diff --git a/.github/workflows/benchmark.yaml b/.github/workflows/benchmark.yaml
index 04802aabd..87fc96820 100644
--- a/.github/workflows/benchmark.yaml
+++ b/.github/workflows/benchmark.yaml
@@ -21,10 +21,7 @@ jobs:
       - name: Install dependencies
         run: |
           julia --project=benchmark -e 'using Pkg;
-          Pkg.rm("EpiAware");
-          Pkg.resolve();
-          Pkg.instantiate();
-          Pkg.develop(path = "./EpiAware")'
+          Pkg.instantiate()'
       - name: Run benchmarks
         run: julia --project=benchmark -e 'using BenchmarkCI; BenchmarkCI.judge(; baseline = "origin/main", retune = true)'
       - name: Post results
diff --git a/.gitignore b/.gitignore
index 8ad444c64..58790f702 100644
--- a/.gitignore
+++ b/.gitignore
@@ -391,3 +391,5 @@ EpiAware/docs/src/examples/*.md
 EpiAware/docs/src/getting-started/tutorials/censored-obs.md
 */Manifest*.toml
 EpiAware/docs/Manifest*.toml
+
+!benchmark/Manifest.toml
diff --git a/EpiAware/Project.toml b/EpiAware/Project.toml
index 91561cb00..0b618be0d 100644
--- a/EpiAware/Project.toml
+++ b/EpiAware/Project.toml
@@ -17,6 +17,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 ManifoldDiff = "af67fdf4-a580-4b9f-bbec-742ef357defd"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Pathfinder = "b1d3bc72-d0e7-4279-b92f-7fa5d6d2d454"
 QuadGK = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -33,19 +34,20 @@ AdvancedHMC = "0.6"
 DataFramesMeta = "0.15"
 Distributions = "0.25"
 DocStringExtensions = "0.9"
-DynamicPPL = "0.27, 0.28, 0.29, 0.30"
+DynamicPPL = "0.30"
 FillArrays = "1.11"
 HybridArrays = "0.4.16"
 LinearAlgebra = ">= 1.9"
 LogExpFunctions = "0.3"
 MCMCChains = "6.0"
 ManifoldDiff = "0.3.10"
-Pathfinder = "0.8"
+OrdinaryDiffEq = "6.89.0"
+Pathfinder = "0.9"
 QuadGK = "2.9"
-Random = ">= 1.9"
+Random = "1.11"
 Reexport = "1.2"
-SparseArrays = "1.10"
-Statistics = "1.10"
+SparseArrays = "1.11"
+Statistics = "1.11"
 Tables = "1.11"
-Turing = "0.32, 0.33, 0.34"
-julia = ">= 1.9"
+Turing = "0.35"
+julia = "1.11"
diff --git a/EpiAware/src/EpiAwareUtils/prefix_submodel.jl b/EpiAware/src/EpiAwareUtils/prefix_submodel.jl
index 85d95bd68..ccf85800d 100644
--- a/EpiAware/src/EpiAwareUtils/prefix_submodel.jl
+++ b/EpiAware/src/EpiAwareUtils/prefix_submodel.jl
@@ -20,7 +20,7 @@ submodel = prefix_submodel(FixedIntercept(0.1), generate_latent, string(1), 2)
 
 We can now draw a sample from the submodel.
 
