Parameter reference

fleet is not yet validated for real use; see the status note.

This article records, argument by argument, how fleet handles the user-facing parameterisation functions of the malariasimulation individual-based model (IBM), v3.0.0: whether each argument is reproduced exactly, what is lost if not, and what to do about it. The parameters argument, the list being modified, is omitted throughout. Section references (§) are to vignette("model"); vignette("using") covers what to do differently because fleet is a mean field.

Status labels

Label	Meaning	Action
Exact	Modelled exactly, or algebraically equivalently for the mean field	None. Differences from the IBM fall within its Monte Carlo noise
Approximate	Modelled, but the mean field loses something; the row says what	Suitable for comparing scenarios that share the approximation. Check against the IBM if the reported result is dominated by this mechanism
Inert	Accepted without error but has no effect on the run, by design rather than by omission	None. Do not tune it
Ignored	Not modelled; the run continues, in some cases with a warning	Decide whether the run is valid without it
Unsupported	Errors at input, or never reaches the model	Remove it from the parameter list

Where a row carries two labels, the second is a caveat on the first.

Index

Function	Status
get_parameters()	Exact
set_species()	Exact
set_equilibrium()	Exact
set_demography()	Approximate
set_drugs()	Approximate
set_clinical_treatment()	Approximate
set_antimalarial_resistance()	Approximate
set_bednets()	Approximate
set_spraying()	Approximate
set_carrying_capacity()	Approximate
set_mda(), set_smc()	Approximate
set_pmc()	Approximate
create_pev_profile()	Exact
set_pev_epi()	Approximate
set_mass_pev()	Approximate
set_tbv()	Approximate
set_epi_outputs()	Exact
set_parameter_draw()	Exact
Helpers and run functions	Mixed

get_parameters()

Argument	malariasimulation meaning	fleet
overrides	Named list overriding any default parameter	Exact. Passed through whole; see the breakdown by group below
parasite	"falciparum" or "vivax"	Exact. Both. "vivax" runs the P. vivax block (§V). MDA, SMC and PMC are refused under vivax, as malariasimulation cannot run them

