Release v2.0.0.

.gitignore

@@ -63,3 +63,6 @@ todo.txt
 tex2pdf*
 ergebnisse.tex
 *.pdf
+*.loc
+*.soc
+_minted-*/

README.md

@@ -4,7 +4,19 @@ This repository contains the technical paper describing
 [sid](https://github.com/covid-19-impact-lab/sid).
 
 The current published version is available as an [IZA discussion
-paper](https://www.iza.org/publications/dp/13899).
+paper](https://www.iza.org/publications/dp/13899). Please cite it with
+
+```
+@article{Gabler2020,
+  title={
+    People Meet People: A Microlevel Approach to Predicting the Effect of Policies on
+    the Spread of COVID-19
+  },
+  author={Gabler, Janos and Raabe, Tobias and R{\"o}hrl, Klara},
+  year={2020},
+  publisher={IZA Discussion Paper}
+}
+```
 
 
 ## Related publications
@@ -17,9 +29,46 @@ Here is a list of publications which rely on sid.
   Covid-19-Welle](https://www.ifo.de/publikationen/2020/aufsatz-zeitschrift/wenn-menschen-keine-menschen-treffen-simulation).
   ifo Schnelldienst Digital, 1(15).
 
+- Gabler, J., Raabe, T., Röhrl, K. & von Gaudecker, H. M. (2020). [Die Bedeutung
+  individuellen Verhaltens über den Jahreswechsel für die Weiterentwicklung der
+  Covid-19- Pandemie in Deutschland](http://ftp.iza.org/sp99.pdf). IZA Standpunkte Nr.
+  99.
+
 
 ## Versions
 
 - v1 - 2020-11-25
 
   The first version of the paper was published to accompany Dorn et al. (2020).
+
+- v2 - 2020-12-20
+
+  The second version of the paper was published to accompany Gabler, Raabe, Röhrl, von
+  Gaudecker (2020).
+
+
+## Development
+
+Here are some useful things to know.
+
+- Uncomment the following line in ``paper/preamble.tex`` to remove all comments from the
+  compiled paper.
+
+  ```latex
+  % \PassOptionsToPackage{final}{changes}
+  ```
+
+- Register yourself as a commentator with your own color in ``paper/preamble.tex`` as
+  seen here:
+
+  ```latex
+  \definechangesauthor[name={Tobias}, color=alizarin]{T}
+  ```
+
+  Then, comment or propose a replacement or deletion with
+
+  ```latex
+  \comment[id=T]{<some-comment>}
+  \delete[id=T]{<comment>}{<to-be-deleted>}
+  \replace[id=T]{<comment>}{<to-be-replaced>}
+  ```

environment.yml

@@ -0,0 +1,12 @@
+name: sid-paper
+
+channels:
+  - conda-forge
+  - pytask
+
+dependencies:
+  - python=3.8
+
+  - pytask
+  - pytask-latex
+  - Pygments

paper/abstract.tex

@@ -1,10 +1,18 @@
-\begin{abstract}
-
-Governments worldwide are adopting nuanced policy measures to reduce the number of Covid-19 cases with minimal social and economic costs. Epidemiological models have a hard time predicting the effects of such fine grained policies. We propose a novel simulation-based model to address this shortcoming. We build on state-of-the-art agent-based simulation models but replace the way contacts between susceptible and infected people take place. Firstly, we allow for heterogeneity in the types of contacts (e.g. recurrent or random) and in the infectiousness of each contact type. Secondly, we strictly separate the number of contacts from the probabilities that a contact leads to an infection. The number of contacts changes with social distancing policies, the infection probabilities remain invariant. This allows us to model many types of fine grained policies that cannot easily be incorporated into other models. To validate our model, we show that it can accurately predict the effect of the German November lockdown even if no similar policy has been observed in the time series that were used to estimate the model parameters.
+\noindent Governments worldwide are adopting nuanced policy measures to reduce the
+number of Covid-19 cases and to balance social and economic costs. Epidemiological
+models have a hard time predicting the effects of fine-grained policies. We propose a
+novel simulation-based model to address this shortcoming. We build on state-of-the-art
+agent-based simulation models but replace the way contacts between susceptible and
+infected people take place. Firstly, we allow for heterogeneity in the types of contacts
+(e.g. recurrent or random) and in the infectiousness of each contact type. Secondly, we
+strictly separate the number of contacts from the probabilities that a contact leads to
+an infection. The number of contacts changes with social distancing policies, the
+infection probabilities remain invariant. This allows us to model many types of targeted
+policies that cannot be incorporated into other models. To validate our model, we show
+that it can predict the effect of the German November lockdown even if no similar policy
+has been observed during the time period that was used to estimate the model parameters.
 
 \vspace{1cm}
-JEL Classification: C63, I18
-
-Keywords: Covid-19, agent based simulation model, public health measures
+\noindent JEL Classification: C63, I18
 
-\end{abstract}
+\noindent Keywords: Covid-19, agent based simulation model, public health measures

paper/acknowledgments.tex

@@ -0,0 +1,3 @@
+Gabler and Röhrl are grateful for financial support by the German Research Foundation
+(DFG) through CRC-TR 224 (Projects C01 and A02, respectively). Gabler is grateful for
+funding by IZA Institute of Labor Economics.