{ "cells": [ { "cell_type": "markdown", "id": "e97f32dd", "metadata": {}, "source": [ "# Online COVID-19 forecasting with a time-varying growth rate\n", "\n", "The defining parameter of an unfolding epidemic is its growth rate $g_t$, the weekly\n", "change in log-cases, which serves as a proxy for whether the effective reproduction\n", "number sits above or below 1. It is a textbook *time-varying parameter*: it climbs at\n", "the start of every wave and falls negative as the wave breaks. This example runs\n", "*bayesloop* in online mode, ingesting the weekly case series one point at a time, in\n", "order to forecast next week's cases as data arrive, to show that adapting the growth\n", "rate improves on assuming it is constant, and to raise a real-time flag when a new wave\n", "begins.\n", "\n", "A useful property of bayesloop in this setting is that, because the `OnlineStudy` is\n", "forward-only, the model evidence it accumulates is the one-step-ahead predictive\n", "log-likelihood summed over the whole stream. This is a *prequential* score that is\n", "out-of-sample by construction, with no train/test split required (Dawid 1984). The\n", "components we use are `OnlineStudy` (sample-by-sample `step()` streaming), a `Gaussian`\n", "observation model, a `GaussianRandomWalk` transition model compared against a `Static`\n", "one, and real-time probability queries on the filtering posterior." ] }, { "cell_type": "code", "execution_count": 1, "id": "3a774238", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:52:54.899870Z", "iopub.status.busy": "2026-06-12T09:52:54.899792Z", "iopub.status.idle": "2026-06-12T09:52:56.372464Z", "shell.execute_reply": "2026-06-12T09:52:56.372043Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import io\n", "import urllib.request\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "\n", "import bayesloop as bl\n", "\n", "try:\n", " import seaborn as sns\n", " sns.set_style(\"whitegrid\")\n", "except ImportError:\n", " plt.style.use(\"default\")\n", "mpl.rcParams.update({\n", " \"figure.dpi\": 110, \"font.size\": 10.5, \"axes.titlesize\": 12,\n", " \"axes.titleweight\": \"bold\", \"axes.edgecolor\": \"#444444\", \"grid.color\": \"#d9d9d9\",\n", "})\n", "C = {\"data\": \"#8a8a8a\", \"accent\": \"#c1272d\", \"accent2\": \"#0b6e99\",\n", " \"green\": \"#2e7d32\", \"amber\": \"#e8a33d\", \"muted\": \"#8a8a8a\",\n", " \"band\": \"#c1272d\", \"band2\": \"#0b6e99\"}\n", "\n", "\n", "def posterior_band(S, name, lo=0.05, hi=0.95):\n", " \"\"\"Posterior mean and an equal-tailed credible band for a time-varying parameter.\"\"\"\n", " x, p = S.get_parameter_distributions(name, density=False)\n", " p = np.asarray(p, dtype=float)\n", " p /= p.sum(axis=1, keepdims=True)\n", " mean = (p * x[None, :]).sum(axis=1)\n", " cdf = np.cumsum(p, axis=1)\n", " low = np.array([np.interp(lo, cdf[t], x) for t in range(p.shape[0])])\n", " high = np.array([np.interp(hi, cdf[t], x) for t in range(p.shape[0])])\n", " return mean, low, high\n", "\n", "\n", "def prob_in(S, name, lo=None, hi=None):\n", " \"\"\"Posterior probability the parameter lies in (lo, hi] at each time step.\"\"\"\n", " x, p = S.get_parameter_distributions(name, density=False)\n", " p = np.asarray(p, dtype=float)\n", " p /= p.sum(axis=1, keepdims=True)\n", " mask = np.ones_like(x, dtype=bool)\n", " if lo is not None:\n", " mask &= x > lo\n", " if hi is not None:\n", " mask &= x <= hi\n", " return p[:, mask].sum(axis=1)" ] }, { "cell_type": "markdown", "id": "6b15f938", "metadata": {}, "source": [ "## The data\n", "\n", "We use US daily confirmed COVID-19 cases from\n", "[Our World in Data](https://github.com/owid/covid-19-data) (compiled from US CDC / JHU\n", "reporting) and aggregate them to weekly sums to smooth the strong day-of-week reporting\n", "cycle. The raw file holds every country, so we fetch it once, immediately filter to the\n", "United States, and discard