overrides by group

Group	Parameters	fleet
Initial state proportions	s_proportion, d_proportion, a_proportion, u_proportion, t_proportion	Unsupported. Not read. fleet seeds at the malariaEquilibrium fixed point for init_EIR (or, under a custom demography, for the EIR the IBM’s mosquito sizing supports; see set_equilibrium()), not at user-supplied proportions
Initial immunity	init_ib, init_ica, init_iva, init_icm, init_ivm, init_id, and for vivax init_iaa, init_iam, init_hyp	Unsupported. Not read, for the same reason (vivax seeds at the malariaEquilibriumVivax solution)
Population size	human_population	Exact. Scales output counts only. The model is per capita, so run time does not depend on it
	human_population_timesteps	Unsupported. Not read. Population is conserved; there is no dynamic population size
Baseline demography	average_age	Exact. Sets $\eta = 1/\text{average_age}$, the constant death hazard and the equilibrium age structure when custom_demography = FALSE
	custom_demography	Exact. Switches to the set_demography() path
Biting heterogeneity	a0, rho	Exact. $\psi_i = 1 - \rho e^{-a_i/a_0}$
	sigma_squared	Exact. Log-normal variance of $\zeta$
	n_heterogeneity_groups	Exact. Number of Gauss–Hermite nodes $n_z$. Must be a whole number $\geq 3$: fewer nodes are not a quadrature on the log-normal at all and only move every human off it
	enable_heterogeneity	Exact. FALSE collapses to a single node with $\zeta = 1$. Under vivax the seed is the IBM’s for this case: everyone from the heterogeneous equilibrium’s first group, among mosquitoes sized from the whole of it (§V.6)
Aquatic mosquito	del, dl, dpl, me, ml, mup, gamma	Exact. Used as given in the $E$/$L$/$P_L$ equations
Adult mosquito	mum, beta, total_M, blood_meal_rates, Q0, foraging_time, species, species_proportions	Exact. Per species. total_M is re-derived: from init_EIR under the default demography, and as the IBM’s own value under a custom one (set_equilibrium())
	init_foim	Unsupported. Not read. fleet recomputes FOIM from its own seeded human infectivity, which must be consistent with its seed
Seasonality	model_seasonality, g0, g, h, rainfall_floor	Exact. Truncated Fourier rainfall drives $K_s(t) = K_{0,s}\,\text{scaler}(t)\,\text{rainfall}(t)/\bar{R}$ on a daily grid
Flexible carrying capacity	carrying_capacity, carrying_capacity_timesteps	Exact. See set_carrying_capacity(). carrying_capacity_scalers is not a get_parameters() default and cannot be passed in overrides; only set_carrying_capacity() creates it
	carrying_capacity_values	Unsupported. Not read, by fleet or by malariasimulation; only the _scalers form is used
Human disease and immunity constants	dd, dt, da, du	Exact. Back-translated to $r_D, r_T, r_A, r_U$, then converted to the IBM’s per-day exit probability $1-e^{-1/d}$ so the realised dwell matches
	rb, rc, rva, rid, rm, rvm	Exact. Immunity decay time constants $d_{I_B}, d_{I_{CA}}, d_{I_{VA}}, d_{I_D}$ and the maternal decay $d_m, d_{vm}$
	ub, uc, uv, ud	Exact. Refractory periods, applied as $u_{\text{eff}} = \lceil u\rceil - 1$ (§B.5)
	pcm, pvm	Exact. $P_M$, $P_{VM}$ maternal transfer fractions
	b0, b1, ib0, kb	Exact. $b$ Hill function
	phi0, phi1, ic0, kc	Exact. $\phi$ Hill function
	theta0, theta1, iv0, kv, fv0, av, gammav	Exact. $\theta$ Hill function and its age modifier $f_v$
	d1, id0, kd, fd0, ad, gammad	Exact. $q$ Hill function and its age modifier $f_d$
	cd, cu, ct, gamma1	Exact. Infectivity by state. ct is superseded by the drug-linked $c_T(t)$ whenever clinical drugs are set
	de, delay_gam, dem	Exact. $\tau_E$, $\tau_l$, $\tau_M$: the three delay lines, read as the IBM reads them
Vector-control shape	phi_bednets, phi_indoors, k0	Exact. Per species, in the $a(t)$/$\mu(t)$ algebra
Output bands	age_group_rendering_*, incidence_rendering_*, clinical_incidence_rendering_*, severe_incidence_rendering_*, prevalence_rendering_*	Exact. Each drives its own output family, as in malariasimulation (§G)
	ib_/id_/ica_/iva_/icm_/ivm_rendering_*, and for vivax iaa_/iam_/hypnozoites_rendering_*	Exact. Mean immunity by band, <name>_mean_*, with the band’s population as n_*, as malariasimulation renders it. idm_rendering_* names no immunity the IBM has, and renders nothing in either model
Metapopulation test-and-treat	rdt_intercept, rdt_coeff	Unsupported. Not read. They parameterise the PCR-to-RDT conversion used by run_metapop_simulation()’s mixing, which fleet does not support (Helpers and run functions)
Engine and solver	mosquito_limit, individual_mosquitoes, r_tol, a_tol, ode_max_steps, progress_bar	Unsupported. Not read. fleet is always compartmental and advances one day at a time, with no ODE solver to control
Vivax only	b, f, gammal, kmax, philm_*, alm50, klm, dpcr_*, apcr50, kpcr, ca, ra, ua	Exact under parasite = "vivax" (§V). Approximate: for kmax, the batch counts from 4 to kmax - 1 share levels in pairs, with a Poisson distribution inside each (§V.1); for ua, IAA’s 44-day refractory window runs as four 11-day stages (§V.3). The shared names (da, dd, dt, cd, cu, ct, rc, uc, rm, pcm, phi0, phi1, ic0, kc) carry the vivax values on a vivax list
	n_with_hypnozoites_rendering_*, and incidence_relapse_rendering_* set on the list	Exact. n_with_hypnozoites_*, with the band’s population as n_*, and n_inc_relapse_*

Fields specific to fleet

These four fields are not malariasimulation parameters. Each may be added to the list, and each defaults to the validated choice.

