From 8478a53940cb32c5c3b5604ed58f186b002ac3c5 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Thu, 18 Apr 2024 13:29:16 +0000 Subject: [PATCH] differences for PR #39 --- clean-data.md | 434 + config.yaml | 86 - data/ebola_line_cases.csv | 15001 ++++++++++++++++ data/simulated_ebola_2.csv | 15001 ++++++++++++++++ delays-functions.md | 16 +- describe-cases.md | 260 + ...-functions-rendered-unnamed-chunk-16-1.png | Bin 50804 -> 50602 bytes ...-functions-rendered-unnamed-chunk-17-1.png | Bin 52299 -> 52268 bytes ...-functions-rendered-unnamed-chunk-19-1.png | Bin 52727 -> 0 bytes ...-functions-rendered-unnamed-chunk-20-1.png | Bin 51477 -> 52368 bytes ...-functions-rendered-unnamed-chunk-21-1.png | Bin 33204 -> 32832 bytes ...-functions-rendered-unnamed-chunk-23-1.png | Bin 51632 -> 0 bytes ...lays-reuse-rendered-unnamed-chunk-14-1.png | Bin 11595 -> 0 bytes ...cribe-cases-rendered-unnamed-chunk-6-1.png | Bin 0 -> 5791 bytes ...cribe-cases-rendered-unnamed-chunk-7-1.png | Bin 0 -> 9266 bytes ...issibility-rendered-unnamed-chunk-17-1.png | Bin 31489 -> 31343 bytes ...issibility-rendered-unnamed-chunk-18-1.png | Bin 30336 -> 30619 bytes ...issibility-rendered-unnamed-chunk-21-1.png | Bin 83577 -> 82760 bytes fig/report_demo.png | Bin 0 -> 81199 bytes ...le-analysis-rendered-unnamed-chunk-1-1.png | Bin 0 -> 21079 bytes ...le-analysis-rendered-unnamed-chunk-2-1.png | Bin 0 -> 20476 bytes ...le-analysis-rendered-unnamed-chunk-5-1.png | Bin 0 -> 22930 bytes ...le-analysis-rendered-unnamed-chunk-6-1.png | Bin 0 -> 20947 bytes md5sum.txt | 30 +- quantify-transmissibility.md | 114 +- read-cases.md | 161 + reference.md | 2 + renv.lock | 2703 --- simple-analysis.md | 178 + 29 files changed, 31123 insertions(+), 2863 deletions(-) create mode 100644 clean-data.md delete mode 100644 config.yaml create mode 100644 data/ebola_line_cases.csv create mode 100644 data/simulated_ebola_2.csv create mode 100644 describe-cases.md delete mode 100644 fig/delays-functions-rendered-unnamed-chunk-19-1.png delete mode 100644 fig/delays-functions-rendered-unnamed-chunk-23-1.png delete mode 100644 fig/delays-reuse-rendered-unnamed-chunk-14-1.png create mode 100644 fig/describe-cases-rendered-unnamed-chunk-6-1.png create mode 100644 fig/describe-cases-rendered-unnamed-chunk-7-1.png create mode 100644 fig/report_demo.png create mode 100644 fig/simple-analysis-rendered-unnamed-chunk-1-1.png create mode 100644 fig/simple-analysis-rendered-unnamed-chunk-2-1.png create mode 100644 fig/simple-analysis-rendered-unnamed-chunk-5-1.png create mode 100644 fig/simple-analysis-rendered-unnamed-chunk-6-1.png create mode 100644 read-cases.md delete mode 100644 renv.lock create mode 100644 simple-analysis.md diff --git a/clean-data.md b/clean-data.md new file mode 100644 index 00000000..0438b4e2 --- /dev/null +++ b/clean-data.md @@ -0,0 +1,434 @@ +--- +title: 'Clean and validate' +teaching: 20 +exercises: 10 +--- + +:::::::::::::::::::::::::::::::::::::: questions + +- How to clean and standardize case data? +- How to convert raw dataset into a `linelist` object? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: objectives + +- Explain how to clean, curate, and standardize case data using `{cleanepi}` package +- Demonstrate how to covert case data to `linelist` data + +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Introduction +In the process of analyzing outbreak data, it's essential to ensure that the dataset is clean, curated, standardized, and validate to facilitate accurate and reproducible analysis. This episode focuses on cleaning epidemics and outbreaks data using the [cleanepi](https://epiverse-trace.github.io/cleanepi/) package, and validate it using the [linelist](https://epiverse-trace.github.io/linelist/) package. For demonstration purposes, we'll work with a simulated dataset of Ebola cases. + + +The first step is to import the dataset following the guidelines outlined in the [Read case data](../episodes/read-cases.Rmd) episode. This involves loading the dataset into our environment and view its structure and content. + + +```r +library("rio") +library("here") +raw_ebola_data <- rio::import( + here::here("built", "data", "simulated_ebola_2.csv") +) +``` + +```{.error} +Error: No such file: /home/runner/work/tutorials-early/tutorials-early/site/built/built/data/simulated_ebola_2.csv +``` + +```r +utils::head(raw_ebola_data, 5) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'raw_ebola_data' not found +``` + +## A quick inspection + +Quick exploration and inspection of the dataset are crucial before diving into any analysis tasks. The `{cleanepi}` package simplifies this process with the `scan_data()` function. Let's take a look at how you can use it: + + +```r +library("cleanepi") +cleanepi::scan_data(raw_ebola_data) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'raw_ebola_data' not found +``` + + +The results provides an overview of the content of every column, including column names, and the percent of some data types per column. +You can see that the column names in the dataset are descriptive but lack consistency, as some they are composed of multiple words separated by white spaces. Additionally, some columns contain more than one data type, and there are missing values in others. + +## Common operations + +This section demonstrate how to perform some common data cleaning operations using the `{cleanepi}` package. + +### Standardizing column names + +For this example dataset, standardizing column names typically involves removing spaces and connecting different words with “_”. This practice helps maintain consistency and readability in the dataset. +However, the function used for standardizing column names offers more options. Type `?cleanepi::standardize_column_names` for more details. + + +```r +sim_ebola_data <- cleanepi::standardize_column_names(raw_ebola_data) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'raw_ebola_data' not found +``` + +```r +names(sim_ebola_data) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +::::::::::::::::::::::::::::::::::::: challenge + +- What differences you can observe in the column names? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +If you want to maintain certain column names without subjecting them to the standardization process, you can utilize the `keep` parameter of the `standardize_column_names()` function. This parameter accepts a vector of column names that are intended to be kept unchanged. + +**Exercise:** Standardize the column names of the input dataset, but keep the “V1” column as is. + +### Removing irregularities + +Raw data may contain irregularities such as duplicated and empty rows and columns, as well as constant columns. `remove_duplicates` and `remove_constants` functions from `{cleanepi}` remove such irregularities as demonstrated in the below code chunk. + + +```r +sim_ebola_data <- cleanepi::remove_constant(sim_ebola_data) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +```r +sim_ebola_data <- cleanepi::remove_duplicates(sim_ebola_data) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +Note that, our simulated Ebola does not contain duplicated nor constant rows or columns. + +### Replacing missing values + +In addition to the regularities, raw data can contain missing values that may be encoded by different strings, including the empty. To ensure robust analysis, it is a good practice to replace all missing values by `NA` in the entire dataset. Below is a code snippet demonstrating how you can achieve this in `{cleanepi}`: + + +```r +sim_ebola_data <- cleanepi::replace_missing_values(sim_ebola_data) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +### Validating subject IDs + +Each entry in the dataset represents a subject and should be distinguishable by a specific column formatted in a particular way, such as falling within a specified range, containing certain prefixes and/or suffixes, containing a specific number of characters. The `{cleanepi}` package offers the `check_subject_ids` function designed precisely for this task as shown in the below code chunk. This function validates whether they are unique and meet the required criteria. + + +```r +# remove this chunk code once `{cleanepi}` is updated. The coercion made here +# cwill be accounted for within `{cleanepi}` +sim_ebola_data$case_id <- as.character(sim_ebola_data$case_id) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + + +```r +sim_ebola_data <- cleanepi::check_subject_ids(sim_ebola_data, + target_columns = "case_id", + range = c(0, 15000) +) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +Note that our simulated dataset does contain duplicated subject IDS. + +### Standardizing dates + +Certainly an epidemic dataset contains date columns for different events, such as the date of infection, date of symptoms onset, ..etc, and these dates can come in different date forms, and it good practice to unify them. The `{cleanepi}` package provides functionality for converting date columns in epidemic datasets into ISO format, ensuring consistency across the different date columns. Here's how you can use it on our simulated dataset: + + +```r +sim_ebola_data <- cleanepi::standardize_dates( + sim_ebola_data, + target_columns = c( + "date_onset", + "date_sample" + ) +) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +```r +utils::head(sim_ebola_data) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +This function coverts the values in the target columns, or will automatically figure out the date columns within the dataset (if `target_columns = NULL`) and convert them into the **Ymd** format. + +### Converting to numeric values + +In the raw dataset, some column can come with mixture of character and numerical values, and you want to covert the character values explicitly into numeric. For example, in our simulated data set, in the age column some entries are written in words. +The `convert_to_numeric()` function in `{cleanepi}` does such conversion as illustrated in the below code chunk. + +```r +sim_ebola_data <- cleanepi::convert_to_numeric(sim_ebola_data, + target_columns = "age" +) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +```r +utils::head(sim_ebola_data) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +## Epidemiology related operations + +In addition to common data cleansing tasks, such as those discussed in the above section, the `{cleanepi}` package offers +additional functionalities tailored specifically for processing and analyzing outbreak and epidemic data. This section +covers some of these specialized tasks. + +### Checking sequence of dated-events + +Ensuring the correct order and sequence of dated events is crucial in epidemiological data analysis, especially +when analyzing infectious diseases where the timing of events like symptom onset and sample collection is essential. +The `{cleanepi}` package provides a helpful function called `check_date_sequence()` precisely for this purpose. + +Here's an example code chunk demonstrating the usage of `check_date_sequence()` function in our simulated Ebola dataset + + ```r + sim_ebola_data <- cleanepi::check_date_sequence( + data = sim_ebola_data, + target_columns = c("date_onset", "date_sample"), + remove = TRUE + ) + ``` + + ```{.error} + Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found + ``` + +This functionality is crucial for ensuring data integrity and accuracy in epidemiological analyses, as it helps identify +any inconsistencies or errors in the chronological order of events, allowing yor to address them appropriately. + +### Dictionary-based substitution + +In the realm of data pre-processing, it's common to encounter scenarios where certain columns in a dataset, such as the “gender” column in our simulated Ebola dataset, +are expected to have specific values or factors. However, it's also common for unexpected or erroneous values to appear in these columns, which need to be replaced with appropriate values. The `{cleanepi}` package offers support for dictionary-based substitution, a method that allows you to replace values in specific columns based on mappings defined in a dictionary. +This approach ensures consistency and accuracy in data cleaning. + +Moreover, `{cleanepi}` provides a built-in dictionary specifically tailored for epidemiological data. The example dictionary below includes mappings for the “gender” column. + + +```r +test_dict <- base::readRDS( + system.file("extdata", "test_dict.RDS", package = "cleanepi") +) +base::print(test_dict) +``` + +```{.output} + options values grp orders +1 1 male gender 1 +2 2 female gender 2 +3 M male gender 3 +4 F female gender 4 +5 m male gender 5 +6 f female gender 6 +``` + +Now, we can use this dictionary to standardize values of the the “gender” column according to predefined categories. Below is an example code chunk demonstrating how to utilize this functionality: + + +```r +sim_ebola_data <- cleanepi::clean_using_dictionary( + sim_ebola_data, + dictionary = test_dict +) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +```r +utils::head(sim_ebola_data) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +This approach simplifies the data cleaning process, ensuring that categorical data in epidemiological datasets is accurately categorized and ready for further analysis. + +> Note that, when the column in the dataset contains values that are not in the dictionary, the clean_using_dictionary() will raise an error. Users can use the cleanepi::add_to_dictionary() function to include the missing value into the dictionary. See the corresponding section in the package [vignette](https://epiverse-trace.github.io/cleanepi/articles/cleanepi.html) for more details. + +### Calculating time span between different date events + +In epidemiological data analysis it is also useful to track and analyze time-dependent events, such as the progression of a disease