diff --git a/config.toml b/config.toml
index c63b3f4cd..0c184e9b2 100644
--- a/config.toml
+++ b/config.toml
@@ -111,3 +111,9 @@ relativeURLs = false
mission = "Develop the theory and practice of epidemiological forecasting, with a long-term vision of making this technology as universally accepted and useful as weather forecasting is today."
apiUrl = "https://cmu-delphi.github.io/delphi-epidata"
twitter = "CmuDelphi"
+ feedbackForm = "https://docs.google.com/forms/d/e/1FAIpQLSeIeOJtrAhdOriEyiRY7LkpQX8DZBY19dl6De8l56Q9CZhmxw/viewform?usp=pp_url&entry.1245962748="
+ feedbackLikelihoodMobile = 0.2
+ feedbackLikelihoodDesktop = 1
+ feedbackDelayMin = 10 # in sec
+ feedbackDelayMax = 100 # in sec
+ feedbackDuration = 60 # show it for 60sec
\ No newline at end of file
diff --git a/content/blog/2021-01-15-causal-effect-mobility.Rmd b/content/blog/2021-01-15-causal-effect-mobility.Rmd
new file mode 100644
index 000000000..2dfb03b89
--- /dev/null
+++ b/content/blog/2021-01-15-causal-effect-mobility.Rmd
@@ -0,0 +1,540 @@
+---
+title: Causal Inference for Social Mobility and COVID-19
+author: Matteo Bonvini, Edward Kennedy, Valérie Ventura, and Larry Wasserman
+date: 2021-01-19
+tags:
+ - COVIDcast
+authors:
+ - mbonvini
+ - ekennedy
+ - vventura
+ - lwasserman
+heroImage: /blog/images/causal-inference-mobility-full-size.jpg
+heroImageThumb: /blog/images/causal-inference-mobility-thumb.jpg
+summary: |
+ How can we estimate the causal effect of mobility on COVID-19 when there could
+ be many confounding variables?
+acknowledgements: |
+ We'd like to thank the Delphi engineering team for making this data available.
+ And we'd like to thank Roni Rosenfeld and Ryan Tibshirani for posing the
+ challenge of making counterfactual predictions. We thank Rob Tibshirani for
+ suggesting several improvements on this post, and Alex Reinhart for getting
+ our post into the appropriate format.
+related:
+ - 2020-08-28-api
+output:
+ blogdown::html_page:
+ toc: true
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(collapse = TRUE)
+```
+
+Most pandemic research has focused on questions related to prediction such as:
+how many COVID cases do we expect to see next week in Allegheny County? Causal
+inference, on the other hand, is concerned with "what if" questions. For
+example: what would happen if everyone wore masks? How many cases would there be
+if there was a lockdown? How many deaths would occur if 40 percent of the
+population is vaccinated? These questions involve hypothetical interventions on
+a population.
+
+Estimating causal effects is tricky. We all know that "correlation
+isn't causation." For example, mask usage and cases could both
+increase over time. But that doesn't mean that masks cause more cases
+to occur. Activation of "Buckle your seatbelt" signs on airplanes is
+correlated with turbulence. But activating the sign does not cause
+turbulence.
+
+So how do we estimate causal effects? There is a collection of methods
+for this task. The important thing is that we can't just use standard
+prediction methods. We need to use specialized methods that were designed
+for causal inference. In this post we will discuss our work on
+the causal effect of social mobility on deaths from COVID. The social mobility
+variable we will use is the proportion of people staying home. We can think of
+this of anti-mobility, and expect that higher values of this variable will lead
+to fewer deaths from COVID.
+
+Let’s start with a brief introduction to causal inference.
+
+
+## Causal Inference
+
+Causal inference
+is a huge, complex topic.
+We give a very brief
+exposition of some key ideas here.
+To learn more,
+we recommend [the book
+by Hernan and Robins](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/) and
+the paper
+"[Statistics and Causal Inference](https://www.jstor.org/stable/2289064)"
+by Paul Holland
+(*Journal of the American Statistical Association* 1986, pp. 945-960).
+And one can find many tutorials
+on the web.
+
+Suppose we have
+three variables
+$X$, $A$ and $Y$, where
+$Y$ is the outcome of interest
+(such as deaths from COVID),
+$A$ is a treatment or exposure
+(such as mobility level),
+and $X$ are confounding variables,
+which are variables that affect both
+$A$ and $Y$ (such as age).
+The joint density is
+
+$$
+p(x,a,y) = p(x)p(a|x)p(y|x,a).
+$$
+
+We want to know: what would be the mean of $Y$
+if we could set $A$ to be equal to some value $a$?
+For example,
+what would the number of COVID cases be
+if 92 percent of people wore masks?
+We let $Y^a$
+denote this hypothetical outcome,
+which is called a counterfactual or potential outcome.
+And we denote the mean of $Y^a$ by
+$\mathbb{E}[Y^a]$.
+
+If we have measured all the confounding variables
+then the answer
+(ignoring a few technical details)
+is given by
+Robins' $g$-formula:
+
+$$
+\mathbb{E}[Y^a] = \int \mu(x,a) p(x) dx = \mathbb{E}[\mu(X,a)]
+$$
+
+where
+$\mu(x,a) = \mathbb{E}[Y|X=x,A=a]$
+is the mean of $Y$ given $X$ and $A$.
+This is not equal to
+the mean of $Y$ given $A=a$, which is
+$\mathbb{E}[Y|A=a] = \int \mu(x,a) p(x|a) dx$.
+This is the difference between causation
+and prediction (correlation).
+You can think of
+the $g$-formula as the mean of $Y$
+if we replace $p(a|x)$
+in the joint density
+with a point mass at $a$.
+Using the $g$-formula is one example of *adjusting for
+confounding*.
+
+Now suppose we observe data
+$(X_1,A_1,Y_1),\dots,(X_n,A_n,Y_n)$.
+How do we estimate
+$\mathbb{E}[Y^a]$?
+The simplest approach is to
+get an estimate $\hat\mu(x,a)$ of $\mu(x,a)$
+(this is just regression) and replace the integral
+in the $g$-formula
+with a sample average to get the estimate
+
+$$
+\hat{\mathbb{E}(Y^a)} = \frac{1}{n}\sum_i \hat\mu(X_i,a).
+$$
+
+This is called the plug-in estimator. (Note that for prediction we would not use
+this formula. We would just use $\hat \mu(X, A)$.)
+There are often better estimators,
+but we won't get into that here.
+The important thing
+is:
+there is a formula for the causal effect
+and we can estimate it.
