23 Time series and outbreak detection

23.1 Overview

This tab demonstrates the use of several packages for time series analysis. It primarily relies on packages from the tidyverts family, but will also use the RECON trending package to fit models that are more appropriate for infectious disease epidemiology.

Note in the below example we use a dataset from the surveillance package on Campylobacter in Germany (see the data chapter, of the handbook for details). However, if you wanted to run the same code on a dataset with multiple countries or other strata, then there is an example code template for this in the r4epis github repo.

Topics covered include:

1. Time series data
2. Descriptive analysis
3. Fitting regressions
4. Relation of two time series
5. Outbreak detection
6. Interrupted time series

23.2 Preparation

Packages

This code chunk shows the loading of packages required for the analyses. In this handbook we emphasize p_load() from pacman, which installs the package if necessary and loads it for use. You can also load packages with library() from base R. See the page on R basics for more information on R packages.

pacman::p_load(rio,          # File import
here,         # File locator
tidyverse,    # data management + ggplot2 graphics
tsibble,      # handle time series datasets
slider,       # for calculating moving averages
imputeTS,     # for filling in missing values
feasts,       # for time series decomposition and autocorrelation
forecast,     # fit sin and cosin terms to data (note: must load after feasts)
trending,     # fit and assess models
tmaptools,    # for getting geocoordinates (lon/lat) based on place names
ecmwfr,       # for interacting with copernicus sateliate CDS API
stars,        # for reading in .nc (climate data) files
units,        # for defining units of measurement (climate data)
yardstick,    # for looking at model accuracy
surveillance  # for aberration detection
)

You can download all the data used in this handbook via the instructions in the Download handbook and data page.

The example dataset used in this section is weekly counts of campylobacter cases reported in Germany between 2001 and 2011.

This dataset is a reduced version of the dataset available in the surveillance package. (for details load the surveillance package and see ?campyDE)

Import these data with the import() function from the rio package (it handles many file types like .xlsx, .csv, .rds - see the Import and export page for details).

# import the counts into R
counts <- rio::import("campylobacter_germany.xlsx")

The first 10 rows of the counts are displayed below.

Clean data

The code below makes sure that the date column is in the appropriate format. For this tab we will be using the tsibble package and so the yearweek function will be used to create a calendar week variable. There are several other ways of doing this (see the Working with dates page for details), however for time series its best to keep within one framework (tsibble).

## ensure the date column is in the appropriate format
counts$date <- as.Date(counts$date)

## create a calendar week variable
## fitting ISO definitons of weeks starting on a monday
counts <- counts %>%
mutate(epiweek = yearweek(date, week_start = 1))

In the relation of two time series section of this page, we will be comparing campylobacter case counts to climate data.

Climate data for anywhere in the world can be downloaded from the EU’s Copernicus Satellite. These are not exact measurements, but based on a model (similar to interpolation), however the benefit is global hourly coverage as well as forecasts.

You can download each of these climate data files from the Download handbook and data page.

For purposes of demonstration here, we will show R code to use the ecmwfr package to pull these data from the Copernicus climate data store. You will need to create a free account in order for this to work. The package website has a useful walkthrough of how to do this. Below is example code of how to go about doing this, once you have the appropriate API keys. You have to replace the X’s below with your account IDs. You will need to download one year of data at a time otherwise the server times-out.

If you are not sure of the coordinates for a location you want to download data for, you can use the tmaptools package to pull the coordinates off open street maps. An alternative option is the photon package, however this has not been released on to CRAN yet; the nice thing about photon is that it provides more contextual data for when there are several matches for your search.