-```julia
+```@example
 rand(submodel)
 ```
 "
diff --git a/EpiAware/src/EpiInfModels/EpiInfModels.jl b/EpiAware/src/EpiInfModels/EpiInfModels.jl
index 22bc6498e..0c08a120e 100644
--- a/EpiAware/src/EpiInfModels/EpiInfModels.jl
+++ b/EpiAware/src/EpiInfModels/EpiInfModels.jl
@@ -6,10 +6,15 @@ module EpiInfModels
 using ..EpiAwareBase
 using ..EpiAwareUtils
 
-using Turing, Distributions, DocStringExtensions, LinearAlgebra, LogExpFunctions
+using LogExpFunctions: xexpy
+
+using Turing, Distributions, DocStringExtensions, LinearAlgebra, OrdinaryDiffEq
+
+#Export parameter helpers
+export EpiData
 
 #Export models
-export EpiData, DirectInfections, ExpGrowthRate, Renewal
+export DirectInfections, ExpGrowthRate, Renewal, ODEProcess
 
 #Export functions
 export R_to_r, r_to_R, expected_Rt
@@ -20,6 +25,7 @@ include("DirectInfections.jl")
 include("ExpGrowthRate.jl")
 include("RenewalSteps.jl")
 include("Renewal.jl")
+include("ODEProcess.jl")
 include("utils.jl")
 
 end
diff --git a/EpiAware/src/EpiInfModels/ODEProcess.jl b/EpiAware/src/EpiInfModels/ODEProcess.jl
new file mode 100644
index 000000000..ab7cc6510
--- /dev/null
+++ b/EpiAware/src/EpiInfModels/ODEProcess.jl
@@ -0,0 +1,306 @@
+@doc raw"""
+A structure representing an infection process modeled by an Ordinary Differential Equation (ODE).
+At a high level, an `ODEProcess` struct object combines:
+
+- An `AbstractTuringParamModel` which defines the ODE model in terms of `OrdinaryDiffEq` types,
+     the parameters of the ODE model and a method to generate the parameters.
+- A technique for solving and interpreting the ODE model using the `SciML` ecosystem. This includes
+    the solver used in the ODE solution, keyword arguments to send to the solver and a function
+    to map the `ODESolution` solution object to latent infections.
+
+# Constructors
+- `ODEProcess(prob::ODEProblem; ts, solver, sol2infs)`: Create an `ODEProcess`
+object with the ODE problem `prob`, time points `ts`, solver `solver`, and function `sol2infs`.
+
+# Predefined ODE models
+Two basic ODE models are provided in the `EpiAware` package: `SIRParams` and `SEIRParams`.
+In both cases these are defined in terms of the proportions of the population in each compartment
+of the SIR and SEIR models respectively.
+
+## SIR model
+
+```math
+\begin{aligned}
+\frac{dS}{dt} &= -\beta SI \\
+\frac{dI}{dt} &= \beta SI - \gamma I \\
+\frac{dR}{dt} &= \gamma I
+\end{aligned}
+```
+Where `S` is the proportion of the population that is susceptible, `I` is the proportion of the
+population that is infected and `R` is the proportion of the population that is recovered. The
+parameters are the infectiousness `β` and the recovery rate `γ`.
+
+```jldoctest sirexample; output = false
+using EpiAware, OrdinaryDiffEq, Distributions
+
+# Create an instance of SIRParams
+sirparams = SIRParams(
+    tspan = (0.0, 100.0),
+    infectiousness = LogNormal(log(0.3), 0.05),
+    recovery_rate = LogNormal(log(0.1), 0.05),
+    initial_prop_infected = Beta(1, 99)
+)
+nothing
+
+# output
+
+```
+
+## SEIR model
+
+```math
+\begin{aligned}
+\frac{dS}{dt} &= -\beta SI \\
+\frac{dE}{dt} &= \beta SI - \alpha E \\
+\frac{dI}{dt} &= \alpha E - \gamma I \\
+\frac{dR}{dt} &= \gamma I
+\end{aligned}
+```
+Where `S` is the proportion of the population that is susceptible, `E` is the proportion of the
+population that is exposed, `I` is the proportion of the population that is infected and `R` is
+the proportion of the population that is recovered. The parameters are the infectiousness `β`,
+the incubation rate `α` and the recovery rate `γ`.
+
+```jldoctest; output = false
+using EpiAware, OrdinaryDiffEq, Distributions, Random
+Random.seed!(1234)
+
+# Create an instance of SIRParams
+seirparams = SEIRParams(
+    tspan = (0.0, 100.0),
+    infectiousness = LogNormal(log(0.3), 0.05),
+    incubation_rate = LogNormal(log(0.1), 0.05),
+    recovery_rate = LogNormal(log(0.1), 0.05),
+    initial_prop_infected = Beta(1, 99)
+)
+nothing
+
+# output
+
+```
+
+# Usage example with `ODEProcess` and predefined SIR model
+
+In this example we define an `ODEProcess` object using the predefined `SIRParams` model from
+above. We then generate latent infections using the `generate_latent_infs` function, and refit
+the model using a `Turing` model.
+
+We assume that the latent infections are observed with a Poisson likelihood around their
+ODE model prediction. The population size is `N = 1000`, which we put into the `sol2infs` function,
+which maps the ODE solution to the number of infections. Recall that the `EpiAware` default SIR
+implementation assumes the model is in density/proportion form. Also, note that since the `sol2infs`
+function is a link function that maps the ODE solution to the expected number of infections we also
+apply the `LogExpFunctions.softplus` function to ensure that the expected number of infections is non-negative.
+Note that the `softplus` function is a smooth approximation to the ReLU function `x -> max(0, x)`.
+The utility of this approach is that small negative output from the ODE solver (e.g. ~ -1e-10) will be
+mapped to small positive values, without needing to use strict positivity constraints in the model.
+
+First, we define the `ODEProcess` object which combines the SIR model with the `sol2infs` link
+function and the solver options.
+
+```jldoctest sirexample; output = false
+using Turing, LogExpFunctions
+N = 1000.0
+
+sir_process = ODEProcess(
+    params = sirparams,
+    sol2infs = sol -> softplus.(N .* sol[2, :]),
+    solver_options = Dict(:verbose => false, :saveat => 1.0)
+)
+nothing
+
+# output
+
+```
+
+Second, we define a `PoissionError` observation model for linking the  the number of infections.
+
+```jldoctest sirexample; output = false
+pois_obs = PoissonError()
+nothing
+
+# output
+
+```
+
+Next, we create a `Turing` model for the full generative process: this solves the ODE model for
+the latent infections and then samples the observed infections from a Poisson distribution with this
+as the average.
+
+NB: The `nothing` argument is a dummy latent process, e.g. a log-Rt time series, that is not
+used in the SIR model, but might be used in other models.
+
+```jldoctest sirexample; output = false
+@model function fit_ode_model(data)
+    @submodel I_t = generate_latent_infs(sir_process, nothing)
+    @submodel y_t = generate_observations(pois_obs, data, I_t)
+
+    return y_t
+end
+nothing
+
+# output
+
+```
+
+We can generate some test data from the model by passing `missing` as the argument to the model.
+This tells `Turing` that there is no data to condition on, so it will sample from the prior parameters
+and then generate infections. In this case, we do it in a way where we cache the sampled parameters
+as `θ` for later use.
+
+```jldoctest sirexample; output = false
+# Sampled parameters
+gen_mdl = fit_ode_model(missing)
+θ = rand(gen_mdl)
+test_data = (gen_mdl | θ)()
+nothing
+
+# output
+
+```
+
+Now, we can refit the model but this time we condition on the test data. We suppress the
+output of the sampling process to keep the output clean, but you can remove the `@suppress` macro.
+
+```jldoctest sirexample; output = false
+using Suppressor
+inference_mdl = fit_ode_model(test_data)
+chn = Suppressor.@suppress sample(inference_mdl, NUTS(), 2_000)
+summarize(chn)
+nothing
+
+# output
+
+```
+
+We can compare the summarized chain to the sampled parameters in `θ` to see that the model is
+fitting the data well and recovering a credible interval containing the true parameters.
+
+# Custom ODE models
+
+To define a custom ODE model, you need to define:
+
+- Some `CustomModel <: AbstractTuringLatentModel` struct
+    that contains the ODE problem as a field called `prob`, as well as sufficient fields to
+    define or sample the parameters of the ODE model.
+- A method for `EpiAwareBase.generate_latent(params::CustomModel, Z_t)` that generates the
+    initial condition and parameters of the ODE model, potentially conditional on a sample from a latent process `Z_t`.
+    This method must return a `Tuple` `(u0, p)` where `u0` is the initial condition and `p` is the parameters.
+
+Here is an example of a simple custom ODE model for _specified_ exponential growth:
+
+```jldoctest customexample; output = false
+using EpiAware, Turing, OrdinaryDiffEq
+# Define a simple exponential growth model for testing
+function expgrowth(du, u, p, t)
+    du[1] = p[1] * u[1]
+end
+
+r = log(2) / 7 # Growth rate corresponding to 7 day doubling time
+
+# Define the ODE problem using SciML
+prob = ODEProblem(expgrowth, [1.0], (0.0, 10.0), [r])
+
+# Define the custom parameters struct
+struct CustomModel <: AbstractTuringLatentModel
+    prob::ODEProblem
+    r::Float64
+    u0::Float64
+end
+custom_ode = CustomModel(prob, r, 1.0)
+
+# Define the custom generate_latent function
+@model function EpiAwareBase.generate_latent(params::CustomModel, n)
+    return ([params.u0], [params.r])
+end
+nothing
+
+# output
+
+```
+
+This model is not random! But we can still use it to generate latent infections.
+
+```jldoctest customexample; output = false
+# Define the ODEProcess
+expgrowth_model = ODEProcess(
+    params = custom_ode,
+    sol2infs = sol -> sol[1, :]
+)
+infs = generate_latent_infs(expgrowth_model, nothing)()
+nothing
+
+# output
+
+```
+"""
+@kwdef struct ODEProcess{
+    P <: AbstractTuringLatentModel, S, F <: Function, D <:
+                                                      Union{Dict, NamedTuple}} <:
+              EpiAwareBase.AbstractTuringEpiModel
+    "The ODE problem and parameters, where `P` is a subtype of `AbstractTuringLatentModel`."
+    params::P
+    "The solver used for the ODE problem. Default is `AutoVern7(Rodas5())`, which is an auto
+    switching solver aimed at medium/low tolerances."
+    solver::S = AutoVern7(Rodas5())
+    "A function that maps the solution object of the ODE to infection counts."
+    sol2infs::F
+    "The extra solver options for the ODE problem. Can be either a `Dict` or a `NamedTuple`
+    containing the solver options."
+    solver_options::D = Dict(:verbose => false, :saveat => 1.0)
+end
+
+@doc raw"""
+Implement the `generate_latent_infs` function for the `ODEProcess` model.
+
+This function remakes the ODE problem with the provided initial conditions and parameters,
+    solves it using the specified solver, and then transforms the solution into latent infections
+    using the `sol2infs` function.
+
+# Example usage with predefined SIR model
+
+In this example we define an `ODEProcess` object using the predefined `SIRParams` model and
+generate an expected infection time series using SIR model parameters sampled from their priors.
+
+```jldoctest; output = false
+using EpiAware, OrdinaryDiffEq, Distributions, Turing, LogExpFunctions
+
+# Create an instance of SIRParams
+sirparams = SIRParams(
+    tspan = (0.0, 100.0),
+    infectiousness = LogNormal(log(0.3), 0.05),
+    recovery_rate = LogNormal(log(0.1), 0.05),
+    initial_prop_infected = Beta(1, 99)
+)
+
+#Population size
+
+N = 1000.0
+
+sir_process = ODEProcess(
+    params = sirparams,
+    sol2infs = sol -> softplus.(N .* sol[2, :]),
+    solver_options = Dict(:verbose => false, :saveat => 1.0)
+)
+
+generated_It = generate_latent_infs(sir_process, nothing)()
+nothing
+
+# output
+
+```
+
+"""
+@model function EpiAwareBase.generate_latent_infs(epi_model::ODEProcess, Z_t)
+    prob, solver, sol2infs, solver_options = epi_model.params.prob,
+    epi_model.solver, epi_model.sol2infs, epi_model.solver_options
+    n = isnothing(Z_t) ? 0 : size(Z_t, 1)
+
+    @submodel u0, p = generate_latent(epi_model.params, n)
+
+    _prob = remake(prob; u0 = u0, p = p)
+    sol = solve(_prob, solver; solver_options...)
+    I_t = sol2infs(sol)
+
+    return I_t
+end
diff --git a/EpiAware/src/EpiLatentModels/EpiLatentModels.jl b/EpiAware/src/EpiLatentModels/EpiLatentModels.jl
index 50a4fd251..214f88bab 100644
--- a/EpiAware/src/EpiLatentModels/EpiLatentModels.jl
+++ b/EpiAware/src/EpiLatentModels/EpiLatentModels.jl
@@ -11,10 +11,14 @@ using LogExpFunctions: softmax
 
 using FillArrays: Fill
 
-using Turing, Distributions, DocStringExtensions, LinearAlgebra
+using Turing, Distributions, DocStringExtensions, LinearAlgebra, SparseArrays,
+      OrdinaryDiffEq
 
 #Export models
-export FixedIntercept, Intercept, IDD, RandomWalk, AR, MA, HierarchicalNormal
+export FixedIntercept, Intercept, IID, RandomWalk, AR, MA, HierarchicalNormal
+
+#Export ODE definitions
+export SIRParams, SEIRParams
 
 # Export tools for manipulating latent models
 export CombineLatentModels, ConcatLatentModels, BroadcastLatentModel
@@ -32,12 +36,16 @@ export DiffLatentModel, TransformLatentModel, PrefixLatentModel, RecordExpectedL
 export define_arma, define_arima
 
 include("docstrings.jl")
+include("utils.jl")
 include("models/Intercept.jl")
-include("models/IDD.jl")
+include("models/IID.jl")
 include("models/RandomWalk.jl")
 include("models/AR.jl")
 include("models/MA.jl")
 include("models/HierarchicalNormal.jl")
+include("models/Null.jl")
+include("odemodels/SIRParams.jl")
+include("odemodels/SEIRParams.jl")
 include("modifiers/DiffLatentModel.jl")
 include("modifiers/TransformLatentModel.jl")
 include("modifiers/PrefixLatentModel.jl")
@@ -49,6 +57,5 @@ include("manipulators/broadcast/rules.jl")
 include("manipulators/broadcast/helpers.jl")
 include("combinations/define_arma.jl")
 include("combinations/define_arima.jl")
-include("utils.jl")
 
 end
diff --git a/EpiAware/src/EpiLatentModels/models/HierarchicalNormal.jl b/EpiAware/src/EpiLatentModels/models/HierarchicalNormal.jl
index 42d2f5b5b..109369a2f 100644
--- a/EpiAware/src/EpiLatentModels/models/HierarchicalNormal.jl
+++ b/EpiAware/src/EpiLatentModels/models/HierarchicalNormal.jl
@@ -47,7 +47,7 @@ Generate latent variables from the hierarchical normal distribution.
 "
 @model function EpiAwareBase.generate_latent(obs_model::HierarchicalNormal, n)
     std ~ obs_model.std_prior
-    @submodel ϵ_t = generate_latent(IDD(Normal()), n)
+    @submodel ϵ_t = generate_latent(IID(Normal()), n)
 
     η_t = obs_model.mean .+ std .* ϵ_t
     return η_t
diff --git a/EpiAware/src/EpiLatentModels/models/IDD.jl b/EpiAware/src/EpiLatentModels/models/IID.jl
similarity index 56%
rename from EpiAware/src/EpiLatentModels/models/IDD.jl
rename to EpiAware/src/EpiLatentModels/models/IID.jl
index b0ca2f683..55982faec 100644
--- a/EpiAware/src/EpiLatentModels/models/IDD.jl
+++ b/EpiAware/src/EpiLatentModels/models/IID.jl
@@ -3,7 +3,7 @@ Model latent process ``\epsilon_t`` as independent and identically distributed r
 
 ## Mathematical specification
 
-The IDD process ``\epsilon_t`` is specified as a sequence of independent and identically distributed random variables,
+The IID process ``\epsilon_t`` is specified as a sequence of independent and identically distributed random variables,
 
 ```math
 \epsilon_t \sim \text{Prior}, \quad t = 1, 2, \ldots
@@ -13,41 +13,41 @@ where Prior is the specified distribution.
 