+
+The first plot below shows an example where we would predict
+higher values of $Y$ when $A$ is large. For pure prediction, this is
+the correct conclusion. The second plot shows that once we
+account for $X = \text{age}$ (corresponding to different colors) there is
+a negative relationship between $Y$ and $A$: for a given person with a fixed
+age, increasing $A$ would *decrease* $Y$. In this case, age is a
+confounder and the $g$-formula would correctly recover the negative
+relationship. For causal inference, this is the correct conclusion.
+
+
+
+Things get trickier
+when there are time varying variables.
+Consider weekly mobility and death data
+$(A_1,Y_1),\dots, (A_T,Y_T)$
+in one state.
+For simplicity, we'll assume that there are no $X$ variables. But we'll see that at time $t$, the
+variables $Y_1, \dots, Y_{t-1}$ are confounding variables for the causal effect of
+mobility on $Y_t$.
+Define
+$\overline{A}_t = (A_1,\dots, A_t)$ and
+$\overline{Y}_t = (Y_1,\dots, Y_t)$
+for $t\geq 1$.
+The density
+of $(\overline{y}_t,\overline{a}_t)$
+can be written as
+
+$$
+p(\overline{y}_t,\overline{a}_t) =
+\prod_{s=1}^t p(y_s|\overline{y}_{s-1},\overline{a}_{s}) p(a_s|\overline{a}_{s-1},\overline{y}_{s-1}).
+$$
+
+Now consider
+the causal question:
+
+What would $Y_t$ be if we set
+$\overline{A}_t$ equal to some value
+$\overline{a}_t =(a_1,\dots, a_t)$?
+
+Let
+$Y^{\overline{a}_t}_t$
+denote this counterfactual quantity.
+For example, if $A_t$ is mobility,
+measuring, say, the percentage of people who stay home,
+and
+$\overline{a}_t = (12,12,12,6,6,6)$
+then
+$Y^{\overline{a}_t}_t$
+is the number of deaths
+we would observe if we set mobility to 12 for three weeks
+then set it to 6 for three weeks.
+
+How do we
+find the mean $\mathbb{E}[Y^{\overline{a}_t}_t]$?
+As a general rule,
+we adjust for pre-treatment variables but
+not for post-treatment variables.
+But in the time varying case,
+this rule makes no sense.
+Each $Y_t$ is both a pre-treatment variable
+(it occurs before $A_{t+1}$) and
+a post-treatment variable
+(it occurs after $A_{t-1}$).
+The correct way to
+get the mean of $\mathbb{E}[Y^{\overline{a}_t}]$
+is to use the more general form
+of the $g$-formula:
+
+$$
+\psi(\overline{a}_t)\equiv
+\mathbb{E}[Y^{\overline{a}_t}_t] =
+\int\cdots\int
+\mathbb{E} \left[Y_t | \overline{A}_t = \overline{a}_t, \overline{Y}_{t-1}=\overline{y}_{t-1}\right]
+\prod_{s=1}^{t-1} p(y_s|\overline{y}_{s-1},\overline{a}_{s})
+d y_s.
+$$
+
+You can ignore that equation
+if you like;
+suffice it to say that
+there is indeed a formula
+for the quantity we are interested in.
+
+We note, by the way, that some authors denote
+$\mathbb{E}[Y^{\overline{a}_t}_t]$ by
+$\mathbb{E}[Y_t| {\rm do}(\overline{a}_t)]$.
+They mean the same thing.
+
+Having a formula for
+$\psi(\overline{a}_t)$
+is great, but how do we estimate it?
+Again we could plug-in estimates
+of all the quantities in
+the $g$-formula.
+But there are
+better things we can do,
+as we now explain.
+
+## A Marginal Structural Model
+
+The approach we use is
+to make use of
+something called
+a *marginal structural model* (MSM).
+
+Usually when we construct
+a statistical model,
+we are trying to
+come up with
+a way to express the distribution
+of the data, in this case,
+$(A_1,Y_1),\dots, (A_T,Y_T)$.
+In effect, we want a formula
+that explains how we would generate
+a dataset
+like the one we have.
+But to do so in a tractable way
+usually involves making a bunch of
+assumptions about the model.
+Instead, with an MSM,
+we specify a plausible
+model for
+the form of the function
+$\mathbb{E}[Y^{\overline{a}_t}]$
+but we otherwise leave the model
+for the data unspecified.
+For example, we might assume
+that $\mathbb{E}[Y^{\overline{a}_t}]\approx \beta_0 + \beta \sum_s a_s$.
+In a sense, we are spending our modeling assumptions wisely:
+we want the effect of the treatment to be smooth
+and fairly simple.
+But we otherwise don't want to impose assumptions.
+
+The model we use is
+
+$$
+\mathbb{E}[L_t^{\overline{a}_t}]= s(t) + \beta M_t
+$$
+
+where
+$L_t = \log (1+Y_t)$ is log-deaths,
+$s(t)$ is an arbitrary, smooth, increasing function
+and
+
+$$
+M_t = \sum_{s=1}^{t-\delta}(a_s-a_1)
+$$
+
+is cumulative mobility (compared to baseline mobility).
+Based on published studies, we take $\delta = 4$ weeks.
+(We switch from deaths to log-deaths mainly for
+certain numerical reasons related to the software we are using.)
+Here, $s(t)$ represents the
+evolution of the pandemic if there were no
+interventions and no reduction in
+susceptible individuals.
+The term $\beta M_t$ captures
+the effect of mobility.
+With no change in mobility,
+deaths increase exponentially as $e^{s(t)}$.
+
+The intuition for using cumulative mobility
+comes from ideas from epidemic modeling.
+Roughly,
+if $I_t$ denotes new infections in week $t$,
+we might assume that
+$I_t \approx e^{\nu(t) + \beta A_t} I_{t-1}$
+for some function $\nu(t)$.
+This implies that
+$I_{t-1} \approx e^{\nu(t-1) + \beta A_{t-1}} I_{t-2}$
+and so on.
+If we continue back to $t=1$ we find that
+$I_t \approx e^{s(t) + \beta \sum_s A_s} I_1$
+where $s(t)=\sum_{s=1}^t \nu(s)$.
+If we further assume that
+some fraction of people infected a time $t$
+will die in $\delta$ weeks,
+then we can derive the above model.
+(More complex models are discussed in the paper.)
+
+To re-cap:
+we are leaving the
+distribution $P$ of the data
+$(A_1,Y_1),\dots, (A_T,Y_T)$
+unspecified,
+except that we require:
+if you apply
+the $g$-formula
+to $P$ you must get
+$s(t) + \beta M_t$.
+In other words,
+the statistical model is the
+set of all densities
+$p(\overline{\ell}_t,\overline{a}_t)$ that satisfy the condition
+
+$$
+\int\cdots\int
+\mathbb{E}[L_t | \overline{A}_t = \overline{a}_t, \overline{L}_{t-1}=\overline{\ell}_{t-1}]
+\prod_{s=1}^{t-1} p(\ell_s|\overline{\ell}_{s-1},\overline{a}_{s})
+d \ell_s =
+s(t) + \beta M_t.
