Function to generate a Barabasi-Albert network

Description

Function to generate a Barabasi-Albert network

Usage

barabasiAlbert(N, K)

Arguments

Value

An object of class CM (contact matrix)

Examples

cm3=barabasiAlbert(200, 4)

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>

Daily measures of malaria infected mice.

Description

Daily data on laboratory mice infected with various strains of Plasmodium chabaudi

Usage

chabaudi

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

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:10.1038/nature07084>

Edition 1 Gradient-function for Coyne et al's rabies model

Description

Edition 1 Gradient-function for Coyne et al's rabies model

Usage

coyne(t, logx, parms)

Arguments

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))

Edition 2 Gradient-function for Coyne et al's rabies model

Description

Edition 2 Gradient-function for Coyne et al's rabies model

Usage

coyne2(t, logx, parms)

Arguments

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(S=12.69/2, E1=0.1, E2=0.1, I = 0.1, R = 0.1))
out = as.data.frame(ode(start, times, coyne, paras))

Colorado Springs network

Description

Network and individual characteristics among 749 sex workers and clients in Colorado Springs as surveyed between 1988 and 1991

Usage

cspring

Format

A list of two items the first ($nodes) is a frame with 749 rows and 6 variables, The second ($cm) is a 749 x 749 relational matrix of presence/absence of sexual contacts among each pair of individuals.

$nodes$id

individual identifier

$nodes$gender

gender; 1 = female, 2 = male

$nodes$sex.worker

sex worker status; 1 = yes, 0 = no

$nodes$pimp

pimp status; 1 = yes, 0 = no

$nodes$sex.work.client

client status; 1 = yes, 2 = no

$nodes$type

node classifier; 1 = client, 2 = worker, 3 = both

$cm

the relational (contact) matrix among the individuals in the network.

Source

Woodhouse et al. (1994) Mapping a social network of heterosexuals at high risk for HIV Infection. AIDS 8:1331-1336. doi:10.1097/00002030-199409000-00018

Klovdahl et al. (1994) Social networks and infectious disease: The Colorado Springs study. Social Science and Medicine 38:79-88. <doi:10.1016/0277-9536(94)90302-6>

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: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

https://stacks.cdc.gov/view/cdc/31408

Euthamia graminifolia rust data.

Description

Data on a fungal pathogen of the aster Euthamia graminifolia collected by Jennifer Keslow.

Usage

euthamia

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

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: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: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:10.1371/journal.pone.0007359>

flowField

Description

Plots the flow or velocity field for a one- or two-dimensional autonomous ODE system. This function was written Michael J Grayling as part of the archived phaseR package and re-published in the this 'epimdr2' package under original GPL-3 lisence

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

Value

Returns a list with the following components (the exact make up is dependent on the value of system):

Author(s)

Michael J Grayling

See Also

arrows, plot

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

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)

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:10.1136/sti.2003.007187>

A function to calculate the matrix of gravity coupling based on distance and population size

Description

A function to calculate the matrix of gravity coupling based on distance and population size

Usage

gravity(tau1, tau2, rho, pop, distance)

Arguments

Value

A matrix of gravity coupling

See Also

sirSpatmod

Examples

require(ncf)
data(usflu)
usdist = gcdist(usflu$Longitude, usflu$Latitude)
G = gravity(0.3, 0.6, 3, usflu$Pop, usdist)

Defoliated by gypsy moth each in northeast US 1975-2002.

Description

A list 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

gypsymoth

Format

A list with two matrices each with 1086 rows:

xy

A matrix with two columns representing UTM coordinates

defoliation

A matrix with 28 columns representing pixel-wise defoliation between 1976 and 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:10.1890/08-1246.1>

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:10.1073/pnas.1616052113>

A Jacobian matrix calculator

Description

A general-purpose function to construct and evaluate Jacobian matrices

Usage

jacobian(states, elist, parameters, pts)

Arguments

Value

The Jacobian matrix

Examples

#The SEIR model
states=c("S", "E", "I", "R")
elist=c(dS = quote(mu * (N  - S)  - beta * S * I / N),
 dE = quote(beta * S * I / N - (mu + sigma) * E),
dI = quote(sigma * E - (mu + gamma+alpha) * I),
dR = quote(gamma * I - mu * R))
paras  = c(mu = 1/50, N = 1, beta =  1000, 
sigma = 365/8, gamma = 365/5, alpha=0)
deq=list(S = 1, E = 0, I = 0, R = 0)
jacobian(states=states, elist=elist, parameters=paras, pts=deq)

A function to calculate asymptotic growth, sensitivity and elasticity for age-structured populations

Description

A function to calculate asymptotic growth, sensitivity and elasticity for age-structured populations

Usage

leslie(L)

Arguments

Value

A list consisting of the following components:

