Function reference • mobwork

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Compute Scaling Relationships

xde_scaling(): Construct an eirpr object for an arbitary model

xde_scaling_Z(): Compute eir-pr scaling relationships

xde_scaling_eir(): Compute eir-pr scaling relationships

xde_scaling_lambda(): Compute lambda from an eirpr object using the Ross-Macdonald model

pr2Lambda(): Using the eirpr matrix and a RM model, convert pr to Lambda

ssMYZ(): Set up the MYZss object for xde_scaling_lambda

Convert Scaling Relationships

xde_eir2ni(): Convert eir to ni

xde_eir2pr(): Convert eir to pr

xde_pr2eir(): Convert pr to eir

xde_pr2ni(): Convert pr to ni

xde_pr2m(): Convert pr to mosquito density

xde_pr2lambda(): Convert pr to lambda

Adult Mosquito Models

The model library for adult mosquito infection dynamics

The Adult Mosquito RMG Model

F_eggs(<RMG>): Number of eggs laid by adult mosquitoes

F_fqZ(<RMG>): Blood feeding rate of the infective mosquito population

F_fqM(<RMG>): Blood feeding rate of the infective mosquito population

MBionomics(<RMG>): Reset bloodfeeding and mortality rates to baseline

dMYZdt(<RMG>): Derivatives for adult mosquitoes

get_inits_MYZ(<RMG>): Return initial values as a vector

make_MYZinits_RMG(): Make inits for RMG adult mosquito model

make_MYZpar_RMG(): Make parameters for RM ODE adult mosquito model

make_indices_MYZ(<RMG>): Add indices for adult mosquitoes to parameter list

make_inits_MYZ_RMG(): Make inits for RMG adult mosquito model

make_parameters_MYZ_RMG(): Make parameters for RMG ODE adult mosquito model

parse_deout_MYZ(<RMG>): Parse the output of deSolve and return variables for the RMG model

setup_MYZpar(<RMG>): Setup MYZpar for the RMG model

setup_MYZinits(<RMG>): Setup initial values for the RMG model

update_inits_MYZ(<RMG>): Make inits for RMG adult mosquito model

Human / vertebrate host infection & Immunity

The model library

New Utilities

split_stratum_by_biting(): Split a stratum into two strata, assigning

The SIS Model

split_stratum_by_biting(<SIS>): Split a stratum into two strata, assigning a fraction p a new biting weight that is multiplied by a factor fac compared with the old one. The biting weight for the remaining 1-p gets a new factor 1/fac

The Garki model

F_X(<garki>): Size of effective infectious human population

F_H(<garki>): Size of effective infectious human population

F_pr(<garki>): Compute the "true" prevalence of infection / parasite rate

F_b(<garki>): Infection blocking pre-erythrocytic immunity

dXdt(<garki>): Derivatives for human population

setup_Xpar(<garki>): Setup Xpar.garki

setup_Xinits(<garki>): Setup Xinits.garki

make_Xpar_garki(): Make parameters for Garki human model

make_Xinits_garki(): Make inits for Garki human model. Note that the variables should sum up to H, so the initial value of x1 is not set. The values are passed in the same order as they are presented in the original paper.

make_indices_X(<garki>): Add indices for human population to parameter list

parse_deout_X(<garki>): Parse the output of deSolve and return variables for the Garki model

get_inits_X(<garki>): Return initial values as a vector for the Garki model

update_inits_X(<garki>): Update Xinits for the Garki model

HTC(<garki>): Compute the HTC for the garki model

xde_plot_X(<garki>): Plot the density of infected individuals for the Garki model

xde_lines_X_garki(): Add lines for the density of infected individuals for the Garki model

The SIR model

F_X(<SIR>): Size of effective infectious human population

F_H(<SIR>): Size of human population

F_pr(<SIR>): Compute the "true" prevalence of infection / parasite rate

F_b(<SIR>): Infection blocking pre-erythrocytic immunity

dXdt(<SIR>): Derivatives for human population

setup_Xinits(<SIR>): Setup Xinits.SIR

setup_Xpar(<SIR>): Setup Xpar.SIR

make_Xpar_SIR(): Make parameters for SIR human model, with defaults

make_Xinits_SIR(): Make initial values for the SIR human model, with defaults

make_indices_X(<SIR>): Add indices for human population to parameter list

parse_deout_X(<SIR>): Parse the output of deSolve and return variables for the SIR model

get_inits_X(<SIR>): Return initial values as a vector

update_inits_X(<SIR>): Update inits for the SIR human model from a vector of states

HTC(<SIR>): Compute the HTC for the SIR model

xde_plot_X(<SIR>): Plot the density of infected individuals for the SIR model

xde_lines_X_SIR(): Add lines for the density of infected individuals for the SIR model

The SEIR model

F_X(<SEIR>): Size of effective infectious human population

F_H(<SEIR>): Size of human population

F_pr(<SEIR>): Compute the "true" prevalence of infection / parasite rate

F_b(<SEIR>): Infection blocking pre-erythrocytic immunity

dXdt(<SEIR>): Derivatives for human population

setup_Xpar(<SEIR>): Setup Xpar.SEIR

setup_Xinits(<SEIR>): Setup Xinits.SEIR

make_Xpar_SEIR(): Make parameters for SEIR human model, with defaults

make_Xinits_SEIR(): Make initial values for the SEIR human model, with defaults

make_indices_X(<SEIR>): Add indices for human population to parameter list

parse_deout_X(<SEIR>): Parse the output of deSolve and return variables for the SEIR model

get_inits_X(<SEIR>): Return initial values as a vector

update_inits_X(<SEIR>): Update inits for the SEIR human model from a vector of states

HTC(<SEIR>): Compute the HTC for the SEIR model

xde_plot_X(<SEIR>): Plot the density of infected individuals for the SEIR model

xde_lines_X_SEIR(): Add lines for the density of infected individuals for the SEIR model

Plot terms

plot_eirVpr(): Plot EIR(t) vs. the PR(t)

lines_eirVpr(): Add lines for the EIR(t) vs. the PR(t)

plot_eirpr(): Plot the eir-pr scaling relationship

lines_eirpr(): Add lines for an eir-pr scaling relationship