{ "cells": [ { "cell_type": "markdown", "id": "4432747a-903e-450d-a9c5-3173aff8c4ac", "metadata": {}, "source": [ "# Add arbitrary Stan code to an HBOMS model\n", "\n", "One goal of HBOMS is to produce human-readable Stan models that can be further developed and customized by the user. However, modifying an HBOMS-generated model makes it hard to change the basic parameter structure, as the modifications would have to be re-applied after each change. Therefore, the user can also include \"plugin\" Stan code in the HBOMS model definition. This plugin code is added as-is at the end of the code blocks (or beginning for the `functions` block). \n", "\n", "To use this functionality, the user must provide a Python dictionary with keys equal to the names of the code blocks. For example\n", "\n", "```python\n", "plugin_code = {\n", " \"data\" : \"real SomeDataPoint;\",\n", " \"model\" : \"SomeDataPoint ~ exponential(lambda);\"\n", "}\n", "```\n", "\n", "will add the declaration for `SomeDataPoint` to the `data` block, and a \"sampling statement\" to the model block. This sampling statement uses a parameter `lambda` and so it is important that this is defined in the HBOMS model.\n", "\n", "The `plugin_code` dictionary is then passed as a keyword argument in the `HbomsModel` initiation\n", "\n", "```python\n", "model = hboms.HbomsModel(\n", " name = \"my_model\",\n", " # other arguments...\n", " plugin_code = plugin_code\n", ")\n", "```\n", "\n", "\n", "## Example: SEIR model with serial interval data\n", "\n", "As an example, we look at an epidemiological model of an infectious disease in which we have time series data, but also additional observations in the form of observed serial intervals (the time between symptom onset of a primary and secondary patient). The epidemiological model will be the SEIR model (susceptible, exposed, infected, recovered), and we will pretend that the serial interval is the same as the generation interval (the time between infections). \n", "\n", "The ODEs for the SEIR model are given by \n", "\n", "\\begin{equation}\n", "\\begin{split}\n", "\\frac{dS}{dt} &= -\\beta SI \\\\\n", "\\frac{dE}{dt} &= \\beta SI - \\alpha E \\\\\n", "\\frac{dI}{dt} &= \\alpha E - \\gamma I\n", "\\end{split}\n", "\\end{equation}\n", "\n", "and initial condition $S(0) = 1-\\epsilon$, $E(0) = 0$, $I(0) = \\epsilon$.\n", "We'll assument that the observed data at time `t` follows a Poisson distribution with mean `SampleSize * I(t)`.\n", "Let's start with creating the nessecary ingredients of an HBOMS model." ] }, { "cell_type": "code", "execution_count": 11, "id": "c7056445-e6e5-4a60-a6c4-b966cbe844f1", "metadata": {}, "outputs": [], "source": [ "import hboms\n", "\n", "params = [\n", " hboms.Parameter(\"beta\", 1.0, \"random\", scale=0.1), \n", " hboms.Parameter(\"alpha\", 1.0, \"random\", scale=0.1),\n", " hboms.Parameter(\"gamma\", 0.5, \"random\", scale=0.1),\n", " hboms.Parameter(\"SampleSize\", 1e2, \"const\")\n", "]\n", "\n", "init = \"\"\"\n", "S_0 = 0.998;\n", "E_0 = 0.0;\n", "I_0 = 0.002; // we're lazy and use a hard-coded epsilon\n", "\"\"\"\n", "\n", "odes = \"\"\"\n", "ddt_S = -beta * S * I;\n", "ddt_E = beta * S * I - alpha * E;\n", "ddt_I = alpha * E - gamma * I;\n", "\"\"\"\n", "\n", "state = [hboms.Variable(\"S\"), hboms.Variable(\"E\"), hboms.Variable(\"I\")]\n", "obs = [hboms.Observation(\"Counts\", data_type=\"int\")]\n", "dists = [hboms.StanDist(\"poisson\", \"Counts\", [\"I * SampleSize\"])]" ] }, { "cell_type": "markdown", "id": "637962a3-d236-4ed3-97f1-c7ece398974c", "metadata": {}, "source": [ "As additional data, we have a number of observed generation intervals $T_G$ which in the SEIR model are hypoexponentially distributed with rate parameters $\\alpha$ and $\\beta$. A hypoexponential random variable is the sum of two exponentially distributed random variables. Hence it is like the Erlang distribution, but the rates can differ between the summands.