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Lela Boermeester
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ch1.ipynb

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"id": "6d8438c9-5d1c-42a6-9f8f-03c622857bf6",
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"metadata": {},
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"source": [
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"The plots above show trends in weighted ILI over the past decade. The y-axis is the weighted percent ILI and the x-axis shows time in years. The top left panel displays just the data on the nation level in red, it illustrates clear seasonal peaks each year, besides around 2020 when COVID-19 occurred. The national level data is continuously displayed in red on all 4 plots. The top right panel shows HHS Region 1 (see above for states) in blue. Both lines follow similar patterns with HHS1 having higher peaks in recent years. In the bottom left HHS Region 2 seems to have consistently larger peaks then at the National level. We see this repeated with HHS Region 6 in the bottom right plot, where peaks in the early years are almost double that of the National Level. These high peaks indicate a high number of ILI cases and a more severe flu season. \n",
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"### Build a scatterplot of ILI at time t versus ILI at time t-1 for national level. \n",
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"#### put a line of best fit on there and the correlation value. \n",
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"### Build a scatterplot of ILI at time t versus ILI at time t-7 for national level. \n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2c5c98e6-caa1-4fb0-a1e9-bb7cd5d1c1e3",
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"metadata": {},
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"source": [
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"The two plots above illustrate the percent change from baseline when using the values from 1 day and 7 days before to predict the next time point. The x axis represents the previous time step (t-1 and t-7) and the y axis is the percent change from baseline. The black line is the line of best fit and the r value shows the correlation coefficient. \n",
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"\n",
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"These plots demonstrate that observations closer in time to the current time step have a stronger relationship with it, while observations further away show a weaker association."
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]
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},
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{
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"cell_type": "markdown",
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"id": "7614c9a3-efc5-4037-b68d-9a9d97bef067",

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