References

Caswell, H. 2001. Matrix Population Models: Construction, Analysis, and Interpretation. 2nd edn Sinauer Associates Inc., Sunderland, MA,

Examples

fa<-c(0, 0.5, 1.2)
sa<-c(0.8, 0.8, 0)
L<-matrix(0, nrow=3, ncol=3)
#inserting fa vector in first row
L[1,]<-fa
#inserting sa in the subdiagonal:
L[row(L)==col(L)+1] <-sa[1:2]
leslie(L)

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

Value

the negative log-likelihood 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 piecewise-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

Value

The negative log-likelihood for a candidate piecewise constant catalytic model

Examples

x=c(1,4,8,12,18,24)
para=rep(0.1,length(x)+1)
optim(par=log(para),fn=llik.pc, age=rabbit$a, num=rabbit$inf, denom=rabbit$n, up=x)

Measles in England and Wales 1944-1994.

Description

The weekly reported cases of measles in each of the 354 cities and villages between 1944 and 1994.

Usage

m4494

Format

A list with four items:

measles

A matrix with 354 rows and 2661 columns. Rows represents community and columns represent week.

ps

A matrix with 354 rows and 41 columns. Rows represents community and columns represent population size for each of the years in the data set.

longlat

A matrix with 354 rows and two columns. Rows represents community and columns longitude and latitude.

year

A vector of length 354 that represents time for each week as yearly decimals.

coverage

A vector of length 2661 that represents reported annual vaccine coverage for each week.

Source

Grenfell, B.T., Bjornstad, O.N., & Kappey, J. 2001. Travelling waves and spatial hierarchies in measles epidemics. Nature 414: 716-723. <doi:10.1038/414716a>

Lau, M.S.Y., Becker, A.D, Korevaar, H.M., Caudron, Q., Shaw, D.J., Metcalf, C.J.E., Bjornstad, O.N. and Grenfell, B.T. 2020. A competing-risks model explains hierarchical spatial coupling of measles epidemics en route to national elimination. Nature Ecology & Evolution. <doi:10.1038/s41559-020-1186-6>

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

https://www.tycho.pitt.edu

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

if(interactive()){may.app}

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:10.2307/3100023>

The Nicholson-Bailey model

Description

Function to simulate the Nicholson-Bailey Parasitoid-host model

Usage

nbmod(R, a, T = 100, H0 = 10, P0 = 1)

Arguments

Value

A list of simulated Host and Parasitoid numbers

Examples

sim= nbmod(R=1.1,a=0.1)

Launch a shiny-app simulating the spatially-extended Nicholson-Bailey parasitoid model

Description

Launch a shiny-app simulating the spatially-extended Nicholson-Bailey parasitoid model

Usage

nbspat.app

Format

An object of class shiny.appobj of length 5.

Details

Launch app for details

Examples

if(interactive()){nbspat.app}

Next generation matrix R0 calculator

Description

Calculates R0 for arbitrarily complex compartmental flows using the method of Diekmann et al. (1990).

Usage

nextgenR0(Istates, Flist, Vlist, parameters, dfe)

Arguments

Value

The next generation matrix estimate of R0

Source

Diekmann, O., Heesterbeek, J. A. P. and Metz, J. A. J. 1990. On the Definition and the Computation of the Basic Reproduction Ratio R0 in Models for Infectious-Diseases in Heterogeneous Populations. Journal of Mathematical Biology 28: 365-382.

Examples

#The SEIR model
#Infected classes are $E$ and $I$
istates=c("E", "I")
flist=c(dEdt=quote(beta * S * I / N), dIdt=quote(0))
Vm1=quote(mu * E + sigma * E)
Vm2=quote(mu * I + alpha * I + gamma * I)
Vp1=0
Vp2=quote(sigma * E)
V1=substitute(a-b, list(a=Vm1, b=Vp1))
V2=substitute(a-b, list(a=Vm2, b=Vp2))
vlist = c(V1,V2)
para = list(mu = 0, alpha = 0, beta = 5, gamma = .8, sigma = 1.2, N = 1)
df = list(S = 1, E = 0, I = 0, R = 0)
nextgenR0(Istates=istates, Flist=flist, Vlist=vlist, parameters=para, dfe=df)

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. <doi: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: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

if(interactive()){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

https://www.tycho.pitt.edu

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

https://www.tycho.pitt.edu

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

https://www.tycho.pitt.edu

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

https://www.tycho.pitt.edu

Cumulative death count of harbor seals from CDV.

Description

The cumulative count of dead seals washed ashore across 25 Northern European areas during the 2002 epidemic starting in May and running through the end of the year.