+$$
+
+This is an example of a semiparametric model:
+a model that has some sort of constraint
+but is otherwise nonparametric.
+
+
+Estimating the MSM is a bit involved.
+A key step is estimating the following function
+
+$$
+W_t = \frac{p(a_t|\overline{a}_{t-1})}{p(a_t| \overline{y}_{t-1},\overline{a}_{t-1},\overline{x}_{t-1})}
+$$
+
+where $\overline{x}_{t-1}$
+represents potential confounding variables.
+The model is fit with these weights
+and the weights are crucial
+because that is how we adjust for confounding in the MSM.
+We won't go into detail here.
+Instead,
+let's look at the data
+and the estimates.
+
+## The Data and the Results
+
+The data we use
+are weekly deaths
+and weekly averaged mobility
+starting February 15 2019.
+The mobility signal
+we'll consider here is the
+proportion of people of who stay home,
+which is kindly provided to Delphi by
+SafeGraph.
+(We [publish this
+data](`r blogdown::shortcode_html("apiref", "api/covidcast-signals/safegraph.html")`)
+in our [COVIDcast Epidata API](`r blogdown::shortcode_html("apiref", "api/covidcast.html")`),
+so it is available to anyone interested in studying mobility during the pandemic.)
+We use data at the state level.
+Note that this is actually a sort of anti-mobility measure:
+the higher it is, the less mobility there is.
+Here are plots of
+weekly deaths and mobility
+starting February 15, 2020,
+for a few states:
+
+```{r deaths-by-state, message=FALSE}
+library(covidcast)
+library(directlabels)
+library(dplyr)
+library(lubridate)
+library(ggplot2)
+
+# Top 5 most populated states according to
+# https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States_by_population
+states_of_interest <- c("ca", "tx", "fl", "ny", "pa")
+
+states_deaths <- covidcast_signal(
+ "jhu-csse", "deaths_incidence_num",
+ start_day = "2020-02-15", end_day = "2020-12-16",
+ geo_type = "state", geo_values = states_of_interest
+)
+
+states_mobility <- covidcast_signal(
+ "safegraph", "completely_home_prop",
+ start_day = "2020-02-15", end_day = "2020-12-16",
+ geo_type = "state", geo_values = states_of_interest
+)
+
+dat <- full_join(x = select(states_deaths, time_value, geo_value, value),
+ y = select(states_mobility, time_value, geo_value, value),
+ by = c("time_value" = "time_value", "geo_value" = "geo_value"))
+
+dat <- dat %>%
+ rename(date = time_value,
+ state = geo_value,
+ stay_home = value.y,
+ deaths = value.x) %>%
+ group_by(state) %>%
+ # epi week is from saturday to sunday
+ mutate(weekday = wday(date, label = TRUE, week_start = 6),
+ weeknum = wday(date, week_start = 6),
+ week = cumsum(weeknum == 1))
+
+dat_week <- dat %>%
+ group_by(state, week) %>%
+ mutate(weekly_deaths = sum(deaths),
+ weekly_deaths = 1 + weekly_deaths, # avoid 0 in the log scale
+ weekly_stay_home = mean(stay_home)) %>%
+ select(week, state, weekly_stay_home, weekly_deaths, weekly_deaths) %>%
+ unique()
+
+ggplot(dat_week, aes(x = week, y = weekly_deaths, color = state)) +
+ geom_line() +
+ scale_y_log10() +
+ geom_dl(aes(label = toupper(state)), method = "last.bumpup") +
+ labs(x = "Week", y = "# deaths (log scale)",
+ title = "Reported deaths over time",
+ subtitle = "From JHU CSSE COVID-19 data",
+ caption = "Data from Delphi COVIDcast, delphi.cmu.edu") +
+ theme_bw() +
+ guides(color = FALSE)
+```
+
+```{r mobility-by-state}
+ggplot(dat_week, aes(x = week, y = weekly_stay_home, color = state)) +
+ geom_line() +
+ geom_dl(aes(label = toupper(state)), method = "last.bumpup") +
+ labs(x = "Week", y = "Proportion staying at home",
+ title = "(Anti)mobility over time",
+ subtitle = "From SafeGraph",
+ caption = "Data from Delphi COVIDcast, delphi.cmu.edu") +
+ theme_bw() +
+ guides(color = FALSE)
+```
+
+After fitting the MSM,
+we can estimate various quantities.
+Let's consider the effect
+of changing the observed
+stay-at-home series
+$(A_1,\dots, A_T)$
+to the hypothetical value
+$\overline{a}_T = (A_1+\gamma,\dots, A_T+\gamma)$.
+In other words,
+what would have happened
+if we had increased stay-at-home by $\gamma$?
+Below is the estimated
+difference in deaths
+(with 90 percent pointwise confidence bands)
+for a few states:
+Arizona, California, Florida and Georgia.
+The plot shows the mean of
+
+$$
+Y_t^\gamma - Y_t
+$$
+
+where $Y_t$ is the number of deaths
+and
+$Y_t^\gamma$
+is the (counterfactual) number of deaths
+we would have observed of mobility had
+been increases by $\gamma$ at all times,
+where we take $\gamma = 10$
+(a ten percent increase in stay-at-home).
+The fact that the values
+are negative
+indicates that increasing
+stay-at-home decreases deaths, as we would expect.
+(We remind the reader
+that these results are preliminary.)
+
+
+
+
+## What's Next?
+
+One important question in every
+causal analysis is: what if there
+is unobserved confounding?
+In the current problem,
+a confounder
+is a variable that affects
+both mobility and death from COVID.
+We try to include as many plausible
+observed confounding variables
+as possible.
+So far we use all
+past values of death and mobility as
+confounding variables.
+But of course,
+it is always possible
+that there are unobserved confounders.
+Currently,
+we are conducting a sensitivity analysis
+to assess how robust the estimates are
+to unobserved confounders.
+
+In this post we have only
+addressed mobility.
+As soon as the mobility analysis
+is complete
+we will turn our attention
+to estimating
+the causal effect of masks on COVID cases.
diff --git a/content/blog/2021-01-15-causal-effect-mobility.html b/content/blog/2021-01-15-causal-effect-mobility.html
new file mode 100644
index 000000000..17e9f640b
--- /dev/null
+++ b/content/blog/2021-01-15-causal-effect-mobility.html
@@ -0,0 +1,499 @@
+---
+title: Causal Inference for Social Mobility and COVID-19
+author: Matteo Bonvini, Edward Kennedy, Valérie Ventura, and Larry Wasserman
+date: 2021-01-19
+tags:
+ - COVIDcast
+authors:
+ - mbonvini
+ - ekennedy
+ - vventura
+ - lwasserman
+heroImage: /blog/images/causal-inference-mobility-full-size.jpg
+heroImageThumb: /blog/images/causal-inference-mobility-thumb.jpg
+summary: |
+ How can we estimate the causal effect of mobility on COVID-19 when there could
+ be many confounding variables?
