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index.Rmd
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---
title: 'Model-based Inference in Ecology and Epidemiology'
date: ICTP Spring College<br>9--20 May 2016
output: html_document
bibliography: course.bib
csl: ecology.csl
---
```{r knitr-opts,include=FALSE,purl=FALSE,cache=FALSE}
library(knitr)
require(plyr)
require(reshape2)
require(magrittr)
prefix <- "syllabus"
opts_chunk$set(
progress=TRUE,
prompt=FALSE,tidy=FALSE,highlight=TRUE,
strip.white=TRUE,
warning=FALSE,
message=FALSE,
error=FALSE,
echo=FALSE,
cache=FALSE,
cache.extra=rand_seed,
results='markup',
fig.show='asis',
size='small',
fig.lp="fig:",
fig.path=paste0("figure/",prefix,"-"),
cache.path=paste0("cache/",prefix,"-"),
fig.pos="h!",
fig.align='center',
fig.height=4,fig.width=6.83,
dpi=300,
dev='png',
dev.args=list(bg='transparent')
)
options(
keep.source=TRUE,
stringsAsFactors=FALSE,
encoding="UTF-8"
)
```
### Instructor
Prof. Aaron A. King, Ph.D.
Departments of Ecology & Evolutionary Biology and Mathematics
University of Michigan
Email: [email protected]
### Perspective
Ecological and epidemiological systems are particularly interesting from the physical point of view.
Their complexity and high-dimensionality makes it natural to approach them as stochastic, nonlinear dynamical systems and within this context, many questions of both intrinsic interest and practical concern can be formulated.
To answer these questions, it is necessary to rigorously confront hypothetical models with data.
In this regard, time-series data are of particular value inasmuch as they have the potential to express the characteristic signatures of causal mechanisms.
This course will take students into the heart of these issues via an introduction to ecological and epidemiological stochastic dynamical systems models using a series of examples with real data.
Students will learn how to formulate questions as models and answer the questions using state-of-the-art inference algorithms.
### Course objectives
1. to introduce partially observed Markov process (POMP) models as tools for scientific investigation
1. to give students the ability to formulate POMP models of their own
1. to teach efficient approaches for performing scientific inference using POMP models
1. to familiarize students with the **pomp** package
1. to give students opportunities to work with such inference methods
1. to provide documented examples for student re-use
### Prerequisites
- Familiarity with deterministic dynamics (discrete-time maps, ordinary differential equations) and probability.
- Some programming experience, in any language.
- Completion of the [**R** tutorial](http://kingaa.github.io/R_Tutorial) before the beginning of the course.
- A sense of humor.
### Format and expectations
The course will be taught using a mixture of lectures and computational laboratory exercises.
Students are expected to complete assigned readings before class meetings, keep up with assigned homework, participate fully in discussions, and work on course activities during class meetings.
### Final examination
The examination is scheduled for session 10 on 20 May.
It will have both a written and a computational component, both of which will be similar to the exercises.
### Computing in **R**
We will make extensive use of the open-source **R** statistical computing environment and the **pomp** package for inference based on partially-observed Markov process models.
Students with laptops should install **R** and **RStudio** on their computers before the first day of the course.
[Instructions for doing so can be found here](./prep/preparation.html).
The course does not assume familiarity with **R**, but students should work through the [**R** tutorial](http://kingaa.github.io/R_Tutorial) before the course commences.
In particular, students should work through the the exercises in the tutorial.
### Readings
The following papers serve as background for some of the central issues:
- S. N. Wood (2010) Statistical inference for noisy nonlinear ecological dynamic systems. *Nature*, **466**: 1102--1104. [DOI: 10.1038/nature09319](http://dx.doi.org/10.1038/nature09319).
- A. A. King, E. L. Ionides, M. Pascual, and M. J. Bouma (2008) Inapparent infections and cholera dynamics. *Nature*, **454**: 877--880. [DOI: 10.1038/nature07084](http://dx.doi.org/10.1038/nature07084)
- S. Shrestha, A. A. King, and P. Rohani (2011) Statistical Inference for Multi-Pathogen Systems. *PLoS Comput. Biol.*, **7**: e1002135. [DOI: 10.1371/journal.pcbi.1002135](http://dx.doi.org/10.1371/journal.pcbi.1002135)
A good reference for modeling in infectious disease epidemiology is:
- Keeling, M. & Rohani, P. *Modeling infectious diseases in humans and animals*. Princeton University Press, 2008.
The **pomp** package is described and illustrated in
- A. A. King, D. Nguyen, and E. L. Ionides (2016) Statistical Inference for Partially Observed Markov Processes via the R Package pomp. *J. Stat. Soft.*, **69**: 1--43. [DOI: 10.18637/jss.v069.i12](http://dx.doi.org/10.18637/jss.v069.i12)
### Preparing for the course
Students should do the following **before the first day of the course**:
1. Follow the [instructions to prepare your laptop by installing **R**, **RStudio**, and the needed **R** packages](http://kingaa.github.io/short-course/prep/preparation.html).
1. If you are not proficient in **R**, work through the [**R** tutorial](http://kingaa.github.io/R_Tutorial).
If you are proficient in **R**, look through the tutorial nevertheless.
## Tentative Schedule
```{r sched1,echo=FALSE}
begin.date <- as.Date("2016-05-09")
data.frame(date=begin.date-3+cumsum(c(rep(c(3,1,1,1,1),times=2)))) -> dates
read.csv(colClasses=c(Date="Date"),comment.char="#",
text='
Date,Topic
2016-05-09,"[Introduction to partially observed Markov processes](./intro/intro.html)"
2016-05-10,"[Introduction to inference: parameter estimation I](./parest/parest.html)"
2016-05-11,"[Introduction to inference: parameter estimation II](./parest/parest.html#fitting-deterministic-dynamical-epidemiological-models-to-data)"
2016-05-12,"[Introduction to inference: parameter estimation III](./parest/parest.html#modeling-the-noise)"
2016-05-13,"[Simulation of stochastic dynamic models.](./stochsim/stochsim.html)"
2016-05-16,"[Likelihood for POMP models: theory and practice](./pfilter/pfilter.html)"
2016-05-17,"[Iterated filtering: principles and practice](./mif/mif.html)"
2016-05-18,"[Case study: measles in large and small towns](./measles/measles.html)"
2016-05-19,"[Case study: the persistence of polio.](./polio/polio.html)"
2016-05-20,Examination
') %>%
mutate(Topic=mapvalues(as.character(Topic),NA,""),
Date=format(Date,"%b %e")) %>%
kable()
```
----------------------
[**R** Tutorial](http://kingaa.github.io/R_Tutorial)
[Tutorial on data munging with `plyr`, `reshape2`, and `magrittr`](http://kingaa.github.io/short-course/hadley/munging.html)
[Tutorial on `ggplot2`](http://kingaa.github.io/short-course/hadley/viz.html)
[Source code for these notes](http://github.com/kingaa/short-course/)
[**pomp** homepage](http://kingaa.github.io/pomp)
----------------------
[The materials in this course are licensed under the Creative Commons Attribution-NonCommercial license](http://creativecommons.org/licenses/by-nc/4.0/).
Please share and remix noncommercially, mentioning its origin.
