| Title: | Functions and Data for "Epidemics: Models and Data in R" |
| Version: | 0.6-5 |
| Date: | 2020-01-23 |
| Author: | Ottar N. Bjornstad [aut, cre] |
| Maintainer: | Ottar N. Bjornstad <onb1@psu.edu> |
| Description: | Functions, data sets and shiny apps for "Epidemics: Models and Data in R" by Ottar N. Bjornstad (ISBN 978-3-319-97487-3) https://www.springer.com/gp/book/9783319974866. The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course https://www.coursera.org/learn/epidemics. |
| Depends: | R (≥ 3.3.2), shiny, deSolve, polspline |
| Suggests: | ade4, bbmle, fields, forecast, imputeTS, lme4, ncf, nleqslv, nlme, nlts, plotrix, pomp, rootSolve, Rwave, statnet, |
| License: | GPL-3 |
| URL: | https://github.com/objornstad/epimdr, https://www.springer.com/gp/book/9783319974866, http://ento.psu.edu/directory/onb1 |
| BugReports: | https://github.com/objornstad/epimdr/issues |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2020-01-24 23:32:09 UTC; onb1 |
| Repository: | CRAN |
| Date/Publication: | 2020-01-25 05:30:02 UTC |
Function to generate a Barabasi-Albert network
Description
Function to generate a Barabasi-Albert network
Usage
BarabasiAlbert(N, K)
Arguments
N |
the number of nodes |
K |
the number of neighbors to which each node is connected so degree = 2*K |
Value
An object of class CM (contact matrix)
Examples
cm3=BarabasiAlbert(200, 4)
Monthly incidence of influenza-like illness in Iceland between 1980 and 2009.
Description
A dataset containing the monthly ILI incidence in Iceland between 1980 and 2009.
Usage
Icelandflu
Format
A data frame with 360 rows and 3 variables:
- month
the month
- year
the year
- ili
ILI incidence
Source
Bjornstad ON, Viboud C. Timing and periodicity of influenza epidemics. Proceedings of the National Academy of Sciences. 2016 Nov 15;113(46):12899-901. doi.org/10.1073/pnas.1616052113.
Launch a shiny-app simulating May's Parasitoid-host Model model
Description
Launch a shiny-app simulating May's Parasitoid-host Model model
Usage
May.app
Format
An object of class shiny.appobj of length 5.
Details
Launch app for details
Examples
## Not run: May.app
The Nicholson-Bailey model
Description
Function to simulate the Nicholson-Bailey Parasitoid-host model
Usage
NB(R, a, T = 100, H0 = 10, P0 = 1)
Arguments
R |
the host reproductive rate |
a |
the parasitoid search efficiency |
T |
the length of simulation (number of time-steps) |
H0 |
initial host numbers |
P0 |
initial parasitoid numbers |
Value
A list of simulated Host and Parasitoid numbers
Examples
sim= NB(R=1.1,a=0.1)
Function to simulate an epidemic on a network
Description
Function to simulate a stochastic (discrete time) Reed-Frost SIR model on a social network
Usage
NetworkSIR(CM, tau, gamma)
Arguments
CM |
a contact matrix |
tau |
the transmission probability |
gamma |
the recovery probability |
Value
An object of class netSIR with infectious status for each node through time
Examples
cm1=BarabasiAlbert(N=200,K=2)
sim1=NetworkSIR(cm1,.3,0.1)
summary(sim1)
## Not run: plot(sim1)
Launch a shiny-app simulating the seasonal SEIR model
Description
Launch a shiny-app simulating the seasonal SEIR model
Usage
SEIR.app
Format
An object of class shiny.appobj of length 5.
Details
Launch app for details
Examples
## Not run: SEIR.app
Launch a shiny-app simulating the SEIRS model
Description
Launch a shiny-app simulating the SEIRS model
Usage
SEIRS.app
Format
An object of class shiny.appobj of length 5.
Details
Launch app for details
Examples
## Not run: SEIRS.app
Daily measures of malaria infected mice.
Description
Daily data on laboratory mice infected with various strains of Plasmodium chaudaudi
Usage
SH9
Format
A data frame with 1300 rows and 11 variables:
- Line
line number
- Day
day of infection
- Box
Cage number
- Mouse
Mouse identifier
- Treatment
Plasmodium strain
- Ind2
Unique mouse identifier
- Weight
Mouse weight
- Glucose
Blood glucose level
- RBC
Red blood cell count
- Sample
Sample number
- Para
Parasite count
Source
Sylvie Huijben
Launch a shiny-app simulating the SIR model
Description
Launch a shiny-app simulating the SIR model
Usage
SIR.app
Format
An object of class shiny.appobj of length 5.