Field	Default	Effect
bite_dedup	1	P. falciparum only; vivax counts every bite, as the IBM does. 1 reproduces the IBM’s per-timestep bite deduplication (saturating hazard, §B.2). 0 uses the linear $b\varepsilon$ that malariaEquilibrium assumes, which reduces the drift of prevalence off the seed from about 0.4% to about 0.1% at EIR 20. It does not remove it, since it changes only the hazard and not the boosting or sojourn forms. Values other than 0 or 1 (TRUE/FALSE accepted) are an error
acquired_immunity_offset	0, or 0.5 for vivax	$\delta$ in the $b$/$\phi$/$\theta$ Hill functions. The IBM adds 0.5 to each person’s positive acquired immunity. Falciparum reads its curves at a stratum mean, where 0 is the better match against the IBM ensemble mean; the vivax curves are read at quadrature nodes that stand for people, and take the IBM’s 0.5 (and 0 with immunity_spread = FALSE, back at the mean). Must lie in $[0, 1]$
immunity_spread	TRUE	P. vivax only. Carries the second moment of immunity in each age, heterogeneity and batch cell, and averages the immunity curves over a gamma distribution with that mean and variance (§V.3); FALSE reads them at the cell mean, with no offset. Must be TRUE or FALSE
hold_init_EIR	FALSE	Has an effect only under set_demography(). By default fleet matches the IBM’s mosquito density rather than its nominal EIR, so the same list realises the same transmission in both models; init_EIR is then the EIR the IBM was sized at, not the one fleet realises. TRUE seeds at init_EIR directly. Must be TRUE or FALSE

set_species()

Argument	malariasimulation meaning	fleet
species	List of species parameter lists (gamb_params, arab_params, fun_params, kol_params, steph_params, or custom)	Exact. Each entry’s blood_meal_rates, foraging_time, Q0, phi_bednets, phi_indoors and mum become per-species vectors driving an independent mosquito sub-model, coupled to the others only through the shared human population
proportions	Relative abundance, summing to 1	Exact. Splits total_M and hence each species’ baseline carrying capacity. A species at proportion 0 stays inert; its $K_0$ is floored at a negligible positive value so the larval term does not evaluate $0/0$

set_equilibrium()

Argument	malariasimulation meaning	fleet
init_EIR	Target EIR to seed from	Exact. Stored as parameters$init_EIR, which is where run_simulation_ode() reads it; the function has no init_EIR argument of its own. It means the same in both models: under the default demography it is the EIR fleet realises; under set_demography() it sizes the mosquito population exactly as the IBM’s does, and fleet seeds at the EIR that density supports (set_demography())
eq_params	Custom malariaEquilibrium parameter set	Exact. Merged over fleet’s own back-translation, taking precedence. set_equilibrium() writes this field even when passed NULL (storing its own back-translation), so after any call to it that stored set, not R/translate_params.R, supplies every shared constant (§F)
EIR_population_input	"adult" (default) or "total"	Exact. Handled upstream: malariasimulation converts a total-population EIR to adult EIR before storing it, and fleet always interprets init_EIR as adult EIR (bites per adult per year)

set_equilibrium() also calls parameterise_mosquito_equilibrium(), which sizes the adult mosquito population total_M from the human equilibrium under the IBM’s default exponential age structure (equilibrium_total_M(); custom mortality never enters). fleet never reads the stored parameters$total_M. It re-derives the quantity itself, in one of two ways:

• Default demography. total_M is chosen so that $\sum_s a_s I_{M,s} = \text{init_EIR}/365$ holds exactly under the human infectivity of fleet’s own equilibrium at the treatment coverage in force at timestep 1. This is the IBM’s formula evaluated on fleet’s grid, so the two agree up to discretisation and init_EIR is the EIR the mosquitoes deliver. The humans themselves are seeded under the coverage at timestep 0, as the IBM seeds them (§F).
• Custom demography (set_demography()). Holding init_EIR here would give the two models different mosquito populations, and so different transmission, from the same parameter list: under the custom age structure the IBM’s mosquito density supports less (or more) transmission, and the IBM drifts there. fleet therefore reproduces the IBM’s total_M exactly (ibm_total_M(): the same human_equilibrium() call on the IBM’s 0–99.9 y grid and the same equilibrium_total_M() arithmetic), root-finds the EIR at which its own equilibrium under the custom age structure has that density, and seeds there. parameters$hold_init_EIR = TRUE seeds at init_EIR instead. Either way the seed is the malariaEquilibrium solution at that EIR, so it relaxes slightly, as every seed does (§F).