+acknowledgements: |
+ We'd like to thank the Delphi engineering team for making this data available.
+ And we'd like to thank Roni Rosenfeld and Ryan Tibshirani for posing the
+ challenge of making counterfactual predictions. We thank Rob Tibshirani for
+ suggesting several improvements on this post, and Alex Reinhart for getting
+ our post into the appropriate format.
+related:
+ - 2020-08-28-api
+output:
+ blogdown::html_page:
+ toc: true
+---
+
+
+
+
+
Most pandemic research has focused on questions related to prediction such as:
+how many COVID cases do we expect to see next week in Allegheny County? Causal
+inference, on the other hand, is concerned with “what if” questions. For
+example: what would happen if everyone wore masks? How many cases would there be
+if there was a lockdown? How many deaths would occur if 40 percent of the
+population is vaccinated? These questions involve hypothetical interventions on
+a population.
+
Estimating causal effects is tricky. We all know that “correlation
+isn’t causation.” For example, mask usage and cases could both
+increase over time. But that doesn’t mean that masks cause more cases
+to occur. Activation of “Buckle your seatbelt” signs on airplanes is
+correlated with turbulence. But activating the sign does not cause
+turbulence.
+
So how do we estimate causal effects? There is a collection of methods
+for this task. The important thing is that we can’t just use standard
+prediction methods. We need to use specialized methods that were designed
+for causal inference. In this post we will discuss our work on
+the causal effect of social mobility on deaths from COVID. The social mobility
+variable we will use is the proportion of people staying home. We can think of
+this of anti-mobility, and expect that higher values of this variable will lead
+to fewer deaths from COVID.
+
Let’s start with a brief introduction to causal inference.
+
+
Causal Inference
+
Causal inference
+is a huge, complex topic.
+We give a very brief
+exposition of some key ideas here.
+To learn more,
+we recommend the book
+by Hernan and Robins and
+the paper
+“Statistics and Causal Inference”
+by Paul Holland
+(Journal of the American Statistical Association 1986, pp. 945-960).
+And one can find many tutorials
+on the web.
+
Suppose we have
+three variables
+\(X\), \(A\) and \(Y\), where
+\(Y\) is the outcome of interest
+(such as deaths from COVID),
+\(A\) is a treatment or exposure
+(such as mobility level),
+and \(X\) are confounding variables,
+which are variables that affect both
+\(A\) and \(Y\) (such as age).
+The joint density is
+
\[
+p(x,a,y) = p(x)p(a|x)p(y|x,a).
+\]
+
We want to know: what would be the mean of \(Y\)
+if we could set \(A\) to be equal to some value \(a\)?
+For example,
+what would the number of COVID cases be
+if 92 percent of people wore masks?
+We let \(Y^a\)
+denote this hypothetical outcome,
+which is called a counterfactual or potential outcome.
+And we denote the mean of \(Y^a\) by
+\(\mathbb{E}[Y^a]\).
+
If we have measured all the confounding variables
+then the answer
+(ignoring a few technical details)
+is given by
+Robins’ \(g\)-formula:
where
+\(\mu(x,a) = \mathbb{E}[Y|X=x,A=a]\)
+is the mean of \(Y\) given \(X\) and \(A\).
+This is not equal to
+the mean of \(Y\) given \(A=a\), which is
+\(\mathbb{E}[Y|A=a] = \int \mu(x,a) p(x|a) dx\).
+This is the difference between causation
+and prediction (correlation).
+You can think of
+the \(g\)-formula as the mean of \(Y\)
+if we replace \(p(a|x)\)
+in the joint density
+with a point mass at \(a\).
+Using the \(g\)-formula is one example of adjusting for
+confounding.
+
Now suppose we observe data
+\((X_1,A_1,Y_1),\dots,(X_n,A_n,Y_n)\).
+How do we estimate
+\(\mathbb{E}[Y^a]\)?
+The simplest approach is to
+get an estimate \(\hat\mu(x,a)\) of \(\mu(x,a)\)
+(this is just regression) and replace the integral
+in the \(g\)-formula
+with a sample average to get the estimate
This is called the plug-in estimator. (Note that for prediction we would not use
+this formula. We would just use \(\hat \mu(X, A)\).)
+There are often better estimators,
+but we won’t get into that here.
+The important thing
+is:
+there is a formula for the causal effect
+and we can estimate it.
+
The first plot below shows an example where we would predict
+higher values of \(Y\) when \(A\) is large. For pure prediction, this is
+the correct conclusion. The second plot shows that once we
+account for \(X = \text{age}\) (corresponding to different colors) there is
+a negative relationship between \(Y\) and \(A\): for a given person with a fixed
+age, increasing \(A\) would decrease\(Y\). In this case, age is a
+confounder and the \(g\)-formula would correctly recover the negative
+relationship. For causal inference, this is the correct conclusion.
+
+
Things get trickier
+when there are time varying variables.
+Consider weekly mobility and death data
+\((A_1,Y_1),\dots, (A_T,Y_T)\)
+in one state.
+For simplicity, we’ll assume that there are no \(X\) variables. But we’ll see that at time \(t\), the
+variables \(Y_1, \dots, Y_{t-1}\) are confounding variables for the causal effect of
+mobility on \(Y_t\).
+Define
+\(\overline{A}_t = (A_1,\dots, A_t)\) and
+\(\overline{Y}_t = (Y_1,\dots, Y_t)\)
+for \(t\geq 1\).
+The density
+of \((\overline{y}_t,\overline{a}_t)\)
+can be written as
What would \(Y_t\) be if we set
+\(\overline{A}_t\) equal to some value
+\(\overline{a}_t =(a_1,\dots, a_t)\)?
+
Let
+\(Y^{\overline{a}_t}_t\)
+denote this counterfactual quantity.
+For example, if \(A_t\) is mobility,
+measuring, say, the percentage of people who stay home,
+and
+\(\overline{a}_t = (12,12,12,6,6,6)\)
+then
+\(Y^{\overline{a}_t}_t\)
+is the number of deaths
+we would observe if we set mobility to 12 for three weeks
+then set it to 6 for three weeks.
+
How do we
+find the mean \(\mathbb{E}[Y^{\overline{a}_t}_t]\)?
+As a general rule,
+we adjust for pre-treatment variables but
+not for post-treatment variables.