Usage

pdv

Format

A list with three items:

coord

A data frame with 3 columns and 25 rows. Column location represents location name, latitude is latitude and longitude is longitude

ts

A data frame with 26 columns and 269 rows. The first column is the day since beginning of outbreak and the next 25 columns are cumulative count of stranded seal carcasses

fs

A 25-by-25 matrix representing the seaway friction distance among the haulouts.

Source

Harding, K. C., Härkönen, T. and Caswell, H. (2002), The 2002 European seal plague: epidemiology and population consequences. Ecology Letters, 5: 727-732. <doi:10.1046/j.1461-0248.2002.00390.x>

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

Value

A plot of the contact matrix

Examples

cm=ringlattice(N=20,K=4)
plot(cm)

Function to plot a netSir object

Description

Function to plot a netSir object

Usage

## S3 method for class 'netSir'
plot(x, ...)

Arguments

Value

A network plot of a netSir object

See Also

sirNetmod

POLYMOD contact-rate data by Age.

Description

Age-specific contact rates from the diary study by Mossong et al. 2008.

Usage

polymod

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:10.1371/journal.pmed.0050074>.

Function to calculate R0 from a contact matrix

Description

Function to calculate R0 from a contact matrix

Usage

r0fun(CM, tau, gamma)

Arguments

Value

the R0

Examples

cm1=barabasiAlbert(N=200,K=2)
r0fun(cm1, 0.3, 0.1)

Rabbit Bordetella bronchiseptica data.

Description

Rabbits infected by B. bronchiseptica 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: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

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

Value

An object of class CM (contact matrix)

Examples

cm=ringlattice(N=20,K=4)

Launch a shiny-app simulating a Ross-Macdonald model

Description

Launch a shiny-app simulating a Ross-Macdonald model

Usage

ross.app

Format

An object of class shiny.appobj of length 5.

Details

Launch app for details.

Examples

if(interactive()){ross.app}

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

if(interactive()){seir.app}

Gradient-function for the SEIR model

Description

Gradient-function for the SEIR model

Usage

seirmod(t, y, parameters)

Arguments

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(start, times, seirmod, paras)

Gradient-function for the forced SEIR model

Description

Gradient-function for the forced SEIR model

Usage

seirmod2(t, y, parameters)

Arguments

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(start, times, seirmod2, paras)

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

if(interactive()){seirs.app}

Antler smut on wild campion.

Description

Data on a fungal pathogen of the wild campion collected by Janis Antonovics.

Usage

silene

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.

Function to simulate the chain-binomial model

Description

Function to simulate the chain-binomial model

Usage

sim.cb(S0, beta)

Arguments

Value

A data-frame with time series of susceptibles and infected

Examples

sim=sim.cb(S0=6500, beta=23)

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

if(interactive()){sir.app}

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, parameters)

Arguments

Value

A list of gradients

Examples

ra=rep(1,4)
n=length(ra)
W=matrix(1, ncol=4, nrow=4)
paras =list(N=1, gamma=365/14, mu=0.02, beta=500, W=W,v=rep(0,4), r=ra)
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, sirAgemod, paras))

Gradient-function for the chain-SIR model

Description

Gradient-function for the chain-SIR model

Usage

sirChainmod(t, logx, parameters)

Arguments

Value

A list of gradients

Examples

require(deSolve)
times  = seq(0, 10, by=1/52)
paras  = 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, paras["u"]-1)), R = 0.0001))
out = as.data.frame(ode(xstart2, times, sirChainmod, paras))

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

sirNetmod(CM, tau, gamma)

Arguments

Value

An object of class netSir with infectious status for each node through time

Examples

cm1=barabasiAlbert(N=200,K=2)
sim1=sirNetmod(cm1,.3,0.1)
summary(sim1)
plot(sim1)

Gradient function for a spatially-extended SIR model

Description

Gradient function for a spatially-extended SIR model given some spatial topology

Usage

sirSpatmod(t, y, parameters)

Arguments

Value

A list of gradients

Examples

require(deSolve)
require(ncf)
data(usflu)
usdist = gcdist(usflu$Longitude, usflu$Latitude)
G = gravity(0.3, 0.6, 3, usflu$Pop, usdist)
gamma = 1/3.5
R0 = 1.8
beta = R0 * gamma/usflu$Pop
m = 1 / 1000 / sum(usflu$Pop)
parms = list(beta = beta, m = m, gamma  =  gamma, G = G) 
S = usflu$Pop
R = I = rep(0, length(usflu$Pop))
I[31] = 1 
inits = c(S = S, I = I, R = R)  
times = 0:200
out = ode(inits, times, sirSpatmod, parms)
L=length(usflu$Pop)
matplot(out[, 50+(1:L)], type = "l", ylab = "Prevalence", xlab = "Day")

Gradient-function for the SIR model

Description

Gradient-function for the SIR model

Usage

sirmod(t, y, parameters)