 ## Constructors
 
-- `IDD(prior::Distribution = Normal(0, 1))`: Create an IDD model with the specified prior distribution.
+- `IID(prior::Distribution = Normal(0, 1))`: Create an IID model with the specified prior distribution.
 
 ## Examples
 
-```jldoctest IDD; filter =  r\"\b\d+(\.\d+)?\b\" => \"*\"
+```jldoctest IID; filter =  r\"\b\d+(\.\d+)?\b\" => \"*\"
 using EpiAware, Distributions
 
-model = IDD(Normal(0, 1))
+model = IID(Normal(0, 1))
 # output
-IDD{Normal{Float64}}(ϵ_t = Normal{Float64}(μ=0.0, σ=1.0))
+IID{Normal{Float64}}(ϵ_t = Normal{Float64}(μ=0.0, σ=1.0))
 
 ```
 
-```jldoctest IDD; filter =  r\"\b\d+(\.\d+)?\b\" => \"*\"
+```jldoctest IID; filter =  r\"\b\d+(\.\d+)?\b\" => \"*\"
 idd = generate_latent(model, 10)
 idd()
 # output
 
 ```
 "
-@kwdef struct IDD{D <: Sampleable} <: AbstractTuringLatentModel
+@kwdef struct IID{D <: Sampleable} <: AbstractTuringLatentModel
     ϵ_t::D = Normal(0, 1)
 end
 
 @doc raw"
- Generate latent variables from the IDD (Independent and Identically Distributed) model.
+ Generate latent variables from the IID (Independent and Identically Distributed) model.
 
 # Arguments
-- `model::IDD`: The IDD model.
+- `model::IID`: The IID model.
 - `n`: Number of latent variables to generate.
 
 # Returns
 - `ϵ_t`: Generated latent variables.
 "
-@model function EpiAwareBase.generate_latent(model::IDD, n)
+@model function EpiAwareBase.generate_latent(model::IID, n)
     ϵ_t ~ filldist(model.ϵ_t, n)
     return ϵ_t
 end
diff --git a/EpiAware/src/EpiLatentModels/models/MA.jl b/EpiAware/src/EpiLatentModels/models/MA.jl
index 8420f549b..b48f29243 100644
--- a/EpiAware/src/EpiLatentModels/models/MA.jl
+++ b/EpiAware/src/EpiLatentModels/models/MA.jl
@@ -2,7 +2,7 @@
 The moving average (MA) model struct.
 
 # Constructors
-- `MA(θ::Distribution, σ::Distribution; q::Int = 1, ϵ::AbstractTuringLatentModel = IDD(Normal()))`: Constructs an MA model with the specified prior distributions.
+- `MA(θ::Distribution, σ::Distribution; q::Int = 1, ϵ::AbstractTuringLatentModel = IID(Normal()))`: Constructs an MA model with the specified prior distributions.
 
 - `MA(; θ::Vector{C} = [truncated(Normal(0.0, 0.05), -1, 1)], ϵ::AbstractTuringLatentModel = HierarchicalNormal) where {C <: Distribution}`: Constructs an MA model with the specified prior distributions.
 
@@ -11,7 +11,7 @@ The moving average (MA) model struct.
 # Parameters
 - `θ`: Prior distribution for the MA coefficients. For MA(q), this should be a vector of q distributions or a multivariate distribution of dimension q.
 - `q`: Order of the MA model, i.e., the number of lagged error terms.
-- `ϵ_t`: Distribution of the error term, typically standard normal. Defaults to `IDD(Normal())`.
+- `ϵ_t`: Distribution of the error term, typically standard normal. Defaults to `IID(Normal())`.
 
 # Examples
 
diff --git a/EpiAware/src/EpiLatentModels/models/Null.jl b/EpiAware/src/EpiLatentModels/models/Null.jl
new file mode 100644
index 000000000..064d43320
--- /dev/null
+++ b/EpiAware/src/EpiLatentModels/models/Null.jl
@@ -0,0 +1,24 @@
+@doc raw"
+A null model struct. This struct is used to indicate that no latent variables are to be generated.
+"
+struct Null <: AbstractTuringLatentModel end
+
+@doc raw"
+Generates `nothing` as latent variables for the given `latent_model` of type `Null`.
+
+# Example
+```jldoctest
+using EpiAware
+null = Null()
+null_mdl = generate_latent(null, 10)
+isnothing(null_mdl())
+
+# output
+
+true
+
+```
+"
+@model function EpiAwareBase.generate_latent(latent_model::Null, n)
+    return nothing
+end
diff --git a/EpiAware/src/EpiLatentModels/models/RandomWalk.jl b/EpiAware/src/EpiLatentModels/models/RandomWalk.jl
index 2592eb44e..72514e312 100644
--- a/EpiAware/src/EpiLatentModels/models/RandomWalk.jl
+++ b/EpiAware/src/EpiLatentModels/models/RandomWalk.jl
@@ -13,7 +13,7 @@ Z_t = Z_0 + \sigma \sum_{i = 1}^t \epsilon_t
 Constructing a random walk requires specifying:
 - An `init_prior` as a prior for ``Z_0``. Default is `Normal()`.
 - A `std_prior` for ``\sigma``. The default is HalfNormal with a mean of 0.25.
-- An `ϵ_t` prior for the white noise sequence. The default is `IDD(Normal())`.
+- An `ϵ_t` prior for the white noise sequence. The default is `IID(Normal())`.
 