+But in the time varying case,
+this rule makes no sense.
+Each \(Y_t\) is both a pre-treatment variable
+(it occurs before \(A_{t+1}\)) and
+a post-treatment variable
+(it occurs after \(A_{t-1}\)).
+The correct way to
+get the mean of \(\mathbb{E}[Y^{\overline{a}_t}]\)
+is to use the more general form
+of the \(g\)-formula:
You can ignore that equation
+if you like;
+suffice it to say that
+there is indeed a formula
+for the quantity we are interested in.
+
We note, by the way, that some authors denote
+\(\mathbb{E}[Y^{\overline{a}_t}_t]\) by
+\(\mathbb{E}[Y_t| {\rm do}(\overline{a}_t)]\).
+They mean the same thing.
+
Having a formula for
+\(\psi(\overline{a}_t)\)
+is great, but how do we estimate it?
+Again we could plug-in estimates
+of all the quantities in
+the \(g\)-formula.
+But there are
+better things we can do,
+as we now explain.
+
+
+
A Marginal Structural Model
+
The approach we use is
+to make use of
+something called
+a marginal structural model (MSM).
+
Usually when we construct
+a statistical model,
+we are trying to
+come up with
+a way to express the distribution
+of the data, in this case,
+\((A_1,Y_1),\dots, (A_T,Y_T)\).
+In effect, we want a formula
+that explains how we would generate
+a dataset
+like the one we have.
+But to do so in a tractable way
+usually involves making a bunch of
+assumptions about the model.
+Instead, with an MSM,
+we specify a plausible
+model for
+the form of the function
+\(\mathbb{E}[Y^{\overline{a}_t}]\)
+but we otherwise leave the model
+for the data unspecified.
+For example, we might assume
+that \(\mathbb{E}[Y^{\overline{a}_t}]\approx \beta_0 + \beta \sum_s a_s\).
+In a sense, we are spending our modeling assumptions wisely:
+we want the effect of the treatment to be smooth
+and fairly simple.
+But we otherwise don’t want to impose assumptions.
where
+\(L_t = \log (1+Y_t)\) is log-deaths,
+\(s(t)\) is an arbitrary, smooth, increasing function
+and
+
\[
+M_t = \sum_{s=1}^{t-\delta}(a_s-a_1)
+\]
+
is cumulative mobility (compared to baseline mobility).
+Based on published studies, we take \(\delta = 4\) weeks.
+(We switch from deaths to log-deaths mainly for
+certain numerical reasons related to the software we are using.)
+Here, \(s(t)\) represents the
+evolution of the pandemic if there were no
+interventions and no reduction in
+susceptible individuals.
+The term \(\beta M_t\) captures
+the effect of mobility.
+With no change in mobility,
+deaths increase exponentially as \(e^{s(t)}\).
+
The intuition for using cumulative mobility
+comes from ideas from epidemic modeling.
+Roughly,
+if \(I_t\) denotes new infections in week \(t\),
+we might assume that
+\(I_t \approx e^{\nu(t) + \beta A_t} I_{t-1}\)
+for some function \(\nu(t)\).
+This implies that
+\(I_{t-1} \approx e^{\nu(t-1) + \beta A_{t-1}} I_{t-2}\)
+and so on.
+If we continue back to \(t=1\) we find that
+\(I_t \approx e^{s(t) + \beta \sum_s A_s} I_1\)
+where \(s(t)=\sum_{s=1}^t \nu(s)\).
+If we further assume that
+some fraction of people infected a time \(t\)
+will die in \(\delta\) weeks,
+then we can derive the above model.
+(More complex models are discussed in the paper.)
+
To re-cap:
+we are leaving the
+distribution \(P\) of the data
+\((A_1,Y_1),\dots, (A_T,Y_T)\)
+unspecified,
+except that we require:
+if you apply
+the \(g\)-formula
+to \(P\) you must get
+\(s(t) + \beta M_t\).
+In other words,
+the statistical model is the
+set of all densities
+\(p(\overline{\ell}_t,\overline{a}_t)\) that satisfy the condition
where \(\overline{x}_{t-1}\)
+represents potential confounding variables.
+The model is fit with these weights
+and the weights are crucial
+because that is how we adjust for confounding in the MSM.
+We won’t go into detail here.
+Instead,
+let’s look at the data
+and the estimates.
+
+
+
The Data and the Results
+
The data we use
+are weekly deaths
+and weekly averaged mobility
+starting February 15 2019.
+The mobility signal
+we’ll consider here is the
+proportion of people of who stay home,
+which is kindly provided to Delphi by
+SafeGraph.
+(We }}">publish this
+data
+in our }}">COVIDcast Epidata API,
+so it is available to anyone interested in studying mobility during the pandemic.)
+We use data at the state level.
+Note that this is actually a sort of anti-mobility measure:
+the higher it is, the less mobility there is.
+Here are plots of
+weekly deaths and mobility
+starting February 15, 2020,
+for a few states:
ggplot(dat_week, aes(x = week, y = weekly_stay_home, color = state)) +
+ geom_line() +
+ geom_dl(aes(label = toupper(state)), method = "last.bumpup") +
+ labs(x = "Week", y = "Proportion staying at home",
+ title = "(Anti)mobility over time",
+ subtitle = "From SafeGraph",
+ caption = "Data from Delphi COVIDcast, delphi.cmu.edu") +
+ theme_bw() +
+ guides(color = FALSE)
+
+
After fitting the MSM,
+we can estimate various quantities.
+Let’s consider the effect
+of changing the observed
+stay-at-home series
+\((A_1,\dots, A_T)\)
+to the hypothetical value
+\(\overline{a}_T = (A_1+\gamma,\dots, A_T+\gamma)\).
+In other words,
+what would have happened
+if we had increased stay-at-home by \(\gamma\)?
+Below is the estimated
+difference in deaths
+(with 90 percent pointwise confidence bands)
+for a few states:
+Arizona, California, Florida and Georgia.
+The plot shows the mean of
+
\[
+Y_t^\gamma - Y_t
+\]
+
where \(Y_t\) is the number of deaths
+and
+\(Y_t^\gamma\)
+is the (counterfactual) number of deaths
+we would have observed of mobility had
+been increases by \(\gamma\) at all times,
+where we take \(\gamma = 10\)
+(a ten percent increase in stay-at-home).
+The fact that the values
+are negative
+indicates that increasing
+stay-at-home decreases deaths, as we would expect.
+(We remind the reader
+that these results are preliminary.)
+
+
+
+
What’s Next?
+
One important question in every
+causal analysis is: what if there
+is unobserved confounding?
+In the current problem,
+a confounder
+is a variable that affects
+both mobility and death from COVID.
+We try to include as many plausible
+observed confounding variables
+as possible.