Details
Launch app for details
Examples
## Not run: SIR.app
Function to simulate the stochastic TSIR
Description
Function to simulate the stochastic TSIR assuming stochasticity in transmission and a Poisson birth-death process
Usage
SimTsir(alpha = 0.97, B = 2300, beta = 25, sdbeta = 0, S0 = 0.06,
I0 = 180, IT = 520, N = 3300000)
Arguments
alpha |
the exponent on I |
B |
the birth rate |
beta |
the transmission rate |
sdbeta |
the standard deviation on beta |
S0 |
the initial susceptible fraction |
I0 |
the initial number of infecteds |
IT |
the length of simulation |
N |
the population size |
Value
A list with time series of simulated infected and susceptible hosts
Examples
out = SimTsir()
Function to simulate the seasonally-forced TSIR
Description
Function to simulate the stochastic TSIR assuming stochasticity in transmission and a Poisson birth-death process
Usage
SimTsir2(beta, alpha, B, N, inits = list(Snull = 0, Inull = 0),
type = "det")
Arguments
beta |
the seasonal transmission coefficients |
alpha |
the exponent on I |
B |
a vector of Births (the length of which determines the length of the simulation) |
N |
the population size |
inits |
a list containing initial S and I |
type |
an argument "det" or "stoc" that determines whether a deterministic or stochastic simulation is done |
Value
A list with time series of simulated infected and susceptible hosts
Examples
## Not run: see chapter 8 in book
Launch a shiny-app simulating TSIR model
Description
Launch a shiny-app simulating TSIR model
Usage
TSIR.app
Format
An object of class shiny.appobj of length 5.
Details
Launch app for details
Examples
## Not run: TSIR.app
Function to calculate the local Lyapunov exponents for the TSIR
Description
Function to calculate the local Lyapunov exponents from an object of class lyap.
Usage
TSIRllyap(x, m = 1)
Arguments
x |
an object of class |
m |
number of forward iterations on the attractor |
Value
An object of class llyap with the local Lyapunov exponent and S-I data
Examples
## Not run: see chapter 10 in book
Function to do Lyapunov exponent calculations from a TSIR simulation
Description
Function to do Lyapunov exponent calculations from a TSIR simulation
Usage
TSIRlyap(I, S, alpha, bt, N)
Arguments
I |
a vector containing the time series of Is |
S |
vector containing the time series of Ss |
alpha |
the exponent on I |
bt |
the seasonal transmission coefficients |
N |
the population size |
Value
An object of class lyap with the lyapunov exponent, values for the Jacobians, parameters and data
Examples
## Not run: see chapter 10 in book
Function to generate a Watts-Strogats network
Description
Function to generate a Watts-Strogats network
Usage
WattsStrogatz(N, K, Prw)
Arguments
N |
the number of nodes |
K |
the number of neighbors to which each node is connected so degree = 2*K |
Prw |
the rewiring probability |
Value
An object of class CM (contact matrix)
Examples
cm2=WattsStrogatz(N=20, K=4, Prw=.3)
Black's measles seroprevalence data.
Description
Seroprevalence-by-age-bracket for measles in prevaccination New Haven as studied by Black (1959).
Usage
black
Format
A data frame with 42 rows and 3 variables:
- age
age-bracket (in years)
- mid
mid-point of age-bracket (in years)
- n
number of tests
- pos
number seropositive
- neg
number seronegative
- f
seroprevalence
Source
Black (1959) Measles antibodies in the population of New Haven, Connecticut. Journal of Immunology 83:74-83
Burnett's Parasitoid-Host data.
Description
Data is of 22 generations of greenhouse white flies (Trialeurodes vaporariorum) and its parasitoid, Encarsia formosa. Column names are self explanatory.
Usage
burnett
Format
A data frame with 22 rows and 7 variables:
- Generation
- NumberofHostsExposed
- NumberofHostsParasitized
- NumberofHostsUnparasitized
- NumberofParasiteEggsLaid
- NumberofParasitesSearching
- PercentageofHostsParasitized
Source
Burnett, T. A. (1958) Model of host-parasite interaction Proceedings of the 10th International Congress, Entomology, 1958, 2, 679-686
UK measles CCS data.
Description
The fraction of weeks measles was absent from each of the 954 cities and towns of England and Wales between 1944 and 1965.
Usage
ccs
Format
A data frame with 954 rows and 14 variables:
- fade3
Average duration of fadeout (of at least 3 weeks of length)
- ext
Fraction of time when measles was absent
- size
Median population size
- fade
Average duration of fadeouts (of a week or longer)
- se3
Standard error fade3
- se
Standard error of fade
- n3
The number of fadeouts (of at least 3 weeks of length)
- n
The number of fadeout of a week or longer
- names
City/town name
Source
Bjornstad and Grenfell (2008) Hazards, spatial transmission and timing of outbreaks in epidemic metapopulations. Environmental and Ecological Statistics 15: 265-277. doi:10.1007/s10651-007-0059-3.
Gradient-function for the chain-SIR model
Description
Gradient-function for the chain-SIR model
Usage
chainSIR(t, logx, params)
Arguments
t |
Implicit argument for time |
logx |
A vector with values for the log-states |
params |
A vector with parameter values for the chain-SIR system |
Value
A list of gradients
Examples
require(deSolve)
times = seq(0, 10, by=1/52)
paras2 = c(mu = 1/75, N = 1, beta = 625, gamma = 365/14, u=5)
xstart2 = log(c(S=.06, I=c(0.001, rep(0.0001, paras2["u"]-1)), R = 0.0001))
out = as.data.frame(ode(xstart2, times, chainSIR, paras2))
Dacca cholera death data.
Description
Monthly deaths from cholera in Dacca, East Bengal between 1891 and 1940.