the rest. We restrict to the period March 2020 to February\n", "2023, during which reporting was dense and comparable, and model the weekly log-growth\n", "$g_t = \\log(\\text{cases}_t) - \\log(\\text{cases}_{t-1})$." ] }, { "cell_type": "code", "execution_count": 2, "id": "1b1b9cbb", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:52:56.373982Z", "iopub.status.busy": "2026-06-12T09:52:56.373852Z", "iopub.status.idle": "2026-06-12T09:52:58.914075Z", "shell.execute_reply": "2026-06-12T09:52:58.913692Z" }, "lines_to_next_cell": 1 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "155 weekly growth observations, 2020-03-15..2023-02-26\n" ] } ], "source": [ "url = (\"https://raw.githubusercontent.com/owid/covid-19-data/master/\"\n", " \"public/data/cases_deaths/full_data.csv\")\n", "req = urllib.request.Request(url, headers={\"User-Agent\": \"Mozilla/5.0 (bayesloop docs)\"})\n", "raw = pd.read_csv(io.BytesIO(urllib.request.urlopen(req, timeout=120).read()),\n", " usecols=[\"date\", \"location\", \"new_cases\"], parse_dates=[\"date\"])\n", "# Filter to the US immediately and drop the rest to keep memory small.\n", "us = raw[raw[\"location\"] == \"United States\"].sort_values(\"date\").set_index(\"date\")\n", "del raw\n", "\n", "wk = us[\"new_cases\"].clip(lower=0).resample(\"W\").sum()\n", "wk = wk[(wk.index >= \"2020-03-08\") & (wk.index <= \"2023-03-01\")]\n", "wk = wk.replace(0, np.nan).ffill()\n", "cases = wk.values.astype(float)\n", "dates = wk.index\n", "y = np.log(cases)\n", "g = np.diff(y) # weekly log-growth, length N-1\n", "gdates = dates[1:]\n", "N = len(g)\n", "print(f\"{N} weekly growth observations, {gdates.min().date()}..{gdates.max().date()}\")" ] }, { "cell_type": "markdown", "id": "0ce5958e", "metadata": {}, "source": [ "## The observation model\n", "\n", "We treat each week's log-growth $g_t$ as Gaussian with a time-varying mean (the growth\n", "rate we want to track) and a free standard deviation that absorbs week-to-week reporting\n", "noise. We wrap it in a small factory so that both studies below receive an identical\n", "fresh copy, which is a prerequisite for their evidences to be directly comparable." ] }, { "cell_type": "code", "execution_count": 3, "id": "7108fa7a", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:52:58.915134Z", "iopub.status.busy": "2026-06-12T09:52:58.915061Z", "iopub.status.idle": "2026-06-12T09:52:58.916820Z", "shell.execute_reply": "2026-06-12T09:52:58.916507Z" } }, "outputs": [], "source": [ "def gauss():\n", " return bl.om.Gaussian(\"growth\", bl.cint(-1.0, 1.0, 200), \"noise\", bl.oint(0.02, 0.6, 60))" ] }, { "cell_type": "markdown", "id": "b926fbd5", "metadata": {}, "source": [ "## The adaptive model\n", "\n", "The first `OnlineStudy` gives the growth rate a `GaussianRandomWalk` transition, so that\n", "it is free to drift smoothly from week to week. We stream the 155 weekly observations\n", "through it with `step()`, and at each step we record the filtered growth-rate mean, the\n", "cumulative predictive log-evidence (`S.log_evidence`, in nats), the real-time\n", "probability P(growth > 0) that the epidemic is currently growing, and the full filtered\n", "posterior band. Setting `store_history=True` lets us recover the per-step distributions\n", "afterwards." ] }, { "cell_type": "code", "execution_count": 4, "id": "9eccd16e", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:52:58.917742Z", "iopub.status.busy": "2026-06-12T09:52:58.917677Z", "iopub.status.idle": "2026-06-12T09:53:00.727036Z", "shell.execute_reply": "2026-06-12T09:53:00.726573Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Added transition model: adaptive (20 combination(s) of the following hyper-parameters: ['sigma'])\n", "+ Start model fit\n", " + Set prior (function): jeffreys. Values have been re-normalized.