+So far we use all
+past values of death and mobility as
+confounding variables.
+But of course,
+it is always possible
+that there are unobserved confounders.
+Currently,
+we are conducting a sensitivity analysis
+to assess how robust the estimates are
+to unobserved confounders.
+
In this post we have only
+addressed mobility.
+As soon as the mobility analysis
+is complete
+we will turn our attention
+to estimating
+the causal effect of masks on COVID cases.
+
diff --git a/content/covidcast/_index.md b/content/covidcast/_index.md
index 20a090b1f..5b01dd126 100644
--- a/content/covidcast/_index.md
+++ b/content/covidcast/_index.md
@@ -7,4 +7,5 @@ order: 1
modeTitle: Map Overview
icon: solid/map
heroImage: /images/landing-page/hero-images/covidcast_withfill.jpg
+feedback: true
---
diff --git a/content/covidcast/export.md b/content/covidcast/export.md
index 926fef821..4f359ccb0 100644
--- a/content/covidcast/export.md
+++ b/content/covidcast/export.md
@@ -7,4 +7,5 @@ app_mode: export
order: 6
icon: solid/download
heroImage: /images/landing-page/hero-images/covidcast_withfill.jpg
+feedback: true
---
diff --git a/content/covidcast/release-log/headless/v1.12.4.md b/content/covidcast/release-log/headless/v1.12.4.md
new file mode 100644
index 000000000..7ed26a873
--- /dev/null
+++ b/content/covidcast/release-log/headless/v1.12.4.md
@@ -0,0 +1,14 @@
+---
+title: v1.12.4
+date: 2021-01-19
+---
+
+This release improves the UX of the survey dashboard for both desktop and mobile experience.
+
+#### New Features
+
+- [#724](https://github.com/cmu-delphi/www-covidcast/pull/724), [#717](https://github.com/cmu-delphi/www-covidcast/pull/717) Survey dashboards improvements
+
+#### Bug-fixes
+
+- [#719](https://github.com/cmu-delphi/www-covidcast/pull/719) (re: [#718](https://github.com/cmu-delphi/www-covidcast/issues/718)) fix Export Data bug
diff --git a/content/covidcast/single.md b/content/covidcast/single.md
index 0ef1ab851..bbc62999d 100644
--- a/content/covidcast/single.md
+++ b/content/covidcast/single.md
@@ -7,4 +7,5 @@ app_mode: single
order: 4
icon: location-solid
heroImage: /images/landing-page/hero-images/covidcast_withfill.jpg
+feedback: true
---
diff --git a/content/covidcast/survey-results.md b/content/covidcast/survey-results.md
index 80894e212..27f352c26 100644
--- a/content/covidcast/survey-results.md
+++ b/content/covidcast/survey-results.md
@@ -8,4 +8,5 @@ app_mode: survey-results
order: 5
icon: solid/poll
heroImage: /images/landing-page/hero-images/covidcast_survey.jpg
+feedback: true
---
diff --git a/content/covidcast/timelapse.md b/content/covidcast/timelapse.md
index 931c0241f..fd6c999ff 100644
--- a/content/covidcast/timelapse.md
+++ b/content/covidcast/timelapse.md
@@ -7,4 +7,5 @@ app_mode: timelapse
order: 2
icon: solid/clock
heroImage: /images/landing-page/hero-images/covidcast_withfill.jpg
+feedback: true
---
diff --git a/content/covidcast/top10.md b/content/covidcast/top10.md
index 263badb3b..2a2b3aefe 100644
--- a/content/covidcast/top10.md
+++ b/content/covidcast/top10.md
@@ -7,4 +7,5 @@ app_mode: top10
order: 3
icon: solid/list
heroImage: /images/landing-page/hero-images/covidcast_withfill.jpg
+feedback: true
---
diff --git a/data/authors.yaml b/data/authors.yaml
index da08074f5..2d4d7e4b5 100644
--- a/data/authors.yaml
+++ b/data/authors.yaml
@@ -22,3 +22,19 @@
name: Kathryn Mazaitis
link: https://cs.cmu.edu/~krivard
description: manages Delphi's engineering team, and is a Senior Research Programmer in the Machine Learning Department at CMU.
+- key: mbonvini
+ name: Matteo Bonvini
+ link: https://www.linkedin.com/in/matteobonvini
+ description: is a graduate student in the Department of Statistics & Data Science at CMU.
+- key: ekennedy
+ name: Edward Kennedy
+ link: http://www.ehkennedy.com/
+ description: is an Assistant Professor in the Department of Statistics & Data Science at CMU
+- key: vventura
+ name: Valérie Ventura
+ link: http://www.stat.cmu.edu/~vventura/
+ description: is a Professor in the Department of Statistics & Data Science at CMU and a member of Delphi.
+- key: lwasserman
+ name: Larry Wasserman
+ link: http://www.stat.cmu.edu/~larry/
+ description: is a Professor in the Department of Statistics & Data Science at CMU as well as in the Machine Learning Department and is a member of Delphi.