 ## Constructors
 
diff --git a/EpiAware/src/EpiLatentModels/odemodels/SEIRParams.jl b/EpiAware/src/EpiLatentModels/odemodels/SEIRParams.jl
new file mode 100644
index 000000000..3a5f2ac7b
--- /dev/null
+++ b/EpiAware/src/EpiLatentModels/odemodels/SEIRParams.jl
@@ -0,0 +1,183 @@
+@doc raw"""
+Internal function for the vector field of a basic SEIR model written in density/per-capita
+form. The function is used to define the ODE problem for the SIR model.
+"""
+function _seir_vf(du, u, p, t)
+    S, E, I, R = u
+    β, α, γ = p
+    du[1] = -β * S * I
+    du[2] = β * S * I - α * E
+    du[3] = α * E - γ * I
+    du[4] = γ * I
+    return nothing
+end
+
+@doc raw"""
+Sparse Jacobian matrix prototype for the basic SEIR model written in density/per-capita form.
+"""
+const _seir_jac_prototype = sparse([1.0 0.0 1.0 0.0;
+                                    1.0 1.0 1.0 0.0;
+                                    0.0 1.0 1.0 0.0;
+                                    0.0 0.0 1.0 0.0])
+
+@doc raw"""
+Internal function for the Jacobian of the basic SEIR model written in density/per-capita
+form. The function is used to define the ODE problem for the SEIR model. The Jacobian
+is used to speed up the solution of the ODE problem when using a stiff solver.
+"""
+function _seir_jac(J, u, p, t)
+    S, E, I, R = u
+    β, α, γ = p
+    J[1, 1] = -β * I
+    J[1, 3] = -β * S
+    J[2, 1] = β * I
+    J[2, 2] = -α
+    J[2, 3] = β * S
+    J[3, 2] = α
+    J[3, 3] = -γ
+    J[4, 3] = γ
+    nothing
+end
+
+@doc raw"""
+Internal function for the ODE function of the basic SIR model written in density/per-capita
+form. The function passes vector field and Jacobian functions to the ODE solver.
+"""
+_seir_function = ODEFunction(_seir_vf; jac = _seir_jac)
+
+@doc raw"""
+A structure representing the SEIR (Susceptible-Exposed-Infectious-Recovered) model and priors for the
+infectiousness and recovery rate parameters.
+
+# Constructors
+- `SEIRParams(; tspan, infectiousness::Distribution, incubation_rate::Distribution,
+    recovery_rate::Distribution, initial_prop_infected::Distribution)` :
+Construct a `SEIRParams` object with the specified time span for ODE solving, infectiousness
+prior, incubation rate prior and recovery rate prior.
+
+## SEIR model
+
+```math
+\begin{aligned}
+\frac{dS}{dt} &= -\beta SI \\
+\frac{dE}{dt} &= \beta SI - \alpha E \\
+\frac{dI}{dt} &= \alpha E - \gamma I \\
+\frac{dR}{dt} &= \gamma I
+\end{aligned}
+```
+Where `S` is the proportion of the population that is susceptible, `E` is the proportion of the
+population that is exposed, `I` is the proportion of the population that is infected and `R` is
+the proportion of the population that is recovered. The parameters are the infectiousness `β`,
+the incubation rate `α` and the recovery rate `γ`.
+
+```jldoctest; output = false
+using EpiAware, OrdinaryDiffEq, Distributions
+
+# Create an instance of SIRParams
+seirparams = SEIRParams(
+    tspan = (0.0, 30.0),
+    infectiousness = LogNormal(log(0.3), 0.05),
+    incubation_rate = LogNormal(log(0.1), 0.05),
+    recovery_rate = LogNormal(log(0.1), 0.05),
+    initial_prop_infected = Beta(1, 99)
+)
+nothing
+
+# output
+
+```
+"""
+struct SEIRParams{
+    P <: ODEProblem, D <: Sampleable, E <: Sampleable, F <: Sampleable, G <: Sampleable} <:
+       AbstractTuringLatentModel
+    "The ODE problem instance for the SIR model."
+    prob::P
+    "Prior distribution for the infectiousness parameter."
+    infectiousness::D
+    "Prior distribution for the incubation rate parameter."
+    incubation_rate::E
+    "Prior distribution for the recovery rate parameter."
+    recovery_rate::F
+    "Prior distribution for initial proportion of the population that is infected."
+    initial_prop_infected::G
+end
+
+function SEIRParams(;
+        tspan, infectiousness::Distribution, incubation_rate::Distribution,
+        recovery_rate::Distribution, initial_prop_infected::Distribution)
+    seir_prob = ODEProblem(_seir_function, [0.99, 0.05, 0.05, 0.0], tspan)
+    return SEIRParams{
+        typeof(seir_prob), typeof(infectiousness), typeof(incubation_rate),
+        typeof(recovery_rate), typeof(initial_prop_infected)}(
+        seir_prob, infectiousness, incubation_rate,
+        recovery_rate, initial_prop_infected)
+end
+
+@doc raw"""
+Generates the initial parameters and initial conditions for the basic SEIR model.
+
+## SEIR model
+
+```math
+\begin{aligned}
+\frac{dS}{dt} &= -\beta SI \\
+\frac{dE}{dt} &= \beta SI - \alpha E \\
+\frac{dI}{dt} &= \alpha E - \gamma I \\
+\frac{dR}{dt} &= \gamma I
+\end{aligned}
+```
+Where `S` is the proportion of the population that is susceptible, `E` is the proportion of the
+population that is exposed, `I` is the proportion of the population that is infected and `R` is
+the proportion of the population that is recovered. The parameters are the infectiousness `β`,
+the incubation rate `α` and the recovery rate `γ`.
+
+## Initial conditions
+
+For this version of the SEIR model we sample the initial proportion of the population that is
+_infected_ (exposed or infectious). The proportion of the infected group that is exposed is
+`α / (α + γ)` and the proportion of the infected group that is infectious is `γ / (α + γ)`. The
+reason for this is that these are the equilibrium proportions in a constant incidence environment.
+
+# Example
+
+```jldoctest; output = false
+using EpiAware, OrdinaryDiffEq, Distributions
+
+# Create an instance of SIRParams
+seirparams = SEIRParams(
+    tspan = (0.0, 30.0),
+    infectiousness = LogNormal(log(0.3), 0.05),
+    incubation_rate = LogNormal(log(0.1), 0.05),
+    recovery_rate = LogNormal(log(0.1), 0.05),
+    initial_prop_infected = Beta(1, 99)
+)
+
+seirparam_mdl = generate_latent(seirparams, nothing)
+
+# Sample the parameters of SEIR model
+sampled_params = rand(seirparam_mdl)
+nothing
+
+# output
+
+```
+
+# Returns
+  A tuple `(u0, p)` where:
+  - `u0`: A vector representing the initial state of the system `[S₀, E₀, I₀, R₀]` where `S₀`
+  is the initial proportion of susceptible individuals, `E₀` is the initial proportion of exposed
+  individuals,`I₀` is the initial proportion of infected individuals, and `R₀` is the initial
+  proportion of recovered individuals.
+  - `p`: A vector containing the parameters `[β, α, γ]` where `β` is the infectiousness rate,
+  `α` is the incubation rate, and `γ` is the recovery rate.
+"""
+@model function EpiAwareBase.generate_latent(params::SEIRParams, n)
+    β ~ params.infectiousness
+    α ~ params.incubation_rate
+    γ ~ params.recovery_rate
+    initial_infs ~ params.initial_prop_infected
+
+    u0 = [1.0 - initial_infs, initial_infs * γ / (α + γ), initial_infs * α / (α + γ), 0.0]
+    p = [β, α, γ]
+    return (u0, p)
+end
diff --git a/EpiAware/src/EpiLatentModels/odemodels/SIRParams.jl b/EpiAware/src/EpiLatentModels/odemodels/SIRParams.jl
new file mode 100644
index 000000000..e13c4aaa5
--- /dev/null
+++ b/EpiAware/src/EpiLatentModels/odemodels/SIRParams.jl
@@ -0,0 +1,158 @@
+@doc raw"""
+Internal function for the vector field of a basic SIR model written in density/per-capita
+form. The function is used to define the ODE problem for the SIR model.
+"""
+function _sir_vf(du, u, p, t)
+    S, I, R = u
+    β, γ = p
+    du[1] = -β * S * I
+    du[2] = β * S * I - γ * I
+    du[3] = γ * I
+    return nothing
+end
+
+@doc raw"""
+Sparse Jacobian matrix prototype for the basic SIR model written in density/per-capita form.
+"""
+const _sir_jac_prototype = sparse([1.0 1.0 0.0;
+                                   1.0 1.0 0.0;
+                                   0.0 1.0 0.0])
+
+@doc raw"""
+Internal function for the Jacobian of the basic SIR model written in density/per-capita
+form. The function is used to define the ODE problem for the SIR model. The Jacobian
+is used to speed up the solution of the ODE problem when using a stiff solver.
+"""
+function _sir_jac(J, u, p, t)
+    S, I, R = u
+    β, γ = p
+    J[1, 1] = -β * I
+    J[1, 2] = -β * S
+    J[2, 1] = β * I
+    J[2, 2] = β * S - γ
+    J[3, 2] = γ
+    nothing
+end
+
+@doc raw"""
+Internal function for the ODE function of the basic SIR model written in density/per-capita
+form. The function passes vector field and Jacobian functions to the ODE solver.
+"""
+_sir_function = ODEFunction(_sir_vf; jac = _sir_jac)
+
+@doc raw"""
+A structure representing the SIR (Susceptible-Infectious-Recovered) model and priors for the
+infectiousness and recovery rate parameters.
+
+# Constructors
+- `SIRParams(; tspan,
+    infectiousness::Distribution,
+    recovery_rate::Distribution,
+    initial_prop_infected::Distribution)` :
+Construct an `SIRParams` object with the specified time span for ODE solving, infectiousness
+prior, and recovery rate prior.
+
+## SIR model
+
+```math
+\begin{aligned}
+\frac{dS}{dt} &= -\beta SI \\
+\frac{dI}{dt} &= \beta SI - \gamma I \\
+\frac{dR}{dt} &= \gamma I
+\end{aligned}
+```
+Where `S` is the proportion of the population that is susceptible, `I` is the proportion of the
+population that is infected and `R` is the proportion of the population that is recovered. The
+parameters are the infectiousness `β` and the recovery rate `γ`.
+
+# Example
+
+```jldoctest; output = false
+ using EpiAware, OrdinaryDiffEq, Distributions
+
+# Create an instance of SIRParams
+sirparams = SIRParams(
+    tspan = (0.0, 30.0),
+    infectiousness = LogNormal(log(0.3), 0.05),
+    recovery_rate = LogNormal(log(0.1), 0.05),
+    initial_prop_infected = Beta(1, 99)
+)
+nothing
+
+# output
+
+```
+"""
+struct SIRParams{P <: ODEProblem, D <: Sampleable, E <: Sampleable, F <: Sampleable} <:
+       AbstractTuringLatentModel
+    "The ODE problem instance for the SIR model."
+    prob::P
+    "Prior distribution for the infectiousness parameter."
+    infectiousness::D
+    "Prior distribution for the recovery rate parameter."
+    recovery_rate::E
+    "Prior distribution for initial proportion of the population that is infected."
+    initial_prop_infected::F
+end
+
+function SIRParams(;
+        tspan, infectiousness::Distribution, recovery_rate::Distribution,
+        initial_prop_infected::Distribution)
+    sir_prob = ODEProblem(_sir_function, [0.99, 0.01, 0.0], tspan)
+    return SIRParams{typeof(sir_prob), typeof(infectiousness),
+        typeof(recovery_rate), typeof(initial_prop_infected)}(
+        sir_prob, infectiousness, recovery_rate, initial_prop_infected)
+end
+
+@doc raw"""
+Generates the initial parameters and initial conditions for the basic SIR model.
+
+## SIR model
+
+```math
+\begin{aligned}
+\frac{dS}{dt} &= -\beta SI \\
+\frac{dI}{dt} &= \beta SI - \gamma I \\
+\frac{dR}{dt} &= \gamma I
+\end{aligned}
+```
+Where `S` is the proportion of the population that is susceptible, `I` is the proportion of the
+population that is infected and `R` is the proportion of the population that is recovered. The
+parameters are the infectiousness `β` and the recovery rate `γ`.
+
+# Example
+
+```jldoctest; output = false
+using EpiAware, OrdinaryDiffEq, Distributions
+
+# Create an instance of SIRParams
+sirparams = SIRParams(
+    tspan = (0.0, 30.0),
+    infectiousness = LogNormal(log(0.3), 0.05),
+    recovery_rate = LogNormal(log(0.1), 0.05),
+    initial_prop_infected = Beta(1, 99)
+)
+
+sirparam_mdl = generate_latent(sirparams, nothing)
+
+#sample the parameters of SIR model
+sampled_params = rand(sirparam_mdl)
+nothing
+
+# output
+
+```
+
+# Returns
+- A tuple `(u0, p)` where:
+  - `u0`: A vector representing the initial state of the system `[S₀, I₀, R₀]` where `S₀` is the initial proportion of susceptible individuals, `I₀` is the initial proportion of infected individuals, and `R₀` is the initial proportion of recovered individuals.
+  - `p`: A vector containing the parameters `[β, γ]` where `β` is the infectiousness rate and `γ` is the recovery rate.
+"""
+@model function EpiAwareBase.generate_latent(params::SIRParams, n)
+    β ~ params.infectiousness
+    γ ~ params.recovery_rate
+    I₀ ~ params.initial_prop_infected
+    u0 = [1.0 - I₀, I₀, 0.0]
+    p = [β, γ]
+    return (u0, p)
+end
diff --git a/EpiAware/src/EpiLatentModels/utils.jl b/EpiAware/src/EpiLatentModels/utils.jl
index c4f1558ce..e0f882ca8 100644
--- a/EpiAware/src/EpiLatentModels/utils.jl
+++ b/EpiAware/src/EpiLatentModels/utils.jl
@@ -1,3 +1,10 @@
+"""
+Internal function to expand a vector of distributions into a product distribution.
+
+## Implementation note
+If all distributions in the input vector `dist` are the same, it returns a filled distribution using `filldist`.
+Otherwise, it returns an array distribution using `arraydist`.
+"""
 function _expand_dist(dist::Vector{D} where {D <: Distribution})
     d = length(dist)
     product_dist = all(first(dist) .== dist) ?
diff --git a/EpiAware/test/EpiAwareUtils/generate_epiware.jl b/EpiAware/test/EpiAwareUtils/generate_epiware.jl
index 0cbeb0bf5..b2a7ac579 100644
--- a/EpiAware/test/EpiAwareUtils/generate_epiware.jl
+++ b/EpiAware/test/EpiAwareUtils/generate_epiware.jl
@@ -8,9 +8,8 @@
     #Define the epi_model
     epi_model = DirectInfections(data, Normal())
 
