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finalized intro lecture
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60 changes: 44 additions & 16 deletions VB_IntroTimeDepData.qmd
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Expand Up @@ -50,21 +50,37 @@ insert_html_math <- make_latex_decorator("", "$$")
## classoption: aspectratio=169
```

## Learning Objectives

1. Understand basic concepts in how we understand and model time-dependent population data in VBD applications
2. Review basic idea of forecasting
3. Overview of course

## Population dynamics of disease

The number of hosts, vectors, pathogens, and infected individuals change over time
The number of hosts, vectors, pathogens, and infected individuals change over time.

We use models to understand and to forecast/predict
We use models models of various types to:
- understand relationships
- forecast/predict possible future outcomes

## What types of models?

tactical to strategic
<center>

![](graphics/model_continuum.png)

</center>

::: columns
::: {.column width="50%"}

- Focus on describing/quantifying patterns
- Statistical Models, e.g. regression, time series

:::

::: {.column width="50%"}
- Seek to understand mechanisms, less prediction
- ODEs, dynamical systems, Stochastic DEs, Individual Based Models

:::
:::

We'll focus on the tactical end of things here (i.e., no dif eqs)

Expand Down Expand Up @@ -153,7 +169,9 @@ for(j in 1:length(sigma)){

## Observation Models

We also have to go out into the field and take some observations of the populations. Let's say that we observe $N_{\mathrm{obs}}(t)$ individuals at time $t$. How does this relate to the true population size? One possibility is: \begin{align*}
We also have to go out into the field and take some observations of the populations.

Let's say that we observe $N_{\mathrm{obs}}(t)$ individuals at time $t$. How does this relate to the true population size? One possibility is: \begin{align*}
N_{\mathrm{obs}}(t) = N(t) + V(t)
\end{align*} where $V(t)$ is our "observation uncertainty", and all together this equation describes our `r myblue("observation model")`.

Expand All @@ -162,19 +180,29 @@ N_{\mathrm{obs}}(t) = N(t) + V(t)
With the observation model, our full system (process + observation models) might be \begin{align*}
N(t) & = s N(t-1) + b(t-1) +W(t)\\
N_{\mathrm{obs}}(t) & = N(t) + V(t)
\end{align*} plus distributions for $W(t)$, $V(t)$, and the initial population size.
\end{align*}

plus:

- distributions for $W(t)$, $V(t)$
- the initial population size.

## Observation process matters

Analysis approach depends on the sampling -- evenly or unevenly spaced, goal of the modeling exercise (understanding vs forecasting/prediction).
Analysis approach depends on not only the goal of the modeling exercise (understanding vs forecasting/prediction) but also on details of the way that data are observed

- evenly or unevenly spaced observations
- consistent sampling locations
- types of observation/instrument (automated? trap type?)

## Forecasting process
Although we focus on tools this week, we'll also talk about general modeling considerations and how to think some of these as we conduct an analyses.

something about forecasting

## Outline of Course

1. Abundance data from VecDyn and NEON
2. Regression refresher for time dep data -- basics plus time dependent predictors, transformations, simple AR
0. Introductions and Goals
1. Regression refresher focusing on diagnostics and transformations
2. Regression approaches for time dependent data -- basics plus time dependent predictors, transformations, simple AR
3. Analysis of evenly-spaced data: basic time-series methods
4. Advanced modeling with Gaussian Process Models
4. Abundance data from VecDyn and NEON + climate and meteorological variables
5. Advanced modeling with Gaussian Process Models
59 changes: 38 additions & 21 deletions docs/VB_IntroTimeDepData.html
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Expand Up @@ -337,23 +337,30 @@ <h1 class="title">VectorByte Methods Training</h1>
</div>
</div>

</section>
<section id="learning-objectives" class="slide level2">
<h2>Learning Objectives</h2>
<ol type="1">
<li>Understand basic concepts in how we understand and model time-dependent population data in VBD applications</li>
<li>Review basic idea of forecasting</li>
<li>Overview of course</li>
</ol>
</section>
<section id="population-dynamics-of-disease" class="slide level2">