diff --git a/package-lock.json b/package-lock.json
index 57980328e..7877755c7 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -1,6 +1,6 @@
{
"name": "www-main",
- "version": "1.12.3",
+ "version": "1.12.4",
"lockfileVersion": 1,
"requires": true,
"dependencies": {
@@ -2673,8 +2673,8 @@
"dev": true
},
"www-covidcast": {
- "version": "https://github.com/cmu-delphi/www-covidcast/releases/download/v1.12.3/www-covidcast-1.12.3.tgz",
- "integrity": "sha512-cpigyX/+azM9/mZzcXhq986vy1j9CeWGElUs+xhu8uoFVaLsm8cr+okFiCM+Bm/RJUe80P3yalY8yyn9U9M2WA==",
+ "version": "https://github.com/cmu-delphi/www-covidcast/releases/download/v1.12.4/www-covidcast-1.12.4.tgz",
+ "integrity": "sha512-02Fg7/yXqYAbZPy8jsWvTUpFSNtIGUR9Iq2oAeQgz5PRAew/beeJjv6bT6u0cJrOb5cvIGxsparqgwuhbaIudg==",
"requires": {
"uikit": "^3.6.5"
}
diff --git a/package.json b/package.json
index 67c334de2..54f5cf88c 100644
--- a/package.json
+++ b/package.json
@@ -1,13 +1,13 @@
{
"name": "www-main",
- "version": "1.12.3",
+ "version": "1.12.4",
"private": true,
"dependencies": {
"@fortawesome/fontawesome-free": "^5.15.1",
"highlight.js": "^10.5.0",
"katex": "^0.12.0",
"uikit": "^3.6.5",
- "www-covidcast": "https://github.com/cmu-delphi/www-covidcast/releases/download/v1.12.3/www-covidcast-1.12.3.tgz"
+ "www-covidcast": "https://github.com/cmu-delphi/www-covidcast/releases/download/v1.12.4/www-covidcast-1.12.4.tgz"
},
"devDependencies": {
"hugo-bin": "^0.67.1",
diff --git a/static/blog/2021-01-15-causal-effect-mobility_files/anchor-sections-1.0/anchor-sections.css b/static/blog/2021-01-15-causal-effect-mobility_files/anchor-sections-1.0/anchor-sections.css
new file mode 100644
index 000000000..07aee5fcb
--- /dev/null
+++ b/static/blog/2021-01-15-causal-effect-mobility_files/anchor-sections-1.0/anchor-sections.css
@@ -0,0 +1,4 @@
+/* Styles for section anchors */
+a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
+a.anchor-section::before {content: '#';}
+.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/static/blog/2021-01-15-causal-effect-mobility_files/anchor-sections-1.0/anchor-sections.js b/static/blog/2021-01-15-causal-effect-mobility_files/anchor-sections-1.0/anchor-sections.js
new file mode 100644
index 000000000..570f99a0a
--- /dev/null
+++ b/static/blog/2021-01-15-causal-effect-mobility_files/anchor-sections-1.0/anchor-sections.js
@@ -0,0 +1,33 @@
+// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
+document.addEventListener('DOMContentLoaded', function() {
+ // Do nothing if AnchorJS is used
+ if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
+ return;
+ }
+
+ const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
+
+ // Do nothing if sections are already anchored
+ if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
+ return null;
+ }
+
+ // Use section id when pandoc runs with --section-divs
+ const section_id = function(x) {
+ return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
+ ? x.id : '');
+ };
+
+ // Add anchors
+ h.forEach(function(x) {
+ const id = x.id || section_id(x.parentElement);
+ if (id === '') {
+ return null;
+ }
+ let anchor = document.createElement('a');
+ anchor.href = '#' + id;
+ anchor.classList = ['anchor-section'];
+ x.classList.add('hasAnchor');
+ x.appendChild(anchor);
+ });
+});
diff --git a/static/blog/2021-01-15-causal-effect-mobility_files/figure-html/deaths-by-state-1.svg b/static/blog/2021-01-15-causal-effect-mobility_files/figure-html/deaths-by-state-1.svg
new file mode 100644
index 000000000..cd1c1c794
--- /dev/null
+++ b/static/blog/2021-01-15-causal-effect-mobility_files/figure-html/deaths-by-state-1.svg
@@ -0,0 +1,82 @@
+
+
diff --git a/static/blog/2021-01-15-causal-effect-mobility_files/figure-html/mobility-by-state-1-1.svg b/static/blog/2021-01-15-causal-effect-mobility_files/figure-html/mobility-by-state-1-1.svg
new file mode 100644
index 000000000..642fa9d73
--- /dev/null
+++ b/static/blog/2021-01-15-causal-effect-mobility_files/figure-html/mobility-by-state-1-1.svg
@@ -0,0 +1,75 @@
+
+
diff --git a/static/blog/2021-01-15-causal-effect-mobility_files/figure-html/mobility-by-state-1.svg b/static/blog/2021-01-15-causal-effect-mobility_files/figure-html/mobility-by-state-1.svg
new file mode 100644
index 000000000..642fa9d73
--- /dev/null
+++ b/static/blog/2021-01-15-causal-effect-mobility_files/figure-html/mobility-by-state-1.svg
@@ -0,0 +1,75 @@
+
+
diff --git a/static/blog/images/causal-effect-mobility-bigstates.svg b/static/blog/images/causal-effect-mobility-bigstates.svg
new file mode 100644
index 000000000..da8f42919
--- /dev/null
+++ b/static/blog/images/causal-effect-mobility-bigstates.svg
@@ -0,0 +1,1349 @@
+
+
diff --git a/static/blog/images/causal-inference-mobility-full-size.jpg b/static/blog/images/causal-inference-mobility-full-size.jpg
new file mode 100644
index 000000000..8afe34890
Binary files /dev/null and b/static/blog/images/causal-inference-mobility-full-size.jpg differ
diff --git a/static/blog/images/causal-inference-mobility-full-size.webp b/static/blog/images/causal-inference-mobility-full-size.webp
new file mode 100644
index 000000000..c225766ae
Binary files /dev/null and b/static/blog/images/causal-inference-mobility-full-size.webp differ
diff --git a/static/blog/images/causal-inference-mobility-thumb.jpg b/static/blog/images/causal-inference-mobility-thumb.jpg
new file mode 100644
index 000000000..306a0c13c
Binary files /dev/null and b/static/blog/images/causal-inference-mobility-thumb.jpg differ
diff --git a/static/blog/images/causal-inference-mobility-thumb.webp b/static/blog/images/causal-inference-mobility-thumb.webp
new file mode 100644
index 000000000..05f425ee9
Binary files /dev/null and b/static/blog/images/causal-inference-mobility-thumb.webp differ
diff --git a/static/blog/images/causal-simple-confounder.svg b/static/blog/images/causal-simple-confounder.svg
new file mode 100644
index 000000000..699763a0a
--- /dev/null
+++ b/static/blog/images/causal-simple-confounder.svg
@@ -0,0 +1,900 @@
+
+
diff --git a/themes/delphi/assets/css/components/_feedback.scss b/themes/delphi/assets/css/components/_feedback.scss
new file mode 100644
index 000000000..b75189ed4
--- /dev/null
+++ b/themes/delphi/assets/css/components/_feedback.scss
@@ -0,0 +1,62 @@
+.feedback-modal {