-    #Define the latent process model
-    rwp = RandomWalk(Normal(0.0, 1.0),
-        truncated(Normal(0.0, 0.05), 0.0, Inf))
+    #Define the latent process model using defaults
+    rwp = RandomWalk()
 
     #Define the observation model
     delay_distribution = Gamma(2.0, 5 / 2)
@@ -38,7 +37,7 @@ end
 @testitem "`generate_epiaware` with Exp growth rate and RW latent process runs" begin
     using Distributions, Turing, DynamicPPL
     # Define test inputs
-    y_t = missing# rand(1:10, 365) # Data will be generated from the model
+    y_t = missing # Data will be generated from the model
     data = EpiData([0.2, 0.3, 0.5], exp)
 
     #Define the epi_model
@@ -46,9 +45,10 @@ end
 
     #Define the latent process model
     r_3 = log(2) / 3.0
-    rwp = RandomWalk(
-        truncated(Normal(0.0, r_3 / 3), -r_3, r_3), # 3 day doubling time at 3 sigmas in prior
-        truncated(Normal(0.0, 0.01), 0.0, 0.1))
+    init_prior = truncated(Normal(0.0, r_3 / 3), -r_3, r_3)# initial 3 day doubling time at 3 sigmas in prior
+    step_size_dist = HierarchicalNormal(std_prior = truncated(Normal(0.0, 0.01), 0.0, 0.1))
+    rwp = RandomWalk(init_prior = init_prior,
+        ϵ_t = step_size_dist)
 
     #Define the observation model - no delay model
     time_horizon = 5
@@ -83,9 +83,10 @@ end
 
     #Define the latent process model
     r_3 = log(2) / 3.0
-    rwp = RandomWalk(
-        truncated(Normal(0.0, r_3 / 3), -r_3, r_3), # 3 day doubling time at 3 sigmas in prior
-        truncated(Normal(0.0, 0.01), 0.0, 0.1))
+    init_prior = truncated(Normal(0.0, r_3 / 3), -r_3, r_3)# initial 3 day doubling time at 3 sigmas in prior
+    step_size_dist = HierarchicalNormal(std_prior = truncated(Normal(0.0, 0.01), 0.0, 0.1))
+    rwp = RandomWalk(init_prior = init_prior,
+        ϵ_t = step_size_dist)
 
     #Define the observation model - no delay model
     time_horizon = 5
diff --git a/EpiAware/test/EpiAwareUtils/turing-methods.jl b/EpiAware/test/EpiAwareUtils/turing-methods.jl
index 47e77c9e2..b175f64ce 100644
--- a/EpiAware/test/EpiAwareUtils/turing-methods.jl
+++ b/EpiAware/test/EpiAwareUtils/turing-methods.jl
@@ -67,11 +67,8 @@ end
 @testitem "Turing method for generate_epiaware with two latent processes" begin
     using Distributions, Turing
 
-    # Latent model
-    damp_prior = truncated(Normal(0.0, 0.05), 0.0, 1)
-    std_prior = truncated(Normal(0.0, 0.05), 0.0, Inf)
-    init_prior = Normal()
-    ar_process = AR(damp_prior, std_prior, init_prior)
+    # Latent model - default AR model
+    ar_process = AR()
 
     # Used again in obs model
 
diff --git a/EpiAware/test/EpiInfModels/ODEProcess.jl b/EpiAware/test/EpiInfModels/ODEProcess.jl
new file mode 100644
index 000000000..24818bf7d
--- /dev/null
+++ b/EpiAware/test/EpiInfModels/ODEProcess.jl
@@ -0,0 +1,150 @@
+@testitem "ODEProcess for SIR model constructor + `generate_latent_infs` tests" begin
+    using OrdinaryDiffEq, Distributions
+    # Define the time span for the ODE problem
+    tspan = (0.0, 30.0)
+
+    # Define prior distributions
+    infectiousness = LogNormal(log(0.3), 0.05)
+    recovery_rate = LogNormal(log(0.1), 0.05)
+    initial_prop_infected = Beta(1, 99)
+
+    # Create an instance of SIRParams
+    sirparams = SIRParams(
+        tspan = tspan,
+        infectiousness = infectiousness,
+        recovery_rate = recovery_rate,
+        initial_prop_infected = initial_prop_infected
+    )
+
+    # Define the SIR ODEProcess model
+    # using solver_options as a Dict
+    sir_process = ODEProcess(
+        params = sirparams,
+        sol2infs = sol -> sol[2, :],
+        solver_options = Dict(:verbose => false, :saveat => 1.0, :reltol => 1e-10)
+    )
+    # Define the SIR ODEProcess model
+    # using solver_options as a NamedTuple
+    sir_process2 = ODEProcess(
+        params = sirparams,
+        sol2infs = sol -> sol[2, :],
+        solver_options = (verbose = false, saveat = 1.0, reltol = 1e-10)
+    )
+    @testset "Constructor tests" begin
+        # Check the types of the fields
+        @test sir_process.params isa SIRParams
+        @test sir_process.sol2infs isa Function
+        @test sir_process.solver_options isa Dict
+        @test sir_process2.solver_options isa NamedTuple
+    end
+
+    @testset "generate_latent_infs tests equal with both solver_options" begin
+        Z_t = rand(10) # dummy latent process
+        inf_mdl = generate_latent_infs(sir_process, Z_t)
+        inf_mdl2 = generate_latent_infs(sir_process2, Z_t)
+        θ = rand(inf_mdl)
+        θ2 = rand(inf_mdl2)
+        @test keys(θ) == keys(θ2)
+        It = (inf_mdl | θ)()
+        It2 = (inf_mdl2 | θ)()
+
+        @test It ≈ It2
+    end
+end
+
+@testitem "ODEProcess for SEIR model constructor + `generate_latent_infs` tests" begin
+    using OrdinaryDiffEq, Distributions
+    # Define the time span for the ODE problem
+    tspan = (0.0, 30.0)
+
+    # Define prior distributions
+    infectiousness = LogNormal(log(0.3), 0.05)
+    incubation_rate = LogNormal(log(0.1), 0.05)
+    recovery_rate = LogNormal(log(0.1), 0.05)
+    initial_prop_infected = Beta(1, 99)
+
+    # Create an instance of SIRParams
+    seirparams = SEIRParams(
+        tspan = tspan,
+        infectiousness = infectiousness,
+        incubation_rate = incubation_rate,
+        recovery_rate = recovery_rate,
+        initial_prop_infected = initial_prop_infected
+    )
+
+    # Define the SIR ODEProcess model
+    # using solver_options as a Dict
+    seir_process = ODEProcess(
+        params = seirparams,
+        sol2infs = sol -> sol[2, :],
+        solver_options = Dict(:verbose => false, :saveat => 1.0, :reltol => 1e-10)
+    )
+    # Define the SIR ODEProcess model
+    # using solver_options as a NamedTuple
+    seir_process2 = ODEProcess(
+        params = seirparams,
+        sol2infs = sol -> sol[2, :],
+        solver_options = (verbose = false, saveat = 1.0, reltol = 1e-10)
+    )
+    @testset "Constructor tests" begin
+        # Check the types of the fields
+        @test seir_process.params isa SEIRParams
+        @test seir_process.sol2infs isa Function
+        @test seir_process.solver_options isa Dict
+        @test seir_process2.solver_options isa NamedTuple
+    end
+
+    @testset "generate_latent_infs tests equal with both solver_options" begin
+        Z_t = rand(10) # dummy latent process
+        inf_mdl = generate_latent_infs(seir_process, Z_t)
+        inf_mdl2 = generate_latent_infs(seir_process2, Z_t)
+        θ = rand(inf_mdl)
+        θ2 = rand(inf_mdl2)
+        @test keys(θ) == keys(θ2)
+        It = (inf_mdl | θ)()
+        It2 = (inf_mdl2 | θ)()
+
+        @test It ≈ It2
+    end
+end
+
+@testitem "Custom ODE model" begin
+    using OrdinaryDiffEq, Distributions, Turing
+
+    # Define a simple exponential growth model for testing
+    function expgrowth(du, u, p, t)
+        du[1] = p[1] * u[1]
+    end
+
+    r = log(2) / 7 # Growth rate corresponding to 7 day doubling time
+
+    # Define the ODE problem using SciML
+    prob = ODEProblem(expgrowth, [1.0], (0.0, 10.0), [r])
+
+    # Define the custom parameters struct
+    struct CustomParams <: AbstractTuringLatentModel
+        prob::ODEProblem
+        r::Float64
+        u0::Float64
+    end
+    params = CustomParams(prob, r, 1.0)
+
+    # Define the custom generate_latent function
+    @model function EpiAwareBase.generate_latent(params::CustomParams, Z_t)
+        return ([params.u0], [params.r])
+    end
+
+    # Define the ODEProcess
+    expgrowth_model = ODEProcess(
+        params = params,
+        sol2infs = sol -> sol[1, :]
+    )
+
+    # Generate the latent infections
+    Z_t = rand(10) # dummy latent process
+    inf_mdl = generate_latent_infs(expgrowth_model, Z_t)
+    @test rand(inf_mdl) == NamedTuple()
+    I_t = inf_mdl()
+    @test length(I_t) == 11
+    @test I_t ≈ [exp(r * t) for t in 0:10]
+end
diff --git a/EpiAware/test/EpiLatentModels/manipulators/broadcast/LatentModel.jl b/EpiAware/test/EpiLatentModels/manipulators/broadcast/LatentModel.jl
index 9843bf140..3afdb70c7 100644
--- a/EpiAware/test/EpiLatentModels/manipulators/broadcast/LatentModel.jl
+++ b/EpiAware/test/EpiLatentModels/manipulators/broadcast/LatentModel.jl
@@ -28,7 +28,7 @@ end
     @test length(rand_model.ϵ_t) == 2
     fix_model = fix(
         broadcasted_model,
-        (σ_RW = 2.0, rw_init = 1.0, ϵ_t = [1, 2])
+        (std = 2.0, rw_init = 1.0, ϵ_t = [1, 2])
     )
     out = fix_model()
     @test out == vcat(fill(1.0, 5), fill(3.0, 5), fill(7.0, 5))
diff --git a/EpiAware/test/EpiLatentModels/models/AR.jl b/EpiAware/test/EpiLatentModels/models/AR.jl
index 0e99b7b25..e3e98358c 100644
--- a/EpiAware/test/EpiLatentModels/models/AR.jl
+++ b/EpiAware/test/EpiLatentModels/models/AR.jl
@@ -3,11 +3,12 @@
 
     damp_prior = truncated(Normal(0.0, 0.05), 0.0, 1)
     std_prior = truncated(Normal(0.0, 0.05), 0.0, Inf)
+    ϵ_t = HierarchicalNormal(; std_prior)
     init_prior = Normal()
-    ar_process = AR(damp_prior, std_prior, init_prior)
+    ar_process = AR(damp_prior, init_prior; ϵ_t)
 
