From 66e4076a92cf1f8b874ee05f923ce610f712bc7a Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Fri, 5 Apr 2024 11:13:47 +0000 Subject: [PATCH] differences for PR #39 --- clean-data.md | 221 + config.yaml | 8 +- data/simulated_ebola_2.csv | 15001 ++++++++++++++++ describe-cases.md | 139 + ...cribe-cases-rendered-unnamed-chunk-3-1.png | Bin 0 -> 5687 bytes ...cribe-cases-rendered-unnamed-chunk-4-1.png | Bin 0 -> 7184 bytes ...cribe-cases-rendered-unnamed-chunk-5-1.png | Bin 0 -> 7402 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 | 6 +- read-cases.md | 143 + simple-analysis.md | 176 + 14 files changed, 15689 insertions(+), 5 deletions(-) create mode 100644 clean-data.md create mode 100644 data/simulated_ebola_2.csv create mode 100644 describe-cases.md create mode 100644 fig/describe-cases-rendered-unnamed-chunk-3-1.png create mode 100644 fig/describe-cases-rendered-unnamed-chunk-4-1.png create mode 100644 fig/describe-cases-rendered-unnamed-chunk-5-1.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 create mode 100644 simple-analysis.md diff --git a/clean-data.md b/clean-data.md new file mode 100644 index 00000000..f644562d --- /dev/null +++ b/clean-data.md @@ -0,0 +1,221 @@ +--- +title: 'Clean outbreaks data' +teaching: 10 +exercises: 2 +--- + +:::::::::::::::::::::::::::::::::::::: questions + +- How to clean and standardize case data? +- How to convert raw dataset into a `linelist` object? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: objectives + +- Explain how 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 +requireNamespace("rio", quietly = TRUE) +raw_ebola_data <- rio::import( + file.path("episodes", "data", "simulated_ebola_2.csv") +) +``` + +```{.error} +Error: No such file: episodes/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 +``` + +## 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 +requireNamespace("cleanepi", quietly = TRUE) +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 data cleaning 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 will 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") +``` + +```{.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. +Note that this function makes call of functions from the `{numberize}` package. + + +```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 previous 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. +### Dictionary-based substitution + +### Calculating age at different time scales + +### Calulating age categories + +## Multiple operations at once diff --git a/config.yaml b/config.yaml index 615e9bfb..0a14c930 100644 --- a/config.yaml +++ b/config.yaml @@ -59,10 +59,10 @@ contact: 'andree.valle-campos@lshtm.ac.uk' # Order of episodes in your lesson episodes: -#- read-cases.Rmd -#- clean-data.Rmd -#- describe-cases.Rmd -#- simple-analysis.Rmd +- read-cases.Rmd +- clean-data.Rmd +- describe-cases.Rmd +- simple-analysis.Rmd - delays-reuse.Rmd - quantify-transmissibility.Rmd - delays-functions.Rmd diff --git a/data/simulated_ebola_2.csv b/data/simulated_ebola_2.csv new file mode 100644 index 00000000..ac7c7af3 --- /dev/null +++ b/data/simulated_ebola_2.csv @@ -0,0 +1,15001 @@ +"","case id","age","gender","status","date onset","date sample" +"1",14905,"90","1","confirmed","03/15/2015","06/04/2015" +"2",13043,"twenty-five","2","","Sep /11/Y","03/01/2014" +"3",14364,"54","f",NA,"09/02/2014","03/03/2015" +"4",14675,"ninety",NA,"","10/19/2014","31/ 12 /14" +"5",12648,"74","F","","08/06/2014","10/10/2016" +"6",14274,"seventy-six","female","","Apr /05/Y","01/23/2016" +"7",14132,"sixteen","male","confirmed","Dec /29/Y","05/10/2015" +"8",14715,"44","f","confirmed","Apr /06/Y","04/24/2016" +"9",13435,"26","1","","09/07/2014","20/ 09 /14" +"10",14816,"thirty","f","","06/29/2015","06/02/2015" +"11",13993,"forty-nine","2","suspected","01/21/2015","06/18/2016" +"12",13698,"four","2","suspected","11/27/2014","05/28/2015" +"13",13976,"sixty-seven","M","suspected","10/20/2014","06/26/2016" +"14",14123,"eighty-three","m","","Dec /23/Y","26/ 08 /15" +"15",12320,"66","M",NA,"12/11/2014","01/25/2015" +"16",13358,"62",NA,"suspected","Apr /24/Y","01/17/2015" +"17",13139,"90","f",NA,"Dec /16/Y","02/27/2015" +"18",13810,"50","1","","11/16/2014","09/25/2015" +"19",13670,"thirteen","male",NA,"05/29/2014","06/14/2015" +"20",14566,"twelve","F","suspected","07/11/2015","07/10/2015" +"21",12323,"twenty-one","f","confirmed","02/26/2015","11/23/2016" +"22",14926,"ninety","1",NA,"06/25/2014","04/ 07 /15" +"23",14029,"fifty-nine","1","suspected","Mar /24/Y","24/ 02 /15" +"24",12596,"ninety-seven","f","suspected","07/23/2015","28/ 08 /14" +"25",13828,"twenty-nine","f","confirmed","03/14/2014","14/ 06 /14" +"26",14301,"ninety-three","male","suspected","05/22/2014","03/31/2014" +"27",14820,"fifty-nine","1","","03/10/2015","29/ 03 /15" +"28",14413,"twenty-seven","M","","08/14/2014","07/10/2015" +"29",13734,"seventy-three","male",NA,"11/24/2015","09/07/2015" +"30",12616,"24","m",NA,"Aug /11/Y","10/21/2015" +"31",13314,"fifty-nine","1","suspected","07/28/2014","11/13/2015" +"32",14279,"59","f",NA,"07/14/2014","02/06/2016" +"33",11967,"thirty-two","female","suspected","May /18/Y","08/ 04 /15" +"34",13654,"sixty-two","1",NA,"May /18/Y","07/23/2016" +"35",13666,"87","m",NA,"Mar /15/Y","02/28/2015" +"36",13056,"0","M",NA,"Mar /06/Y","20/ 07 /14" +"37",14222,"32",NA,"confirmed","Nov /29/Y","08/17/2016" +"38",13921,"58","F","suspected","Aug /06/Y","09/04/2014" +"39",13617,"54","F","","03/11/2015","31/ 01 /15" +"40",14751,"39","F",NA,"Mar /21/Y","04/08/2014" +"41",13873,"62","2","confirmed","Sep /24/Y","03/29/2014" +"42",14387,"77","f","","04/11/2014","03/20/2016" +"43",13272,"48","1",NA,"Dec /08/Y","08/08/2014" +"44",13856,"forty-one","f",NA,"02/26/2015","04/03/2014" +"45",12468,"2","1","","Jan /22/Y","04/22/2016" +"46",12547,"forty-eight","f","","08/03/2015","02/ 02 /15" +"47",14199,"63","f","confirmed","07/19/2015","04/16/2016" +"48",14997,"twenty-two","M","suspected","02/28/2015","03/11/2015" +"49",13641,"thirty-six","1",NA,"May /04/Y","02/27/2015" +"50",13457,"95","2","","Oct /22/Y","09/30/2016" +"51",14016,"55","2",NA,"Aug /07/Y","01/ 09 /14" +"52",12100,"eighty-four",NA,"","Oct /17/Y","06/ 01 /15" +"53",13057,"thirty-nine","1","","Aug /05/Y","15/ 07 /14" +"54",12922,"9","M","suspected","11/10/2015","07/ 06 /14" +"55",14153,"nine","M","suspected","May /29/Y","12/21/2015" +"56",12645,"ten","2","confirmed","Aug /21/Y","26/ 06 /14" +"57",12848,"ninety-four","female",NA,"Jun /05/Y","12/04/2015" +"58",13343,"twenty-eight","m","","07/05/2015","06/15/2016" +"59",14272,"89","2","confirmed","10/19/2015","30/ 01 /15" +"60",12445,"67","F","suspected","09/25/2014","10/ 08 /14" +"61",14238,"52","female","confirmed","04/06/2014","09/10/2014" +"62",12936,"27","2",NA,"04/12/2014","04/24/2014" +"63",12921,"eight","F","suspected","10/23/2015","08/28/2015" +"64",12816,"eighty-one","f",NA,"08/12/2015","06/12/2016" +"65",12827,"zero","m","confirmed","10/15/2014","22/ 07 /14" +"66",12657,"35","1","suspected","05/25/2015","09/26/2015" +"67",13143,"ninety-eight","M","confirmed","12/16/2014","01/30/2016" +"68",13039,"forty-nine","f","","Feb /14/Y","12/03/2014" +"69",14252,"69","F",NA,"Jul /14/Y","08/22/2015" +"70",14294,"eight",NA,NA,"12/13/2014","05/21/2014" +"71",12338,"forty-six","2",NA,"10/22/2014","10/ 02 /15" +"72",12772,"eleven","m","suspected","11/17/2014","02/07/2016" +"73",14420,"45","female","suspected","07/30/2015","03/08/2014" +"74",12547,"thirty-seven","M",NA,"06/16/2015","06/06/2016" +"75",14250,"63","F","confirmed","Aug /16/Y","05/18/2015" +"76",12274,"78","f",NA,"Jun /17/Y","04/26/2016" +"77",14249,"88","female","","10/08/2014","01/16/2016" +"78",13017,"fifty-five","f","","05/19/2015","10/05/2015" +"79",12889,"twenty-nine","f","","05/14/2015","12/26/2015" +"80",13592,"13","female",NA,"02/18/2015","02/ 09 /15" +"81",12830,"thirty",NA,NA,"03/17/2015","08/16/2014" +"82",14004,"13","2","confirmed","Jun /28/Y","05/17/2016" +"83",12705,"53","f","","Jan /27/Y","06/15/2015" +"84",13105,"85","2","suspected","03/07/2014","19/ 08 /14" +"85",14539,"twelve","1",NA,"01/09/2015","25/ 08 /14" +"86",12822,"ninety-nine","male","suspected","04/07/2014","02/12/2015" +"87",13097,"80","2","","Oct /10/Y","09/27/2014" +"88",12770,"44",NA,"confirmed","05/25/2015","17/ 02 /15" +"89",14197,"39","F",NA,"11/06/2014","15/ 07 /14" +"90",14131,"seventy-seven","F","confirmed","04/20/2015","04/06/2014" +"91",12353,"seventy-five",NA,"confirmed","Jul /31/Y","02/17/2016" +"92",14194,"59","1","suspected","03/09/2014","03/06/2016" +"93",14828,"100","f","confirmed","Jul /21/Y","04/06/2014" +"94",11927,"5","male","confirmed","11/20/2015","12/06/2015" +"95",12829,"fifty-three","F","suspected","07/19/2015","11/25/2015" +"96",13959,"50","f",NA,"Apr /15/Y","23/ 11 /14" +"97",14223,"fifty-three",NA,"confirmed","03/24/2014","05/07/2016" +"98",12964,"fifteen","F",NA,"Jul /12/Y","09/18/2016" +"99",12543,"eighty-nine","F","","12/22/2014","28/ 08 /14" +"100",14877,"5","2","suspected","May /25/Y","25/ 02 /15" +"101",12985,"sixty-four","m",NA,"02/25/2015","04/01/2014" +"102",13859,"76","male","confirmed","Mar /20/Y","06/ 12 /14" +"103",12964,"fifty-four","male","","06/24/2015","16/ 07 /14" +"104",13847,"84","2","","06/08/2014","08/ 06 /14" +"105",14540,"forty-three","m","","08/20/2015","07/26/2014" +"106",14764,"70","male","suspected","11/18/2014","27/ 10 /14" +"107",12625,"67","1",NA,"06/19/2015","07/ 02 /15" +"108",13221,"93","male","confirmed","Aug /20/Y","13/ 11 /14" +"109",13140,"zero","1","","07/03/2015","14/ 05 /15" +"110",13192,"93","F","","07/30/2015","08/17/2014" +"111",11925,"sixty-one","female",NA,"11/25/2015","02/09/2016" +"112",14280,"29",NA,"confirmed","Jan /25/Y","04/ 08 /15" +"113",13684,"eighty-eight","male",NA,"04/19/2015","10/07/2014" +"114",12452,"62","male","","08/13/2015","05/15/2016" +"115",12545,"38","f","","Nov /30/Y","08/26/2015" +"116",13526,"fifty-three",NA,"","Aug /16/Y","02/28/2015" +"117",14884,"seventy-five",NA,"suspected","04/28/2015","11/23/2014" +"118",12170,"sixty-three","female","suspected","10/05/2015","19/ 06 /15" +"119",11915,"6","female","confirmed","03/22/2014","06/23/2014" +"120",13978,"56","male",NA,"07/03/2014","10/17/2016" 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+"137",12526,"ninety-two","female","confirmed","09/20/2015","10/25/2014" +"138",14181,"44","male",NA,"08/30/2014","07/ 07 /14" +"139",12405,"twenty-eight","F","suspected","Jun /01/Y","07/21/2014" +"140",12059,"seventy-one","F","confirmed","May /06/Y","06/ 01 /15" +"141",13976,"forty-two","female","confirmed","06/06/2014","01/05/2015" +"142",12308,"seventy-seven","1","confirmed","10/12/2015","09/27/2015" +"143",14193,"33","F","suspected","Nov /16/Y","03/04/2014" +"144",13887,"twenty-four","F","suspected","08/29/2014","02/12/2016" +"145",14375,"four","f","","Apr /04/Y","08/21/2014" +"146",12268,"75","female","suspected","02/27/2015","23/ 03 /15" +"147",13850,"18",NA,"suspected","Jun /12/Y","06/18/2016" +"148",12348,"sixty-one",NA,NA,"03/01/2015","08/06/2016" +"149",12350,"94","m","","09/06/2015","12/08/2016" +"150",12845,"49","F","suspected","12/09/2014","16/ 01 /15" +"151",14091,"nineteen","2","suspected","Jun /11/Y","11/07/2015" 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+- Convert case data to incidence +- Create epidemic curves from incidence data +- Estimate the growth rate from incidence curves +- Create