\n", "In e.g. [Roberts and Heesterbeek](https://doi.org/10.1007/s00285-007-0112-8), we find an explicit formula for the PDF\n", "\n", "$$ f(t) = \\frac{\\alpha\\gamma}{\\gamma-\\alpha} (e^{-\\alpha t} - e^{-\\gamma t}) $$\n", "\n", "This equation is a bit annoying for estimating $\\alpha$ and $\\gamma$, though, because it has a removable singularity at $\\alpha = \\gamma$. Therefore, we use a somewhat less explicit, but equivalent, equation\n", "\n", "$$ f(t) = -(1,0) \\exp(\\Theta t) \\Theta (1,1)'\\quad \\mbox{where}\\quad \\Theta = \\left(\\begin{array}{cc}-\\alpha & \\alpha \\\\ 0 &-\\gamma \\end{array}\\right) $$\n", "and $\\exp$ is the matrix exponential and we trust that the implementation of the matrix exponential in Stan is efficient and accurate.\n", "\n", "To implement this, we will add some plugin code to the `functions`, `data` and `model` blocks.\n", "We define the `hypoexp` distribution in the `functions` block, declare a data array (and it's size) in the `data` block and write a \"sampling statement\" for the `model` block. For defining the data array, we can make use of the variable `R` which is the number of units.\n", "For the sampling statements, we'll make use of the parameters $\\alpha$ and $\\gamma$.\n", "\n", ":::{caution}\n", "As these are random parameters, we will have to index them. If you were to change $\\alpha$ or $\\gamma$ to fixed parameters, you would have to make sure the indexing is removed in the plugin code.\n", ":::\n", "\n", "With this plugin code in place, we finally initiate the HBOMS model." ] }, { "cell_type": "code", "execution_count": 2, "id": "9df0ecf3-65a8-4096-ab60-1d6458808f0e", "metadata": {}, "outputs": [], "source": [ "plugin_code_functions = \"\"\"\n", "real hypoexp_lpdf(real t, real a, real b) {\n", " matrix[2, 2] Theta = [[-a, a], [0,-b]];\n", " return log(sum((-matrix_exp(Theta .* t) * Theta)[1,:]));\n", "}\n", "\"\"\"\n", "\n", "plugin_code_data = \"\"\"\n", "int NumIntervalSamples;\n", "array[R, NumIntervalSamples] real IntervalSamples;\n", "\"\"\"\n", "\n", "plugin_code_model = \"\"\"\n", "for ( r in 1:R ) {\n", " for ( n in 1:NumIntervalSamples ) {\n", " IntervalSamples[r,n] ~ hypoexp(alpha[r], gamma[r]);\n", " }\n", "}\n", "\"\"\"\n", "\n", "plugin_code = {\n", " \"functions\" : plugin_code_functions,\n", " \"data\" : plugin_code_data,\n", " \"model\" : plugin_code_model\n", "}\n", "\n", "model = hboms.HbomsModel(\n", " \"plugin_model\",\n", " state,\n", " odes,\n", " init,\n", " params,\n", " obs,\n", " dists,\n", " plugin_code=plugin_code\n", ")" ] }, { "cell_type": "markdown", "id": "b9fd9796-8dc3-4e00-8f96-4fbc6d723293", "metadata": {}, "source": [ "Using the `show_stan_model` utility, we can have a look at what HBOMS has generated.\n", "Notice that the plugin function is defined before the other functions. The motivation is that we might want to define functions that are used in the ODE model definition. The other plugin code is added at the end of the code blocks." ] }, { "cell_type": "code", "execution_count": 3, "id": "9a157227-3a47-4ce7-a35d-ec1e89bd46ed", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "

\n", "\n", "
functions {\n",
       "    /* User-provided plugin code */\n",
       "    real hypoexp_lpdf(real t, real a, real b) {\n",
       "        matrix[2, 2] Theta = [[-a, a], [0,-b]];\n",
       "        return log(sum((-matrix_exp(Theta .* t) * Theta)[1,:]));\n",
       "    }\n",
       "    /* vector field */\n",
       "    vector ode_fun(real t, vector state, real beta, real alpha, real gamma) {\n",