     @test ar_process.damp_prior == filldist(damp_prior, 1)
-    @test ar_process.std_prior == std_prior
+    @test ar_process.ϵ_t.std_prior == std_prior
     @test ar_process.init_prior == filldist(init_prior, 1)
 end
 
@@ -20,7 +21,7 @@ end
     end
 
     @testset "std_prior" begin
-        std_AR = rand(ar.std_prior)
+        std_AR = rand(ar.ϵ_t.std_prior)
         @test std_AR >= 0.0
     end
 
@@ -32,10 +33,12 @@ end
 
 @testitem "Test AR(2)" begin
     using Distributions
+    std_prior = truncated(Normal(0.0, 0.05), 0.0, Inf)
+    ϵ_t = HierarchicalNormal(; std_prior)
     ar = AR(
         damp_priors = [truncated(Normal(0.0, 0.05), 0.0, 1),
             truncated(Normal(0.0, 0.05), 0.0, 1)],
-        std_prior = truncated(Normal(0.0, 0.05), 0.0, Inf),
+        ϵ_t = ϵ_t,
         init_priors = [Normal(), Normal()]
     )
     @testset "damp_prior" begin
@@ -46,7 +49,7 @@ end
     end
 
     @testset "std_prior" begin
-        std_AR = rand(ar.std_prior)
+        std_AR = rand(ar.ϵ_t.std_prior)
         @test std_AR >= 0.0
     end
 
@@ -70,7 +73,7 @@ end
     ar_init = [0.0]
 
     model = generate_latent(ar_model, n)
-    fixed_model = fix(model, (σ_AR = σ_AR, damp_AR = damp, ar_init = ar_init))
+    fixed_model = fix(model, (std = σ_AR, damp_AR = damp, ar_init = ar_init))
 
     n_samples = 100
     samples = sample(fixed_model, Prior(), n_samples; progress = false) |>
diff --git a/EpiAware/test/EpiLatentModels/models/HierarchicalNormal.jl b/EpiAware/test/EpiLatentModels/models/HierarchicalNormal.jl
index 5fd843f79..02548a710 100644
--- a/EpiAware/test/EpiLatentModels/models/HierarchicalNormal.jl
+++ b/EpiAware/test/EpiLatentModels/models/HierarchicalNormal.jl
@@ -8,7 +8,7 @@
     int_def = HierarchicalNormal()
     @test typeof(int_def) <: AbstractTuringLatentModel
     @test int_def.mean == 0.0
-    @test int_def.std_prior == truncated(Normal(0, 1), 0, Inf)
+    @test int_def.std_prior == truncated(Normal(0, 0.1), 0, Inf)
 
     @test int == HierarchicalNormal(mean = 0.1, std_prior = truncated(Normal(0, 2), 0, Inf))
 end
diff --git a/EpiAware/test/EpiLatentModels/models/IDD.jl b/EpiAware/test/EpiLatentModels/models/IID.jl
similarity index 76%
rename from EpiAware/test/EpiLatentModels/models/IDD.jl
rename to EpiAware/test/EpiLatentModels/models/IID.jl
index acc03602a..478e0385e 100644
--- a/EpiAware/test/EpiLatentModels/models/IDD.jl
+++ b/EpiAware/test/EpiLatentModels/models/IID.jl
@@ -1,40 +1,40 @@
-@testitem "Testing IDD constructor" begin
+@testitem "Testing IID constructor" begin
     using Distributions, Turing
 
     normal_prior = Normal(0.0, 1.0)
-    idd_process = IDD(normal_prior)
+    idd_process = IID(normal_prior)
 
     @test idd_process.ϵ_t == normal_prior
 end
 
-@testitem "Test IDD defaults" begin
+@testitem "Test IID defaults" begin
     using Distributions
-    idd = IDD()
+    idd = IID()
     @test idd.ϵ_t == Normal(0, 1)
 end
 
-@testitem "Test IDD with different distributions" begin
+@testitem "Test IID with different distributions" begin
     using Distributions
 
     @testset "Uniform distribution" begin
-        idd = IDD(Uniform(0, 1))
+        idd = IID(Uniform(0, 1))
         sample = rand(idd.ϵ_t)
         @test 0 <= sample <= 1
     end
 
     @testset "Exponential distribution" begin
-        idd = IDD(Exponential(1))
+        idd = IID(Exponential(1))
         sample = rand(idd.ϵ_t)
         @test sample >= 0
     end
 end
 
-@testitem "Testing IDD process against theoretical properties" begin
+@testitem "Testing IID process against theoretical properties" begin
     using DynamicPPL, Turing
     using HypothesisTests: ExactOneSampleKSTest, pvalue
     using Distributions
 
-    idd_model = IDD(Normal(2, 3))
+    idd_model = IID(Normal(2, 3))
     n = 1000
 
     model = generate_latent(idd_model, n)
diff --git a/EpiAware/test/EpiLatentModels/models/MA.jl b/EpiAware/test/EpiLatentModels/models/MA.jl
index 4ed2ef714..cb9dff5bf 100644
--- a/EpiAware/test/EpiLatentModels/models/MA.jl
+++ b/EpiAware/test/EpiLatentModels/models/MA.jl
@@ -8,7 +8,7 @@
     @test ma_process.θ_prior == filldist(θ_prior, 1)
     @test ma_process.σ_prior == σ_prior
     @test ma_process.q == 1
-    @test ma_process.ϵ_t isa IDD
+    @test ma_process.ϵ_t isa IID
 end
 
 @testitem "Test MA(2)" begin
@@ -19,7 +19,7 @@ end
         σ_prior = HalfNormal(0.1)
     )
     @test ma.q == 2
-    @test ma.ϵ_t isa IDD
+    @test ma.ϵ_t isa IID
     @test ma.θ_prior == filldist(θ_prior, 2)
 end
 