Usage
cholera
Format
A data frame with 600 rows and 4 variables:
- Year
Year
- Month
Month of the year
- Dacca
Monthly cholera deaths
- Population
Population size of district
Source
King, A.A., Ionides, E.L., Pascual, M. and Bouma, M. J. (2008) Inapparent infections and cholera dynamics. Nature, 454:877-880. doi.org/10.1038/nature07084.
Gradient-function for Coyne et al's rabies model
Description
Gradient-function for Coyne et al's rabies model
Usage
coyne(t, logx, parms)
Arguments
t |
Implicit argument for time |
logx |
A vector with values for the log-states |
parms |
A vector with parameter values for the dynamical system |
Value
A list of gradients for the log system
Examples
require(deSolve)
times = seq(0, 50, by=1/520)
paras = c(gamma = 0.0397, b = 0.836, a = 1.34, sigma = 7.5,
alpha = 66.36, beta = 33.25, c = 0, rho = 0.8)
start = log(c(X=12.69/2, H1=0.1, H2=0.1, Y = 0.1, I = 0.1))
out = as.data.frame(ode(start, times, coyne, paras))
Measles incidence across 40 US cities
Description
A dataset of Measles incidence across 40 US cities with relevant demographic data
Usage
dalziel
Format
A data frame with 44,720 rows and 10 variables:
- biweek
biweek of the year
- cases
incidence
- year
year
- loc
city name
- pop
population size
- rec
susceptible recruits
- country
country
- lon
city longitude
- lat
city latitude
- decimalYear
time counter
Source
Dalziel et al. 2016. Persistent chaos of measles epidemics in the prevaccination United States caused by a small change in seasonal transmission patterns. PLoS Computational Biology 2016: e1004655. doi.org/10.1371/journal.pcbi.1004655.
Sierra-Leone Ebola 2015 data.
Description
The daily number of cases of ebola in Sierra Leone during the 2015 epidemic.
Usage
ebola
Format
A data frame with 103 rows and 4 variables:
- date
date
- day
day
- cum_cases
cumulative incidence
- cases
incidence calculated by differencing the cumcases and setting negatives to zero.
Source
http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/cumulative-cases-graphs.html
Ferrari et al. 2005 outbreak data.
Description
The incidence aggregated by serial interval of a number of outbreaks studied by Ferrari et al. 2005.
Usage
ferrari
Format
A data frame with 15 rows and 7 variables:
- Eboladeaths00
Number of deaths from ebola during the 2000 Uganda outbreak
- Ebolacases00
Number of cases of ebola during the 2000 Uganda outbreak
- Ebolacases95
Number of cases of ebola during the 1995 DRC outbreak
- FMDfarms
Number of farms infected with FMD during the 2000-01 UK outbreak
- HogCholera
Number of cases of swine fever in pigs in the 1997-98 outbreak in the Netherlands
- SarsHk
Number of cases of SARS in Hong Kong during the 2003 outbreak
- SarsSing
Number of cases of SARS in Singapore during the 2003 outbreak
Source
Ferrari et al. (2005) Estimation and inference of R-0 of an infectious pathogen by a removal method. Mathematical Biosciences 198: 14-26. doi.org/10.1016/j.mbs.2005.08.002.
Filipendula rust data.
Description
Rust infection status of 162 populations of Filipendula ulmaria in a Swedish Island archipelago
Usage
filipendula
Format
A data frame with 162 rows and 4 variables:
- y94
infection status in 1994
- y95
infection status in 1995
- X
X coordinate
- Y
Y coordinate
Source
Smith et al. 2003. Epidemiological patterns at multiple spatial scales: an 11-year study of a Triphragmium ulmariae – Filipendula ulmaria metapopulation. Journal of Ecology, 91(5), pp.890-903. doi.org/10.1046/j.1365-2745.2003.00811.x.
FIV infection in cats.
Description
Immunological measures on cats infected with different strains of FIV
Usage
fiv
Format
A data frame with 238 rows and 18 variables:
- Id
Individual identifier
- CD4
CD4 cell count
- CD8B
CD8B cell count
- CD25
CD25 cell count
- FAS_L
FAS ligand
- FAS
FAS
- IFNg
Interferon gamma
- IL_10
Interleukin 10
- IL_12
Interleukin 12
- IL_4
Interleukin 4
- lymphocyte
lymphocyte count
- neutrophils
neutrophil count
- TNF_a
Tumor necrosis factor
- provirus
provirus count
- viremia
viremia
- Day
day
- No
unique identifier
- Treatment
Experimental treatment
Source
Roy et al. 2009. Multivariate statistical analyses demonstrate unique host immune responses to single and dual lentiviral infection. PloS one 4, e7359. doi.org/10.1371/journal.pone.0007359.
Flowfield
Description
Plots the flow or velocity field for a one- or two-dimensional autonomous ODE system.
Usage
flowField(deriv, xlim, ylim, parameters = NULL, system = "two.dim",
points = 21, col = "gray", arrow.type = "equal",
arrow.head = 0.05, frac = 1, add = TRUE, xlab = if (system ==
"two.dim") state.names[1] else "t", ylab = if (system == "two.dim")
state.names[2] else state.names[1], ...)