       "        /* unpack the state variables */\n",
       "        real S = state[1];\n",
       "        real E = state[2];\n",
       "        real I = state[3];\n",
       "        /* declare derivatives of state variables */\n",
       "        real ddt_S, ddt_E, ddt_I;\n",
       "        /* user-defined ODEs */\n",
       "        ddt_S = -beta * S * I;\n",
       "        ddt_E = beta * S * I - alpha * E;\n",
       "        ddt_I = alpha * E - gamma * I;\n",
       "        /* return literal vector with derivatives */\n",
       "        return [ddt_S, ddt_E, ddt_I]';\n",
       "    }\n",
       "    /* initial condition */\n",
       "    vector gen_init() {\n",
       "        /* declare initial variables */\n",
       "        real S_0, E_0, I_0;\n",
       "        /* user-defined initial condition */\n",
       "        S_0 = 0.998;\n",
       "        E_0 = 0.0;\n",
       "        I_0 = 0.002;\n",
       "        /* return literal vector with initival state variables */\n",
       "        return [S_0, E_0, I_0]';\n",
       "    }\n",
       "    /* IVP solver function */\n",
       "    array[] vector solve_ivp(data array[] real Time, real beta, real alpha, real gamma) {\n",
       "        int N = num_elements(Time); /* number of time points */\n",
       "        vector[3] init_state = gen_init(); /* generate initial state */\n",
       "        array[N] vector[3] sol; /* allocate space for solution */\n",
       "        sol[1:N, 1:3] = ode_rk45(ode_fun, init_state, 0.0, Time[1:N], beta, alpha, gamma); /* solve initial value problem */\n",
       "        return sol; /* return the solution */\n",
       "    }\n",
       "    /* auxiliary function for map_rect */\n",
       "    vector map_rect_helper_fun(vector ppar, vector upar, data array[] real rdat, data array[] int idat) {\n",
       "        int N = idat[1]; /* number of time points */\n",
       "        /* solve initial value problem */\n",
       "        array[N] vector[3] sol = solve_ivp(rdat[1:N + 0], upar[1], upar[2], upar[3]);\n",
       "        /* concatenate solution into a vector */\n",
       "        vector[3 * N] res;\n",
       "        for ( n in 1:N ) res[(n - 1) * 3 + 1:n * 3] = sol[n];\n",
       "        return res;\n",
       "    }\n",
       "    /* log-likelihood function */\n",
       "    vector loglik_fun(int Counts, vector state, data real SampleSize) {\n",
       "        /* unpack required state variables */\n",
       "        real I = state[3];\n",
       "        /* declare log-lik variables */\n",
       "        real ll_Counts = 0.0;\n",
       "        /* user-defined log-likelihood */\n",
       "        ll_Counts = poisson_lpmf(Counts | I * SampleSize); /* poisson log-likelihood */\n",
       "        return [ll_Counts]';\n",
       "    }\n",
       "    /* rng function */\n",
       "    int Counts_rng(vector state, data real SampleSize) {\n",
       "        /* unpack required state variables */\n",
       "        real I = state[3];\n",
       "        /* declare variable to-be-returned */\n",
       "        int Counts;\n",
       "        /* user-defined sampler */\n",
       "        Counts = poisson_rng(I * SampleSize); /* random poisson sample */\n",
       "        return Counts;\n",
       "    }\n",
       "}\n",
       "\n",
       "data {\n",
       "    int<lower=0> R; /* number of units */\n",
       "    array[R] int<lower=0> N; /* number of observations per unit */\n",
       "    array[R, max(N)] real<lower=0.0> Time; /* observation times */\n",
       "    /* observations */\n",
       "    array[R, max(N)] int Counts;\n",
       "    int<lower=0> NSim; /* number of simulation time points */\n",