Calling parameterise_total_M() or parameterise_mosquito_equilibrium() directly does no harm and has no effect.

set_demography()

Argument	malariasimulation meaning	fleet
agegroups	Upper edges of the death-rate age groups, in days	Exact. Model age groups are binned into them by midpoint using right-closed intervals, matching .bincode(age, c(0, agegroups))
timesteps	When each death-rate row takes effect	Exact. Knots of a constant-interpolated $\mu_i(t)$, so custom demography is time-varying: a demographic transition is modelled, not frozen. Each row applies from its own timestep, as the IBM reads it (§H)
deathrates	[length(timesteps) × length(agegroups)] daily death rates	Exact. Used directly as $\mu_i(t)$. The $t = 0$ row also sets the equilibrium age structure onto which the human seed is rescaled. The open-ended top model group is looked up one day above its lower edge, so it takes the rate of the band it belongs to rather than the one below. Approximate: model ages above the top agegroups edge take the top rate, whereas the IBM removes them. A warning is given; raise default_age_lower(max_age =) or extend agegroups to match

With a custom demography the mosquito population is sized as the IBM sizes it (see set_equilibrium()): fleet takes the IBM’s total_M and seeds at the EIR that its own equilibrium under this age structure supports, which is generally not init_EIR. An older population sustains less transmission from the same mosquitoes: the comparison article’s scenario seeds at EIR 13.9 for init_EIR = 20, matching the IBM’s realised 13.8.

set_drugs()

Argument	malariasimulation meaning	fleet
drugs	List of 4-element vectors c(efficacy, rel_c, prophylaxis_shape, prophylaxis_scale) (for example AL_params, DHA_PQP_params, SP_AQ_params)	Approximate. Each element is handled as below
drug_efficacy	Probability treatment clears the infection	Exact. Multiplies coverage into $f_t^{\text{eff}}$, and multiplies chemoprevention pulse fractions
drug_rel_c	Infectivity of a treated case relative to cd	Exact. $c_T = c_d \times \text{rel_c}$, coverage-share-weighted across active drugs and time-varying
drug_prophylaxis_shape, drug_prophylaxis_scale	Weibull prophylaxis survival	Exact in both moments, on the protected days a dose gives, $W(1), W(2), \dots$ The post-treatment chain carries the protected days left once out of $T$, $\sum_n (1-(1-r_T)^{n-1})W(n)$ (§B.4), and its length $k_P$ matches the variance of the whole $T + P$ sojourn: 9 stages for SP-AQ, 10 for DHA-PQP, 1 for AL. The chemoprevention chain, with no $T$ stage in front, matches the dose’s own mean and variance: 10 stages for SP-AQ, 9 for DHA-PQP. No chain has more stages than its mean has days, and none more than 20. ode_tuning(n_ph =) overrides the count. Approximate: a stage chain is not a Weibull, and it protects fully while it lasts, where the IBM’s protection is partial and fades. The $T$ stage adds variability the IBM’s parallel clock does not have, so for AL the protection curve is reproduced in its integral, not its shape
drug_hypnozoite_efficacy, drug_hypnozoite_prophylaxis_shape, drug_hypnozoite_prophylaxis_scale	P. vivax radical cure	Exact. The treated lose every batch with the hypnozoite efficacy, drawn independently of blood-stage clearance, and start liver-stage protection: a chain of levels matched to the protected days, $M^2/(V+M)$ stages, eleven for tafenoquine (§V.5). Approximate: a chain protects fully while it lasts; there is none where the protection ends inside the blood-stage protection given with it, as primaquine’s does inside chloroquine’s

set_clinical_treatment()

Argument	malariasimulation meaning	fleet
drug	Index into the drug table	Exact. Identifies which drug’s efficacy, rel_c and prophylaxis enter the blend
timesteps	When coverage changes	Exact. Knots of the step-interpolated $f_t(t)$ and of the drug-mix series
coverages	Fraction of clinical cases treated with this drug	Exact. $f_t(t) = \min\left(1, \sum_{\text{drugs}} \text{cov}_d(t)\right)$. set_clinical_treatment() errors if the summed coverage exceeds 1 at any timestep, so the cap at 1 is defensive and never binds on a valid parameter set. Approximate: the drug-linked quantities $\text{eff}(t)$, $c_T(t)$, $r_P(t)$ are blended by each drug’s instantaneous coverage share, so a first-line switch is modelled, but a mixed first line is represented by its blend rather than as parallel sub-populations

set_antimalarial_resistance()

