Name: Peruvian Social Contact Network
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Peruvian Social Contact Network

Detailed Contact-Level Interactions and Data

By [source]

About this dataset

This dataset contains information about the social contact and interaction networks of people living in Peru. It draws on data collected from participants through the 2015 Grijalva survey, as well as household data. This richly detailed dataset focuses on a wide range of topics, including participant characteristics such as age, gender and household size; sday-level observations including days associated to each participant; and contact-level observations such as type, frequency and physical contact. The comprehensive nature of this database provides an invaluable resource for mapping complex social contacts between specific individuals within a community or wider geographical area. From understanding everyday interactions to examining long-term changes in disease prevalence – essential elements when studying risk factors associated with infectious diseases – this dataset enables researchers to map details regarding social contacts at both the individual level and population level

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How to use the dataset

This dataset on the social contact network of Peru can provide a wealth of information about Peruvian society and individuals in it. To start exploring this dataset, you should first become familiar with the different variables and values that are recorded within it. The data is divided into four main categories: participant-level, household-level, sday-level, and contact-level data.

To begin with participant-level data, you can find information about an individual's identification such as their id number as well as characteristics like their age and gender. Participant's household characteristics are also included in this level of data, such as their household's id number and community ID.

Next is the household-level data which records additional details about the household itself including contact related variables such as name of contacts made within that particular area or environment (i.e., home). It also records days and months associated to each individual participant for further exploration on patterns in social activities over a certain period of time or day from Monday to Sunday (sday).

The remaining two levels to explore are sday-level and contact-level data respectively. The sday component includes values on days and months associated to each person; which will be useful for understanding activity patterns during various times frames across days/months for any given subject or group (i.e., seasonal effects). Finally at contact level detail provides us insight into an individual’s contacts where we can uncover attributes like contact name, age range (exact + estimated minimum/maximum age), gender type or even physical interaction or duration conneced with specific parties involved in discourse transactions between multiple participants when applicable - all promising dimensions adding up to create profiles charged both by qualitative + quantitative character traits specified under these labels mentioned above!

Using this dataset therefore allows you to gain an understanding of social relationships among individuals - both collective networks connecting entire households & communities while simultaneously diving into more intricate details between two persons linked together by commonalities...further enrichment may be determined through your own clever combination likely not depicted here directly but certainly hinted at by clues found throughout! Good luck exploring & have fun building connections from A to Z :)

Research Ideas

Estimating the efficacy of social distancing policies in Peru by analyzing contact-level variables, such as contact frequency.

Analyzing demographic differences in contact networks of people living in Peru, such as the gender breakdown of contacts within a household.

Studying how community and household size may be related to social interaction patterns by using the household-level data provided with this dataset

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: 2015_Grijalva_Peru_participant_extra.csv

Column name	Description
CONTD004	Contact duration in minutes. (Integer)
CONTD004S	Contact duration in seconds. (Integer)
CONTD005	Contact location code. (Integer)
CONTD005S	Contact location string. (String)
CONTD006	Contact type code. (Integer)
CONTD007	Contact type string. (String)

File: 2015_Grijalva_Peru_hh_extra.csv

Column name	Description
CONT2	Number of contacts in the last 7 days (Numeric)
CONT3	Number of contacts in the last 14 days (Numeric)
CONT4	Number of contacts in the last 21 days (Numeric)
CONT5	Number of contacts in the last 28 days (Numeric)
CONT6	Number of contacts in the last 35 days (Numeric)
CNT71A	Number of contacts in the last 7 days with people aged 0-4 (Numeric)
CNT71B	Number of contacts in the last 7 days with people aged 5-14 (Numeric)
CNT71C	Number of contacts in the last 7 days with people aged 15-24 (Numeric)
CNT71D	Number of contacts in the last 7 days with people aged 25-44 (Numeric)
CNT71E	Number of contacts in the last 7 days with people aged 45+ (Numeric)
CNT72A	Number of contacts in the last 14 days with people aged 0-4 (Numeric)
CNT72B	Number of contacts in the last 14 days with people aged 5-14 (Numeric)
CNT72C	Number of contacts in the last 14 days with people aged 15-24 (Numeric)
CNT72D	Number of contacts in the last 14 days with people aged 25-44 (Numeric)
CNT72E	Number of contacts in the last 14 days with people aged 45+ (Numeric)
CNT73A	Number of contacts in the last 21 days with people aged 0-4 (Numeric)
CNT73B	Number of contacts in the last 21 days with people aged 5-14 (Numeric)
CNT73C	Number of contacts in the last 21 days with people aged 15-24 (Numeric)
CNT73D	Number of contacts in the last 21 days with people aged 25-44 (Numeric)
CNT73E	Number of contacts in the last 21 days with people aged 45+ (Numeric)
CNT74A	Number of contacts in the last 28 days with people aged 0-4 (Numeric)
CNT74B	Number of contacts in the last 28 days with people aged 5-14 (Numeric)
CNT74C	Number of contacts in the last 28 days with people aged 15-24 (Numeric)
CNT74D	Number of contacts in the last 28 days with people aged 25-44 (Numeric)
CNT74E	Number of contacts in the last 28 days with people aged 45+ (Numeric)
CNT75A	Number of contacts in the last 35 days with people aged 0-4 (Numeric)
CNT75B	Number of contacts in the last 35 days with people aged 5-14 (Numeric)
CNT75C	Number of contacts in the last 35 days with people aged 15-24 (Numeric)
CNT75D	Number of contacts in the last 35 days with people aged 25-44 (Numeric)
CNT75E	Number of contacts in the last 35 days with people aged 45+ (Numeric)
CNT76A	Number of contacts in the last 42 days with people aged 0-4 (Numeric)
CNT76B	Number of contacts in the last 42 days with people aged 5-14 (Numeric)
CNT76C	Number of contacts in the last 42 days with people aged 15-24 (Numeric)
CNT76D	Number of contacts in the last 42 days with people aged 25-44 (Numeric)
CNT76E	Number of contacts in the last 42 days with people aged 45+ (Numeric)
CNT77A	Number of contacts in the last 49 days with people aged 0-4 (Numeric)
CNT77B	Number of contacts in the last 49 days with people aged 5-14 (Numeric)
CNT77C	Number of contacts in the last 49 days with people aged 15-24 (Numeric)
CNT77D	Number of contacts in the last 49 days with people aged 25-44 (Numeric)
CNT78A	Number of contacts in the last 7 days with people aged 0-4 (Numeric)
CNT78B	Number of contacts in the last 7 days with people aged 5-14 (Numeric)
CNT78C	Number of contacts in the last 7 days with people aged 15-24 (Numeric)
CNT78D	Number of contacts in the last 7 days with people aged 25-44 (Numeric)
CNT78E	Number of contacts in the last 7 days with people aged 45+ (Numeric)
CNT79A	Number of contacts in the last 14 days with people aged 0-4 (Numeric)
CNT79B	Number of contacts in the last 14 days with people aged 5-14 (Numeric)
CNT79C	Number of contacts in the last 14 days with people aged 15-24 (Numeric)
CNT79D	Number of contacts in the last 14 days with people aged 25-44 (Numeric)
CNT79E	Number of contacts in the last 14 days with people aged 45+ (Numeric)
CNTRESP	Contact response (Categorical)
CONTDIA	Contact duration (Numeric)
CONT8	Number of contacts in the last 8 days (Numeric)
CNT77E	Number of contacts in the last 49 days with people aged 45+ (Numeric)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