\n" ] } ], "source": [ "Sa = bl.OnlineStudy(store_history=True, silent=True)\n", "Sa.set_observation_model(gauss(), silent=True)\n", "Sa.add_transition_model(\"adaptive\", bl.tm.GaussianRandomWalk(\"sigma\", bl.cint(0, 0.4, 20), target=\"growth\"))\n", "Sa.set_transition_model_prior([1.0], silent=True)\n", "mu_filt, logE_steps = [], []\n", "for val in g:\n", " Sa.step(val)\n", " mu_filt.append(Sa.get_current_parameter_mean_value(\"growth\"))\n", " logE_steps.append(Sa.log_evidence) # cumulative prequential log-likelihood (nats)\n", "mu_filt = np.array(mu_filt)\n", "p_growing = prob_in(Sa, \"growth\", lo=0.0) # real-time P(epidemic growing)\n", "mu_mean, mu_lo, mu_hi = posterior_band(Sa, \"growth\")\n", "logE_adaptive = float(Sa.log_evidence)" ] }, { "cell_type": "markdown", "id": "e325daaf", "metadata": {}, "source": [ "## The static baseline\n", "\n", "The second `OnlineStudy` is identical except that its transition model is `Static()`, so\n", "the growth rate is forced to be one fixed constant for the whole pandemic. Both models\n", "see exactly the same data in the same order, so the difference in their accumulated\n", "predictive log-likelihood is a like-for-like forecasting comparison." ] }, { "cell_type": "code", "execution_count": 5, "id": "8c816c86", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:00.728370Z", "iopub.status.busy": "2026-06-12T09:53:00.728281Z", "iopub.status.idle": "2026-06-12T09:53:00.766862Z", "shell.execute_reply": "2026-06-12T09:53:00.766308Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Added transition model: static (no hyper-parameters)\n", "+ Start model fit\n", " + Set prior (function): jeffreys. Values have been re-normalized.\n" ] } ], "source": [ "Ss = bl.OnlineStudy(store_history=True, silent=True)\n", "Ss.set_observation_model(gauss(), silent=True)\n", "Ss.add_transition_model(\"static\", bl.tm.Static())\n", "Ss.set_transition_model_prior([1.0], silent=True)\n", "logE_steps_static = []\n", "for val in g:\n", " Ss.step(val)\n", " logE_steps_static.append(Ss.log_evidence)\n", "logE_static = float(Ss.log_evidence)" ] }, { "cell_type": "markdown", "id": "65f2b5cc", "metadata": {}, "source": [ "## One-step-ahead point forecasts\n", "\n", "Alongside the probabilistic score we also build causal point forecasts of next week's\n", "growth. The adaptive forecast uses the current filtered mean, $\\hat g_{t+1} = \\mu_t$; we\n", "compare it to a naive persistence rule ($\\hat g_{t+1} = g_t$) and an expanding-mean\n", "static forecast. From the adaptive growth forecast we also obtain a one-step case-count\n", "forecast, $\\text{cases}_{t+1} = \\text{cases}_t \\cdot \\exp(\\mu_t)$, and score it with\n", "MAPE." ] }, { "cell_type": "code", "execution_count": 6, "id": "81d24421", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:00.768298Z", "iopub.status.busy": "2026-06-12T09:53:00.768188Z", "iopub.status.idle": "2026-06-12T09:53:00.770898Z", "shell.execute_reply": "2026-06-12T09:53:00.770481Z" } }, "outputs": [], "source": [ "pred_adaptive = mu_filt[:-1]\n", "pred_persist = g[:-1]\n", "exp_mean = np.array([g[:i + 1].mean() for i in range(N - 1)])\n", "actual = g[1:]\n", "def rmse(p): return float(np.sqrt(np.mean((actual - p) ** 2)))\n", "# case-count one-step forecast from adaptive growth\n", "cases_pred = cases[1:-1] * np.exp(mu_filt[:-1]) # predict cases[t+1]\n", "cases_actual = cases[2:]\n", "mape_cases_adaptive = float(np.mean(np.abs(cases_pred - cases_actual) / cases_actual))" ] }, { "cell_type": "markdown", "id": "18b235ae", "metadata": {}, "source": [ "## Real-time wave detection\n", "\n", "Finally we turn the posterior into an alarm: every week where the real-time P(growing)\n", "crosses 0.5 from below marks the onset of a new wave. Because the OnlineStudy only ever\n", "uses data up to the current week, these crossings are exactly what a live now-casting\n", "dashboard would have flagged in real time." ] }, { "cell_type": "code", "execution_count": 