       "    array[R, NSim] real<lower=0.0> TimeSim; /* simulation time points */\n",
       "    /* constants */\n",
       "    real<lower=0.0> SampleSize;\n",
       "    /* User-provided plugin code */\n",
       "    int NumIntervalSamples;\n",
       "    array[R, NumIntervalSamples] real IntervalSamples;\n",
       "}\n",
       "\n",
       "transformed data {\n",
       "    /* declarations */\n",
       "    array[R, max(N)] real rdats;\n",
       "    array[R, 1] int idats; /* integer data */\n",
       "    /* definitions */\n",
       "    rdats[:, 1:max(N)] = Time;\n",
       "    idats[:, 1] = N;\n",
       "}\n",
       "\n",
       "parameters {\n",
       "    /* individual parameters (and their hyper-parameters) */\n",
       "    array[R] real<lower=0.0> beta;\n",
       "    real loc_beta;\n",
       "    real<lower=0.0> scale_beta;\n",
       "    array[R] real<lower=0.0> alpha;\n",
       "    real loc_alpha;\n",
       "    real<lower=0.0> scale_alpha;\n",
       "    array[R] real<lower=0.0> gamma;\n",
       "    real loc_gamma;\n",
       "    real<lower=0.0> scale_gamma;\n",
       "}\n",
       "\n",
       "transformed parameters {\n",
       "    array[R] vector[3] upars; /* prepare data structure for map_rect */\n",
       "    vector[0] ppar; /* no population parameters required for map_rect */\n",
       "    /* assign unit-parameters to array of vectors */\n",
       "    upars[:, 1] = beta;\n",
       "    upars[:, 2] = alpha;\n",
       "    upars[:, 3] = gamma;\n",
       "}\n",
       "\n",
       "model {\n",
       "    /* solve ODEs in parallel */\n",
       "    vector[sum(N) * 3] concat_res = map_rect(map_rect_helper_fun, ppar, upars, rdats, idats);\n",
       "    /* compute log-likelihood of observations */\n",
       "    for ( r in 1:R ) {\n",
       "        for ( n in 1:N[r] ) {\n",
       "            /* extract state */\n",
       "            int idx = 3 * (sum(N[:r - 1]) + n - 1) + 1;\n",
       "            vector[3] state = concat_res[idx:idx + 2];\n",
       "            /* compute likelihood of observation given state */\n",
       "            target += loglik_fun(Counts[r, n], state, SampleSize);\n",
       "        }\n",
       "    }\n",
       "    /* prior */\n",
       "    beta ~ lognormal(loc_beta, scale_beta);\n",
       "    loc_beta ~ student_t(3.0, 0.0, 2.5);\n",
       "    scale_beta ~ student_t(3.0, 0.0, 2.5);\n",
       "    alpha ~ lognormal(loc_alpha, scale_alpha);\n",
       "    loc_alpha ~ student_t(3.0, 0.0, 2.5);\n",
       "    scale_alpha ~ student_t(3.0, 0.0, 2.5);\n",
       "    gamma ~ lognormal(loc_gamma, scale_gamma);\n",
       "    loc_gamma ~ student_t(3.0, 0.0, 2.5);\n",
       "    scale_gamma ~ student_t(3.0, 0.0, 2.5);\n",
       "    /* User-provided plugin code */\n",
       "    for ( r in 1:R ) {\n",
       "        for ( n in 1:NumIntervalSamples ) {\n",
       "            IntervalSamples[r,n] ~ hypoexp(alpha[r], gamma[r]);\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "generated quantities {\n",
       "    array[R, NSim] real S_sim, E_sim, I_sim;\n",
       "    array[R, max(N)] int Counts_sim;\n",
       "    vector[sum(N) * 1] log_lik; /* vector of log-likelihoods for model comparison */\n",
       "    for ( r in 1:R ) {\n",
       "        array[NSim] vector[3] u_sim = solve_ivp(TimeSim[r], beta[r], alpha[r], gamma[r]); /* solve ODEs at simulation times */\n",
       "        array[N[r]] vector[3] u_sim_obs = solve_ivp(Time[r, 1:N[r]], beta[r], alpha[r], gamma[r]); /* solve ODEs at observation times */\n",
       "        for ( n in 1:NSim ) {\n",
       "            S_sim[r, n] = u_sim[n, 1];\n",
       "            E_sim[r, n] = u_sim[n, 2];\n",
       "            I_sim[r, n] = u_sim[n, 3];\n",