<h2>Population dynamics of disease</h2>
<p>The number of hosts, vectors, pathogens, and infected individuals change over time</p>
<p>We use models to understand and to forecast/predict</p>
<p>The number of hosts, vectors, pathogens, and infected individuals change over time.</p>
<p>We use models models of various types to: - understand relationships - forecast/predict possible future outcomes</p>
</section>
<section id="what-types-of-models" class="slide level2">
<h2>What types of models?</h2>
<p>tactical to strategic</p>
<center>
<p><img data-src="graphics/model_continuum.png"></p>
</center>
<div class="columns">
<div class="column" style="width:50%;">
<ul>
<li>Focus on describing/quantifying patterns</li>
<li>Statistical Models, e.g.&nbsp;regression, time series</li>
</ul>
</div><div class="column" style="width:50%;">
<ul>
<li>Seek to understand mechanisms, less prediction</li>
<li>ODEs, dynamical systems, Stochastic DEs, Individual Based Models</li>
</ul>
</div>
</div>
<p>We’ll focus on the tactical end of things here (i.e., no dif eqs)</p>
</section>
<section id="simple-example-a-first-deterministic-model" class="slide level2">
Expand Down Expand Up @@ -399,7 +406,8 @@ <h2>A first (stochastic) model</h2>
<img data-src="VB_IntroTimeDepData_files/figure-revealjs/unnamed-chunk-2-1.png" class="quarto-figure quarto-figure-center r-stretch" width="960"></section>
<section id="observation-models" class="slide level2">
<h2>Observation Models</h2>
<p>We also have to go out into the field and take some observations of the populations. Let’s say that we observe <span class="math inline">\(N_{\mathrm{obs}}(t)\)</span> individuals at time <span class="math inline">\(t\)</span>. How does this relate to the true population size? One possibility is: <span class="math display">\[\begin{align*}
<p>We also have to go out into the field and take some observations of the populations.</p>
<p>Let’s say that we observe <span class="math inline">\(N_{\mathrm{obs}}(t)\)</span> individuals at time <span class="math inline">\(t\)</span>. How does this relate to the true population size? One possibility is: <span class="math display">\[\begin{align*}
N_{\mathrm{obs}}(t) = N(t) + V(t)
\end{align*}\]</span> where <span class="math inline">\(V(t)\)</span> is our “observation uncertainty”, and all together this equation describes our <span style="color: dodgerblue;">observation model</span>.</p>
</section>
Expand All @@ -408,22 +416,31 @@ <h2>Observation Models</h2>
<p>With the observation model, our full system (process + observation models) might be <span class="math display">\[\begin{align*}
N(t) &amp; = s N(t-1) + b(t-1) +W(t)\\
N_{\mathrm{obs}}(t) &amp; = N(t) + V(t)
\end{align*}\]</span> plus distributions for <span class="math inline">\(W(t)\)</span>, <span class="math inline">\(V(t)\)</span>, and the initial population size.</p>
\end{align*}\]</span></p>
<p>plus:</p>
<ul>
<li>distributions for <span class="math inline">\(W(t)\)</span>, <span class="math inline">\(V(t)\)</span></li>
<li>the initial population size.</li>
</ul>
</section>
<section id="observation-process-matters" class="slide level2">
<h2>Observation process matters</h2>
<p>Analysis approach depends on the sampling – evenly or unevenly spaced, goal of the modeling exercise (understanding vs forecasting/prediction).</p>
</section>
<section id="forecasting-process" class="slide level2">
<h2>Forecasting process</h2>
<p>something about forecasting</p>
<p>Analysis approach depends on not only the goal of the modeling exercise (understanding vs forecasting/prediction) but also on details of the way that data are observed</p>
<ul>
<li>evenly or unevenly spaced observations</li>
<li>consistent sampling locations</li>
<li>types of observation/instrument (automated? trap type?)</li>
</ul>
<p>Although we focus on tools this week, we’ll also talk about general modeling considerations and how to think some of these as we conduct an analyses.</p>
</section>
<section id="outline-of-course" class="slide level2">
<h2>Outline of Course</h2>
<ol type="1">
<li>Abundance data from VecDyn and NEON</li>
<li>Regression refresher for time dep data – basics plus time dependent predictors, transformations, simple AR</li>
<ol start="0" type="1">
<li>Introductions and Goals</li>
<li>Regression refresher focusing on diagnostics and transformations</li>
<li>Regression approaches for time dependent data – basics plus time dependent predictors, transformations, simple AR</li>