Arguments
deriv |
A function computing the derivative at a point for the ODE
system to be analysed. Discussion of the required format of these functions
can be found in the package vignette, or in the help file for the
function |
xlim |
In the case of a two-dimensional system, this sets the limits of
the first dependent variable in which gradient reflecting line segments
should be plotted. In the case of a one-dimensional system, this sets the
limits of the independent variable in which these line segments should be
plotted. Should be a |
ylim |
In the case of a two-dimensional system this sets the limits of
the second dependent variable in which gradient reflecting line segments
should be plotted. In the case of a one-dimensional system, this sets the
limits of the dependent variable in which these line segments should be
plotted. Should be a |
parameters |
Parameters of the ODE system, to be passed to |
system |
Set to either |
points |
Sets the density of the line segments to be plotted;
|
col |
Sets the colour of the plotted line segments. Should be a
|
arrow.type |
Sets the type of line segments plotted. If set to
|
arrow.head |
Sets the length of the arrow heads. Passed to
|
frac |
Sets the fraction of the theoretical maximum length line
segments can take without overlapping, that they can actually attain. In
practice, |
add |
Logical. If |
xlab |
Label for the x-axis of the resulting plot. |
ylab |
Label for the y-axis of the resulting plot. |
... |
Value
Returns a list with the following components (the
exact make up is dependent on the value of system):
add |
As per input. |
arrow.head |
As per input. |
arrow.type |
As per input. |
col |
As per input, but with possible editing if a
|
deriv |
As per input. |
dx |
A |
dy |
A |
frac |
As per input. |
parameters |
As per input. |
points |
As per input. |
system |
As per input. |
x |
A |
xlab |
As per input. |
xlim |
As per input. |
y |
A |
ylab |
As per input. |
ylim |
As per input. |
Author(s)
Michael J Grayling
See Also
Examples
#See archived phaseR package for examples
Boarding school influenza data.
Description
The daily number of children confined to bed in a boarding school in North England during an outbreak in 1978 of the reemerging A/H1N1 strain. The school had 763 boys of which 512 boys were confined to bed sometime during the outbreak.
Usage
flu
Format
A data frame with 14 rows and 2 variables:
- day
day since beginning of outbreak
- cases
number of sick children
Source
Anonymous (1978) EPIDEMIOLOGY: Influenza in a boarding school. British Medical Journal, 4 March 1978 p.587.
Gillespie exact algorithm
Description
Function simulating a dynamical system using the Gillespie exact algorithm
Usage
gillespie(rateqs, eventmatrix, parameters, initialvals, numevents)
Arguments
rateqs |
a list with rate equations |
eventmatrix |
a matrix of changes in state variables associated with each event |
parameters |
a vector of parameter values |
initialvals |
a vector of initial values for the states |
numevents |
number of events to be simulated |
Value
A data frame with simulated time series
Examples
rlist=c(quote(mu * (S+I+R)), quote(mu * S), quote(beta * S * I /(S+I+R)),
quote(mu * I), quote(gamma * I), quote(mu*R))
emat=matrix(c(1,0,0,-1,0,0,-1,1,0,0,-1,0,0,-1,1,0,0,-1),ncol=3, byrow=TRUE)
paras = c(mu = 1, beta = 1000, gamma = 365/20)
inits = c(S=100, I=2, R=0)
sim=gillespie(rlist, emat, paras, inits, 100)
Defoliated by gypsy moth each in northeast US 1975-2002.
Description
A dataset containing the fraction of forest defoliated by the gypsy moth in 20km x 20km pixels across northeast US in each year between 1975 and 2002.
Usage
gm
Format
A data frame with 1086 rows and 30 variables:
- UTMX
UTM x-coordinates
- UTMY
UTM y-coordinates
- d1975
Defolitation in 1975
- d1976
Defolitation in 1976
- d1977
Defolitation in 1977
- d1978
Defolitation in 1978
- d1979
Defolitation in 1979
- d1980
Defolitation in 1980
- d1981
Defolitation in 1981
- d1982
Defolitation in 1982
- d1983
Defolitation in 1983
- d1984
Defolitation in 1984
- d1985
Defolitation in 1985
- d1986
Defolitation in 1986
- d1987
Defolitation in 1987
- d1988
Defolitation in 1988
- d1989
Defolitation in 1989
- d1990
Defolitation in 1990
- d1991
Defolitation in 1991
- d1992
Defolitation in 1992
- d1993
Defolitation in 1993
- d1994
Defolitation in 1994
- d1995
Defolitation in 1995
- d1996
Defolitation in 1996
- d1997
Defolitation in 1997
- d1998
Defolitation in 1998
- d1999
Defolitation in 1999
- d2000
Defolitation in 2000
- d2001
Defolitation in 2001
- d2002
Defolitation in 2002
Source
Bjornstad, O. N., Robinet, C., & Liebhold, A. M. (2010). Geographic variation in North American gypsy moth cycles: subharmonics, generalist predators, and spatial coupling. Ecology, 91(1), 106-118. doi.org/10.1890/08-1246.1.
De et al. 2004 gonorrhea contact matrix
Description
The directed contact network from De et al. (2004) contact-tracing of the spread of gonorrhea across asexual network in Alberta canada
Usage
gonnet
Format
A matrix with 89 rows and 89 columns:
- gonet
a matrix of directional contacts of disease spread
Source
De et al (2004). Sexual network analysis of a gonorrhea outbreak. Sexually transmitted infections 80: 280-285. doi.org/10.1136/sti.2003.007187.