       "        }\n",
       "        for ( n in 1:N[r] ) {\n",
       "            int idx = 1 * (sum(N[:r - 1]) + n - 1) + 1;\n",
       "            log_lik[idx:idx + 0] = loglik_fun(Counts[r, n], u_sim_obs[n], SampleSize); /* record log-likelihood of each observation */\n",
       "            Counts_sim[r, n] = Counts_rng(u_sim_obs[n], SampleSize); /* simulate data at observation times */\n",
       "        }\n",
       "    }\n",
       "}\n",
       "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hboms.utilities.show_stan_model(model.model_code)" ] }, { "cell_type": "markdown", "id": "8f5abd78-4618-4298-aa04-50320c54aa8f", "metadata": {}, "source": [ "## Simulate data with the Stan model\n", "\n", "Next, we use the Stan model to simulate some data. We have to provide time points for each unit at which we want to generate pseudo-observations." ] }, { "cell_type": "code", "execution_count": 4, "id": "df504c26-63e4-4581-bd3e-f8325c663fdc", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "15:05:34 - cmdstanpy - INFO - CmdStan start processing\n", "15:05:34 - cmdstanpy - INFO - Chain [1] start processing\n", "15:05:34 - cmdstanpy - INFO - Chain [1] done processing\n" ] } ], "source": [ "import numpy as np\n", "\n", "R = 6\n", "Time = [np.linspace(1, 40, 40) for _ in range(R)]\n", "data = {\"Time\" : Time}\n", "sims = model.simulate(data=data, num_simulations=10)\n", "# select the first simulated data set, and get the random parameters\n", "sim_data, sim_pars = sims[0]" ] }, { "cell_type": "markdown", "id": "bf7cb0c4-c871-48c9-a4a3-463f62e103e0", "metadata": {}, "source": [ "We will now add simulated generation intervals for each unit. We can simulate these using the sum of two exponentially distributed random deviates, using `scipy.stats`." ] }, { "cell_type": "code", "execution_count": 5, "id": "039a93d2-fcf9-4627-8976-814f78d64bcc", "metadata": {}, "outputs": [], "source": [ "import scipy.stats as sts\n", "\n", "alpha_gt = sim_pars[\"alpha\"]\n", "gamma_gt = sim_pars[\"gamma\"]\n", "\n", "NumIntervalSamples = 100\n", "IntervalSamples = [\n", " sts.expon.rvs(scale=1/alpha, size=NumIntervalSamples) + \\\n", " sts.expon.rvs(scale=1/gamma, size=NumIntervalSamples)\n", " for alpha, gamma in zip(alpha_gt, gamma_gt)\n", "]\n", "\n", "sim_data[\"NumIntervalSamples\"] = NumIntervalSamples\n", "sim_data[\"IntervalSamples\"] = IntervalSamples" ] }, { "cell_type": "markdown", "id": "8cf2d5ca-d4f7-4649-acdd-8e0b24069afb", "metadata": {}, "source": [ "## Fit the Stan model to the simulated data\n", "\n", "Next, we use the `sample` method to fit the model. At this point it is important that the additinal data has been added to the data dictionary `sim_data`, otherwise Stan will raise an exception, indicating that data is missing. This is also true if you use `init_check`." ] }, { "cell_type": "code", "execution_count": 6, "id": "1513a28c-e413-4444-b64e-1f177a00d696", "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "15:05:34 - cmdstanpy - INFO - CmdStan start processing\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "efbbb1c5e58e4c96831ae41766055d9c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "chain 1 | | 00:00 Status" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "eef72f899bb1439eb9e72079efa54039", "version_major": 2, "version_minor": 0 }, "text/plain": [ "chain 2 | | 00:00 Status" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "eaa642f65a1c464cbecf499aa937083a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "chain 3 | | 00:00 Status" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2a2e8acb7e2a4ebcaecc0c385be21012", "version_major": 2, "version_minor": 0 }, "text/plain": [ "chain 4 | | 00:00 Status" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " " ] }, { "name": "stderr", "output_type": "stream", "text": [ "15:09:01 - cmdstanpy - INFO - CmdStan done processing.