<li>Analysis of evenly-spaced data: basic time-series methods</li>
<li>Abundance data from VecDyn and NEON + climate and meteorological variables</li>
<li>Advanced modeling with Gaussian Process Models</li>
</ol>

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10 changes: 5 additions & 5 deletions docs/search.json
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Expand Up @@ -11,14 +11,14 @@
"href": "VB_IntroTimeDepData.html#population-dynamics-of-disease",
"title": "VectorByte Methods Training",
"section": "Population dynamics of disease",
"text": "Population dynamics of disease\nThe number of hosts, vectors, pathogens, and infected individuals change over time\nWe use models to understand and to forecast/predict"
"text": "Population dynamics of disease\nThe number of hosts, vectors, pathogens, and infected individuals change over time.\nWe use models models of various types to: - understand relationships - forecast/predict possible future outcomes"
},
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"href": "VB_IntroTimeDepData.html#what-types-of-models",
"title": "VectorByte Methods Training",
"section": "What types of models?",
"text": "What types of models?\ntactical to strategic\nWe’ll focus on the tactical end of things here (i.e., no dif eqs)"
"text": "What types of models?\n\n\n\n\n\n\nFocus on describing/quantifying patterns\nStatistical Models, e.g. regression, time series\n\n\n\nSeek to understand mechanisms, less prediction\nODEs, dynamical systems, Stochastic DEs, Individual Based Models\n\n\n\nWe’ll focus on the tactical end of things here (i.e., no dif eqs)"
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Expand All @@ -39,14 +39,14 @@
"href": "VB_IntroTimeDepData.html#observation-models",
"title": "VectorByte Methods Training",
"section": "Observation Models",
"text": "Observation Models\nWe also have to go out into the field and take some observations of the populations. Let’s say that we observe \\(N_{\\mathrm{obs}}(t)\\) individuals at time \\(t\\). How does this relate to the true population size? One possibility is: \\[\\begin{align*}\nN_{\\mathrm{obs}}(t) = N(t) + V(t)\n\\end{align*}\\] where \\(V(t)\\) is our “observation uncertainty”, and all together this equation describes our observation model."
"text": "Observation Models\nWe also have to go out into the field and take some observations of the populations.\nLet’s say that we observe \\(N_{\\mathrm{obs}}(t)\\) individuals at time \\(t\\). How does this relate to the true population size? One possibility is: \\[\\begin{align*}\nN_{\\mathrm{obs}}(t) = N(t) + V(t)\n\\end{align*}\\] where \\(V(t)\\) is our “observation uncertainty”, and all together this equation describes our observation model."
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"text": "Observation process matters\nAnalysis approach depends on the sampling – evenly or unevenly spaced, goal of the modeling exercise (understanding vs forecasting/prediction)."
"text": "Observation process matters\nAnalysis approach depends on not only the goal of the modeling exercise (understanding vs forecasting/prediction) but also on details of the way that data are observed\n\nevenly or unevenly spaced observations\nconsistent sampling locations\ntypes of observation/instrument (automated? trap type?)\n\nAlthough we focus on tools this week, we’ll also talk about general modeling considerations and how to think some of these as we conduct an analyses."
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Expand All @@ -60,7 +60,7 @@
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"section": "Outline of Course",
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6 changes: 3 additions & 3 deletions materials_temp.qmd
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Expand Up @@ -38,9 +38,9 @@ We are assuming familiarity with R basics as well as at least introductory stati

# Live Workshop Materials

## Introduction to the Workshop and Time Dependent Abundance Data
## Introduction to the Workshop

- [Lecture (coming soon)]()
- [Lecture](VB_IntroTimeDepData.qmd)

<br> <br>

Expand Down Expand Up @@ -76,7 +76,7 @@ We are assuming familiarity with R basics as well as at least introductory stati

- [The VecDyn website](https://vectorbyte.crc.nd.edu/vecdyn-datasets).
- [About the VecDyn API](https://www.vectorbyte.org/blog/vecdyn-api)
- Other Materials Coming Soon.
- Other Materials Coming Soon!

<br> <br>

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