Euthamia graminifolia rust data.
Description
Data on a fungal pathogen of the aster Euthamia graminifolia collected by Jennifer Keslow.
Usage
gra
Format
A data frame with 360 rows and 8 variables:
- block
the block
- row
row
- plot
plot within block
- xloc
x coordinates
- yloc
y coordinate
- comp
plot composition
- water
treatment: dry or wet
- score
the rust score
Auxillary function used by llik.pc
Description
Auxillary function used by llik.pc
Usage
integrandpc(a, up, foi)
Arguments
a |
a vector with the ages |
up |
a vector with upper age-bracket cut-offs |
foi |
a vector with FoI |
Value
A vector with FoIs matched to data
See Also
llik.pc
Bordetella bronchiseptica in rabbit kittens.
Description
Data on Bordetella bronchiseptica in rabbit kittens in a breeding facility.
Usage
litter
Format
A data frame with 494 rows and 8 variables:
- Facility
breeding facility
- sick
infection status
- Date
date sampled
- Animal.code
animal identifier
- msick
dams infection status
- Litter
litter identifier
- CFU
bacterial count
- Description
unique litter identifier
Source
Long et al (2010) Identifying the Age Cohort Responsible for Transmission in a Natural Outbreak of Bordetella bronchiseptica. PLoS Pathogens 6(12): e1001224. doi:10.1371/journal.ppat.1001224.
Negative log-likelihood function for the chain-binomial model
Description
Negative log-likelihood function for the chain-binomial model
Usage
llik.cb(S0, beta, I)
Arguments
S0 |
a scalar with value for S0 |
beta |
a scalar with value for beta |
I |
a vector incidence aggregated at serial interval |
Value
the negative log-likelhood for the model
Examples
twoweek=rep(1:15, each=2)
niamey_cases1=sapply(split(niamey$cases_1[1:30], twoweek), sum)
llik.cb(S0=6500, beta=23, I=niamey_cases1)
Function to estimate parameters for the picewise-constant catalytic model
Description
This function uses binomial likelihoods to estimate the picewise-constant FoI model from age-incidence data
Usage
llik.pc(par, age, num, denom, up)
Arguments
par |
a vector with initial guesses |
age |
a vector with the ages |
num |
a vector with number infected by age |
denom |
a vector with number tested by age |
up |
a vector with upper age-bracket cut-offs |
Value
The negative log-likelihood for a candidate piecewise constant catalytic model
Examples
x=c(1,4,8,12,18,24)
para=rep(.1,length(x))
## Not run: optim(par=log(para),fn=loglikpc, age=rabbit$a, num=rabbit$inf, denom=rabbit$n, up=x)
Massachusetts gonorrhea data.
Description
Weekly cases of gonorrhea in Massachusetts between 2006 and 2015.
Usage
magono
Format
A data frame with 422 rows and 4 variables:
- number
Weekly case reports
- year
Year
- week
Week of the year
- time
Time in fractions of year
Source
Bi-weekly measles incidence in London from 1944-65.
Description
A dataset containing the biweekly incidence of measles in London from 1944 to 1965
Usage
meas
Format
A data frame with 546 rows and 5 variables:
- year
year
- week
week of the year
- time
time
- London
incidence
- B
Biweekly births
Details
Birth numbers are annual, so in the data set, this number is evenly distributed across the 26 bi-weeks of each year.
Source
Bjornstad et al. (2002) Endemic and epidemic dynamics of measles: Estimating transmission rates and their scaling using a time series SIR model. Ecological Monographs 72: 169-184. doi.org/10.2307/3100023.
POLYMOD contact-rate data by Age.
Description
Age-specific contact rates from the diary study by Mossong et al. 2008.
Usage
mossong
Format
A data frame with 900 rows and 3 variables:
- contactor
end of age-bracket (in years) of contactor group
- contactee
end of age-bracket (in years) of contactee group
- contact.rate
average contact rate
Source
Mossong et al. 2008 Social contacts and mixing patterns relevant to the spread of infectious diseases PLoS Med, Public Library of Science 5:e74. doi.org/10.1371/journal.pmed.0050074.
Weekly measles incidence from 2003-04 in Niamey, Niger.
Description
A dataset containing the weekly incidence of measles in Niamey, Niger during the 2003-04 outbreak
Usage
niamey
Format
A data frame with 31 rows and 13 variables:
- absweek
week since beginning of outbreak
- week
week of the year
- tot_cases
weekly incidence for the whole city
- tot_mort
weekly deaths for the whole city
- lethality
weekly case fatality rate
- tot_attack
weekly attack rates for the whole city
- cases_1
weekly incidence for district 1
- attack_1
weekly attack rates for district 1
- cases_2
weekly incidence for district 2
- attack_2
weekly attack rates for district 2
- cases_3
weekly incidence for district 3
- attack_3
weekly attack rates for district 3
- cum_cases
weekly cumulative incidence for the whole city
Source
Grais et al (2008) Time is of the essence: exploring a measles outbreak response vaccination in Niamey, Niger. Journal of the Royal Society Interface 5: 67-74. https://doi.org/10.1098/rsif.2007.1038.