7, "id": "26a8c758", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:00.771993Z", "iopub.status.busy": "2026-06-12T09:53:00.771898Z", "iopub.status.idle": "2026-06-12T09:53:00.774496Z", "shell.execute_reply": "2026-06-12T09:53:00.774112Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prequential predictive log-likelihood (log10): adaptive -10.9, static -26.3 => gain 15.4\n", "One-step growth RMSE: adaptive 0.241, persistence 0.235, static-mean 0.359\n", "Adaptive one-step case MAPE: 17%\n", "Detected wave onsets (P(growing) up-crossings): ['2020-06-14', '2020-09-27', '2021-04-04', '2021-07-11', '2021-11-21', '2021-12-05', '2022-04-17', '2022-06-26', '2022-11-13', '2022-11-27', '2023-01-08']\n" ] } ], "source": [ "cross_up = [(gdates[i].date().isoformat())\n", " for i in range(1, N) if p_growing[i] > 0.5 and p_growing[i - 1] <= 0.5]\n", "\n", "print(f\"Prequential predictive log-likelihood (log10): adaptive {logE_adaptive/np.log(10):.1f}, \"\n", " f\"static {logE_static/np.log(10):.1f} => gain {(logE_adaptive-logE_static)/np.log(10):.1f}\")\n", "print(f\"One-step growth RMSE: adaptive {rmse(pred_adaptive):.3f}, persistence {rmse(pred_persist):.3f}, \"\n", " f\"static-mean {rmse(exp_mean):.3f}\")\n", "print(f\"Adaptive one-step case MAPE: {100*mape_cases_adaptive:.0f}%\")\n", "print(f\"Detected wave onsets (P(growing) up-crossings): {cross_up}\")" ] }, { "cell_type": "markdown", "id": "c3150565", "metadata": {}, "source": [ "The up-crossings line up with the actual US wave timeline (summer 2020, winter 2020,\n", "Delta, Omicron, BA.2, BA.5, and the winter 2022/23 waves), each flagged as it began.\n", "\n", "## The forecast, the growth rate, and the wave indicator\n", "\n", "The three panels below summarise the analysis: the one-week-ahead case forecast tracking\n", "the actual series (with the unavoidable one-week causal lag), the inferred growth rate\n", "rising above zero at each wave onset and falling negative as each wave breaks, and the\n", "real-time P(growing) alarm." ] }, { "cell_type": "code", "execution_count": 8, "id": "0be1ad73", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:00.775629Z", "iopub.status.busy": "2026-06-12T09:53:00.775541Z", "iopub.status.idle": "2026-06-12T09:53:00.980910Z", "shell.execute_reply": "2026-06-12T09:53:00.980506Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(9.2, 6.4), sharex=True)\n", "ax1.semilogy(dates, cases, color=C[\"data\"], lw=1.3, label=\"weekly cases (actual)\")\n", "ax1.semilogy(dates[2:], cases_pred, color=C[\"accent\"], lw=1.3, ls=\"--\", label=\"1-week-ahead forecast\")\n", "ax1.set_ylabel(\"weekly cases\")\n", "ax1.set_title(\"Online one-week-ahead COVID-19 forecasting with a time-varying growth rate\")\n", "ax1.legend(loc=\"lower left\", fontsize=8)\n", "\n", "ax2.axhline(0, color=\"k\", lw=0.7)\n", "ax2.plot(gdates, mu_mean, color=C[\"accent2\"], lw=1.8, label=\"inferred growth rate\")\n", "ax2.fill_between(gdates, mu_lo, mu_hi, color=C[\"band2\"], alpha=0.2)\n", "ax2.set_ylabel(\"weekly log-growth\")\n", "ax2.legend(loc=\"upper right\", fontsize=8)\n", "\n", "ax3.fill_between(gdates, 0, p_growing, color=C[\"green\"], alpha=0.55)\n", "ax3.axhline(0.5, color=\"k\", ls=\"--\", lw=0.8)\n", "ax3.set_ylabel(\"P(growing)\")\n", "ax3.set_xlabel(\"date\")\n", "ax3.set_ylim(-0.03, 1.05)\n", "for lab, d in [(\"spring'20\", \"2020-03-20\"), (\"winter'20\", \"2020-10-20\"),\n", " (\"Delta\", \"2021-07-15\"), (\"Omicron\", \"2021-12-20\")]:\n", " dd = pd.Timestamp(d)\n", " if gdates.min() <= dd <= gdates.max():\n", " ax3.text(dd, 1.0, lab, fontsize=7, rotation=0, ha=\"center\", va=\"top\", color=C[\"green\"])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "faefab7a", "metadata": {}, "source": [ "## Probabilistic prediction\n", "\n", "Summed over all one-step-ahead forecasts, the adaptive model's predictive log-likelihood\n", "exceeds the static model's by roughly +15 log₁₀; the data stream is about a quadrillion\n", "times more probable under the model that lets the growth rate evolve. The cumulative\n", "curve (left) shows the adaptive model ahead at every step, not just on average.