+ padding-top: 1.5em;
+ height: calc(100vh - 100px);
+ display: block;
+ position: relative;
+}
+
+.feedback-modal > iframe {
+ width: 100%;
+ height: 100%;
+}
+
+.feedback-message {
+ width: unset;
+
+ .uk-notification-message {
+ background: #60a5fa;
+ color: white;
+ border-radius: 5px;
+ padding: 0;
+ font-size: 1rem;
+ cursor: inherit;
+ box-shadow: 0px 0px 12px 0px rgba(0, 0, 0, 0.25);
+
+ > div {
+ padding: 1.5rem 3rem 1.5rem 1.5rem;
+ display: flex;
+ justify-content: flex-start;
+ align-items: center;
+ }
+ }
+
+ .uk-notification-close {
+ color: white;
+ right: 1rem;
+ top: 50%;
+ transform: translateY(-50%);
+ display: block !important;
+ }
+
+ .uk-button {
+ background: #2563eb;
+ border-radius: 3px;
+ // padding: 14px 24px 14px 24px;
+ }
+}
+
+.feedback-text {
+ margin-right: 1em;
+}
+
+@media only screen and (max-width: 550px) {
+ .feedback-message {
+ .uk-notification-message > div {
+ flex-direction: column;
+ }
+
+ .uk-button {
+ margin-top: 0.5em;
+ }
+ }
+}
diff --git a/themes/delphi/assets/css/main.scss b/themes/delphi/assets/css/main.scss
index 3ff6dfdf5..04021c1b0 100644
--- a/themes/delphi/assets/css/main.scss
+++ b/themes/delphi/assets/css/main.scss
@@ -23,6 +23,7 @@
@import "./components/card_grid";
@import "./components/latest_card";
@import "./components/toc";
+@import "./components/feedback";
// Page Designs
@import "./pages/about";
diff --git a/themes/delphi/assets/css/pages/_covidcast.scss b/themes/delphi/assets/css/pages/_covidcast.scss
index eb2d85998..178db8f83 100644
--- a/themes/delphi/assets/css/pages/_covidcast.scss
+++ b/themes/delphi/assets/css/pages/_covidcast.scss
@@ -11,3 +11,20 @@
.covidcast_wrapper > footer {
display: none;
}
+
+.covidcast-modes {
+ position: relative;
+
+ &::before {
+ content: "";
+ position: absolute;
+ bottom: 0;
+ left: 0;
+ right: 0;
+ border-bottom: 1px solid #e5e5e5;
+ }
+
+ .uk-tab::before {
+ display: none;
+ }
+}
diff --git a/themes/delphi/assets/js/feedback-button.js b/themes/delphi/assets/js/feedback-button.js
new file mode 100644
index 000000000..245e7046f
--- /dev/null
+++ b/themes/delphi/assets/js/feedback-button.js
@@ -0,0 +1,88 @@
+import UIkit from "uikit/dist/js/uikit.js";
+
+(() => {
+ const markSubmitted = () => {
+ if (!localStorage) {
+ return;
+ }
+ // expires in a month
+ const expiresAt = new Date();
+ expiresAt.setMonth(expiresAt.getMonth() + 1);
+ localStorage.setItem("feedback-submitted", expiresAt.toString());
+
+ // send a google analytics event
+ if (window.ga) {
+ window.ga("send", "event", "feedback", "open", "show feedback form", "true");
+ }
+ };
+ const wasRecentlySubmitted = () => {
+ if (!localStorage) {
+ return false;
+ }
+ const r = localStorage.getItem("feedback-submitted");
+ if (!r) {
+ return false;
+ }
+ const expiresAt = new Date(r);
+ // check if expired
+ return expiresAt > new Date();
+ };
+
+ const showForm = (formLink) => {
+ const url = `${formLink}${encodeURIComponent(location.href)}&embedded=true`;
+ UIkit.modal.dialog(
+ ``
+ );
+ };
+ const btn = document.querySelector(".feedback-button");
+ if (btn) {
+ btn.addEventListener("click", (e) => {
+ e.preventDefault();
+ markSubmitted();
+ showForm(btn.href);
+ });
+ }
+
+ const container = document.querySelector(".feedback-popup-container");
+
+ if (container && !wasRecentlySubmitted()) {
+ const delayMin = Number.parseInt(container.dataset.delayMin, 10) * 1000;
+ const delayMax = Number.parseInt(container.dataset.delayMax, 10) * 1000;
+ const delay = delayMin + Math.random() * (delayMax - delayMin);
+
+ const feedbackLikelihoodDesktop = Number.parseFloat(container.dataset.likeDesktop);
+ const feedbackLikelihoodMobile = Number.parseFloat(container.dataset.likeMobile);
+ const isMobile = window.matchMedia("only screen and (max-width: 700px)").matches;
+
+ const showByChance =
+ (isMobile && Math.random() <= feedbackLikelihoodMobile) ||
+ (!isMobile && Math.random() <= feedbackLikelihoodDesktop);
+
+ const duration = Number.parseInt(container.dataset.duration, 10) * 1000;
+ const formLink = container.dataset.href;
+
+ const showFeedbackNotification = () => {
+ const elem = UIkit.notification({
+ message: container.innerHTML,
+ pos: "bottom-right",
+ timeout: duration,
+ clsContainer: "feedback-message uk-notification",
+ });
+ elem.$el.querySelector("div").addEventListener("click", (e) => {
+ // stop auto close
+ e.stopPropagation();
+ e.preventDefault();
+ });
+ elem.$el.querySelector(".feedback-button").addEventListener("click", (e) => {
+ e.preventDefault();
+ elem.close(true);
+ markSubmitted();
+ showForm(formLink);
+ });
+ };
+ // initial delay
+ if (showByChance) {
+ setTimeout(showFeedbackNotification, delay);
+ }
+ }
+})();
diff --git a/themes/delphi/assets/js/main.js b/themes/delphi/assets/js/main.js
index 97b340962..dee102a9e 100644
--- a/themes/delphi/assets/js/main.js
+++ b/themes/delphi/assets/js/main.js
@@ -1,5 +1,6 @@
import UIkit from "uikit/dist/js/uikit.js";
import plugin from "uikit/dist/js/uikit-icons.js";
+import "./feedback-button";
UIkit.use(plugin);
// re export for COVIDcast
diff --git a/themes/delphi/layouts/_default/baseof.html b/themes/delphi/layouts/_default/baseof.html
index f70a407fa..f2389b0a8 100644
--- a/themes/delphi/layouts/_default/baseof.html
+++ b/themes/delphi/layouts/_default/baseof.html
@@ -8,7 +8,7 @@
{{ partial "nav.html" . }}
- {{ partial "menu/breadcrumb.html" . }}
+ {{ block "breadcrumb" . }}{{ partial "menu/breadcrumb.html" . }}{{ end }}
{{ block "main" . }} {{ end }}
{{ partial "footer.html" . }}
{{ partial "scripts.html" . }}
diff --git a/themes/delphi/layouts/_default/covidcast_app.html b/themes/delphi/layouts/_default/covidcast_app.html
index ca4038f1c..1f11dcda3 100644
--- a/themes/delphi/layouts/_default/covidcast_app.html
+++ b/themes/delphi/layouts/_default/covidcast_app.html
@@ -5,6 +5,7 @@
{{ end }}
+{{ define "breadcrumb" }}{{ end }}
{{ define "body_class" }}covidcast_wrapper{{ end }}
{{ define "main" }}
{{ partial "covidcast/modes.html" . }}
diff --git a/themes/delphi/layouts/partials/footer.html b/themes/delphi/layouts/partials/footer.html
index fe94b349a..683e0b1ea 100644
--- a/themes/delphi/layouts/partials/footer.html
+++ b/themes/delphi/layouts/partials/footer.html
@@ -40,8 +40,31 @@