diff --git a/EpiAware/test/EpiLatentModels/models/Null.jl b/EpiAware/test/EpiLatentModels/models/Null.jl
new file mode 100644
index 000000000..305760702
--- /dev/null
+++ b/EpiAware/test/EpiLatentModels/models/Null.jl
@@ -0,0 +1,8 @@
+@testitem "Null Model Tests" begin
+    # Test that Null can be instantiated
+    @test Null() isa Null
+
+    # Test that generate_latent returns nothing
+    null = Null()
+    @test isnothing(generate_latent(null, 10)())
+end
diff --git a/EpiAware/test/EpiLatentModels/odemodels/SEIRParams.jl b/EpiAware/test/EpiLatentModels/odemodels/SEIRParams.jl
new file mode 100644
index 000000000..b3803d116
--- /dev/null
+++ b/EpiAware/test/EpiLatentModels/odemodels/SEIRParams.jl
@@ -0,0 +1,60 @@
+@testitem "SEIRParams: constructor and `generate_latent`" begin
+    using OrdinaryDiffEq, Distributions
+    # Define the time span for the ODE problem
+    tspan = (0.0, 10.0)
+
+    # Define prior distributions
+    infectiousness = LogNormal(log(0.3), 0.05)
+    incubation_rate = LogNormal(log(0.1), 0.05)
+    recovery_rate = LogNormal(log(0.1), 0.05)
+    initial_prop_infected = Beta(2, 5)
+
+    # Create an instance of seirparams
+    seirparams = SEIRParams(
+        tspan = tspan,
+        infectiousness = infectiousness,
+        incubation_rate = incubation_rate,
+        recovery_rate = recovery_rate,
+        initial_prop_infected = initial_prop_infected
+    )
+
+    @testset "SEIRParams constructor tests" begin
+        # Check the types of the fields
+        @test seirparams.prob isa ODEProblem
+        @test seirparams.infectiousness isa Distribution
+        @test seirparams.incubation_rate isa Distribution
+        @test seirparams.recovery_rate isa Distribution
+        @test seirparams.initial_prop_infected isa Distribution
+
+        # Check the values of the fields
+        @test seirparams.prob.tspan == tspan
+        @test seirparams.infectiousness == infectiousness
+        @test seirparams.incubation_rate == incubation_rate
+        @test seirparams.recovery_rate == recovery_rate
+        @test seirparams.initial_prop_infected == initial_prop_infected
+    end
+
+    @testset "SEIRParams `generate_latent` tests" begin
+        # Generate parameters
+        Z_t = rand(10) # dummy latent process
+        seirparam_mdl = generate_latent(seirparams, Z_t)
+        sampled_params = rand(seirparam_mdl)
+
+        # Check the types of the fields
+        @test sampled_params.β isa Float64
+        @test sampled_params.α isa Float64
+        @test sampled_params.γ isa Float64
+        @test sampled_params.initial_infs isa Float64
+    end
+end
+
+@testitem "SEIRParams: jac_prototype non-zeros invariant to jac call" begin
+    Jp = deepcopy(EpiAware.EpiLatentModels._seir_jac_prototype)
+    Jp_pattern_before = Jp .!= 0
+    u = rand(4)
+    p = rand(3)
+    EpiAware.EpiLatentModels._seir_jac(Jp, u, p, 0.0)
+    Jp_pattern_after = Jp .!= 0
+
+    @test Jp_pattern_before == Jp_pattern_after
+end
diff --git a/EpiAware/test/EpiLatentModels/odemodels/SIRParams.jl b/EpiAware/test/EpiLatentModels/odemodels/SIRParams.jl
new file mode 100644
index 000000000..000c86366
--- /dev/null
+++ b/EpiAware/test/EpiLatentModels/odemodels/SIRParams.jl
@@ -0,0 +1,55 @@
+@testitem "SIRParams: constructor and `generate_latent`" begin
+    using OrdinaryDiffEq, Distributions
+    # Define the time span for the ODE problem
+    tspan = (0.0, 10.0)
+
+    # Define prior distributions
+    infectiousness = LogNormal(log(0.3), 0.05)
+    recovery_rate = LogNormal(log(0.1), 0.05)
+    initial_prop_infected = Beta(2, 5)
+
+    # Create an instance of SIRParams
+    sirparams = SIRParams(
+        tspan = tspan,
+        infectiousness = infectiousness,
+        recovery_rate = recovery_rate,
+        initial_prop_infected = initial_prop_infected
+    )
+
+    @testset "SIRParams constructor tests" begin
+        # Check the types of the fields
+        @test sirparams.prob isa ODEProblem
+        @test sirparams.infectiousness isa Distribution
+        @test sirparams.recovery_rate isa Distribution
+        @test sirparams.initial_prop_infected isa Distribution
+
+        # Check the values of the fields
+        @test sirparams.prob.tspan == tspan
+        @test sirparams.infectiousness == infectiousness
+        @test sirparams.recovery_rate == recovery_rate
+        @test sirparams.initial_prop_infected == initial_prop_infected
+    end
+
+    @testset "SIRParams `generate_latent` tests" begin
+        # Generate parameters
+        Z_t = rand(10) # dummy latent process
+        sirparam_mdl = generate_latent(sirparams, Z_t)
+        sampled_params = rand(sirparam_mdl)
+
+        # Check the types of the fields
+        @test sampled_params.β isa Float64
+        @test sampled_params.γ isa Float64
+        @test sampled_params.I₀ isa Float64
+    end
+end
+
+@testitem "SIRParams: jac_prototype non-zeros invariant to jac call" begin
+    Jp = deepcopy(EpiAware.EpiLatentModels._sir_jac_prototype)
+    Jp_pattern_before = Jp .!= 0
+    u = rand(3)
+    p = rand(2)
+    EpiAware.EpiLatentModels._sir_jac(Jp, u, p, 0.0)
+    Jp_pattern_after = Jp .!= 0
+
+    @test Jp_pattern_before == Jp_pattern_after
+end
diff --git a/EpiAware/test/EpiObsModels/modifiers/ascertainment/helpers.jl b/EpiAware/test/EpiObsModels/modifiers/ascertainment/helpers.jl
index bcf7ce20e..881fd1fae 100644
--- a/EpiAware/test/EpiObsModels/modifiers/ascertainment/helpers.jl
+++ b/EpiAware/test/EpiObsModels/modifiers/ascertainment/helpers.jl
@@ -5,7 +5,8 @@
         model::AbstractTuringObservationModel
     end
 
-    @model EpiAware.EpiAwareBase.generate_observations(model::ExpectedObs, y_t, Y_t) = begin
+    @model EpiAware.EpiAwareBase.generate_observations(model::ExpectedObs, y_t,
+    Y_t) = begin
         expected_obs := Y_t
         @submodel y_t = generate_observations(model.model, y_t, Y_t)
     end
diff --git a/EpiAware/test/Project.toml b/EpiAware/test/Project.toml
index 158597502..b9cb60592 100644
--- a/EpiAware/test/Project.toml
+++ b/EpiAware/test/Project.toml
@@ -15,6 +15,7 @@ LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 LogDensityProblemsAD = "996a588d-648d-4e1f-a8f0-a84b347e47b1"
 LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Pathfinder = "b1d3bc72-d0e7-4279-b92f-7fa5d6d2d454"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
diff --git a/benchmark/Manifest.toml b/benchmark/Manifest.toml
new file mode 100644
index 000000000..451bcb15d
--- /dev/null
+++ b/benchmark/Manifest.toml
@@ -0,0 +1,2858 @@
+# This file is machine-generated - editing it directly is not advised
+
+julia_version = "1.11.1"
+manifest_format = "2.0"
+project_hash = "9fb2e9db0221fb4b8d0c098c0ecdd8e5e0675214"
+
+[[deps.ADTypes]]
+git-tree-sha1 = "30bb95a372787af850addf28ac937f1be7b79173"
+uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
+version = "1.10.0"
+weakdeps = ["ChainRulesCore", "ConstructionBase", "EnzymeCore"]
+
+    [deps.ADTypes.extensions]
+    ADTypesChainRulesCoreExt = "ChainRulesCore"
+    ADTypesConstructionBaseExt = "ConstructionBase"
+    ADTypesEnzymeCoreExt = "EnzymeCore"
+
+[[deps.AbstractFFTs]]
+deps = ["LinearAlgebra"]
+git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef"
+uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
+version = "1.5.0"
+weakdeps = ["ChainRulesCore", "Test"]
+
+    [deps.AbstractFFTs.extensions]
+    AbstractFFTsChainRulesCoreExt = "ChainRulesCore"
+    AbstractFFTsTestExt = "Test"
+
+[[deps.AbstractMCMC]]
+deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "FillArrays", "LogDensityProblems", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"]
+git-tree-sha1 = "aa469a7830413bd4c855963e3f648bd9d145c2c3"
+uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
+version = "5.6.0"
+
+[[deps.AbstractPPL]]
+deps = ["AbstractMCMC", "Accessors", "DensityInterface", "Random"]
+git-tree-sha1 = "6380a9a03a4207bac53ac310dd3a283bb4df54ef"
+uuid = "7a57a42e-76ec-4ea3-a279-07e840d6d9cf"
+version = "0.8.4"
+
+[[deps.AbstractTrees]]
+git-tree-sha1 = "2d9c9a55f9c93e8887ad391fbae72f8ef55e1177"
+uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
+version = "0.4.5"
+
+[[deps.Accessors]]
+deps = ["CompositionsBase", "ConstructionBase", "InverseFunctions", "LinearAlgebra", "MacroTools", "Markdown"]
+git-tree-sha1 = "b392ede862e506d451fc1616e79aa6f4c673dab8"
+uuid = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
+version = "0.1.38"
+
+    [deps.Accessors.extensions]
+    AccessorsAxisKeysExt = "AxisKeys"
+    AccessorsDatesExt = "Dates"
+    AccessorsIntervalSetsExt = "IntervalSets"
+    AccessorsStaticArraysExt = "StaticArrays"
+    AccessorsStructArraysExt = "StructArrays"
+    AccessorsTestExt = "Test"
+    AccessorsUnitfulExt = "Unitful"
+
+    [deps.Accessors.weakdeps]
+    AxisKeys = "94b1ba4f-4ee9-5380-92f1-94cde586c3c5"
+    Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
+    IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953"
+    Requires = "ae029012-a4dd-5104-9daa-d747884805df"
+    StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+    StructArrays = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
+    Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+    Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
+
+[[deps.Adapt]]
+deps = ["LinearAlgebra", "Requires"]
+git-tree-sha1 = "50c3c56a52972d78e8be9fd135bfb91c9574c140"
+uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
+version = "4.1.1"
+weakdeps = ["StaticArrays"]
+
+    [deps.Adapt.extensions]
+    AdaptStaticArraysExt = "StaticArrays"
+
+[[deps.AdvancedHMC]]
+deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "ProgressMeter", "Random", "Requires", "Setfield", "SimpleUnPack", "Statistics", "StatsBase", "StatsFuns"]
+git-tree-sha1 = "6f6a228808fe00ad05b47d74747c800d3df18acb"
+uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d"
+version = "0.6.4"
+
+    [deps.AdvancedHMC.extensions]
+    AdvancedHMCCUDAExt = "CUDA"
+    AdvancedHMCMCMCChainsExt = "MCMCChains"
+    AdvancedHMCOrdinaryDiffEqExt = "OrdinaryDiffEq"
+
+    [deps.AdvancedHMC.weakdeps]
+    CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+    MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
+    OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+
+[[deps.AdvancedMH]]
+deps = ["AbstractMCMC", "Distributions", "FillArrays", "LinearAlgebra", "LogDensityProblems", "Random", "Requires"]
+git-tree-sha1 = "b5f8159bffa2e6af9e080c11c4e6da8c5b05f079"
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+version = "0.8.4"
+weakdeps = ["DiffResults", "ForwardDiff", "MCMCChains", "StructArrays"]
+
+    [deps.AdvancedMH.extensions]
+    AdvancedMHForwardDiffExt = ["DiffResults", "ForwardDiff"]
+    AdvancedMHMCMCChainsExt = "MCMCChains"
+    AdvancedMHStructArraysExt = "StructArrays"
+
+[[deps.AdvancedPS]]
+deps = ["AbstractMCMC", "Distributions", "Random", "Random123", "Requires", "SSMProblems", "StatsFuns"]
+git-tree-sha1 = "5dcd3de7e7346f48739256e71a86d0f96690b8c8"
+uuid = "576499cb-2369-40b2-a588-c64705576edc"
+version = "0.6.0"
+weakdeps = ["Libtask"]
+
+    [deps.AdvancedPS.extensions]
+    AdvancedPSLibtaskExt = "Libtask"
+
+[[deps.AdvancedVI]]
+deps = ["ADTypes", "Bijectors", "DiffResults", "Distributions", "DistributionsAD", "DocStringExtensions", "ForwardDiff", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "StatsBase", "StatsFuns", "Tracker"]
+git-tree-sha1 = "d23ac9ecdf0ba3dfdd8ea22f42793fabc763afdc"
+uuid = "b5ca4192-6429-45e5-a2d9-87aec30a685c"
+version = "0.2.10"
+
+    [deps.AdvancedVI.extensions]
+    AdvancedVIEnzymeExt = ["Enzyme"]
+    AdvancedVIFluxExt = ["Flux"]
+    AdvancedVIReverseDiffExt = ["ReverseDiff"]
+    AdvancedVIZygoteExt = ["Zygote"]
+
+    [deps.AdvancedVI.weakdeps]
+    Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
+    Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+    ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
+    Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+
+[[deps.AliasTables]]
+deps = ["PtrArrays", "Random"]
+git-tree-sha1 = "9876e1e164b144ca45e9e3198d0b689cadfed9ff"
+uuid = "66dad0bd-aa9a-41b7-9441-69ab47430ed8"
+version = "1.1.3"
+
+[[deps.ArgCheck]]
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+
+[[deps.ArgTools]]
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+version = "1.1.2"
+
+[[deps.ArnoldiMethod]]
+deps = ["LinearAlgebra", "Random", "StaticArrays"]
+git-tree-sha1 = "d57bd3762d308bded22c3b82d033bff85f6195c6"
+uuid = "ec485272-7323-5ecc-a04f-4719b315124d"
+version = "0.4.0"
+
+[[deps.ArrayInterface]]
+deps = ["Adapt", "LinearAlgebra"]
+git-tree-sha1 = "d60a1922358aa203019b7857a2c8c37329b8736c"
+uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
+version = "7.17.0"
+
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+    [deps.Transducers.weakdeps]
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+    [deps.Turing.weakdeps]
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+
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diff --git a/benchmark/bench/EpiLatentModels/EpiLatentModels.jl b/benchmark/bench/EpiLatentModels/EpiLatentModels.jl
index 3d83adb57..b90fb0285 100644
--- a/benchmark/bench/EpiLatentModels/EpiLatentModels.jl
+++ b/benchmark/bench/EpiLatentModels/EpiLatentModels.jl
@@ -4,7 +4,7 @@ using BenchmarkTools, TuringBenchmarking, EpiAware, DynamicPPL
 suite = BenchmarkGroup()
 