Argument	malariasimulation meaning	fleet
drug	Which clinical drug carries resistance	Exact. Matched to the clinical-treatment drug index, and to the MDA, SMC and PMC drug, so a resistant chemoprevention drug clears fewer infections
timesteps	When resistance levels change	Exact. Knots of $\text{ETF}(t)$ and $\text{SPC}(t)$, merged with the treatment-coverage change times
artemisinin_resistance_proportion	Fraction of infections that are artemisinin-resistant	Exact. Multiplies both the ETF and SPC probabilities
partner_drug_resistance_proportion	Partner-drug resistance	Unsupported. malariasimulation requires 0, so it cannot reach the model
slow_parasite_clearance_probability	Probability a treated resistant case clears slowly	Exact. $\text{SPC}(t)$, the split of the treated inflow between $T$ and $T_s$, coverage-share-weighted across drugs
early_treatment_failure_probability	Probability treatment fails early	Exact. $\text{ETF}(t)$ reduces $f_t^{\text{eff}}$, diverting those cases to $D$
late_clinical_failure_probability	Late clinical failure	Unsupported. malariasimulation requires 0
late_parasitological_failure_probability	Late parasitological failure	Unsupported. malariasimulation requires 0
reinfection_during_prophylaxis_probability	Reinfection while prophylactic	Unsupported. malariasimulation requires 0
slow_parasite_clearance_time	Mean duration of slow clearance	Approximate. $r_T^{\text{slow}} = 1 - e^{-1/\text{dt_slow}}$, a single scalar with the same whole-day conversion as the other sojourns (§B.4). With one resistant drug the blend is exact; with several it is the coverage-weighted blend evaluated at peak resistance (peak rather than final, so a rise-then-fall schedule is not collapsed to zero)

set_bednets()

Status: approximate. Net efficacy is averaged over the net-using fraction into per-species $a_s(t)$ and $\mu_s(t)$, so protection is not correlated within individuals across bites. Within that, every argument is handled exactly.

Argument	malariasimulation meaning	fleet
timesteps	Distribution dates	Exact. Every past distribution contributes: at each grid time the population is the mixture over all rounds, each weighted by its most-recent-receipt probability $\text{cov}_d\prod_{j>d}(1-\text{cov}_j)$
coverages	Fraction receiving a net at each distribution	Exact. As above; repeated distributions accumulate rather than only the latest applying
dn0	[n_timesteps × n_species] probability a net kills a mosquito	Exact. Decayed by net age: $d_{n}(s_n) = d_{n0}e^{-s_n/\gamma_n}$
rn	[n_timesteps × n_species] initial repelling probability	Exact. Decayed towards rnm: $r_n(s_n) = (r_n - r_{nm})e^{-s_n/\gamma_n} + r_{nm}$
rnm	[n_timesteps × n_species] minimum (asymptotic) repelling probability	Exact. As above
gamman	Insecticide decay time constant per distribution	Exact. The $\gamma_n$ above; each distribution decays on its own clock
retention	Mean net-retention time (log-uniform model)	Exact. Exponential usage survival $e^{-s_n/\text{retention}}$
logistic_half_life, logistic_k	Alternative logistic retention	Exact. The survival of the IBM’s logistic retention time: $S(s_n) = \exp\!\left(-k\frac{r^2}{1-r^2}\right)$ for $r = s_n/l < 1$ (else 0), with $l = \text{half_life}/\sqrt{1 - k/(k-\log 0.5)}$, which gives $S(\text{half_life}) = 0.5$ exactly

set_spraying()

Status: approximate. As for nets, protection is population-averaged, and exact within that. Sprayed protection never expires in the IBM, so the mixture over past rounds has no retention factor.

Argument	malariasimulation meaning	fleet
timesteps	Spray dates	Exact. Mixture over all past rounds, weighted by most-recent-spray probability
coverages	Fraction of houses sprayed	Exact. As above
ls_theta, ls_gamma	[n_timesteps × n_species] mortality logistic parameters	Exact. $l_s(s_s) = \text{logit}^{-1}(\theta + \gamma s_s)$
ks_theta, ks_gamma	Feeding-success logistic parameters	Exact. $k_s = k_0\,\text{logit}^{-1}(\theta + \gamma s_s)$
ms_theta, ms_gamma	Deterrence logistic parameters	Exact. $m_s = \text{logit}^{-1}(\theta + \gamma s_s)$