Day of appearance of each measles case from 2003-04 outbreak in Niamey, Niger.
Description
A dataset containing the day of appearance of each measles case in Niamey, Niger during the 2003-04 outbreak.
Usage
niamey_daily
Format
A data frame with 10,937 rows and 1 variables:
- day
the day of appearance of each case since day of outbreak
Source
Grais et al. (2008) Time is of the essence: exploring a measles outbreak response vaccination in Niamey, Niger. Journal of the Royal Society Interface 5: 67-74. doi.org/10.1098/rsif.2007.1038.
Launch a shiny-app to study outbreak-response vaccination campaigns
Description
Launch a shiny-app to study outbreak-response vaccination campaigns
Usage
orv.app
Format
An object of class shiny.appobj of length 5.
Details
Launch app for details
Examples
## Not run: orv.app
Weekly incidence of giardia in Pennsylvania between 2006 and 2014.
Description
A dataset containing the weekly incidence of giardia in Pennsylvania between 2006 and 2014.
Usage
pagiard
Format
A data frame with 448 rows and 3 variables:
- PENNSYLVANIA
weekly incidence
- YEAR
the year
- WEEK
the week
Source
Weekly deaths from Influenza-like illness in Pennsylvania between 1972 and 1998.
Description
A dataset containing the weekly ILI related deaths in Pennsylvania between 1972 and 1998.
Usage
paili
Format
A data frame with 1404 rows and 3 variables:
- PENNSYLVANIA
weekly deaths
- YEAR
the year
- WEEK
the week
Source
Weekly incidence of Lymes disease in Pennsylvania between 2006 and 2014.
Description
A dataset containing the weekly incidence of Lymes disease in Pennsylvania between 2006 and 2014.
Usage
palymes
Format
A data frame with 448 rows and 3 variables:
- PENNSYLVANIA
weekly incidence
- YEAR
the year
- WEEK
the week
Source
Weekly incidence of measles in Pennsylvania between 1928 and 1969.
Description
A dataset containing the weekly incidence of measles in Pennsylvania between 2006 and 2014.
Usage
pameasle
Format
A data frame with 448 rows and 3 variables:
- PENNSYLVANIA
weekly incidence
- YEAR
the year
- WEEK
the week
Source
Weekly whooping cough incidence from 1900-1937 in Copenhagen, Denmark.
Description
A dataset containing the weekly incidence of whooping cough from Copenhagen, Denmark between January 1900 and December 1937
Usage
pertcop
Format
A data frame with 1982 rows and 9 variables:
- date
date
- births
births
- day
day of month
- month
month of year
- year
year
- cases
weekly incidence
- deaths
weekly deaths
- popsize
weekly population size interpolated from census data
Source
Lavine et al. 2013. Immune boosting explains regime- shifts in prevaccine-era pertussis dynamics. PLoS One, 8(8):e72086. doi:10.1371/journal.pone.0072086.
Rubella in Peru data.
Description
Rubella incidence by age as studied by Metcalf et al (2011).
Usage
peru
Format
A data frame with 95 rows and 2 variables:
- age
end of age-bracket (in years)
- cumulative
cumulative number of rubella cases
- incidence
number of rubella cases
- n
total cases
Source
Metcalf et al (2011) Rubella metapopulation dynamics and importance of spatial coupling to the risk of congenital rubella syndrome in Peru. Journal of the Royal Society Interface 8: 369-376. doi:10.1371/journal.pone.0072086.
Function to plot an object of class CM
Description
Function to plot an object of class CM
Usage
## S3 method for class 'cm'
plot(x, ...)
Arguments
x |
an object of class cm |
... |
other arguments |
Value
A plot of the contract matrix
Examples
cm=ringlattice(N=20,K=4)
## Not run: plot(cm)
Function to plot a netSIR object
Description
Function to plot a netSIR object
Usage
## S3 method for class 'netSIR'
plot(x, ...)
Arguments
x |
an object of class netSIR |
... |
other arguments |
See Also
Function to calculate R0 from a contact matrix
Description
Function to calculate R0 from a contact matrix
Usage
r0fun(CM, tau, gamma)
Arguments
CM |
an object of class CM |
tau |
= probability of infection across an edge |
gamma |
= probability of removal per time step |
Value
the R0
Examples
cm1=BarabasiAlbert(N=200,K=2)
r0fun(cm1, 0.3, 0.1)
Rabbit Bordetella brochiseptica data.
Description
Rabbits infected by B. brochiseptica by age as studied by Long et al (2010).
Usage
rabbit
Format
A data frame with 42 rows and 3 variables:
- a
end of age-bracket (in months)
- n
number of rabbits tested
- inf
number of rabbits infected with the bacterium
Source
Long et al (2010) Identifying the Age Cohort Responsible for Transmission in a Natural Outbreak of Bordetella bronchiseptica. PLoS Pathogens 6(12): e1001224. doi:10.1371/journal.ppat.1001224.
Raccoon rabies data.
Description
Data is the average monthly number of reported cases of rabid raccoons across all counties within each of 11 east coast US states the time line is from the first reported case in each state (starting in late 1970s for West Virginia).