outbreak or the duration between sample collection and analysis. +The `{cleanepi}` package offers a convenient function for calculating the time elapsed between two dated events at different time scales. For example, the below code snippet utilizes the `span()` function to compute the time elapsed since the date of sample for the case identified + until the date this document was generated (2024-04-18). + + +```r +sim_ebola_data <- cleanepi::span( + sim_ebola_data, + target_column = "date_sample", + end_date = Sys.Date(), + span_unit = "years", + span_column_name = "time_since_sampling_date", + span_remainder_unit = "months" +) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +```r +utils::head(sim_ebola_data) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'sim_ebola_data' not found +``` + +After executing the `span()` function, two new columns named `time_since_sampling_date` and `remainder_months` are added to the **sim_ebola_data** dataset, containing the calculated time elapsed since the date of sampling for each case, measured in years, and the remaining time measured in months. + +## Multiple operations at once + +Performing data cleaning operations individually can be time-consuming and error-prone. The `{cleanepi}` package simplifies this process by offering a convenient wrapper function called `clean_data()`, which allows you to perform multiple operations at once. + +The `clean_data()` function applies a series of predefined data cleaning operations to the input dataset. Here's an example code chunk illustrating how to use `clean_data()` on a raw simulated Ebola dataset: + + +Further more, you can combine multiple data cleaning tasks via the pipe operator in "|>", as shown in the below code snippet. + +```r +# remove the line below once Karim has updated cleanepi +raw_ebola_data$`case id` <- as.character(raw_ebola_data$`case id`) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'raw_ebola_data' not found +``` + +```r +# PERFORM THE OPERATIONS USING THE pipe SYNTAX +cleaned_data <- raw_ebola_data |> + cleanepi::standardize_column_names(keep = "V1", rename = NULL) |> + cleanepi::replace_missing_values(target_columns = NULL) |> + cleanepi::remove_constant(cutoff = 1.0) |> + cleanepi::remove_duplicates(target_columns = NULL) |> + cleanepi::standardize_dates( + target_columns = c("date_onset", "date_sample"), + error_tolerance = 0.4, + format = NULL, + timeframe = NULL + ) |> + cleanepi::check_subject_ids( + target_columns = "case_id", + range = c(1, 15000) + ) |> + cleanepi::convert_to_numeric(target_columns = "age") |> + cleanepi::clean_using_dictionary(dictionary = test_dict) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'raw_ebola_data' not found +``` + +## Printing the clean report + +The `{cleanepi}` package generates a comprehensive report detailing the findings and actions of all data cleansing +operations conducted during the analysis. This report is presented as a webpage with multiple sections. Each section +corresponds to a specific data cleansing operation, and clicking on each section allows you to access the results of +that particular operation. This interactive approach enables users to efficiently review and analyze the outcomes of +individual cleansing steps within the broader data cleansing process. + +You can view the report using `cleanepi::print_report()` function. + +![Example of data cleaning report generated by `{cleanepi}`](fig/report_demo.png) + +## Validating and tagging case data +In outbreak analysis, once you have completed the initial steps of reading and cleaning the case data, +it's essential to establish an additional foundational layer to ensure the integrity and reliability of subsequent + analyses. Specifically, this involves verifying the presence and correct data type of certain input columns within + your dataset, a process commonly referred to as "tagging." Additionally, it's crucial to implement measures to + validate that these tagged columns are not inadvertently deleted during further data processing steps. + + This is achieved by converting the cleaned case data into a `linelist` object using `{linelist}` package, see the + below code chunk. + + +```r +library("linelist") +data <- linelist::make_linelist(cleaned_data, + id = "case_id", + age = "age", + date_onset = "date_onset", + date_reporting = "date_sample", + gender = "gender" +) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'cleaned_data' not found +``` + +```r +utils::head(data, 7) +``` + +```{.output} + +1 function (..., list = character(), package = NULL, lib.loc = NULL, +2 verbose = getOption("verbose"), envir = .GlobalEnv, overwrite = TRUE) +3 { +4 fileExt <- function(x) { +5 db <- grepl("\\\\.[^.]+\\\\.(gz|bz2|xz)$", x) +6 ans <- sub(".*\\\\.", "", x) +7 ans[db] <- sub(".*\\\\.([^.]+\\\\.)(gz|bz2|xz)$", "\\\\1\\\\2", +``` + +::::::::::::::::::::::::::::::::::::: keypoints + +- Use `{cleanepi}` package to clean and standardize epidemic and outbreak data +- Use `{linelist}` to tagg, validate, and prepare case data for downstream analysis. + +:::::::::::::::::::::::::::::::::::::::::::::::: + diff --git a/config.yaml b/config.yaml deleted file mode 100644 index 97381f8f..00000000 --- a/config.yaml +++ /dev/null @@ -1,86 +0,0 @@ -#------------------------------------------------------------ -# Values for this lesson. -#------------------------------------------------------------ - -# Which carpentry is this (swc, dc, lc, or cp)? -# swc: Software Carpentry -# dc: Data Carpentry -# lc: Library Carpentry -# cp: Carpentries (to use for instructor training for instance) -# incubator: The Carpentries Incubator -carpentry: 'incubator' - -# Overall title for pages. -title: 'Accessing and using delays to Quantify transmission' - -# Date the lesson was created (YYYY-MM-DD, this is empty by default) -created: - -# Comma-separated list of keywords for the lesson -keywords: - -# Life cycle stage of the lesson -# possible values: pre-alpha, alpha, beta, stable -life_cycle: 'pre-alpha' - -# License of the lesson materials (recommended CC-BY 4.0) -license: 'CC-BY 4.0' - -# Link to the source repository for this lesson -source: 'https://github.com/epiverse-trace/tutorials-early' - -# Default branch of your lesson -branch: 'main' - -# Who to contact if there are any issues -contact: 'andree.valle-campos@lshtm.ac.uk' - -# Navigation ------------------------------------------------ -# -# Use the following menu items to specify the order of -# individual pages in each dropdown section. Leave blank to -# include all pages in the folder. -# -# Example ------------- -# -# episodes: -# - introduction.md -# - first-steps.md -# -# learners: -# - setup.md -# -# instructors: -# - instructor-notes.md -# -# profiles: -# - one-learner.md -# - another-learner.md - -# Order of episodes in your lesson -episodes: -#- read-cases.Rmd -#- clean-data.Rmd -#- describe-cases.Rmd -#- simple-analysis.Rmd -- delays-reuse.Rmd -- quantify-transmissibility.Rmd -- delays-functions.Rmd - -# Information for Learners -learners: - -# Information for Instructors -instructors: - -# Learner Profiles -profiles: - -# Customisation --------------------------------------------- -# -# This space below is where custom yaml items (e.g. pinning -# sandpaper and varnish versions) should live - - -varnish: epiverse-trace/varnish@epiversetheme -sandpaper: epiverse-trace/sandpaper@patch-renv-github-bug diff --git a/data/ebola_line_cases.csv b/data/ebola_line_cases.csv new file mode 100644 index 00000000..aad32813 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-WARN [2024-04-08 21:25:24] epinow: There were 3 divergent transitions after warmup. See +WARN [2024-04-18 13:23:06] epinow: There were 9 divergent transitions after warmup. See https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup to find out why this is a problem and how to eliminate them. - -WARN [2024-04-08 21:25:24] epinow: Examine the pairs() plot to diagnose sampling problems +WARN [2024-04-18 13:23:06] epinow: Examine the pairs() plot to diagnose sampling problems - ``` @@ -589,10 +589,10 @@ epinow_estimates_cgi <- epinow( ``` ```{.output} -WARN [2024-04-08 21:27:09] epinow: There were 10 divergent transitions after warmup. See +WARN [2024-04-18 13:25:03] epinow: There were 7 divergent transitions after warmup. See https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup to find out why this is a problem and how to eliminate them. - -WARN [2024-04-08 21:27:09] epinow: Examine the pairs() plot to diagnose sampling problems +WARN [2024-04-18 13:25:03] epinow: Examine the pairs() plot to diagnose sampling problems - ``` @@ -736,10 +736,10 @@ epinow_estimates_egi <- epinow( ``` ```{.output} -WARN [2024-04-08 21:30:34] epinow: There were 2 divergent transitions after warmup. See +WARN [2024-04-18 13:28:28] epinow: There were 3 divergent transitions after warmup. See https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup to find out why this is a problem and how to eliminate them. - -WARN [2024-04-08 21:30:34] epinow: Examine the pairs() plot to diagnose sampling problems +WARN [2024-04-18 13:28:28] epinow: Examine the pairs() plot to diagnose sampling problems - ``` diff --git a/describe-cases.md b/describe-cases.md new file mode 100644 index 00000000..4451acf9 --- /dev/null +++ b/describe-cases.md @@ -0,0 +1,260 @@ +--- +title: 'Aggregate and visulaize' +teaching: 20 +exercises: 10 +--- + +:::::::::::::::::::::::::::::::::::::: questions + +- How to aggregate case data? +- How to visualize aggregated data? +- What is distribution of cases in time, place, gender, age? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: objectives + +- Simulate synthetic outbreak data +- Convert linelist data to incidence +- Create epidemic curves from incidence data +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Introduction +In an analytic pipeline, exploratory data analysis (EDA) is an important step before formal modelling. EDA helps +determine relationships between variables and summarize their main characteristics often by means of data visualization. + +This episode focuses on EDA of outbreaks and epidemic data, and how to achieved that using a couples of handy `R` +packages. A key observation in EDA of epidemic analysis is capturing the relationship between time and the number of +reported cases, spanning various categories (confirmed, hospitalized, deaths, and recoveries), locations, and other +demographic factors such as gender, age, etc. + +## Synthetic outbreak data +To illustrate the process of conducting EDA on outbreak data, we will generate a line list +for a hypothetical Ebola outbreak utilizing the `{simulist}` package. This line list dataset offers individual-level +information about the outbreak. For our simulation, we will assume that the dynamics of this outbreak are influenced by +several factors: the contact distribution (average number of contacts for an infected case), distribution of contact +intervals (time period between contacts), and the delay distributions of onset to hospitalization and onset to death. +These latter distributions can be sourced from literature and are conveniently available in the `{epiparameter}` +package, see the below code chunk. + + +```r +library("simulist") +library("epiparameter") +contact_dist <- epiparameter::epidist( + disease = "Ebola", + epi_dist = "contact distribution", + prob_distribution = "pois", + prob_distribution_params = c(mean = 2) +) +cont_interval <- epiparameter::epidist( + disease = "Ebola", + epi_dist = "contact interval", + prob_distribution = "gamma", + prob_distribution_params = c(shape = 1, scale = 1) +) +# Define onset to hospitalized +onset_to_hosp <- contact_dist <- epiparameter::epidist( + disease = "Ebola", + epi_dist = "onset to hospitalisatio", + prob_distribution = "pois", + prob_distribution_params = c(mean = 7) +) + +# get onset to death from {epiparameter} database +onset_to_death <- epiparameter::epidist_db( + disease = "Ebola", + epi_dist = "onset to death", + single_epidist = TRUE +) +infect_period <- epiparameter::epidist( + disease = "Ebola", + epi_dist = "Infectious period", + prob_distribution = "gamma", + prob_distribution_params = c(shape = 1, scale = 1) +) +``` +Additionally, we assume that the outbreak started at the beginning of 2023, is highly contagious with a probability of +infection of 80%, and its minimum and maximum sizes are 1000 and 10,000, respectively. Combining these assumptions with +the mentioned distributions, the code chunk below generates a simulated line list: + +```r +base::set.seed(1) +linelist <- simulist::sim_linelist( + contact_dist, + infect_period, + prob_infect = 0.6, + onset_to_hosp, + onset_to_death, + hosp_risk = 0.2, + hosp_death_risk = 0.5, + non_hosp_death_risk = 0.05, + outbreak_start_date = as.Date("2023-01-01"), + add_names = TRUE, + add_ct = TRUE, + outbreak_size = c(1000, 10000), + population_age = c(1, 90), + case_type_probs = c(suspected = 0.2, probable = 0.1, confirmed = 0.7), + config = simulist::create_config() +) +utils::head(linelist) +``` + +```{.output} + id case_name case_type sex age date_onset date_admission +1 1 Keegan Hardy-Roy confirmed m 3 2023-01-01 +2 6 Alexandria Torres-Perez probable f 13 2023-01-01 +3 7 Mitchell Reinhart confirmed m 74 2023-01-01 2023-01-08 +4 9 Stephanie Loadman-Copeland confirmed f 65 2023-01-01 2023-01-10 +5 10 Sufyaan al-Irani confirmed m 8 2023-01-02 +6 11 Rohan Nguyen confirmed m 27 2023-01-01 + date_death date_first_contact date_last_contact ct_value +1 24.9 +2 2022-12-30 2023-01-02 NA +3 2022-12-31 2023-01-04 24.9 +4 2023-01-03 2023-01-04 24.9 +5 2022-12-31 2023-01-02 24.9 +6 2023-01-01 2023-01-05 24.9 +``` +## Aggregating +Downstream analysis involves working with aggregated data rather than individual cases. This requires grouping linelist +data in the form of incidence data. The [incidence2]((https://www.reconverse.org/incidence2/articles/incidence2.html){.external target="_blank"}) +package offers an essential function, called `incidence`, for grouping case data, usually centered around dated events +and/or other factors. The code chunk provided below demonstrates the creation of an `incidence2` object from the +simulated Ebola `linelist` data based on the date of onset. + + +```r +library("incidence2") +dialy_incidence_data <- incidence2::incidence( + linelist, + date_index = "date_onset", + interval = 1 +) +utils::head(dialy_incidence_data, 5) +``` + +```{.output} +# incidence: 5 x 3 +# count vars: date_onset + date_index count_variable count +* +1 2023-01-01 date_onset 475 +2 2023-01-02 date_onset 4904 +3 2023-01-03 date_onset 5478 +4 2023-01-04 date_onset 5319 +5 2023-01-05 date_onset 3520 +``` +Furthermore, with the `{incidence2}` package, you can specify the desired interval and categorize cases by one or +more factors. Below is a code snippet demonstrating weekly cases grouped by the date of onset and gender. + + +```r +weekly_incidence_data <- incidence2::incidence( + linelist, + date_index = "date_onset", + interval = 7, + groups = c("sex", "case_type") +) +utils::head(weekly_incidence_data, 5) +``` + +```{.output} +# incidence: 5 x 5 +# count vars: date_onset +# groups: sex, case_type + date_index sex case_type count_variable count +* +1 2022-12-29 to 2023-01-04 f confirmed date_onset 5576 +2 2022-12-29 to 2023-01-04 f probable date_onset 766 +3 2022-12-29 to 2023-01-04 f suspected date_onset 1612 +4 2022-12-29 to 2023-01-04 m confirmed date_onset 5760 +5 2022-12-29 to 2023-01-04 m probable date_onset 787 +``` + +::::::::::::::::::::::::::::::::::::: callout +## Notes +When cases are grouped by different factors, it's possible that these groups may have different date ranges in the +resulting `incidence2` object. The `incidence2` package provides a function called `complete_dates()` to ensure that an + incidence object has the same range of dates for each group. By default, missing counts will be filled with 0. + + +```r +dialy_incidence_data_2 <- incidence2::incidence( + linelist, + date_index = "date_onset", + groups = "sex", + interval = 1 +) +incidence2::complete_dates(dialy_incidence_data_2, + expand = TRUE, + fill = 0L, by = 1L, + allow_POSIXct = FALSE +) +``` + +```{.output} +# incidence: 24 x 4 +# count vars: date_onset +# groups: sex + date_index sex count_variable count + + 1 2023-01-01 f date_onset 242 + 2 2023-01-01 m date_onset 233 + 3 2023-01-02 f date_onset 2403 + 4 2023-01-02 m date_onset 2501 + 5 2023-01-03 f date_onset 2709 + 6 2023-01-03 m date_onset 2769 + 7 2023-01-04 f date_onset 2600 + 8 2023-01-04 m date_onset 2719 + 9 2023-01-05 f date_onset 1763 +10 2023-01-05 m date_onset 1757 +# ℹ 14 more rows +``` +:::::::::::::::::::::::::::::::::::::::::::::::: + +## visualization + +"The `incidence2` object can be visualized using the `plot()` function from the base R package. +The resulting graph is referred to as an epidemic curve, or epi-curve for short. The following code +snippets generate epi-curves for the `dialy_incidence_data` and `weekly_incidence_data` incidence objects mentioned above." + + +```r +library("ggplot2") +library("tracetheme") +base::plot(dialy_incidence_data) + ggplot2::labs( + x = "Time (in days)", + y = "Dialy cases" +) + tracetheme::theme_trace() +``` + + + + + +```r +base::plot(weekly_incidence_data) + ggplot2::labs( + x = "Time (in days)", + y = "weekly cases" +) + tracetheme::theme_trace() +``` + + + +::::::::::::::::::::::::::::::::::::: challenge + +## Challenge 1: Can you do it? + + - Using suitable distributions for contacts, contact interval, infectious period, onset to hospitalized, and onset to + death, generate a simulated linelist data for Marburg outbreak that has the probability of $0.5$ infection? + - Aggregate the generated linelist and produce some epidemic curves? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: keypoints + +- Use `{simulist}` package to generate synthetic outbreak data +- Use `{incidence2}` package to aggregate case data based on a date event, and produce epidemic curves. + +:::::::::::::::::::::::::::::::::::::::::::::::: diff --git a/fig/delays-functions-rendered-unnamed-chunk-16-1.png b/fig/delays-functions-rendered-unnamed-chunk-16-1.png index af6eadea089029bec8390f5e749daae9fa19b3c2..e8ce8d3d12bb31391b81fa52c789a92512491349 100644 GIT binary patch literal 50602 zcmaI8bx>P<)GZuZXwgEE;_mM5P~3_W+}(;h6fIKRwRmv}uEjODJH??uaff?&-uM3V z&7ChZnJ_sgll*M&wbovn@Gpu|D2RB7AP@*eM*6b~2n2Kc`hs^23BL;{leEUM;_`M2FWk-#kL<)y?%Q+<7JE+I|EoGn2f9_9_{P&X_r zCf6?#rXx}A$`K}ph&M!w`HOlJf=A%-Nvu_@2{<@x*6y3~fWdoJ4n$kzSR4{k(lp&& zzs{>C6}_9i8z}{Aua+`zFKaLJ_)X6&g8fo2A@%WT)u)nTq47OWVOTME798Yv5GT@< z);GW(L15kL`~gFjRICqfm#-JPyP}DLx(qdV7A8$ta7Gc|^LxGCwP`37(`Cp)Pw;o2 z6$jZoB#2P(T~JrKsYFBihZ z{*Px86=?rmk!y~A=I*?MhN9rxcRpQbXJ?P5-))$m0et#)Uf{l#LJWrbUh&;FB zo*r(P{jUC-t1r7Jlfo~L7bqww$tmvxGVEIpR^8VHzy9ep)<6cv$C6}ZFZHru661TQ 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"ea696deee95293d01dffc3a6438309ca" "site/built/read-cases.md" "2024-04-18" +"episodes/clean-data.Rmd" "2a2c0fc782908822da3aeaa5febd2ab0" "site/built/clean-data.md" "2024-04-18" +"episodes/describe-cases.Rmd" "16873cdb27f6b6104e77a992071dec73" "site/built/describe-cases.md" "2024-04-18" +"episodes/simple-analysis.Rmd" "8b1eacfe982b6c8e13b931f399e94652" "site/built/simple-analysis.md" "2024-04-18" +"episodes/delays-reuse.Rmd" "99c8549fdb699dea71a19f6022ddbeff" "site/built/delays-reuse.md" "2024-04-18" +"episodes/quantify-transmissibility.Rmd" "87679a9120c62fde0f6a647d80b72c46" "site/built/quantify-transmissibility.md" "2024-04-18" +"episodes/delays-functions.Rmd" "53216ab4c9d20d5e157bf4a06429f7a9" "site/built/delays-functions.md" "2024-04-18" +"instructors/instructor-notes.md" "ca3834a1b0f9e70c4702aa7a367a6bb5" "site/built/instructor-notes.md" "2024-04-18" +"learners/reference.md" "106717912e909a7c8d9e3e8fea48e17d" "site/built/reference.md" "2024-04-18" +"learners/setup.md" "64f432f4e2d4e1986341fd106f85b23d" "site/built/setup.md" "2024-04-18" +"profiles/learner-profiles.md" "31b503c4b5bd1f0960ada730eca4a25e" "site/built/learner-profiles.md" "2024-04-18" +"renv/profiles/lesson-requirements/renv.lock" "6accfdaa462995621757b915e5c0e793" "site/built/renv.lock" "2024-04-18" diff --git a/quantify-transmissibility.md b/quantify-transmissibility.md index 097d7225..bef14901 100644 --- a/quantify-transmissibility.md +++ b/quantify-transmissibility.md @@ -406,6 +406,14 @@ estimates <- epinow( ) ``` +```{.output} +WARN [2024-04-18 12:33:19] epinow: There were 7 divergent transitions after warmup. See +https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup +to find out why this is a problem and how to eliminate them. - +WARN [2024-04-18 12:33:19] epinow: Examine the pairs() plot to diagnose sampling problems + - +``` + ### Results We can extract and visualise estimates of the effective reproduction number through time: @@ -437,22 +445,22 @@ summary(estimates) ```{.output} measure estimate -1: New confirmed cases by infection date 7207 (3925 -- 12712) +1: New confirmed cases by infection date 7175 (4090 -- 12537) 2: Expected change in daily cases Likely decreasing -3: Effective reproduction no. 0.89 (0.56 -- 1.3) -4: Rate of growth -0.014 (-0.065 -- 0.039) -5: Doubling/halving time (days) -49 (18 -- -11) +3: Effective reproduction no. 0.89 (0.58 -- 1.3) +4: Rate of growth -0.015 (-0.063 -- 0.038) +5: Doubling/halving time (days) -46 (18 -- -11) ``` As these estimates are based on partial data, they have a wide uncertainty interval. -+ From the summary of our analysis we see that the expected change in daily cases is Likely decreasing with the estimated new confirmed cases 7207 (3925 -- 12712). ++ From the summary of our analysis we see that the expected change in daily cases is Likely decreasing with the estimated new confirmed cases 7175 (4090 -- 12537). -+ The effective reproduction number $R_t$ estimate (on the last date of the data) is 0.89 (0.56 -- 1.3). ++ The effective reproduction number $R_t$ estimate (on the last date of the data) is 0.89 (0.58 -- 1.3). -+ The exponential growth rate of case numbers is -0.014 (-0.065 -- 0.039). ++ The exponential growth rate of case numbers is -0.015 (-0.063 -- 0.038). -+ The doubling time (the time taken for case numbers to double) is -49 (18 -- -11). ++ The doubling time (the time taken for case numbers to double) is -46 (18 -- -11). ::::::::::::::::::::::::::::::::::::: callout ### `Expected change in daily cases` @@ -522,17 +530,17 @@ estimates_regional <- regional_epinow( ``` ```{.output} -INFO [2024-04-08 18:34:58] Producing following optional outputs: regions, summary, samples, plots, latest -INFO [2024-04-08 18:34:58] Reporting estimates using data up to: 2020-04-28 -INFO [2024-04-08 18:34:58] No target directory specified so returning output -INFO [2024-04-08 18:34:58] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber -INFO [2024-04-08 18:34:58] Regions excluded: none -INFO [2024-04-08 19:19:30] Completed regional estimates -INFO [2024-04-08 19:19:30] Regions with estimates: 13 -INFO [2024-04-08 19:19:30] Regions with runtime errors: 0 -INFO [2024-04-08 19:19:30] Producing summary -INFO [2024-04-08 19:19:30] No summary directory specified so returning summary output -INFO [2024-04-08 19:19:30] No target directory specified so returning timings +INFO [2024-04-18 12:33:24] Producing following optional outputs: regions, summary, samples, plots, latest +INFO [2024-04-18 12:33:24] Reporting estimates using data up to: 2020-04-28 +INFO [2024-04-18 12:33:24] No target directory specified so returning output +INFO [2024-04-18 12:33:24] Producing estimates for: East Midlands, East of England, England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and The Humber +INFO [2024-04-18 12:33:24] Regions excluded: none +INFO [2024-04-18 13:21:25] Completed regional estimates +INFO [2024-04-18 13:21:25] Regions with estimates: 13 +INFO [2024-04-18 13:21:25] Regions with runtime errors: 0 +INFO [2024-04-18 13:21:25] Producing summary +INFO [2024-04-18 13:21:25] No summary directory specified so returning summary output +INFO [2024-04-18 13:21:25] No target directory specified so returning timings ``` ```r @@ -542,49 +550,49 @@ estimates_regional$summary$summarised_results$table ```{.output} Region New confirmed cases by infection date - 1: East Midlands 341 (207 -- 547) - 2: East of England 537 (328 -- 840) - 3: England 3542 (2220 -- 5532) - 4: London 297 (191 -- 457) - 5: North East 252 (145 -- 427) - 6: North West 557 (313 -- 879) - 7: Northern Ireland 43 (24 -- 85) - 8: Scotland 283 (157 -- 541) - 9: South East 585 (365 -- 997) -10: South West 420 (291 -- 607) -11: Wales 95 (65 -- 138) -12: West Midlands 268 (145 -- 468) -13: Yorkshire and The Humber 480 (276 -- 780) + 1: East Midlands 346 (211 -- 549) + 2: East of England 535 (327 -- 849) + 3: England 3583 (2262 -- 5584) + 4: London 292 (190 -- 456) + 5: North East 252 (145 -- 408) + 6: North West 557 (330 -- 876) + 7: Northern Ireland 43 (24 -- 82) + 8: Scotland 290 (155 -- 568) + 9: South East 587 (352 -- 983) +10: South West 414 (293 -- 602) +11: Wales 95 (64 -- 136) +12: West Midlands 271 (151 -- 491) +13: Yorkshire and The Humber 481 (282 -- 767) Expected change in daily cases Effective reproduction no. 1: Likely increasing 1.2 (0.84 -- 1.6) - 2: Likely increasing 1.2 (0.83 -- 1.6) - 3: Likely decreasing 0.91 (0.64 -- 1.2) + 2: Likely increasing 1.2 (0.82 -- 1.6) + 3: Likely decreasing 0.92 (0.64 -- 1.3) 4: Likely decreasing 0.79 (0.56 -- 1.1) 5: Likely decreasing 0.91 (0.6 -- 1.3) - 6: Likely decreasing 0.86 (0.55 -- 1.2) - 7: Likely decreasing 0.64 (0.4 -- 1.1) - 8: Likely decreasing 0.9 (0.58 -- 1.4) - 9: Stable 0.98 (0.69 -- 1.4) + 6: Likely decreasing 0.87 (0.58 -- 1.2) + 7: Likely decreasing 0.65 (0.4 -- 1) + 8: Likely decreasing 0.92 (0.58 -- 1.4) + 9: Stable 0.98 (0.67 -- 1.4) 10: Increasing 1.4 (1.1 -- 1.8) -11: Decreasing 0.57 (0.42 -- 0.76) -12: Likely decreasing 0.7 (0.43 -- 1.1) +11: Decreasing 0.57 (0.42 -- 0.75) +12: Likely decreasing 0.71 (0.44 -- 1.1) 13: Stable 1 (0.69 -- 1.4) Rate of growth Doubling/halving time (days) - 1: 0.022 (-0.022 -- 0.067) 31 (10 -- -31) - 2: 0.021 (-0.023 -- 0.065) 32 (11 -- -30) - 3: -0.012 (-0.052 -- 0.03) -57 (23 -- -13) - 4: -0.029 (-0.066 -- 0.011) -24 (64 -- -11) - 5: -0.012 (-0.059 -- 0.037) -57 (19 -- -12) - 6: -0.018 (-0.067 -- 0.023) -38 (30 -- -10) - 7: -0.052 (-0.098 -- 0.0096) -13 (72 -- -7.1) - 8: -0.014 (-0.063 -- 0.048) -51 (14 -- -11) - 9: -0.0022 (-0.044 -- 0.049) -320 (14 -- -16) -10: 0.047 (0.012 -- 0.085) 15 (8.1 -- 59) -11: -0.065 (-0.093 -- -0.033) -11 (-21 -- -7.5) -12: -0.042 (-0.091 -- 0.0089) -17 (78 -- -7.6) -13: 0.0033 (-0.045 -- 0.051) 210 (14 -- -15) + 1: 0.024 (-0.022 -- 0.068) 29 (10 -- -32) + 2: 0.022 (-0.024 -- 0.067) 32 (10 -- -29) + 3: -0.011 (-0.052 -- 0.032) -65 (22 -- -13) + 4: -0.03 (-0.066 -- 0.0092) -23 (75 -- -11) + 5: -0.012 (-0.06 -- 0.031) -59 (22 -- -12) + 6: -0.018 (-0.062 -- 0.022) -39 (31 -- -11) + 7: -0.051 (-0.097 -- 0.0044) -13 (160 -- -7.1) + 8: -0.011 (-0.063 -- 0.05) -64 (14 -- -11) + 9: -0.0024 (-0.048 -- 0.048) -280 (15 -- -14) +10: 0.046 (0.013 -- 0.086) 15 (8.1 -- 54) +11: -0.065 (-0.094 -- -0.034) -11 (-20 -- -7.4) +12: -0.041 (-0.088 -- 0.012) -17 (56 -- -7.8) +13: 0.0032 (-0.044 -- 0.049) 210 (14 -- -16) ``` ```r diff --git a/read-cases.md b/read-cases.md new file mode 100644 index 00000000..cb88af2c --- /dev/null +++ b/read-cases.md @@ -0,0 +1,161 @@ +--- +title: 'Read case data' +teaching: 20 +exercises: 10 +editor_options: + chunk_output_type: inline +--- + +:::::::::::::::::::::::::::::::::::::: questions + +- Where do you usually store your outbreak data? +- How many different data formats can I read? +- Is it possible to import data from database and health APIs? +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: objectives + +- Explain how to import outbreak data from different sources into `R` +environment for analysis. +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: prereq + +## Prerequisites + +This episode requires you to be familiar with: + +**Data science** : Basic programming with R. +::::::::::::::::::::::::::::::::: + +## Introduction + +The initial step in outbreak analysis involves importing the target dataset into the `R` environment from various sources. Outbreak data is typically stored in files of diverse formats, relational database management systems (RDBMS), or health information system (HIS) application program interfaces (APIs) such as [REDCap](https://www.project-redcap.org/), [DHIS2](https://dhis2.org/), etc. The latter option is particularly well-suited for storing institutional health data. This episode will elucidate the process of reading cases from these sources. + +## Reading from files + +Several packages are available for importing outbreak data stored in individual files into `R`. These include [rio](http://gesistsa.github.io/rio/), [readr](https://readr.tidyverse.org/) from the `tidyverse`, [io](https://bitbucket.org/djhshih/io/src/master/), [ImportExport](https://cran.r-project.org/web/packages/ImportExport/index.html), [data.table](https://rdatatable.gitlab.io/data.table/). Together, these packages offer methods to read single or multiple files in a wide range of formats. + +The below example shows how to import a `csv` file into `R` environment using `{rio}` package. + + +```r +library("rio") +library("here") +# read data +case_data <- rio::import(here::here("built", "data", "ebola_cases.csv")) +``` + +```{.error} +Error: No such file: /home/runner/work/tutorials-early/tutorials-early/site/built/built/data/ebola_cases.csv +``` + +```r +head(case_data, 5) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'case_data' not found +``` + +Similarly, you can import files of other formats such as `tsv`, `xlsx`, etc. + +::::::::::::::::::::::::::::::::: challenge + +### Reading compressed data + +Take 1 minute: +- Is it possible to read compressed data in `R`? + +::::::::::::::::: hint + +You can check the [full list of supported file formats](http://gesistsa.github.io/rio/#supported-file-formats) in the `{rio}` package on the package website. Here is a selection of some key ones: + + +Access to all the available formats run: + + +```r +rio::install_formats() +``` + +:::::::::::::::::::::: + +::::::::::::::::: solution + + +```r +rio::import(here::here("some", "where", "downto", "path", "file_name.zip")) +``` + +:::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::::::::: + + +## Reading from databases + +The [DBI](https://dbi.r-dbi.org/) package serves as a versatile interface for interacting with database management systems (DBMS) across different back-ends or servers. It offers a uniform method for accessing and retrieving data from various database systems. + + +The following code chunk demonstrates how to create a temporary SQLite database in memory, store the `case_data` as a table within it, and subsequently read from it: + + +```r +library("DBI") +library("RSQLite") + +# Create a temporary SQLite database in memory +db_con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") + +# Store the 'case_data' dataframe as a table named 'cases' +# in the SQLite database +DBI::dbWriteTable(db_con, "cases", case_data) +``` + +```{.error} +Error in h(simpleError(msg, call)): error in evaluating the argument 'value' in selecting a method for function 'dbWriteTable': object 'case_data' not found +``` + +```r +# Read data from the 'cases' table +result <- DBI::dbReadTable(db_con, "cases") +``` + +```{.error} +Error: no such table: cases +``` + +```r +# Close the database connection +DBI::dbDisconnect(db_con) +# View the result +base::print(utils::head(result)) +``` + +```{.error} +Error in eval(expr, envir, enclos): object 'result' not found +``` + +This code first establishes a connection to an SQLite database created in memory using `dbConnect()`. Then, it writes the `case_data` into a table named 'cases' within the database using the `dbWriteTable()` function. Subsequently, it reads the data from the 'cases' table using `dbReadTable()`. Finally, it closes the database connection with `dbDisconnect()`. More examples about SQL databases and R can be found [here](https://datacarpentry.org/R-ecology-lesson/05-r-and-databases.html). + +:::::::::::::::::::::: callout + +### Run SQL queries in R using dbplyr + +Using a database interface package is invaluable for optimizing memory usage because it can strategically limit the amount of data pulled from the database through queries. By leveraging such a package, one can ensure that most aggregation tasks are performed directly within the database before extracting the necessary information. This approach minimizes the need to load large datasets into local memory for processing, thereby conserving resources and enhancing performance. Conversely, relying solely on external data manipulation processes can lead to suboptimal memory usage. Conducting all data operations outside the database could tax system resources and hinder efficiency. + +Read the [Introduction to dbplyr](https://dbplyr.tidyverse.org/articles/dbplyr.html) vignette to learn how to generate your own queries! + +:::::::::::::::::::::: + + +## Reading from HIS APIs + +Health related data are also increasingly stored in specialized HIS APIs like **Fingertips**, **GoData**, **REDCap**, and **DHIS2**. In such case one can resort to [readepi](https://epiverse-trace.github.io/readepi/) package, which enables reading data from HIS-APIs. +-[TBC] + +::::::::::::::::::::::::::::::::::::: keypoints +- Use `{rio}, {io}, {readr}` and `{ImportExport}` to read data from individual files. +- Use `{readepi}` to read data form HIS APIs and RDBMS. +:::::::::::::::::::::::::::::::::::::::::::::::: diff --git a/reference.md b/reference.md index a84719f5..5d5e0d46 100644 --- a/reference.md +++ b/reference.md @@ -119,6 +119,8 @@ It is a type of sampling bias. It affects backward-looking data and is related t [Reporting delay]{#reportingdelay} : Delay or lag between the time an event occurs (e.g. symptom onset) and the time it is reported ([Lawless, 1994](https://www.jstor.org/stable/3315820)). We can quantify it by comparing the linelist with successive versions of it or up-to-date reported aggregated case counts ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). +[RDBMS]{#RDBMS} +: Relational DataBase Management System. ## S [State variables]{#state} diff --git a/renv.lock b/renv.lock deleted file mode 100644 index c8cdc3c6..00000000 --- a/renv.lock +++ /dev/null @@ -1,2703 +0,0 @@ -{ - "R": { - "Version": "4.3.3", - "Repositories": [ - { - "Name": "carpentries", - "URL": "https://carpentries.r-universe.dev" - }, - { - "Name": "carpentries_archive", - "URL": "https://carpentries.github.io/drat" - }, - { - "Name": "CRAN", - "URL": "https://cran.rstudio.com" - }, - { - "Name": "RSPM", - "URL": "https://packagemanager.posit.co/cran/latest" - } - ] - }, - "Packages": { - "BH": { - "Package": "BH", - "Version": "1.84.0-0", - "Source": "Repository", - "Repository": "CRAN", - "Hash": "a8235afbcd6316e6e91433ea47661013" - }, - "DBI": { - "Package": "DBI", - 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By analyzing the trend, policymakers and public health experts can make informed decisions to mitigate the spread of diseases and protect public health. This episode focuses on how to perform a simple early analysis on incidence data. It uses the same dataset of **Covid-19 case data from England** that utilized it in [Aggregate and visualize](../episodes/describe-cases.Rmd) episode. + +## Simple model + +Aggregated case data over a specific time unit, or incidence data, typically represent the number of cases occurring within that time frame. These data can often be assumed to follow either `Poisson distribution` or a `negative binomial (NB) distribution`, depending on the specific characteristics of the data and the underlying processes generating them. When analyzing such data, one common approach is to examine the trend over time by computing the rate of change, which can indicate whether there is exponential growth or decay in the number of cases. Exponential growth implies that the number of cases is increasing at an accelerating rate over time, while exponential decay suggests that the number of cases is decreasing at a decelerating rate. + +The `i2extras` package provides methods for modelling the trend in case data, calculating moving averages, and exponential growth or decay rate. The code chunk below computes the Covid-19 trend in UK within first 3 months using negative binomial distribution. + + + +```r +# loads the i2extras package, which provides methods for modeling +library("i2extras") +# This line loads the i2extras package, which provides methods for modeling +library("incidence2") +# subset the covid19_eng_case_data to include only the first 3 months of data +covid19_eng_case_data <- outbreaks::covid19_england_nhscalls_2020 +df <- base::subset( + covid19_eng_case_data, + covid19_eng_case_data$date <= min(covid19_eng_case_data$date) + 90 +) +# uses the incidence function from the incidence2 package to compute the +# incidence data +df_incid <- incidence2::incidence(df, date_index = "date", groups = "sex") + +# fit a curve to the incidence data. The model chosen is the negative binomial +# distribution with a significance level (alpha) of 0.05. +fitted_curve_nb <- i2extras::fit_curve(df_incid, model = "negbin", alpha = 0.05) +base::plot(fitted_curve_nb, angle = 45) + ggplot2::labs(x = "Date", y = "Cases") +``` + + + + +::::::::::::::::::::::::::::::::::::: challenge + +## Challenge 1: Poission distribution + +Repeat the above analysis using Poisson distribution? + +:::::::::::::::::::::::: solution + + +```r +fitted_curve_poisson <- i2extras::fit_curve(df_incid, model = "poisson", + alpha = 0.05) +base::plot(fitted_curve_poisson, angle = 45) + + ggplot2::labs(x = "Date", y = "Cases") +``` + + + +::::::::::::::::::::::::::::::::: +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Exponential growth or decay rate + +The exponential growth or decay rate, denoted as $r$, serves as an indicator for the trend in cases, indicating whether they are increasing (growth) or decreasing (decay) on an exponential scale. This rate is computed using the so-called **renewal equation** [(Wallinga et al. 2006)](https://royalsocietypublishing.org/doi/10.1098/rspb.2006.3754), which mechanistically links the reproductive number $R$ of new cases to the generation interval of the disease. This computational method is implemented in the `{i2extras}` package. + +Below is a code snippet demonstrating how to extract the growth/decay rate from the above **NB**-fitted curve using the `growth_rate()` function: + + +```r +rates_nb <- i2extras::growth_rate(fitted_curve_nb) +rates_nb <- base::as.data.frame(rates_nb) |> + subset(select = c(sex, r, r_lower, r_upper)) +base::print(rates_nb) +``` + +```{.output} + sex r r_lower r_upper +1 female -0.008241228 -0.009182635 -0.007300403 +2 male -0.008346783 -0.009316775 -0.007377392 +3 unknown -0.023703987 -0.028179436 -0.019299926 +``` + + +::::::::::::::::::::::::::::::::::::: challenge + +## Challenge 2: Growth rates from **Poisson**-fitted curve + +Extract growth rates from the **Poisson**-fitted curve of **Challenge 1**? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Peak time + +The **Peak time ** is the time at which the highest number of cases is observed in the aggregated data. It can be estimated using the `i2extras::estimate_peak()` function as shown in the below code chunk, which identify peak time from the `incidenc2` object `df_incid`. + + +```r +peaks_nb <- i2extras::estimate_peak(df_incid, progress = FALSE) |> + subset(select = -c(count_variable, bootstrap_peaks)) +base::print(peaks_nb) +``` + +```{.output} +# A data frame: 3 × 6 + sex observed_peak observed_count lower_ci median upper_ci +* +1 female 2020-03-26 1314 2020-03-18 2020-03-24 2020-03-30 +2 male 2020-03-27 1299 2020-03-19 2020-03-23 2020-03-30 +3 unknown 2020-04-10 32 2020-03-24 2020-04-10 2020-04-20 +``` + + +## Moving average + +A moving or rolling average calculates the average number of cases within a specified time period. This can be achieved by utilizing the `add_rolling_average()` function from the `{i2extras}` package on an `incidence2 object`. The following code chunk demonstrates the computation of the weekly average number of cases from the `incidence2` object `df_incid`, followed by visualization. + + +```r +library("ggplot2") +moving_Avg_week <- i2extras::add_rolling_average(df_incid, n = 7L) +base::plot(moving_Avg_week, border_colour = "white", angle = 45) + + ggplot2::geom_line(ggplot2::aes(x = date_index, y = rolling_average, + color = "red")) + + ggplot2::labs(x = "Date", y = "Cases") +``` + + + +::::::::::::::::::::::::::::::::::::: challenge + +## Challenge 3: Monthly moving average + +Compute and visualize the monthly moving average of cases on `df_incid`? + +:::::::::::::::::::::::: solution + + +```r +moving_Avg_mont <- i2extras::add_rolling_average(df_incid, n = 30L) +base::plot(moving_Avg_mont, border_colour = "white", angle = 45) + + ggplot2::geom_line(ggplot2::aes(x = date_index, y = rolling_average, + color = "red")) + + ggplot2::labs(x = "Date", y = "Cases") +``` + + + +::::::::::::::::::::::::::::::::: +:::::::::::::::::::::::::::::::::::::::::::::::: + + +::::::::::::::::::::::::::::::::::::: keypoints + +- Use `{i2extras}` to: + - fit epi-curve using either **Poisson** or **NB** distributions, + - calculate exponential growth or decline of cases, + - find peak time, and + - computing moving average of cases in specified time window. + +:::::::::::::::::::::::::::::::::::::::::::::::: +