## retrieve location coordinates
coords <- geocode_OSM("Germany", geometry = "point")

## pull together long/lats in format for ERA-5 querying (bounding box)
## (as just want a single point can repeat coords)
request_coords <- str_glue_data(coords$coords, "{y}/{x}/{y}/{x}") ## Pulling data modelled from copernicus satellite (ERA-5 reanalysis) ## https://cds.climate.copernicus.eu/cdsapp#!/software/app-era5-explorer?tab=app ## https://github.com/bluegreen-labs/ecmwfr ## set up key for weather data wf_set_key(user = "XXXXX", key = "XXXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXX", service = "cds") ## run for each year of interest (otherwise server times out) for (i in 2002:2011) { ## pull together a query ## see here for how to do: https://bluegreen-labs.github.io/ecmwfr/articles/cds_vignette.html#the-request-syntax ## change request to a list using addin button above (python to list) ## Target is the name of the output file!! request <- request <- list( product_type = "reanalysis", format = "netcdf", variable = c("2m_temperature", "total_precipitation"), year = c(i), month = c("01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"), day = c("01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31"), time = c("00:00", "01:00", "02:00", "03:00", "04:00", "05:00", "06:00", "07:00", "08:00", "09:00", "10:00", "11:00", "12:00", "13:00", "14:00", "15:00", "16:00", "17:00", "18:00", "19:00", "20:00", "21:00", "22:00", "23:00"), area = request_coords, dataset_short_name = "reanalysis-era5-single-levels", target = paste0("germany_weather", i, ".nc") ) ## download the file and store it in the current working directory file <- wf_request(user = "XXXXX", # user ID (for authentication) request = request, # the request transfer = TRUE, # download the file path = here::here("data", "Weather")) ## path to save the data } Load climate data Whether you downloaded the climate data via our handbook, or used the code above, you now should have 10 years of “.nc” climate data files stored in the same folder on your computer. Use the code below to import these files into R with the stars package. ## define path to weather folder file_paths <- list.files( here::here("data", "time_series", "weather"), # replace with your own file path full.names = TRUE) ## only keep those with the current name of interest file_paths <- file_paths[str_detect(file_paths, "germany")] ## read in all the files as a stars object data <- stars::read_stars(file_paths) ## t2m, tp, ## t2m, tp, ## t2m, tp, ## t2m, tp, ## t2m, tp, ## t2m, tp, ## t2m, tp, ## t2m, tp, ## t2m, tp, ## t2m, tp, Once these files have been imported as the object data, we will convert them to a data frame. ## change to a data frame temp_data <- as_tibble(data) %>% ## add in variables and correct units mutate( ## create an calendar week variable epiweek = tsibble::yearweek(time), ## create a date variable (start of calendar week) date = as.Date(epiweek), ## change temperature from kelvin to celsius t2m = set_units(t2m, celsius), ## change precipitation from metres to millimetres tp = set_units(tp, mm)) %>% ## group by week (keep the date too though) group_by(epiweek, date) %>% ## get the average per week summarise(t2m = as.numeric(mean(t2m)), tp = as.numeric(mean(tp))) ## summarise() has grouped output by 'epiweek'. You can override using the .groups argument. 23.3 Time series data There are a number of different packages for structuring and handling time series data. As said, we will focus on the tidyverts family of packages and so will use the tsibble package to define our time series object. Having a data set defined as a time series object means it is much easier to structure our analysis. To do this we use the tsibble() function and specify the “index”, i.e. the variable specifying the time unit of interest. In our case this is the epiweek variable. If we had a data set with weekly counts by province, for example, we would also be able to specify the grouping variable using the key = argument. This would allow us to do analysis for each group. ## define time series object counts <- tsibble(counts, index = epiweek) Looking at class(counts) tells you that on top of being a tidy data frame (“tbl_df”, “tbl”, “data.frame”), it has the additional properties of a time series data frame (“tbl_ts”). You can take a quick look at your data by using ggplot2. We see from the plot that there is a clear seasonal pattern, and that there are no missings. However, there seems to be an issue with reporting at the beginning of each year; cases drop in the last week of the year and then increase for the first week of the next year. ## plot a line graph of cases by week ggplot(counts, aes(x = epiweek, y = case)) + geom_line() DANGER: Most datasets aren’t as clean as this example. You will need to check for duplicates and missings as below. Duplicates tsibble does not allow duplicate observations. So each row will need to be unique, or unique within the group (key variable). The package has a few functions that help to identify duplicates. These include are_duplicated() which gives you a TRUE/FALSE vector of whether the row is a duplicate, and duplicates() which gives you a data frame of the duplicated rows. See the page on De-duplication for more details on how to select rows you want. ## get a vector of TRUE/FALSE whether rows are duplicates are_duplicated(counts, index = epiweek) ## get a data frame of any duplicated rows duplicates(counts, index = epiweek)  Missings We saw from our brief inspection above that there are no missings, but we also saw there seems to be a problem with reporting delay around new year. One way to address this problem could be to set these values to missing and then to impute values. The simplest form of time series imputation is to draw a straight line between the last non-missing and the next non-missing value. To do this we will use the imputeTS package function na_interpolation(). See the Missing data page for other options for imputation. Another alternative would be to calculate a moving average, to try and smooth over these apparent reporting issues (see next section, and the page on Moving averages). ## create a variable with missings instead of weeks with reporting issues counts <- counts %>% mutate(case_miss = if_else( ## if epiweek contains 52, 53, 1 or 2 str_detect(epiweek, "W51|W52|W53|W01|W02"), ## then set to missing NA_real_, ## otherwise keep the value in case case )) ## alternatively interpolate missings by linear trend ## between two nearest adjacent points counts <- counts %>% mutate(case_int = imputeTS::na_interpolation(case_miss) ) ## to