Turing with multiple replicates of data

I have a simple model for which I have single data to be used in inference. I’m new to julia. Right now I’m setting up inference using turing.jl .

My Turing model looks like this :

@model function fit_(data1,  prob)
    # Prior distributions.
    α ~ Uniform(0,0.1)
    β ~ Uniform(0,0.1)

    p = [ α, β]

    prob = remake(problem, p=p)

    predicted = solve(prob, Tsit5(), saveat=1)'

    for i in 1:6
      data1[:,i] .~ MvNormal(predicted[:,i], σ^2 * I)

  return nothing

model = fit_(z1 , problem)

Do you guys think its a correct approach ?  because model is simple and I inferred same model in r but here i'm not getting correct fitting except for first state in my model.


function  new_model(du, u, p, t)
    x, s, a = u
    α, β = p
    du[1] = - (α * x * s)
    du[2] =   β * x * s
    du[3] =  0

# true parameters these are random parameters for setting up problem
p = [0.5, 0.2]
# initial state values 
u0 = [20, 3, 2]
problem  = ODEProblem(new_model, u0, (0.0,10.0), p)