Arguments

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(start, times, sirmod, paras)

Gradient-function for the SIR model with outbreak-response vaccination

Description

Gradient-function for the SIR model with outbreak-response vaccination

Usage

sirvmod(t, x, parms)

Arguments

Value

A list of gradients

See Also

retrospec

Gradient-function for the SIRWS model

Description

Gradient-function for the SIRWS model

Usage

sirwmod(t, logy, parameters)

Arguments

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))

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

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

Value

A data-frame with the time series of susceptible, infected and recovered individuals

See Also

sirNetmod

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

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)

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

if(interactive()){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

Value

An object of class llyap with the local Lyapunov exponent and S-I data

Examples

"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

Value

An object of class lyap with the Lyapunov exponent, values for the Jacobians, parameters and data

Examples

"See chapter 10 in book"

Function to simulate the stochastic TSIR

Description

Function to simulate the stochastic TSIR assuming stochasticity in transmission and a Poisson birth-death process

Usage

tsirSim(
  alpha = 0.97,
  B = 2300,
  beta = 25,
  sdbeta = 0,
  S0 = 0.06,
  I0 = 180,
  IT = 520,
  N = 3300000
)

Arguments

Value

A list with time series of simulated infected and susceptible hosts

Examples

out = tsirSim()

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

tsirSim2(beta, alpha, B, N, inits = list(Snull = 0, Inull = 0), type = "det")

Arguments

Value

A list with time series of simulated infected and susceptible hosts

Examples

"See chapter 8 in book"

Function to simulate the spatially-extended seasonally-forced TSIR

Description

Function to simulate the spatially-extended seasonally-forced TSIR for a patchily distributed host population. Coupling is assumed to be global and according to a commuter model so with p patches and a coupling of c, local transmission is reduced by a fraction (1-c*p).

Usage

tsirSpat(beta, alpha, B, N, p, c, inits, type = "det")

Arguments

Value

A list with time series of simulated infected and susceptible hosts

Examples

"see chapter 15 in book"

Gradient-function for the two-strain SIR model

Description

Gradient-function for the two-strain SIR model

Usage

twostrain(t, y, parameters)

Arguments

Value

A list of gradients

Examples

require(deSolve)
times  = seq(0, 30, by=1/200)
paras  = c(mu = 0.02, N = 1, beta1=500, beta2=750, gamma = 365/5, Theta=0.15, Xi=0.15, Pi=0.8)
start = c(S = 0.999, I1 = 0.001, I2 = 0.00, R1=0, R2=0, J1=0, J2=0, R = 0)
out = as.data.frame(ode(start, times, twostrain, paras))

Launch a shiny-app simulating a two-strain SIR model

Description

Launch a shiny-app simulating a two-strain SIR model

Usage

twostrain.app

Format

An object of class shiny.appobj of length 5.

Details

Launch app for details

Examples

if(interactive()){twostrain.app}

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

https://www.tycho.pitt.edu

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

https://www.tycho.pitt.edu

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

https://www.tycho.pitt.edu

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

https://www.tycho.pitt.edu

2005 US Life table.

Description

Survivorship and fecundities for the US in 2005 by 5 year age-brackets.

Usage

us

Format

A data frame with 20 rows and 4 variables:

a

end of age-bracket (in years)

la

fraction of birth cohort still alive

fa

fecundity at age

sa

survival probabilities per age-bracket

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:10.1126/science.1125237>

US SARS-CoV-2 variant data.

Description

Weekly fraction of identification of the various CoV-2 variants May 2021 through March 2022. From coronavirus.health.ny.gov/clinical-variant-data/

Usage

variants

Format

A data frame with 47 rows and 7 variables:

date

End of week of sample

other

Early variants

B.1.617.2

Delta variant

B.1.1.529

First omicron variant

BA.1.1

Omicron variant BA.1

BA.2

Omicron variant BA.2 and BA.2.12

BA.2.12.1

Omicron variant BA 2.12.1

Rabies month of first appearance across Connecticut.

Description

First month of report of raccoon rabies for 168 townships in Connecticut from March 1991 through January 1995.

Usage

waller

Format

A data frame with 168 rows and 3 variables:

x

Longitudinal distance (in km) from first township of appearance

y

Latitudinal distance (in km) from first township of appearance

month

month since first appearance in the state in March 1991

Source

Waller, L. A., & Gotway, C. A. (2004). Applied spatial statistics for public health data (Vol. 368). John Wiley & Sons.

Function to generate a Watts-Strogatz network

Description

Function to generate a Watts-Strogatz network

Usage

wattsStrogatz(N, K, Prw)

Arguments

Value

An object of class CM (contact matrix)

Examples

cm2=wattsStrogatz(N=20, K=4, Prw=.3)