check what values have been imputed compared to the original ggplot_na_imputations(counts$case_miss, counts$case_int) + ## make a traditional plot (with black axes and white background) theme_classic() 23.4 Descriptive analysis Moving averages If data is very noisy (counts jumping up and down) then it can be helpful to calculate a moving average. In the example below, for each week we calculate the average number of cases from the four previous weeks. This smooths the data, to make it more interpretable. In our case this does not really add much, so we will stick to the interpolated data for further analysis. See the Moving averages page for more detail. ## create a moving average variable (deals with missings) counts <- counts %>% ## create the ma_4w variable ## slide over each row of the case variable mutate(ma_4wk = slider::slide_dbl(case, ## for each row calculate the name ~ mean(.x, na.rm = TRUE), ## use the four previous weeks .before = 4)) ## make a quick visualisation of the difference ggplot(counts, aes(x = epiweek)) + geom_line(aes(y = case)) + geom_line(aes(y = ma_4wk), colour = "red") Periodicity Below we define a custom function to create a periodogram. See the Writing functions page for information about how to write functions in R. First, the function is defined. Its arguments include a dataset with a column counts, start_week = which is the first week of the dataset, a number to indicate how many periods per year (e.g. 52, 12), and lastly the output style (see details in the code below). ## Function arguments ##################### ## x is a dataset ## counts is variable with count data or rates within x ## start_week is the first week in your dataset ## period is how many units in a year ## output is whether you want return spectral periodogram or the peak weeks ## "periodogram" or "weeks" # Define function periodogram <- function(x, counts, start_week = c(2002, 1), period = 52, output = "weeks") { ## make sure is not a tsibble, filter to project and only keep columns of interest prepare_data <- dplyr::as_tibble(x) # prepare_data <- prepare_data[prepare_data[[strata]] == j, ] prepare_data <- dplyr::select(prepare_data, {{counts}}) ## create an intermediate "zoo" time series to be able to use with spec.pgram zoo_cases <- zoo::zooreg(prepare_data, start = start_week, frequency = period) ## get a spectral periodogram not using fast fourier transform periodo <- spec.pgram(zoo_cases, fast = FALSE, plot = FALSE) ## return the peak weeks periodo_weeks <- 1 / periodo$freq[order(-periodo$spec)] * period if (output == "weeks") { periodo_weeks } else { periodo } } ## get spectral periodogram for extracting weeks with the highest frequencies ## (checking of seasonality) periodo <- periodogram(counts, case_int, start_week = c(2002, 1), output = "periodogram") ## pull spectrum and frequence in to a dataframe for plotting periodo <- data.frame(periodo$freq, periodo$spec) ## plot a periodogram showing the most frequently occuring periodicity ggplot(data = periodo, aes(x = 1/(periodo.freq/52), y = log(periodo.spec))) + geom_line() + labs(x = "Period (Weeks)", y = "Log(density)") ## get a vector weeks in ascending order peak_weeks <- periodogram(counts, case_int, start_week = c(2002, 1), output = "weeks") NOTE: It is possible to use the above weeks to add them to sin and cosine terms, however we will use a function to generate these terms (see regression section below) Decomposition Classical decomposition is used to break a time series down several parts, which when taken together make up for the pattern you see. These different parts are: • The trend-cycle (the long-term direction of the data) • The seasonality (repeating patterns) • The random (what is left after removing trend and season) ## decompose the counts dataset counts %>% # using an additive classical decomposition model model(classical_decomposition(case_int, type = "additive")) %>% ## extract the important information from the model components() %>% ## generate a plot autoplot() Autocorrelation Autocorrelation tells you about the relation between the counts of each week and the weeks before it (called lags). Using the ACF() function, we can produce a plot which shows us a number of lines for the relation at different lags. Where the lag is 0 (x = 0), this line would always be 1 as it shows the relation between an observation and itself (not shown here). The first line shown here (x = 1) shows the relation between each observation and the observation before it (lag of 1), the second shows the relation between each observation and the observation before last (lag of 2) and so on until lag of 52 which shows the relation between each observation and the observation from 1 year (52 weeks before). Using the PACF() function (for partial autocorrelation) shows the same type of relation but adjusted for all other weeks between. This is less informative for determining periodicity. ## using the counts dataset counts %>% ## calculate autocorrelation using a full years worth of lags ACF(case_int, lag_max = 52) %>% ## show a plot autoplot() ## using the counts data set counts %>% ## calculate the partial autocorrelation using a full years worth of lags PACF(case_int, lag_max = 52) %>% ## show a plot autoplot() You can formally test the null hypothesis of independence in a time series (i.e. that it is not autocorrelated) using the Ljung-Box test (in the stats package). A significant p-value suggests that there is autocorrelation in the data. ## test for independance Box.test(counts$case_int, type = "Ljung-Box")
##
##  Box-Ljung test
##
## data:  counts$case_int ## X-squared = 462.65, df = 1, p-value < 2.2e-16 23.5 Fitting regressions It is possible to fit a large number of different regressions to a time series, however, here we will demonstrate how to fit a negative binomial regression - as this is often the most appropriate for counts data in infectious diseases. Fourier terms Fourier terms are the equivalent of sin and cosin curves. The difference is that these are fit based on finding the most appropriate combination of curves to explain your data. If only fitting one fourier term, this would be the equivalent of fitting a sin and a cosin for your most frequently occurring lag seen in your periodogram (in our case 52 weeks). We use the fourier() function from the forecast package. In the below code we assign using the $, as fourier() returns two columns (one for sin one for cosin) and so these are added to the dataset as a list, called “fourier” - but this list can then be used as a normal variable in regression.