Usage
rabies
Format
A data frame with 208 rows and 12 variables:
- Month
Month since rabies appearance in the state
- CT
Connecticut
- DE
Delaware
- MD
Maryland
- MA
Massachusetts
- NJ
New Jersey
- NY
New York
- NC
North Carolina
- PA
Pennsylvania
- RI
Rhode Island
- VA
Virginia
- WV
West Virginia
Source
Childs et al. 2000. Predicting the local dynamics of epizootic rabies among raccoons in the United States Proceedings of the National Academy of Sciences 97:13666-13671. doi.org/10.1073/pnas.240326697.
Function to predict efficacy of outbreak-response vaccination campaign
Description
Function to predict efficacy of outbreak-response vaccination campaign
Usage
retrospec(R, day, vaccine_efficacy, target_vaccination,
intervention_length, mtime, LP = 7, IP = 7, N = 10000)
Arguments
R |
reproductive ratio |
day |
first day of ORV campaign |
vaccine_efficacy |
Vaccine efficacy |
target_vaccination |
fraction of population vaccinated during ORV campaign |
intervention_length |
duration of ORV campaign |
mtime |
length of simulation |
LP |
length of latent period |
IP |
length of infectious period |
N |
initial susceptible population size |
Value
A list of gradients
Examples
red1=retrospec(R=1.8, 161, vaccine_efficacy=0.85, target_vaccination=0.5,
intervention_length=10, mtime=250, LP=8, IP=5, N=16000)
1-red1$redn
Function to generate a ring lattice
Description
Function to generate a ring lattice
Usage
ringlattice(N, K)
Arguments
N |
the number of nodes |
K |
the number of neighbors to which each node is connected so degree = 2xK |
Value
An object of class CM (contact matrix)
Examples
cm=ringlattice(N=20,K=4)
Gradient-function for the SEIR model
Description
Gradient-function for the SEIR model
Usage
seirmod(t, y, parms)
Arguments
t |
Implicit argument for time |
y |
A vector with values for the states |
parms |
A vector with parameter values for the SEIR system |
Value
A list of gradients
Examples
require(deSolve)
times = seq(0, 10, by=1/120)
paras = c(mu = 1/50, N = 1, beta = 1000, sigma = 365/8, gamma = 365/5)
start = c(S=0.06, E=0, I=0.001, R = 0.939)
out=ode(y=start, times=times, func=seirmod, parms=paras)
Gradient-function for the forced SEIR model
Description
Gradient-function for the forced SEIR model
Usage
seirmod2(t, y, parms)
Arguments
t |
Implicit argument for time |
y |
A vector with values for the states |
parms |
A vector with parameter values for the SIR system |
Value
A list of gradients
Examples
require(deSolve)
times = seq(0, 10, by=1/120)
paras = c(mu = 1/50, N = 1, beta0 = 1000, beta1 = 0.2, sigma = 365/8, gamma = 365/5)
start = c(S=0.06, E=0, I=0.001, R = 0.939)
out=ode(y=start, times=times, func=seirmod2, parms=paras)
Antler smut on wild campion.
Description
Data on a fungal pathogen of the wild campion collected by Janis Antonovics
Usage
silene2
Format
A data frame with 876 rows and 5 variables:
- X
road segment number
- lat
latitude
- long
longitude
- hmean
number of healthy plants
- dmean
number of diseased plants
Source
Antonovics, J. 2004. Long-term study of a plant-pathogen metapopulation. In: Hanski, Ilkka, and Oscar E. Gaggiotti. Ecology, genetics, and evolution of metapopulations. Academic Press. doi.org/10.1371/journal.pone.0007359.
Function to simulate the chain-binomial model
Description
Function to simulate the chain-binomial model
Usage
sim.cb(S0, beta)
Arguments
S0 |
a scalar with value for S0 |
beta |
a scalar with value for beta |
Value
A data-frame with time series of susceptibles and infecteds
Examples
sim=sim.cb(S0=6500, beta=23)
Gradient-function for the age-structured SIR model with possibly heterogeneous mixing
Description
Gradient-function for the age-structured SIR model with possibly heterogeneous mixing
Usage
siragemod(t, logx, parms)
Arguments
t |
Implicit argument for time |
logx |
A vector with log-values for the log-states |
parms |
A vector with parameter values for the age-structured SIR system |
Value
A list of gradients
Examples
a=rep(1,4)
n=length(a)
betaM=matrix(1, ncol=4, nrow=4)
pars =list(N=1, gamma=365/14, mu=0.02, sigma=0.2, beta=500, betaM=betaM,p=rep(0,4), a=a)
xstart<-log(c(S=rep(0.099/n,n), I=rep(0.001/n,n), R=rep(0.9/n,n)))
times=seq(0,10,by=14/365)
out=as.data.frame(ode(xstart, times=times, func=siragemod, parms=pars))
Gradient-function for the SIR model
Description
Gradient-function for the SIR model
Usage
sirmod(t, y, parms)
Arguments
t |
Implicit argument for time |
y |
A vector with values for the states |
parms |
A vector with parameter values for the SIR system |
Value
A list of gradients
Examples
require(deSolve)
times = seq(0, 26, by=1/10)
paras = c(mu = 0, N = 1, beta = 2, gamma = 1/2)
start = c(S=0.999, I=0.001, R = 0)
out=ode(y=start, times=times, func=sirmod, parms=paras)
Gradient-function for the SIRWS model
Description
Gradient-function for the SIRWS model
Usage
sirwmod(t, logy, parms)
Arguments
t |
Implicit argument for time |
logy |
A vector with values for the log(states) |
parms |
A vector with parameter values for the SIRWS system |
Value
A list of gradients (in log-coordinates)
Examples
require(deSolve)
times = seq(0, 26, by=1/10)
paras = c(mu = 1/70, p=0.2, N = 1, beta = 200, omega = 1/10, gamma = 17, kappa=30)
start = log(c(S=0.06, I=0.01, R=0.92, W = 0.01))
out = as.data.frame(ode(start, times, sirwmod, paras))
Gradient-function for the SIR model with outbreak-response vaccination
Description
Gradient-function for the SIR model with outbreak-response vaccination
Usage
sivmod(t, x, parms)
Arguments
t |
Implicit argument for time |
x |
A vector with values for the states |
parms |
A vector with parameter values for the SIR system |
Value
A list of gradients
See Also
Function to calculate the degree distribution for an object of class CM
Description
Function to calculate the degree distribution for an object of class CM
Usage
## S3 method for class 'cm'
summary(object, plot = FALSE, ...)