quick descriptive and comparison tables + +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Introduction + +A comprehensive description of data is pivotal for conducting insightful explanatory and exploratory analyses. This episode focuses on describing and visualizing epidemic data. The examples are built around the **Covid-19 case data from England** dataset that contained in the [outbreaks](http://www.reconverse.org/outbreaks/) package. The first step is to read this dataset, and we recommend utilizing the [readr](../links.md#readr) package for this purpose (or employing alternative methods as outlined in the [Read case data](../episodes/read-cases.Rmd) episode. + + +```r +requireNamespace("outbreaks", quietly = TRUE) +covid19_eng_case_data <- outbreaks::covid19_england_nhscalls_2020 +utils::head(covid19_eng_case_data, 5) +``` + +```{.output} + site_type date sex age ccg_code +1 111 2020-03-18 female missing e38000062 +2 111 2020-03-18 female missing e38000163 +3 111 2020-03-18 female 0-18 e38000001 +4 111 2020-03-18 female 0-18 e38000002 +5 111 2020-03-18 female 0-18 e38000004 + ccg_name count postcode +1 nhs_gloucestershire_ccg 1 gl34fe +2 nhs_south_tyneside_ccg 1 ne325nn +3 nhs_airedale_wharfedale_and_craven_ccg 8 bd57jr +4 nhs_ashford_ccg 7 tn254ab +5 nhs_barking_and_dagenham_ccg 35 rm13ae + nhs_region day weekday +1 South West 0 rest_of_week +2 North East and Yorkshire 0 rest_of_week +3 North East and Yorkshire 0 rest_of_week +4 South East 0 rest_of_week +5 London 0 rest_of_week +``` + +## Incidence data + +Downstream analysis involves working with aggregated data rather than individual cases. This requires aggregating case 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 `covid19_eng_case_data` based on the sampling date. + + +```r +requireNamespace("incidence2", quietly = TRUE) +covid19_eng_incidence_data <- incidence2::incidence( + covid19_eng_case_data, + date_index = "date" +) +utils::head(covid19_eng_incidence_data, 5) +``` + +```{.output} +# incidence: 5 x 3 +# count vars: date + date_index count_variable count +* +1 2020-03-18 date 2579 +2 2020-03-19 date 2602 +3 2020-03-20 date 2615 +4 2020-03-21 date 2588 +5 2020-03-22 date 2603 +``` + +The `incidence2` object can be visualized using the `plot()` function from base R package. + + +```r +base::plot(covid19_eng_incidence_data) +``` + + + +Moreover, `{incidence2}` has other functions that allow for aggregating case data based on a dated event and other factors such as the individual gender, the sampling location, etc. In the example below, we calculate weekly counts of Covid-19 cases in England grouping them by `sex` type. + + +```r +weekly_covid19_eng_incidence <- incidence2::incidence( + covid19_eng_case_data, + date_index = "date", + interval = "week", + groups = "sex" +) +base::plot(weekly_covid19_eng_incidence, angle = 45) +``` + + + + + +::::::::::::::::::::::::::::::::::::: challenge + +## Challenge 1: Can you do it? + + - Using the above `covid91_eng_case_data` dataset, produce monthly epi-curves for Covid-19 cases in England based on regional places in England? + +:::::::::::::::::::::::: solution + + + +```r +monthly_covid19_eng_incidence <- incidence2::incidence( + covid19_eng_case_data, + date_index = "date", + interval = "month", + groups = "sex" +) +base::plot(monthly_covid19_eng_incidence, angle = 45) +``` + + + +::::::::::::::::::::::::::::::::: + +:::::::::::::::::::::::::::::::::::::::::::::::: + + +::::::::::::::::::::::::::::::::::::: keypoints + +- Use `{incidence2}` to aggregate case data based on a date event. +:::::::::::::::::::::::::::::::::::::::::::::::: + 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a/md5sum.txt b/md5sum.txt index 1bc668c4..6f8ab097 100644 --- a/md5sum.txt +++ b/md5sum.txt @@ -1,9 +1,13 @@ "file" "checksum" "built" "date" "CODE_OF_CONDUCT.md" "549f00b0992a7743c2bc16ea6ce3db57" "site/built/CODE_OF_CONDUCT.md" "2024-03-28" "LICENSE.md" "14377518ee654005a18cf28549eb30e3" "site/built/LICENSE.md" "2024-03-28" -"config.yaml" "8d9e4aaa445394c028620657cdf476cd" "site/built/config.yaml" "2024-04-02" +"config.yaml" "620f494a91107c771e1afb198211b08b" "site/built/config.yaml" "2024-04-05" "index.md" "32bc80d6f4816435cc0e01540cb2a513" "site/built/index.md" "2024-03-28" "links.md" "fe82d0a436c46f4b07b82684ed2cceaf" "site/built/links.md" "2024-03-28" +"episodes/read-cases.Rmd" "f6a65075bc938f938f27d3f6c9d458d0" "site/built/read-cases.md" "2024-04-05" +"episodes/clean-data.Rmd" "531fa8d39df8d9d174b2e274dd9c74e8" "site/built/clean-data.md" "2024-04-05" +"episodes/describe-cases.Rmd" "3575756733d3286161c4dc26c9d9187c" "site/built/describe-cases.md" "2024-04-05" +"episodes/simple-analysis.Rmd" "adbcb6b21e6523bd96b528bd5ba61fae" "site/built/simple-analysis.md" "2024-04-05" "episodes/delays-reuse.Rmd" "bb6ad865f2600bf267f833cb2c4b406d" "site/built/delays-reuse.md" "2024-04-02" "episodes/quantify-transmissibility.Rmd" "f552d70266c0967dc2a4203599b3ad25" "site/built/quantify-transmissibility.md" "2024-04-02" "episodes/delays-functions.Rmd" "d8cd5448a0977811709efa43d7d1505f" "site/built/delays-functions.md" "2024-04-02" diff --git a/read-cases.md b/read-cases.md new file mode 100644 index 00000000..cdcc9a92 --- /dev/null +++ b/read-cases.md @@ -0,0 +1,143 @@ +--- +title: 'Read case data' +teaching: 20 +exercises: 2 +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 +requireNamespace("rio", quietly = TRUE) +case_data <- rio::import(file.path("episodes", "data", "ebola_cases.csv")) +``` + +```{.error} +Error: No such file: episodes/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 supported file formats in the `{rio}` package as follows: + +```r +requireNamespace("rio", quietly = TRUE) +rio::install_formats() +``` + +:::::::::::::::::::::: + +::::::::::::::::: solution + + +```r +requireNamespace("rio", quietly = TRUE) +rio::import("path/to/file/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 +requireNamespace("DBI", quietly = TRUE) +requireNamespace("RSQLite", quietly = TRUE) +# 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). + +## 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/simple-analysis.md b/simple-analysis.md new file mode 100644 index 00000000..486ec986 --- /dev/null +++ b/simple-analysis.md @@ -0,0 +1,176 @@ +--- +title: 'Early analysis' +teaching: 10 +exercises: 2 +--- + +:::::::::::::::::::::::::::::::::::::: questions + +- What is the growth rate of the epidemic? +- How to identify the peak time of an outbreak? +- How to compute moving average of cases? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: objectives + +- Perform an early analysis of outbreak data +- Identify trends, exponential, doubling, and peak time + +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Introduction + +Understanding the trend in case data is crucial for various purposes, such as forecasting future case counts, implementing public health interventions, and assessing the effectiveness of control measures. 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 +requireNamespace("i2extras", quietly = TRUE) +requireNamespace("incidence2", quietly = TRUE) # This line loads the i2extras package, which provides methods for modeling + +# 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 2020-03-23 2020-03-30 +2 male 2020-03-27 1299 2020-03-18 2020-03-23 2020-03-30 +3 unknown 2020-04-10 32 2020-03-24 2020-04-07 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 +requireNamespace("ggplot2", quietly = TRUE) +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. + +:::::::::::::::::::::::::::::::::::::::::::::::: +