In AndySouth/resistanceGame: prototyping an insecticide resistance management computer game

This document demonstrates how a few simple equations can simulate vector populations and insecticide resistance over time depending on the use of insecticides. Parameters within the simple equations can be modified to generate different patterns.

We intend to tweak the input parameters to generate reasonable scenarios. We are looking for assessment of the scenarios themselves rather than the input parameter values. The input parameters are simply a means by which we can generate reasonable scenarios.

In the following plots time is represented on the x axis, the top panel shows insecticide use, the middle panel shows vector population and the lower panel shows resistance (phenotypic). The code included is there merely to show us as developers how the scenarios were generated.

For an interactive version of the equations used to generate these plots see https://andysouth.shinyapps.io/shinyGame4.

#### load required packages
require(resistanceGame)

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scenario 1 : no insecticide use

plot_sim( run_sim() )

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scenario 2 : continuous pyr use in presence of resistance

l_config <- read_config()
l_config2 <- config_plan(l_config, t_strt=1, t_stop=20, control_id='irs_pyr')
plot_sim( run_sim(l_config=l_config2) )

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scenario 3 : continuous use of ops with no resistance

l_config2 <- config_plan(l_config, t_strt=1, t_stop=20, control_id='irs_ops')
plot_sim( run_sim(l_config=l_config2) )

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scenario 4 : pyr used in alternate steps

l_config2 <- config_plan(l_config, t_strt=seq(1,20,2), t_stop=seq(1,20,2),
                            control_id='irs_pyr')
plot_sim( run_sim(l_config=l_config2) )

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scenario 5 : alternate use of ops and pyr

l_config2 <- config_plan(l_config, t_strt=seq(1,20), t_stop=seq(1,20), control_id=c('irs_pyr','irs_ops'))
plot_sim( run_sim(l_config=l_config2) )

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scenario 6 : 10 steps pyr, 10 steps ops

l_config2 <- config_plan(l_config, t_strt=c(1,11), t_stop=c(10,20),
                            control_id=c('irs_pyr','irs_ops'))
plot_sim( run_sim(l_config=l_config2) )

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scenario 7 : 10 steps pyr, 10 steps ddt

l_config2 <- config_plan(l_config, t_strt=c(1,11), t_stop=c(10,20),
                            control_id=c('irs_pyr','irs_ddt'))
plot_sim( run_sim(l_config=l_config2) )

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scenario 8 : no insecticide use, 50% randomness added

plot_sim( run_sim(randomness=0.5) )

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scenario 9 : same as previous but just a different randomisation

plot_sim( run_sim(randomness=0.5) )

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scenario 10 : no insecticide use, 10% randomness added

plot_sim( run_sim(randomness=0.1) )

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scenario 11 : continuous pyr use, resistance_modifier < 1 decreases effect of resistance

l_config2 <- config_plan(l_config, t_strt=1, t_stop=20, control_id='irs_pyr')
plot_sim( run_sim(l_config=l_config2, resistance_modifier=0.1) )

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scenario 12 : continuous pyr use, resistance_modifier > 1 increases effect of resistance

plot_sim( run_sim(l_config=l_config2, resistance_modifier=10) )

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scenario 13 : set a different control and resistance mechanism and control plan

l_config2 <- config_controls(l_config, control_id='new_ai', vector_id='an_gamb',
                             control_kill_rate=0.4)
l_config2 <- config_resistances(l_config2, control_id='new_ai', resistance_id="target_site",
                                resistance_strength=0.8,
                                resistance_incr=0.2,
                                resistance_decr=0.1)
l_config2 <- config_plan(l_config2, t_strt=1, t_stop=20, control_id='new_ai')
plot_sim( run_sim(l_config=l_config2) )

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scenario 14 : change emergence over time (6 tstep(month) cycle).

emergence = c(rep(0.5,6),rep(0,6))
plot_sim( run_sim(emergence=emergence, survival=0.7), plot_emergence=TRUE )

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scenario 15 : emergence gradual change.

emergence = seq(0.5,0,-0.05)
plot_sim( run_sim(emergence=emergence, survival=0.7), plot_emergence=TRUE )

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scenario 16 : emergence annual pattern of monthly values, daily time step

emergence = c(rep(0.3,180),rep(0,180))
plot_sim( run_sim(num_tsteps=360,emergence=emergence, survival=0.7), plot_emergence=TRUE )

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scenario 17 : as previous, with control

emergence = c(rep(0.3,180),rep(0,180))
l_config2 <- config_plan(l_config, t_strt=c(1,181), t_stop=c(180,360),
                         control_id=c('irs_ops','irs_pyr'))
plot_sim( run_sim(l_config=l_config2, 
                  num_tsteps=360, emergence=emergence, survival=0.7,
                  resist_incr=0.02, resist_decr = 0.01), 
          plot_emergence=TRUE )

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How controls and resistance mechanisms can be specified.

Our generic approach allows us to specify any combination of controls and resistance mechanisms. The controls cause a specified kill rate(s) on specified vector(s). The resistance mechanisms specify which controls they apply to and how fast resistance increases and decreases in the presence and absence respectively of that control. Cross resistance can be specified simply by specifiying multiple controls for one resistance mechanism.

The relationships between vectors, controls and resistance mechanisms are specified in simple configuration files. Here is a simple example of a collection of such configuration files :

places.csv

places <- read.csv( system.file('extdata','config1','places.csv', package='resistanceGame'))
print(places) 

vectors.csv

vectors <- read.csv( system.file('extdata','config1','vectors.csv', package='resistanceGame'))
print(vectors)

controls.csv

controls <- read.csv( system.file('extdata','config1','controls.csv', package='resistanceGame'))
print(controls)

resistances.csv

resistances <- read.csv( system.file('extdata','config1','resistances.csv', package='resistanceGame'))
print(resistances)

control_plan.csv

control_plan <- read.csv( system.file('extdata','config1','control_plan.csv', package='resistanceGame'))
print(control_plan)

AndySouth/resistanceGame documentation built on May 5, 2019, 6:01 a.m.