Arguments
object |
an object of class cm |
plot |
if TRUE a bar plot of the degree distribution is produced |
... |
other arguments |
Value
A plot of the contract matrix
Examples
cm=WattsStrogatz(N=20, K=4, Prw=.3)
summary(cm)
Function to summarize a netSIR object
Description
Function to summarize a netSIR object
Usage
## S3 method for class 'netSIR'
summary(object, ...)
Arguments
object |
an object of class netSIR |
... |
other arguments |
Value
A data-frame with the time series of susceptible, infected and recovered individuals
See Also
Gillespie tau-leap algorithm
Description
Function simulating a dynamical system using the Gillespie tau-leap approximation
Usage
tau(rateqs, eventmatrix, parameters, initialvals, deltaT, endT)
Arguments
rateqs |
a list with rate equations |
eventmatrix |
a matrix of changes in state variables associated with each event |
parameters |
a vector of parameter values |
initialvals |
a vector of initial values for the states |
deltaT |
the tau-leap time interval |
endT |
the time length of simulation |
Value
A data frame with simulated time series
Examples
rlist2=c(quote(mu * (S+E+I+R)), quote(mu * S), quote(beta * S * I/(S+E+I+R)),
quote(mu*E), quote(sigma * E), quote(mu * I), quote(gamma * I), quote(mu*R))
emat2=matrix(c(1,0,0,0,-1,0,0,0,-1,1,0,0,0,-1,0,0,0,-1,1,0,0,0,-1,0,0,0,-1,1,0,0,0,-1),
ncol=4, byrow=TRUE)
paras = c(mu = 1, beta = 1000, sigma = 365/8, gamma = 365/5)
inits = c(S=999, E=0, I=1, R = 0)
sim2=tau(rlist2, emat2, paras, inits, 1/365, 1)
Weekly incidence of diphtheria in Philadelphia between 1914 and 1947.
Description
A dataset containing the weekly incidence incidence of diphtheria in Philadelphia between 1914 and 1947.
Usage
tydiphtheria
Format
A data frame with 1774 rows and 4 variables:
- YEAR
the year
- WEEK
the week
- PHILADELPHIA
weekly diphtheria incidence
- TIME
the time counter
Source
Weekly incidence of measles in Philadelphia between 1914 and 1947.
Description
A dataset containing the weekly incidence incidence of measles in Philadelphia between 1914 and 1947.
Usage
tymeasles
Format
A data frame with 1774 rows and 4 variables:
- YEAR
the year
- WEEK
the week
- PHILADELPHIA
weekly measles incidence
- TIME
the time counter
Source
Weekly incidence of scarlet fever in Philadelphia between 1914 and 1947.
Description
A dataset containing the weekly incidence incidence of scarlet fever in Philadelphia between 1914 and 1947.
Usage
tyscarlet
Format
A data frame with 1774 rows and 4 variables:
- YEAR
the year
- WEEK
the week
- PHILADELPHIA
weekly scarlet fever incidence
- TIME
the time counter
Source
Weekly incidence of whooping cough in Philadelphia between 1925 and 1947.
Description
A dataset containing the weekly incidence incidence of whooping cough in Philadelphia between 1925 and 1947.
Usage
tywhooping
Format
A data frame with 1200 rows and 5 variables:
- YEAR
the year
- WEEK
the week
- PHILADELPHIA
weekly whooping cough incidence
- TIME
the time counter
- TM
observation counter
Source
US 1975/76 ILI data.
Description
Influenza-like illness data for the lower 48 states and the District of Columbia during the 1975/76 season dominated by A/H3N2/Victoria strain
Usage
usflu
Format
A data frame with 49 rows and 7 variables:
- State
State number
- Acronym
State code
- Pop
Population size
- Latitude
Latitude
- Longitude
Longitude
- Start
Week of start of epidemic
- Peak
Week of peak of epidemic
Source
Viboud C, Bjornstad ON, Smith DL, Simonsen L, Miller MA, Grenfell BT (2006) Synchrony, waves, and spatial hierarchies in the spread of influenza. Science 312: 447-451. doi.org/10.1126/science.1125237.