The repellency $r_s$ and survival $s_s$ derived from these are the same spray outcome per individual, so fleet forms the joint mean $\overline{(1-r_s)s_s}$ rather than multiplying two independent averages. The IBM recomputes these every timestep; fleet reads $a_s(t)$ and $\mu_s(t)$ once a day from series that follow the within-round logistic decay.

set_carrying_capacity()

Argument	malariasimulation meaning	fleet
timesteps	When each scaler row takes effect	Exact. Step change in $K_s(t)$, composed multiplicatively with seasonality. $K_s$ is read at the start of each day, as the IBM reads it, so a change scheduled for timestep $t$ applies from day $t + 1$, as in the IBM (§D, §H)
carrying_capacity_scalers	[n_timesteps × n_species] multipliers on baseline $K_0$	Exact. Multiplies baseline $K_0$ per species. Approximate: a scaler of exactly 0 is floored at $K_0 \times 10^{-4}$ to keep the larval term finite, so complete vector elimination is approached but not reproduced exactly

set_mda() and set_smc()

set_smc() takes the same arguments as set_mda() and is handled identically.

Status: approximate. A mass campaign is applied as a pulse rather than as per-individual events, and its protection is all or nothing while it lasts. As in the IBM, the clinical and LM-detectable asymptomatic cases a round treats pass through the treated state first, detectable and infectious for the Tr stay.

Argument	malariasimulation meaning	fleet
drug	Drug administered	Exact. Its drug_efficacy scales the treated fraction. Its Weibull protection sets the chains’ means and stage counts: $k = M^2/(V+M)$ for the protection from the dose, and the post-treatment rule for the protection left after the Tr stay (§B.4). Its drug_rel_c scales the infectivity of the treated. Antimalarial-resistance ETF on this drug reduces the treated fraction, and SPC sends its share of the detectable to the slow-clearing treated state
timesteps	Round dates	Exact. The run pauses after each round’s day; the pulse takes effect the day after the scheduled timestep
coverages	Fraction of the target band reached	Exact. Treated fraction $= \text{coverage}\times\text{efficacy}\times(1-\text{ETF})\times o_i$. A zero-coverage round has no effect
min_ages, max_ages	Per-round vectors (one entry per timesteps entry) giving the target band in days, inclusive at both ends	Exact. Per-round bands are honoured, each mapped onto the model age grid by fractional overlap $o_i$, so narrow bands are not dropped and coarse groups are not over-treated. The absorbing top group is treated as fully covered if and only if the band reaches its lower edge. Approximate: fleet treats the band as half-open, $[\ell, u)$, one day narrower than the IBM’s; this is negligible except for very narrow (PMC-style) bands

Co-deployed chemoprevention types (SMC, MDA, PMC) share the two chemoprevention chains, moment-matched to the mixture of their drugs’ protection. Each drug is weighted by its type’s total scheduled coverage (the sum over all its rounds), so a type with more rounds carries proportionally more weight, whatever its per-round coverage.

set_pmc()

Argument	malariasimulation meaning	fleet
drug	Drug administered	Exact. As for MDA and SMC
timesteps	Coverage-change schedule (not dose dates; PMC delivery is age-triggered)	Exact. Read as a coverage schedule
coverages	Fraction of infants receiving each dose	Exact. The coverage in force at each pulse time is used
ages	Ages (days) at which doses are given	Approximate. The IBM gives a dose when an individual reaches each dose age. fleet approximates this as pulses every 30 days over a 30-day band starting at each dose age, with per-group overlap weighting, keeping the effective dose at about coverage × efficacy

create_pev_profile()

Argument	malariasimulation meaning	fleet
vmax	Maximum efficacy	Exact.
alpha, beta	Hill shape and scale mapping antibody titre to efficacy	Exact.
cs	c(mu, sigma) of $\log$ peak antibody titre	Exact, including the sigma
rho	c(mu, sigma) of the logit short-lived antibody fraction	Exact, including the sigma
ds, dl	c(mu, sigma) of $\log$ short and long antibody half-lives	Exact, including the sigmas

malariasimulation re-draws all four antibody parameters from their profile distributions at every timestep, for every vaccinated individual; they are not stored per person. Population efficacy is therefore exactly $\mathbb{E}\left[\text{Hill}(\text{Ab})\right]$, not $\text{Hill}(\text{Ab at the median})$. The sigmas are large and the Hill function is nonlinear, so the two differ materially: evaluating at the median overstated R21 efficacy by up to about 4.7 percentage points. fleet integrates over the 4-D distribution with a tensor Gauss–Hermite rule (options(fleet.pev_gq =), default 7 nodes per axis), validated against a Monte Carlo of the IBM’s own sampler to under $5\times10^{-4}$. Because the IBM’s draws are independent between days and between people, there is no within-person correlation for the mean field to lose, and the quadrature is exact rather than approximate. The mean-efficacy curve is cached per profile on a daily grid.

set_pev_epi()

Status: approximate. The routine schedule and the full booster sequence are modelled; seasonal boosters are approximated, with a warning.