## add in fourier terms using the epiweek and case_int variabless
counts$fourier <- select(counts, epiweek, case_int) %>% fourier(K = 1) Negative binomial It is possible to fit regressions using base stats or MASS functions (e.g. lm(), glm() and glm.nb()). However we will be using those from the trending package, as this allows for calculating appropriate confidence and prediction intervals (which are otherwise not available). The syntax is the same, and you specify an outcome variable then a tilde (~) and then add your various exposure variables of interest separated by a plus (+). The other difference is that we first define the model and then fit() it to the data. This is useful because it allows for comparing multiple different models with the same syntax. TIP: If you wanted to use rates, rather than counts you could include the population variable as a logarithmic offset term, by adding offset(log(population). You would then need to set population to be 1, before using predict() in order to produce a rate. TIP: For fitting more complex models such as ARIMA or prophet, see the fable package. ## define the model you want to fit (negative binomial) model <- glm_nb_model( ## set number of cases as outcome of interest case_int ~ ## use epiweek to account for the trend epiweek + ## use the fourier terms to account for seasonality fourier) ## fit your model using the counts dataset fitted_model <- trending::fit(model, counts) ## calculate confidence intervals and prediction intervals observed <- predict(fitted_model, simulate_pi = FALSE) ## plot your regression ggplot(data = observed, aes(x = epiweek)) + ## add in a line for the model estimate geom_line(aes(y = estimate), col = "Red") + ## add in a band for the prediction intervals geom_ribbon(aes(ymin = lower_pi, ymax = upper_pi), alpha = 0.25) + ## add in a line for your observed case counts geom_line(aes(y = case_int), col = "black") + ## make a traditional plot (with black axes and white background) theme_classic() Residuals To see how well our model fits the observed data we need to look at the residuals. The residuals are the difference between the observed counts and the counts estimated from the model. We could calculate this simply by using case_int - estimate, but the residuals() function extracts this directly from the regression for us. What we see from the below, is that we are not explaining all of the variation that we could with the model. It might be that we should fit more fourier terms, and address the amplitude. However for this example we will leave it as is. The plots show that our model does worse in the peaks and troughs (when counts are at their highest and lowest) and that it might be more likely to underestimate the observed counts. ## calculate the residuals observed <- observed %>% mutate(resid = residuals(fitted_model$fitted_model, type = "response"))

## are the residuals fairly constant over time (if not: outbreaks? change in practice?)
observed %>%
ggplot(aes(x = epiweek, y = resid)) +
geom_line() +
geom_point() +
labs(x = "epiweek", y = "Residuals")
## is there autocorelation in the residuals (is there a pattern to the error?)
observed %>%
as_tsibble(index = epiweek) %>%
ACF(resid, lag_max = 52) %>%
autoplot()
## are residuals normally distributed (are under or over estimating?)
observed %>%
ggplot(aes(x = resid)) +
geom_histogram(binwidth = 100) +
geom_rug() +
labs(y = "count") 
## compare observed counts to their residuals
## should also be no pattern
observed %>%
ggplot(aes(x = estimate, y = resid)) +
geom_point() +
labs(x = "Fitted", y = "Residuals")
## formally test autocorrelation of the residuals
## H0 is that residuals are from a white-noise series (i.e. random)
## test for independence
## if p value significant then non-random
Box.test(observed$resid, type = "Ljung-Box") ## ## Box-Ljung test ## ## data: observed$resid
## X-squared = 346.64, df = 1, p-value < 2.2e-16

23.6 Relation of two time series

Here we look at using weather data (specifically the temperature) to explain campylobacter case counts.

