Coronavirus (covid-19) in Sierra Leone

This repository contains datasets relating to coronavirus in Sierra Leone, as well as on demographic and other information from the 2015 Population and Household Census (PHC). It also includes mapping shapefiles by district, so that you can map the district-level coronavirus statistics.

See here for a full description of how the data files have been created from the source data, including the R code.

Last updated: 10 June 2020.

Context

The novel 2019 coronavirus (covid-19) arrived late to West Africa and Sierra Leone in particular. This dataset provides the number of reported cases on a district-by-district basis for Sierra Leone, as well as various additional statistics at the country level. In addition, I provide district-by-district data on demographics and households' main sources of information, both from the 2015 census. For convenience, I also provide shapefiles for mapping the 14 districts of Sierra Leone.

Content

The dataset consists of four main files, which are in the output folder. See the column descriptions below for further details.

1. Coronavirus confirmed cases by district (sl_districts_coronavirus.csv). I found the original data by looking in the static/js/data folder in the source code for covid19.mic.gov.sl, last accessed 10 June 2020. The file contains the cumulative number of confirmed coronavirus cases in the 14 districts of Sierra Leone as a time series. I have used the R tidyverse to reshape the data and ensure naming is consistent with the other data files.

2. Demographic statistics by district (sl_districts_demographics.csv). Data from the 2015 Population and Housing Census (PHC), sourced from Open Data Sierra Leone. The dataset covers the 14 districts of Sierra Leone, which increased to 16 in 2017. Last accessed 10 June 2020.

3. Main Sources of Information by district (sl_districts_info_sources.csv). Data from the 2015 Population and Housing Census (PHC), sourced from Open Data Sierra Leone. The dataset presents the main sources of information, such as television or radio, for households in the 14 districts of Sierra Leone. Last accessed 2 June 2020. I note that I have made one correction to the source data (see R code with correction here).

4. Country-wide coronavirus statistics for Sierra Leone (sl_national_coronavirus.csv). The original data also comes from covid19.mic.gov.sl, last accessed 10 June 2020. The file contains numerous statistics as time series, listed in the Column Description section below. I note that there are various potential issues in the file which I leave the user to decide how to deal with (duplicate datetimes, inconsistent statistics).

Additionally I include a set of five files with district-by-district mapping (shapefiles) and other data, unchanged from their original source. Each file is labelled in the following way: sl_districts_mapping.*. These files come from Direct Relief Open Data on ArcGIS Hub. The data also include district-level data on maternal child health attributes, which was the original context of the mapping data.

Column Descriptions

Coronavirus confirmed cases by district sl_districts_coronavirus.csv:

1. date: Date of reporting
2. district: District of Sierra Leone (based on pre-2017 administrative boundaries)
3. confirmed_cases: Cumulative number of confirmed coronavirus cases; NA if no data reported
4. decrease: Dummy variable indicating whether the number of reported cases has been revised down. NA if no reported cases on that date; 1 if there is a decrease from the last reported cases; 0 otherwise

Demographic statistics by district sl_districts_demographics.csv:

1. district: District of Sierra Leone (based on pre-2017 administrative boundaries)
2. d_code: District code
3. d_id: District id
4. total_pop: Total population in district
5. pop_share: District's share of total country population
6. t_male: Total male population
7. t_female: Total female population
8. s_ratio: (*) Sex ratio at birth (number of males for every 100 females, under the age of 1)
9. t_urban: Total urban population
10. t_rural: Total rural population
11. prop_urban: Proportion urban
12. t_h_pop: Sum of h_male and h_female
13. h_male: (?)
14. h_female: (?)
15. t_i_pop: Sum of i_male and i_female
16. i_male: (?)
17. i_female: (?)
18. working_pop: Working population
19. depend_pop: Dependent population

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