Argument	malariasimulation meaning	fleet
profile	Primary-series PEV profile	Exact.
coverages	Coverage of the routine programme over time	Exact. Time-varying: each cohort is protected at the coverage in force on its own first-dose date, not at a single coverage
timesteps	When each coverage takes effect	Exact. Also gates eligibility: a cohort is protected only once its first dose falls inside the programme. Gating follows malariasimulation, which keys on pev_epi_coverages/pev_epi_timesteps and never reads parameters$pev
age	Age (days) at the first dose	Exact. Efficacy starts at age + max(pev_doses), after the final primary dose
min_wait	Minimum time since the last vaccination	Inert. For a routine EPI programme this guards re-vaccination and seasonal-booster timing, not the primary-plus-booster schedule fleet models, so it has no effect on the result. For mass campaigns it does matter; see set_mass_pev()
booster_spacing	Days from the final primary dose to each booster	Exact. The full booster sequence is modelled: the vaccinated are partitioned by the most recent booster each has reached, and each stratum carries its own decayed efficacy
booster_coverage	Matrix of conditional booster coverages	Exact. Each booster’s coverage is read at its own administration date (matching coverage[match_timestep(timesteps, admin_time), booster]), which reduces to the single-row lookup in the usual case
booster_profile	Per-booster profiles	Exact. Each booster uses its own profile, integrated over the antibody distribution as in create_pev_profile()
seasonal_boosters	Times the first booster relative to the start of the calendar year: booster_spacing[1] becomes a day of the year. It makes no reference to the fitted seasonality; the user chooses the day, for example with peak_season_offset()	Approximate. Treated as a fixed days-since-primary schedule. fleet warns when this is set

set_mass_pev()

Status: approximate. Repeated campaigns are the weak point: min_wait re-vaccination exclusion is not applied, and each campaign reaches people independently of the last. A campaign’s protection ages with the cohort it vaccinated, and campaigns combine as the most recent dose, as in the IBM. A single campaign is unaffected.

Argument	malariasimulation meaning	fleet
profile	Primary-series profile	Exact.
timesteps	Campaign dates	Exact. The IBM keeps only the most recent vaccination per person, so the population protection is a most-recent-dose mixture, $\sum_k c_k w_k \prod_{j>k}(1 - c_j w_j)\,e_k$ over completed campaigns, with $w$ each group’s share of a campaign’s cohort. This is the structure fleet uses for bed nets. Approximate: reaching someone in one campaign does not make them more likely to be reached in the next
coverages	Coverage per campaign (recycled if length 1)	Exact.
min_ages, max_ages	Target age bands	Exact. Every band is applied at every campaign, each mapped onto the model age grid by fractional overlap as for MDA and SMC, so a band narrower than the groups it falls between still vaccinates its share rather than nobody. The vaccinated then age: at each later day the band has moved up by the time since the campaign, so protection stays with the cohort that received it and nobody born since shares it. The bands of one campaign are one campaign: their weights add into a single covered fraction per group, capped at 1. A band overlapping no group gives a warning
min_wait	Minimum time since the last vaccination	Ignored, and it matters here. In malariasimulation each campaign excludes anyone vaccinated within min_wait of it, so with repeated campaigns this is the main control on who is re-vaccinated. fleet treats every campaign as reaching its whole target band independently, so a min_wait longer than the campaign spacing leads to over-vaccination
booster_spacing	Days from the final primary dose to each booster	Exact. Full sequence, as in set_pev_epi()
booster_coverage	Conditional booster coverage matrix	Exact. Read at each booster’s administration date
booster_profile	Per-booster profiles	Exact.

set_tbv()