Merging datasets

We can join our datasets using the week variable. For more on merging see the handbook section on joining.

## left join so that we only have the rows already existing in counts
## drop the date variable from temp_data (otherwise is duplicated)
counts <- left_join(counts,
select(temp_data, -date),
by = "epiweek")

Descriptive analysis

First plot your data to see if there is any obvious relation. The plot below shows that there is a clear relation in the seasonality of the two variables, and that temperature might peak a few weeks before the case number. For more on pivoting data, see the handbook section on pivoting data.

counts %>%
## keep the variables we are interested
select(epiweek, case_int, t2m) %>%
## change your data in to long format
pivot_longer(
## use epiweek as your key
!epiweek,
## move column names to the new "measure" column
names_to = "measure",
## move cell values to the new "values" column
values_to = "value") %>%
## create a plot with the dataset above
## plot epiweek on the x axis and values (counts/celsius) on the y
ggplot(aes(x = epiweek, y = value)) +
## create a separate plot for temperate and case counts
## let them set their own y-axes
facet_grid(measure ~ ., scales = "free_y") +
## plot both as a line
geom_line()

Lags and cross-correlation

To formally test which weeks are most highly related between cases and temperature. We can use the cross-correlation function (CCF()) from the feasts package. You could also visualise (rather than using arrange) using the autoplot() function.

counts %>%
## calculate cross-correlation between interpolated counts and temperature
CCF(case_int, t2m,
## set the maximum lag to be 52 weeks
lag_max = 52,
## return the correlation coefficient
type = "correlation") %>%
## arange in decending order of the correlation coefficient
## show the most associated lags
arrange(-ccf) %>%
## only show the top ten
slice_head(n = 10)
## # A tsibble: 10 x 2 [1W]
##      lag   ccf
##    <lag> <dbl>
##  1    4W 0.749
##  2    5W 0.745
##  3    3W 0.735
##  4    6W 0.729
##  5    2W 0.727
##  6    7W 0.704
##  7    1W 0.695
##  8    8W 0.671
##  9    0W 0.649
## 10  -47W 0.638

We see from this that a lag of 4 weeks is most highly correlated, so we make a lagged temperature variable to include in our regression.

DANGER: Note that the first four weeks of our data in the lagged temperature variable are missing (NA) - as there are not four weeks prior to get data from. In order to use this dataset with the trending predict() function, we need to use the the simulate_pi = FALSE argument within predict() further down. If we did want to use the simulate option, then we have to drop these missings and store as a new data set by adding drop_na(t2m_lag4) to the code chunk below.

counts <- counts %>%
## create a new variable for temperature lagged by four weeks
mutate(t2m_lag4 = lag(t2m, n = 4))

Negative binomial with two variables

We fit a negative binomial regression as done previously. This time we add the temperature variable lagged by four weeks.

CAUTION: Note the use of simulate_pi = FALSE within the predict() argument. This is because the default behaviour of trending is to use the ciTools package to estimate a prediction interval. This does not work if there are NA counts, and also produces more granular intervals. See ?trending::predict.trending_model_fit for details.

## define the model you want to fit (negative binomial)
model <- glm_nb_model(
## set number of cases as outcome of interest
case_int ~
## use epiweek to account for the trend
epiweek +
## use the fourier terms to account for seasonality
fourier +
## use the temperature lagged by four weeks
t2m_lag4
)

## fit your model using the counts dataset
fitted_model <- trending::fit(model, counts)

## calculate confidence intervals and prediction intervals
observed <- predict(fitted_model, simulate_pi = FALSE)

To investigate the individual terms, we can pull the original negative binomial regression out of the trending format using get_model() and pass this to the broom package tidy() function to retrieve exponentiated estimates and associated confidence intervals.

What this shows us is that lagged temperature, after controlling for trend and seasonality, is similar to the case counts (estimate ~ 1) and significantly associated. This suggests that it might be a good variable for use in predicting future case numbers (as climate forecasts are readily available).

fitted_model %>%
## extract original negative binomial regression
get_model() %>%
## get a tidy dataframe of results
tidy(exponentiate = TRUE,
conf.int = TRUE)
## # A tibble: 5 x 7
##   term           estimate  std.error statistic  p.value   conf.low  conf.high
##   <chr>             <dbl>      <dbl>     <dbl>    <dbl>      <dbl>      <dbl>
## 1 (Intercept)   5.83      0.108          53.8  0         5.61       6.04
## 2 epiweek       0.0000846 0.00000774     10.9  8.13e-28  0.0000695  0.0000998
## 3 fourierS1-52 -0.285     0.0214        -13.3  1.84e-40 -0.327     -0.243
## 4 fourierC1-52 -0.195     0.0200         -9.78 1.35e-22 -0.234     -0.157
## 5 t2m_lag4      0.00667   0.00269         2.48 1.30e- 2  0.00139    0.0119

A quick visual inspection of the model shows that it might do a better job of estimating the observed case counts.