 include("../../make_epiaware_suite.jl")
-include("models/IDD.jl")
+include("models/IID.jl")
 include("models/AR.jl")
 include("models/MA.jl")
 include("models/RandomWalk.jl")
diff --git a/benchmark/bench/EpiLatentModels/models/IDD.jl b/benchmark/bench/EpiLatentModels/models/IDD.jl
index 09270d964..180d6566d 100644
--- a/benchmark/bench/EpiLatentModels/models/IDD.jl
+++ b/benchmark/bench/EpiLatentModels/models/IDD.jl
@@ -1,5 +1,5 @@
 let
-    model = IDD(Normal(0, 1))
+    model = IID(Normal(0, 1))
     mdl = generate_latent(model, 10)
-    suite["IDD"] = make_epiaware_suite(mdl)
+    suite["IID"] = make_epiaware_suite(mdl)
 end
diff --git a/pipeline/src/infer/generate_inference_results.jl b/pipeline/src/infer/generate_inference_results.jl
index 03c63d61b..5254feecc 100644
--- a/pipeline/src/infer/generate_inference_results.jl
+++ b/pipeline/src/infer/generate_inference_results.jl
@@ -26,7 +26,8 @@ function generate_inference_results(
     prfx = _inference_prefix(truthdata, inference_config, pipeline)
     _datadir_str = _get_inferencedatadir_str(pipeline)
 
-    inference_results, inferencefile = produce_or_load(
+    inference_results,
+    inferencefile = produce_or_load(
         infer, config, _datadir_str; prefix = prfx)
     return inference_results
 end
@@ -63,7 +64,8 @@ function generate_inference_results(
 
     datadir_name = mktempdir()
 
-    inference_results, inferencefile = produce_or_load(
+    inference_results,
+    inferencefile = produce_or_load(
         infer, config, datadir_name; prefix = prfx)
     return inference_results
 end
diff --git a/pipeline/src/infer/inference_helpers.jl b/pipeline/src/infer/inference_helpers.jl
index 057afbaa3..e4608702b 100644
--- a/pipeline/src/infer/inference_helpers.jl
+++ b/pipeline/src/infer/inference_helpers.jl
@@ -61,7 +61,9 @@ end
 """
 Internal method for setting the data directory path for the inference data.
 """
-_get_inferencedatadir_str(pipeline::AbstractEpiAwarePipeline) = datadir("epiaware_observables")
+function _get_inferencedatadir_str(pipeline::AbstractEpiAwarePipeline)
+    datadir("epiaware_observables")
+end
 function _get_inferencedatadir_str(pipeline::AbstractRtwithoutRenewalPipeline)
     pipeline.testmode ? mktempdir() :
     pipeline.priorpredictive ? datadir("priorpredictive") : datadir("epiaware_observables")
diff --git a/pipeline/src/plotting/figureone.jl b/pipeline/src/plotting/figureone.jl
index e6da279a2..5e05dbc6a 100644
--- a/pipeline/src/plotting/figureone.jl
+++ b/pipeline/src/plotting/figureone.jl
@@ -20,7 +20,8 @@ function _figure_one_scenario(analysis_df, scenario; reference_time, true_gi_cho
     model_plotting_data = analysis_df |>
                           df -> @subset(df, :True_GI_Mean.==true_gi_choice) |>
                                 df -> @subset(df, :Used_GI_Mean.==used_gi_choice) |>
-                                      df -> @subset(df, :Reference_Time.==reference_time) |>
+                                      df -> @subset(df,
+        :Reference_Time.==reference_time) |>
                                             df -> @subset(df, :Scenario.==scenario) |>
                                                   data
 
@@ -141,7 +142,8 @@ function _figure_one_model_panel(
     model_plotting_data = analysis_df |>
                           df -> @subset(df, :True_GI_Mean.==true_gi_choice) |>
                                 df -> @subset(df, :Used_GI_Mean.==used_gi_choice) |>
-                                      df -> @subset(df, :Reference_Time.==reference_time) |>
+                                      df -> @subset(df,
+        :Reference_Time.==reference_time) |>
                                             df -> @subset(df, :Scenario.==scenario) |>
                                                   df -> @subset(df, :Target.==target) |>
                                                         df -> @subset(df,
diff --git a/pipeline/src/scoring/summarise_crps.jl b/pipeline/src/scoring/summarise_crps.jl
index 1530e846c..9e47f5298 100644
--- a/pipeline/src/scoring/summarise_crps.jl
+++ b/pipeline/src/scoring/summarise_crps.jl
@@ -13,7 +13,10 @@ function summarise_crps(config, inference_results, forecast_results, epidata)
     ts = config.tspan[1]:min(config.tspan[2] + config.lookahead, length(config.truth_I_t))
 
     procs_names = (:log_I_t, :rt, :Rt, :I_t, :log_Rt)
-    scores_log_I_t, scores_rt, scores_Rt, scores_I_t, scores_log_Rt = _process_crps_scores(
+    scores_log_I_t, scores_rt,
+    scores_Rt,
+    scores_I_t,
+    scores_log_Rt = _process_crps_scores(
         procs_names, inference_results, forecast_results, config, ts, epidata)
 
     scores_y_t, scores_log_y_t = _cases_crps_scores(forecast_results, config, ts)
diff --git a/pipeline/src/simulate/generate_truthdata.jl b/pipeline/src/simulate/generate_truthdata.jl
index 373d5cb3d..81e2c4543 100644
--- a/pipeline/src/simulate/generate_truthdata.jl
+++ b/pipeline/src/simulate/generate_truthdata.jl
@@ -68,7 +68,8 @@ function generate_truthdata(
         cluster_factor = default_params["cluster_factor"], I0 = default_params["I0"])
     datadir_str = mktempdir()
 
-    truthdata, truthfile = produce_or_load(
+    truthdata,
+    truthfile = produce_or_load(
         simulate, config, datadir(datadir_str); prefix = prefix)
     if plot
         plot_truth_data(truthdata, config, pipeline)
diff --git a/pipeline/src/utils/timeseries_utils.jl b/pipeline/src/utils/timeseries_utils.jl
index ce94e49c2..a3c71d4ae 100644
--- a/pipeline/src/utils/timeseries_utils.jl
+++ b/pipeline/src/utils/timeseries_utils.jl
@@ -25,8 +25,10 @@ end
 """
 Internal function for reducing a sequence of results from calls to `calculate_processes`.
 """
-_process_reduction(procs_1, procs_2) = (; log_I_t = hcat(procs_1.log_I_t, procs_2.log_I_t),
-    Rt = hcat(procs_1.Rt, procs_2.Rt), rt = hcat(procs_1.rt, procs_2.rt))
+function _process_reduction(procs_1, procs_2)
+    (; log_I_t = hcat(procs_1.log_I_t, procs_2.log_I_t),
+        Rt = hcat(procs_1.Rt, procs_2.Rt), rt = hcat(procs_1.rt, procs_2.rt))
+end
 
 """
 Generate quantiles for targets based on the output and EpiData.
diff --git a/pipeline/test/plotting/test_plot_funcs.jl b/pipeline/test/plotting/test_plot_funcs.jl
index 5dd2aa8c0..61e048227 100644
--- a/pipeline/test/plotting/test_plot_funcs.jl
+++ b/pipeline/test/plotting/test_plot_funcs.jl
@@ -8,7 +8,8 @@
     subdirname = "test"
     testpipeline = RtwithoutRenewalPriorPipeline()
 
-    f, path = plot_truth_data(
+    f,
+    path = plot_truth_data(
         data, config, testpipeline; plotsubdir = subdirname, saveplot = false)
     @test f isa Figure
     @test path isa String