\n", "15:09:01 - cmdstanpy - WARNING - Some chains may have failed to converge.\n", "\tChain 1 had 3 divergent transitions (1.5%)\n", "\tChain 2 had 1 divergent transitions (0.5%)\n", "\tChain 4 had 5 divergent transitions (2.5%)\n", "\tUse the \"diagnose()\" method on the CmdStanMCMC object to see further information.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "model.sample(\n", " data=sim_data, \n", " iter_warmup=200, iter_sampling=200, \n", " refresh=1, \n", " step_size=0.01, adapt_delta=0.95,\n", " threads_per_chain=R\n", ")" ] }, { "cell_type": "markdown", "id": "8ff0b836-c20f-41cb-afa2-e9853b4e55c5", "metadata": {}, "source": [ "We then use `post_pred_check` to inspect the fitted model trajectories." ] }, { "cell_type": "code", "execution_count": 7, "id": "2724884c-7148-4b16-b8dc-509caeab8eca", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = model.post_pred_check(data=sim_data, obs_names=[])" ] }, { "cell_type": "markdown", "id": "56c314f5-82af-4c87-9ad4-7609f385cc3f", "metadata": {}, "source": [ "And how the posterior predictive distributions mach with the data." ] }, { "cell_type": "code", "execution_count": 8, "id": "0ef191bc-130d-4891-ba34-fb9073a78e4a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = model.post_pred_check(data=sim_data, state_var_names=[])" ] }, { "cell_type": "markdown", "id": "6d423950-dcb5-44d4-8aa3-7c4d1e2a0b2a", "metadata": {}, "source": [ "Even though we have a lot of data to infer parameter values from, the parameters show high posterior correlations.\n", "We show this for $\\beta$ and $\\gamma$ below" ] }, { "cell_type": "code", "execution_count": 9, "id": "e78581de-16bd-41aa-b740-a914eb754a27", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "def plot_beta_vs_gamma(model, gt_pars, nrows, ncols):\n", " fig, axs = plt.subplots(nrows, ncols, figsize=(3*ncols,3*nrows), sharex=True, sharey=True)\n", " \n", " for i, ax in enumerate(axs.flat):\n", " beta = model.fit.stan_variable(\"beta\")[:,i]\n", " gamma = model.fit.stan_variable(\"gamma\")[:,i]\n", " z = sts.gaussian_kde(bg := np.stack([beta, gamma]))(bg)\n", " ax.scatter(beta, gamma, s=2, linewidths=0, c=z)\n", " ax.set(xlabel=\"$\\\\beta$\", ylabel=\"$\\\\gamma$\")\n", " beta_gt = gt_pars[\"beta\"][i]\n", " gamma_gt = gt_pars[\"gamma\"][i]\n", " ax.axhline(gamma_gt, color='k')\n", " ax.axvline(beta_gt, color='k')\n", "\n", " return fig, axs\n", "\n", "fig, axs = plot_beta_vs_gamma(model, sim_pars, 2, 3)" ] }, { "cell_type": "markdown", "id": "44091d07-d37a-4258-bb5b-208790665db8", "metadata": {}, "source": [ "Of course we can also estimate the basic reproduction number $R_0 = \\beta / \\gamma$. We can quite precisely estimate $R_0$, which would not be the case had we excluded the generation interval data." ] }, { "cell_type": "code", "execution_count": 10, "id": "9c38513e-14a5-46c7-b86e-70d018a4df6d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_R0(model, gt_pars, nrows, ncols):\n", " R0 = model.fit.stan_variable(\"beta\") / model.fit.stan_variable(\"gamma\")\n", " R0_gt = gt_pars[\"beta\"] / gt_pars[\"gamma\"]\n", "\n", " fig, axs = plt.subplots(nrows, ncols, figsize=(3*ncols,3*nrows), sharex=True, sharey=True)\n", "\n", " for i, ax in enumerate(axs.flat):\n", " ax.hist(R0[:,i], 50, density=True, color='b', alpha=0.5, label=\"posterior\")\n", " ax.axvline(R0_gt[i], color='k', label=\"ground truth\")\n", " ax.set_xlabel(\"$R_0$\")\n", "\n", " axs.flat[0].legend(fontsize='small')\n", " return fig, axs\n", "\n", "fig, axs = plot_R0(model, sim_pars, 2, 3)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 5 }