## plot your regression
ggplot(data = observed, aes(x = epiweek)) +
## add in a line for the model estimate
geom_line(aes(y = estimate),
col = "Red") +
## add in a band for the prediction intervals
geom_ribbon(aes(ymin = lower_pi,
ymax = upper_pi),
alpha = 0.25) +
## add in a line for your observed case counts
geom_line(aes(y = case_int),
col = "black") +
## make a traditional plot (with black axes and white background)
theme_classic()

Residuals

We investigate the residuals again to see how well our model fits the observed data. The results and interpretation here are similar to those of the previous regression, so it may be more feasible to stick with the simpler model without temperature.

## calculate the residuals
observed <- observed %>%
mutate(resid = case_int - estimate)

## are the residuals fairly constant over time (if not: outbreaks? change in practice?)
observed %>%
ggplot(aes(x = epiweek, y = resid)) +
geom_line() +
geom_point() +
labs(x = "epiweek", y = "Residuals")
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 4 rows containing missing values (geom_point).
## is there autocorelation in the residuals (is there a pattern to the error?)
observed %>%
as_tsibble(index = epiweek) %>%
ACF(resid, lag_max = 52) %>%
autoplot()
## are residuals normally distributed (are under or over estimating?)
observed %>%
ggplot(aes(x = resid)) +
geom_histogram(binwidth = 100) +
geom_rug() +
labs(y = "count") 
## Warning: Removed 4 rows containing non-finite values (stat_bin).
## compare observed counts to their residuals
## should also be no pattern
observed %>%
ggplot(aes(x = estimate, y = resid)) +
geom_point() +
labs(x = "Fitted", y = "Residuals")
## Warning: Removed 4 rows containing missing values (geom_point).
## formally test autocorrelation of the residuals
## H0 is that residuals are from a white-noise series (i.e. random)
## test for independence
## if p value significant then non-random
Box.test(observed$resid, type = "Ljung-Box") ## ## Box-Ljung test ## ## data: observed$resid
## X-squared = 339.52, df = 1, p-value < 2.2e-16

23.7 Outbreak detection

We will demonstrate two (similar) methods of detecting outbreaks here. The first builds on the sections above. We use the trending package to fit regressions to previous years, and then predict what we expect to see in the following year. If observed counts are above what we expect, then it could suggest there is an outbreak. The second method is based on similar principles but uses the surveillance package, which has a number of different algorithms for aberration detection.

CAUTION: Normally, you are interested in the current year (where you only know counts up to the present week). So in this example we are pretending to be in week 39 of 2011.

surveillance package

In this section we use the surveillance package to create alert thresholds based on outbreak detection algorithms. There are several different methods available in the package, however we will focus on two options here. For details, see these papers on the application and theory of the alogirthms used.

The first option uses the improved Farrington method. This fits a negative binomial glm (including trend) and down-weights past outbreaks (outliers) to create a threshold level.

The second option use the glrnb method. This also fits a negative binomial glm but includes trend and fourier terms (so is favoured here). The regression is used to calculate the “control mean” (~fitted values) - it then uses a computed generalized likelihood ratio statistic to assess if there is shift in the mean for each week. Note that the threshold for each week takes in to account previous weeks so if there is a sustained shift an alarm will be triggered. (Also note that after each alarm the algorithm is reset)

In order to work with the surveillance package, we first need to define a “surveillance time series” object (using the sts() function) to fit within the framework.

## define surveillance time series object
## nb. you can include a denominator with the population object (see ?sts)
counts_sts <- sts(observed = counts$case_int[!is.na(counts$case_int)],
start = c(
## subset to only keep the year from start_date
as.numeric(str_sub(start_date, 1, 4)),
## subset to only keep the week from start_date
as.numeric(str_sub(start_date, 7, 8))),
## define the type of data (in this case weekly)
freq = 52)

## define the week range that you want to include (ie. prediction period)
## nb. the sts object only counts observations without assigning a week or
## year identifier to them - so we use our data to define the appropriate observations
weekrange <- cut_off - start_date

Farrington method

We then define each of our parameters for the Farrington method in a list. Then we run the algorithm using farringtonFlexible() and then we can extract the threshold for an alert using farringtonmethod@upperboundto include this in our dataset. It is also possible to extract a TRUE/FALSE for each week if it triggered an alert (was above the threshold) using farringtonmethod@alarm.