\n", "\n", "The point-error bar chart (right) shows a more nuanced picture. On raw one-step growth\n", "error the adaptive forecast clearly improves on the static-mean forecast but only ties a\n", "naive persistence rule. This is expected: when a parameter behaves like a random walk,\n", "\"tomorrow ≈ today\" is a strong point predictor. The value bayesloop adds here is not a\n", "lower point error but a calibrated predictive distribution (hence the\n", "15-order-of-magnitude likelihood gain) together with a denoised, interpretable\n", "growth-rate estimate with uncertainty, which a persistence rule cannot provide." ] }, { "cell_type": "code", "execution_count": 9, "id": "cb6c6e4b", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:00.982093Z", "iopub.status.busy": "2026-06-12T09:53:00.981995Z", "iopub.status.idle": "2026-06-12T09:53:01.138120Z", "shell.execute_reply": "2026-06-12T09:53:01.137485Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (axa, axb) = plt.subplots(1, 2, figsize=(9.2, 3.4))\n", "axa.plot(gdates, np.array(logE_steps) / np.log(10), color=C[\"accent\"], lw=2, label=\"adaptive (time-varying)\")\n", "axa.plot(gdates, np.array(logE_steps_static) / np.log(10), color=C[\"muted\"], lw=2, ls=\"--\", label=\"static\")\n", "axa.set_ylabel(r\"cumulative $\\log_{10}$ predictive likelihood\")\n", "axa.set_xlabel(\"date\")\n", "axa.set_title(\"Adaptive stays above static at every step\")\n", "axa.legend(fontsize=8, loc=\"lower left\")\n", "labels = [\"adaptive\\n(bayesloop)\", \"persistence\", \"static mean\"]\n", "vals = [rmse(pred_adaptive), rmse(pred_persist), rmse(exp_mean)]\n", "axb.bar(labels, vals, color=[C[\"accent\"], C[\"muted\"], C[\"amber\"]])\n", "axb.set_ylabel(\"one-step growth RMSE\")\n", "axb.set_title(r\"Adaptive $\\gg$ static (+%d $\\log_{10}$ predictive)\" % round((logE_adaptive - logE_static) / np.log(10)))\n", "for i, v in enumerate(vals):\n", " axb.text(i, v, f\"{v:.3f}\", ha=\"center\", va=\"bottom\", fontsize=8)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "37c77a9f", "metadata": {}, "source": [ "## What this example showed\n", "\n", "A one-parameter online model, the time-varying epidemic growth rate, gave us credible\n", "one-week-ahead COVID-19 forecasts, clearly outperformed a static model in proper\n", "probabilistic scoring, and detected the onset of every major US wave in real time. In\n", "doing so we used bayesloop to:\n", "\n", "- stream data one point at a time with `OnlineStudy.step()`, taking each decision from\n", " the current filtering posterior exactly as a live now-casting dashboard would;\n", "- score forecasts probabilistically, where the accumulated online evidence is the\n", " prequential one-step-ahead predictive log-likelihood, out-of-sample by construction\n", " with no train/test split;\n", "- compare parameter dynamics like-for-like, using an identical Gaussian observation\n", " model under `GaussianRandomWalk` versus `Static`, where letting the growth rate drift\n", " wins by about 15 orders of magnitude in predictive likelihood;\n", "- turn the posterior into an alarm, where P(growth > 0) crossing 0.5 flags every wave as\n", " it begins, with a history that matches the US pandemic record;\n", "- distinguish the two kinds of accuracy, since the gain is in probabilistic prediction\n", " and uncertainty calibration rather than in point error versus a persistence rule." ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "main_language": "python", "notebook_metadata_filter": "-all", "text_representation": { "extension": ".py", "format_name": "hydrogen" } }, "kernelspec": { "display_name": "Python 3", "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": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 }