally has two R(0)s, thus they have to make a correction for this, this is calculated in u_adj with the ratio of sampled R(0) from the normal distribution and the “empirical” R(0) as defined by the parameters. Based on the definitions in the paper, I believe this is an adjustment to individual susceptibility to infection. They then use this adjustment to the individual’s susceptibility to infection to correct things downstream.

Levels

Three levels of population grouping: Level 0:

[1] "UK | UNITED KINGDOM"

Level 1:

[1] "UK | ENGLAND"          "UK | WALES"            "UK | SCOTLAND"         "UK | NORTHERN IRELAND"

Level 2:

UK | NORTH EAST
UK | NORTH WEST
UK | YORKSHIRE AND THE HUMBER
UK | EAST MIDLANDS
UK | WEST MIDLANDS
UK | EAST
UK | LONDON
UK | SOUTH EAST
UK | SOUTH WEST
UK | WALES
UK | SCOTLAND
UK | NORTHERN IRELAND

Level 3 & Level 4 have higher granularity still.

References

1. The effect of non-pharmaceutical interventions on COVID-19 cases, deaths and demand for hospital services in the UK: a modelling study, N. G. Davies et. al, 2020, Table S1, pg. 23

2. Early Transmission Dynamics in Wuhan China, of Novel Coronavirus-Infected Pneumonia, L. Q. Guan et. al., 2020, N Engl J Med. 2020;382: 1199-1207.

3. Epidemiology and Transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts, medRxiv. 2020;2020.03.03.20028423.

4. Serial interval of novel coronavirus (2019-nCoV) infections, medRxiv. 2020; 2020.02.03.20019497

5. The contribution of pre-symptomatic infection to the transmission dynamics of COVID-2019, Liu Y et. al, Wellcome Open Research. 2020;5:58.

6. Social contacts and mixing patterns relevant to the spread of infectious diseases, J. Mossong et. al, PLoS Med. 2008;5 e74.

7. A Trial of Lopinavir-Ritonavir in Adults Hospitalized with Sever Covid-19, B. Cao et. al, N Engl H Med. 2020. doi:10.1056/NEJMoa2001282.

8. Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis Of Publicly Available Case Data, N. M. Linton et. al, J Clin Med Res. 2020;9. doi:10.3390/jcm9020538.