## define control
ctrl <- list(
## define what time period that want threshold for (i.e. 2011)
range = which(counts_sts@epoch > weekrange),
b = 9, ## how many years backwards for baseline
w = 2, ## rolling window size in weeks
weightsThreshold = 2.58, ## reweighting past outbreaks (improved noufaily method - original suggests 1)
## pastWeeksNotIncluded = 3, ## use all weeks available (noufaily suggests drop 26)
trend = TRUE,
pThresholdTrend = 1, ## 0.05 normally, however 1 is advised in the improved method (i.e. always keep)
thresholdMethod = "nbPlugin",
populationOffset = TRUE
)

## apply farrington flexible method
farringtonmethod <- farringtonFlexible(counts_sts, ctrl)

## create a new variable in the original dataset called threshold
## containing the upper bound from farrington
## nb. this is only for the weeks in 2011 (so need to subset rows)
counts[which(counts$epiweek >= cut_off & !is.na(counts$case_int)),
"threshold"] <- farringtonmethod@upperbound

We can then visualise the results in ggplot as done previously.

ggplot(counts, aes(x = epiweek)) +
## add in observed case counts as a line
geom_line(aes(y = case_int, colour = "Observed")) +
## add in upper bound of aberration algorithm
geom_line(aes(y = threshold, colour = "Alert threshold"),
linetype = "dashed",
size = 1.5) +
## define colours
scale_colour_manual(values = c("Observed" = "black",
"Alert threshold" = "red")) +
## make a traditional plot (with black axes and white background)
theme_classic() +
## remove title of legend
theme(legend.title = element_blank())

GLRNB method

Similarly for the GLRNB method we define each of our parameters for the in a list, then fit the algorithm and extract the upper bounds.

CAUTION: This method uses “brute force” (similar to bootstrapping) for calculating thresholds, so can take a long time!

See the GLRNB vignette for details.

## define control options
ctrl <- list(
## define what time period that want threshold for (i.e. 2011)
range = which(counts_sts@epoch > weekrange),
mu0 = list(S = 1,    ## number of fourier terms (harmonics) to include
trend = TRUE,   ## whether to include trend or not
refit = FALSE), ## whether to refit model after each alarm
## cARL = threshold for GLR statistic (arbitrary)
## 3 ~ middle ground for minimising false positives
## 1 fits to the 99%PI of glm.nb - with changes after peaks (threshold lowered for alert)
c.ARL = 2,
# theta = log(1.5), ## equates to a 50% increase in cases in an outbreak
ret = "cases"     ## return threshold upperbound as case counts
)

## apply the glrnb method
glrnbmethod <- glrnb(counts_sts, control = ctrl, verbose = FALSE)

## create a new variable in the original dataset called threshold
## containing the upper bound from glrnb
## nb. this is only for the weeks in 2011 (so need to subset rows)
counts[which(counts$epiweek >= cut_off & !is.na(counts$case_int)),
"threshold_glrnb"] <- glrnbmethod@upperbound

Visualise the outputs as previously.

ggplot(counts, aes(x = epiweek)) +
## add in observed case counts as a line
geom_line(aes(y = case_int, colour = "Observed")) +
## add in upper bound of aberration algorithm
geom_line(aes(y = threshold_glrnb, colour = "Alert threshold"),
linetype = "dashed",
size = 1.5) +
## define colours
scale_colour_manual(values = c("Observed" = "black",
"Alert threshold" = "red")) +
## make a traditional plot (with black axes and white background)
theme_classic() +
## remove title of legend
theme(legend.title = element_blank())

23.8 Interrupted timeseries

Interrupted timeseries (also called segmented regression or intervention analysis), is often used in assessing the impact of vaccines on the incidence of disease. But it can be used for assessing impact of a wide range of interventions or introductions. For example changes in hospital procedures or the introduction of a new disease strain to a population. In this example we will pretend that a new strain of Campylobacter was introduced to Germany at the end of 2008, and see if that affects the number of cases. We will use negative binomial regression again. The regression this time will be split in to two parts, one before the intervention (or introduction of new strain here) and one after (the pre and post-periods). This allows us to calculate an incidence rate ratio comparing the two time periods. Explaining the equation might make this clearer (if not then just ignore!).