Argument	malariasimulation meaning	fleet
timesteps	Vaccination dates	Exact. Antibody titre decays from each round’s date. The IBM overwrites each person’s vaccination date, and rounds combine as the most recent dose, as for mass PEV. Approximate: each round reaches people independently of the last
coverages	Fraction vaccinated	Exact.
ages	Integer ages (years) vaccinated	Exact. Each year $y$ is the day interval $[365y, 365(y+1))$, matched to the age groups by fractional overlap, and the vaccinated then age, the year set moving up with them. Overlap has to be fractional because, between its pinned ages, the default grid puts edges wherever its group budget falls (above 15, every 0.38 years), so a whole-year target need not line up with any group edge. A set overlapping no group gives a warning

Transmission-reducing activity is mapped to state-specific transmission-blocking activity by the IBM’s own transform $\text{TBA}(m_x, k, \text{TRA})$, with per-state $m_x \in \{\texttt{tbv_mu}, \texttt{tbv_ma}, \texttt{tbv_md}, \texttt{tbv_mt}\}$, giving four independent per-age multipliers on $U$, $A$, $D$ and $T$ infectivity. The tbv_* shape constants (tbv_tau, tbv_rho, tbv_ds, tbv_dl, tbv_tra_mu, tbv_gamma1, tbv_gamma2, tbv_k) are used as given.

set_epi_outputs()

Argument	malariasimulation meaning	fleet
age_group	Bands for population counts	Exact. n_age_*, also emitted over the union of all other families’ bands, sorted, as malariasimulation does
incidence	Bands for all-infection incidence	Exact. n_inc_*, and p_inc_* (0, as malariasimulation 3.0.0 renders it)
clinical_incidence	Bands for clinical incidence	Exact. n_inc_clinical_*, p_inc_clinical_*
severe_incidence	Bands for severe incidence	Exact. n_inc_severe_*, p_inc_severe_*
prevalence	Bands for prevalence	Exact. n_detect_lm_*, p_detect_lm_* (the expected number detected, not a prevalence), n_detect_pcr_*
ica, icm, iva, ivm, id, ib	Bands for mean immunity	Exact. <name>_mean_*, with the band’s population as n_*
n_with_hypnozoites, hypnozoites, iaa, iam	P. vivax only	Exact. n_with_hypnozoites_* and <name>_mean_*, each with the band’s population as n_*

A family whose band list is empty renders nothing, as in the IBM. A bare get_parameters() leaves four of the five families empty and sets the prevalence bands to 2–10 y, so it renders the 2–10 prevalence columns and no incidence. A band [lower, upper] holds the whole-day ages lower to upper, both ends included, and its tag is formatted as the IBM formats it (n_age_182.5_1825.5, n_inc_clinical_1e.05_2e.05). Every n_*/p_* pair is the IBM’s sampled count and its expectation; a mean field’s expected count is both.

set_parameter_draw()

Argument	malariasimulation meaning	fleet
draw	Index 1–1000 into the fitted posterior draws	Exact. malariasimulation overwrites the immunity and disease constants in the parameter list in place; they then pass through fleet’s back-translation like any other parameter. iv0 is read from the live list: set_equilibrium()’s stored copy holds malariaEquilibrium’s default whatever the draw, because malariasimulation’s back-translation table has that entry the wrong way round, and the IBM’s severe disease reads the live value. Must be called before set_equilibrium(), as in the IBM

Helpers and run functions

Function	malariasimulation meaning	fleet
peak_season_offset(parameters)	Day of peak seasonal transmission	Exact. A pure helper on g0, g and h, which fleet reads; useful for timing SMC and booster schedules
get_correlation_parameters(parameters)	Correlation between intervention recipients	Unsupported. Individual-level, with no mean-field analogue
run_simulation(timesteps, parameters, correlations)	Run the IBM	Exact. run_simulation_ode(timesteps, parameters, correlations, tuning) mirrors the first three arguments; tuning carries the discretisation settings, so nothing epidemiological sits outside the parameter list. A non-NULL correlations is ignored with a warning: the model assumes independence between interventions
run_simulation_with_repetitions(...)	Repeat stochastic runs	Unsupported. Not applicable: fleet is deterministic, so one run is the ensemble mean
run_metapop_simulation(timesteps, parameters, ..., export_mixing, import_mixing)	Coupled patches	Unsupported. fleet is a single patch. There is no guard, so a metapopulation setup passed to it is not honoured
run_resumable_simulation(...)	Resume from saved state	Unsupported. Not implemented. fleet runs are cheap enough to re-run from the seed