Resistance
Overview
**magenta** is an individual-based simulation model of
malaria epidemiology and parasite genetics. It is thus well suited for
simulating resistance.
Demonstration
Base Model
The magenta simulation model is run using the
pipeline function, which handles creating needed parameter
objects, starting the simulation, summarising the simulation model state
at desired intervals and handling how to save the final model output and
state. See Introduction vignette for more detail.
For example to run the model for 10 years with 1000 people and 40% treatment coverage:
In our return object by default, it returns just the Loggers. In this, there is a list called “Treatments” which gives us some information on treatments given out:
str(out$Loggers$Treatments)
## List of 5
## $ Successful_Treatments : num 573
## $ Unsuccesful_Treatments_LPF : num 0
## $ Not_Treated : num 844
## $ daily_bite_counters : int 1073094
## $ daily_infectious_bite_counters: int 5515(See Loggers vignette for more info about what is returned in Loggers).
The Unsuccessful_Treatments_LPF is the total number of
failed treatments, defined as a 28-day late parasitological treatment
failure (LPF). By default, magenta assumes a perfect drug
with no treatment failure regardless of resistance. To start simulating
resistance we need to understand a little bit more about how loci in
magenta work.
In a simulation, we can set the number of genetic parasite loci we track. These loci can also be used to encode information about the resistance phenotype of the parasite. For example, let’s now run a simulation assuming DHA-PPQ is being used as the first line therapy.
out <- pipeline(EIR = 1, N = 1000, years = 10, ft = 0.4,
human_only_full_save = FALSE, full_save = TRUE,
drug_list = drug_list_create(drugs = list(magenta:::drug_create_dhappq()))
)
## Seed set to 580843526
## magenta v1.3.0We have used the full_save option above so we can have a
look at all the model output. If we look at the parameter list in the
model return we can see what drug is being used in terms of the
properties that the model uses:
out <- pipeline(EIR = 1, N = 1000, years = 10, ft = 0.4,
human_only_full_save = FALSE, full_save = TRUE,
update_save = TRUE, summary_saves_only = FALSE,
drug_list = drug_list_create(drugs = list(magenta:::drug_create_dhappq()))
)
## Seed set to 7718812
## magenta v1.3.0
## Starting Stochastic Simulation for 10 years
## Running: [==========================================================================================================================================] 100% eta: It is a list of lists (here just being a list of one list). Each of
the lists is a drug being used. Since we only specified for one drug to
be used (DHA-PPQ) we only have one drug here. The information above
shows the properties we encode for a drug in magenta, which
is created using drug_create() (see documentation for more
info). The main information is which postions in the parasite genetic
barcode are related to drug resistance. Here, we can see that the first
6 positions (0:5) are related to drug properties, with the locus at
barcode positon 5 relating to prophylaxis, i.e. the partner drug.
Because there are 6 loci related to drug resistance, the vector that
determines the propbability of LPF (m_lpf) is 64 long
(2^6), and represents the probability of LPF for each genotype, i.e. for
each combination of the first 6 loci. When an individual is treated, the
probability that they fail treatment is equal to the probability of LPF
for the parasite that triggered the symptomatic infection (if there is
more than one parasite then the lowest probability across all triggering
parasites).
For 6 loci modelled in magenta for drug resistance are crt76, mdr1 86
and 184, mdr1 copy number, kelch13 580 and plasmepsion-2,3 copy number
in that order. Each loci is binary in nature, i.e. wild type (0) or
resistant (1). So for DHA-PPQ, the loci that relate to resistance are 4
and 5 (the last 2). (For full table of drugs etc see
magenta::drug_table) As a result we may expect under
DHA-PPQ, these loci to increase over time:
out <- pipeline(EIR = 1, N = 1000, years = 10, ft = 0.4,
update_length = 30,
update_save = TRUE,
human_update_save = TRUE,
summary_saves_only = TRUE,
genetics_df_without_summarising = TRUE,
full_update_save = TRUE,
num_loci = 6,
drug_list = drug_list_create(drugs = list(magenta:::drug_create_dhappq()))
)
## Seed set to 29293988
## magenta v1.3.0
## Starting Stochastic Simulation for 10 years
## Running: [==========================================================================================================================================] 100% eta: 0sSo let’s have a look at the allele frequency (af) over time
Huh,
it doesn’t. Why? Because we have a lot of control over when resistance
is allowed to be modelled. We do this by using the
drug_list more that we supply to pipeline:
# let's create a drug
drug_list <- drug_list_create(
resistance_flag = c(rep(FALSE, 5), rep(TRUE, 10)), # 5 years not modelling it followed by 10 modelling it
mft_flag = FALSE,
artemisinin_loci = 5,
absolute_fitness_cost_flag = FALSE,
partner_drug_ratios = 1,
drugs = list(magenta:::drug_create_dhappq()),
cost_of_resistance = rep(1, 6),
sequential_cycling = -1,
number_of_drugs = 1,
sequential_update = -1,
number_of_resistance_loci = 6
)
out <- pipeline(EIR = 1, N = 10000, years = 15, ft = 0.4,
update_length = 30,
update_save = TRUE,
human_update_save = TRUE,
summary_saves_only = TRUE,
genetics_df_without_summarising = TRUE,
full_update_save = TRUE,
num_loci = 6,
drug_list = drug_list)
## Seed set to 481191969
## magenta v1.3.0
## Starting Stochastic Simulation for 15 years
## Running: [==========================================================================================================================================] 100% eta: 0sAnd plot it:
By having resistance be switched on, we are thus able to allow a population to reach equilibrium without the effects and then have it come on when ready. So for example, imagine we wanted to simulate artemisinin resistance being neutral in the population and existing at 10% population level allel frequency (plaf) along with PPQ resistance and then initiate DHA-PPQ being used. We can do this as follows:
out <- pipeline(EIR = 1, N = 10000, years = 15, ft = 0.4,
update_length = 30,
update_save = TRUE,
human_update_save = TRUE,
summary_saves_only = TRUE,
genetics_df_without_summarising = TRUE,
full_update_save = TRUE,
num_loci = 6,
drug_list = drug_list,
# use the spatial importation here to have 100% of new infection to be imported
# for first 5 yeaers. This means they are drawn from the plaf
spatial_incidence_matrix = c(rep(1,5),rep(0,10)),
spatial_mosquitoFOI_matrix = c(rep(1,5),rep(0,10)),
spatial_type = "island",
# set up the plaf to be used for the first 5 years to be 1% kelch & plasmepsin
plaf=matrix(c(rep(c(0,0,0,0,0.1,0.1),5),rep(0,6*10)),ncol=6,byrow=TRUE))
## Seed set to 430911093
## magenta v1.3.0
## Starting Stochastic Simulation for 15 years
## Running: [==========================================================================================================================================] 100% eta: 0s
Summary
That is all I have had time to write so far. But there are sufficient options so far to look at the following:
- DHA-PPQ, AL, ASAQ
- Resistance arising through mutation
- Resistance arising through importation
- Multiple First Line Therapies vs Sequential Therapies
- Setting your own drug properties
- Deciding how much of each drug to use in MFT and which drugs to cycle
- Deciding the thresholds for which cycling occurs at
- Lots of other features I have not had time/forgotten about to explain