The negative binomial regression can be defined as follows:

$\log(Y_t)= β_0 + β_1 \times t+ β_2 \times δ(t-t_0) + β_3\times(t-t_0 )^+ + log(pop_t) + e_t$

Where: $$Y_t$$is the number of cases observed at time $$t$$
$$pop_t$$ is the population size in 100,000s at time $$t$$ (not used here)
$$t_0$$ is the last year of the of the pre-period (including transition time if any)
$$δ(x$$ is the indicator function (it is 0 if x≤0 and 1 if x>0)
$$(x)^+$$ is the cut off operator (it is x if x>0 and 0 otherwise)
$$e_t$$ denotes the residual Additional terms trend and season can be added as needed.

$$β_2 \times δ(t-t_0) + β_3\times(t-t_0 )^+$$ is the generalised linear part of the post-period and is zero in the pre-period. This means that the $$β_2$$ and $$β_3$$ estimates are the effects of the intervention.

We need to re-calculate the fourier terms without forecasting here, as we will use all the data available to us (i.e. retrospectively). Additionally we need to calculate the extra terms needed for the regression.

## add in fourier terms using the epiweek and case_int variabless
counts$fourier <- select(counts, epiweek, case_int) %>% as_tsibble(index = epiweek) %>% fourier(K = 1) ## define intervention week intervention_week <- yearweek("2008-12-31") ## define variables for regression counts <- counts %>% mutate( ## corresponds to t in the formula ## count of weeks (could probably also just use straight epiweeks var) # linear = row_number(epiweek), ## corresponds to delta(t-t0) in the formula ## pre or post intervention period intervention = as.numeric(epiweek >= intervention_week), ## corresponds to (t-t0)^+ in the formula ## count of weeks post intervention ## (choose the larger number between 0 and whatever comes from calculation) time_post = pmax(0, epiweek - intervention_week + 1)) We then use these terms to fit a negative binomial regression, and produce a table with percentage change. What this example shows is that there was no significant change. CAUTION: Note the use of simulate_pi = FALSE within the predict() argument. This is because the default behaviour of trending is to use the ciTools package to estimate a prediction interval. This does not work if there are NA counts, and also produces more granular intervals. See ?trending::predict.trending_model_fit for details. ## define the model you want to fit (negative binomial) model <- glm_nb_model( ## set number of cases as outcome of interest case_int ~ ## use epiweek to account for the trend epiweek + ## use the furier terms to account for seasonality fourier + ## add in whether in the pre- or post-period intervention + ## add in the time post intervention time_post ) ## fit your model using the counts dataset fitted_model <- trending::fit(model, counts) ## calculate confidence intervals and prediction intervals observed <- predict(fitted_model, simulate_pi = FALSE) ## show estimates and percentage change in a table fitted_model %>% ## extract original negative binomial regression get_model() %>% ## get a tidy dataframe of results tidy(exponentiate = TRUE, conf.int = TRUE) %>% ## only keep the intervention value filter(term == "intervention") %>% ## change the IRR to percentage change for estimate and CIs mutate( ## for each of the columns of interest - create a new column across( all_of(c("estimate", "conf.low", "conf.high")), ## apply the formula to calculate percentage change .f = function(i) 100 * (i - 1), ## add a suffix to new column names with "_perc" .names = "{.col}_perc") ) %>% ## only keep (and rename) certain columns select("IRR" = estimate, "95%CI low" = conf.low, "95%CI high" = conf.high, "Percentage change" = estimate_perc, "95%CI low (perc)" = conf.low_perc, "95%CI high (perc)" = conf.high_perc, "p-value" = p.value) ## # A tibble: 1 x 7 ## IRR 95%CI low 95%CI high Percentage change 95%CI low (perc) 95%CI high (perc) p-value ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 -0.0661 -0.135 0.00305 -107. -113. -99.7 0.0645 As previously we can visualise the outputs of the regression. ggplot(observed, aes(x = epiweek)) + ## add in observed case counts as a line geom_line(aes(y = case_int, colour = "Observed")) + ## add in a line for the model estimate geom_line(aes(y = estimate, col = "Estimate")) + ## add in a band for the prediction intervals geom_ribbon(aes(ymin = lower_pi, ymax = upper_pi), alpha = 0.25) + ## add vertical line and label to show where forecasting started geom_vline( xintercept = as.Date(intervention_week), linetype = "dashed") + annotate(geom = "text", label = "Intervention", x = intervention_week, y = max(observed$upper_pi),
angle = 90,
vjust = 1
) +
## define colours
scale_colour_manual(values = c("Observed" = "black",
"Estimate" = "red")) +
## make a traditional plot (with black axes and white background)
theme_classic()
## Warning: Removed 13 row(s) containing missing values (geom_path).