N 2015 Grijalva Peru Contact Common

@kaggle.thedevastator_peruvian_social_contact_network.n_2015_grijalva_peru_contact_common

88.32 kB
9,019 rows
15 columns

CREATE TABLE n_2015_grijalva_peru_contact_common (
  "part_id" BIGINT,
  "cont_id" VARCHAR,
  "cnt_age_exact" DOUBLE,
  "cnt_age_est_min" DOUBLE,
  "cnt_age_est_max" DOUBLE,
  "cnt_gender" VARCHAR,
  "cnt_home" VARCHAR,
  "cnt_work" BOOLEAN,
  "cnt_school" VARCHAR,
  "cnt_transport" BOOLEAN,
  "cnt_leisure" VARCHAR,
  "cnt_otherplace" VARCHAR,
  "frequency_multi" DOUBLE,
  "phys_contact" DOUBLE,
  "duration_multi" DOUBLE
);

N 2015 Grijalva Peru Hh Common

@kaggle.thedevastator_peruvian_social_contact_network.n_2015_grijalva_peru_hh_common

3.42 kB
114 rows
3 columns

CREATE TABLE n_2015_grijalva_peru_hh_common (
  "hh_id" BIGINT,
  "country" VARCHAR,
  "hh_size" BIGINT
);

N 2015 Grijalva Peru Hh Extra

@kaggle.thedevastator_peruvian_social_contact_network.n_2015_grijalva_peru_hh_extra

32.51 kB
114 rows
55 columns

CREATE TABLE n_2015_grijalva_peru_hh_extra (
  "hh_id" BIGINT,
  "commid" BIGINT,
  "cont2" VARCHAR,
  "cont3" VARCHAR,
  "cont4" VARCHAR,
  "cont5" VARCHAR,
  "cont6" VARCHAR,
  "cnt71a" VARCHAR,
  "cnt71b" VARCHAR,
  "cnt71c" DOUBLE,
  "cnt71d" DOUBLE,
  "cnt71e" VARCHAR,
  "cnt72a" VARCHAR,
  "cnt72b" VARCHAR,
  "cnt72c" DOUBLE,
  "cnt72d" DOUBLE,
  "cnt72e" VARCHAR,
  "cnt73a" VARCHAR,
  "cnt73b" VARCHAR,
  "cnt73c" VARCHAR,
  "cnt73d" VARCHAR,
  "cnt73e" VARCHAR,
  "cnt74a" VARCHAR,
  "cnt74b" VARCHAR,
  "cnt74c" VARCHAR,
  "cnt74d" VARCHAR,
  "cnt74e" VARCHAR,
  "cnt75a" VARCHAR,
  "cnt75b" VARCHAR,
  "cnt75c" VARCHAR,
  "cnt75d" VARCHAR,
  "cnt75e" VARCHAR,
  "cnt76a" VARCHAR,
  "cnt76b" VARCHAR,
  "cnt76c" VARCHAR,
  "cnt76d" VARCHAR,
  "cnt76e" VARCHAR,
  "cnt77a" VARCHAR,
  "cnt77b" VARCHAR,
  "cnt77c" VARCHAR,
  "cnt77d" VARCHAR,
  "cnt77e" VARCHAR,
  "cnt78a" VARCHAR,
  "cnt78b" VARCHAR,
  "cnt78c" VARCHAR,
  "cnt78d" VARCHAR,
  "cnt78e" VARCHAR,
  "cnt79a" VARCHAR,
  "cnt79b" VARCHAR,
  "cnt79c" VARCHAR,
  "cnt79d" VARCHAR,
  "cnt79e" VARCHAR,
  "cntresp" DOUBLE,
  "contdia" VARCHAR,
  "cont8" VARCHAR
);

N 2015 Grijalva Peru Participant Common

@kaggle.thedevastator_peruvian_social_contact_network.n_2015_grijalva_peru_participant_common

8.46 kB
588 rows
4 columns

CREATE TABLE n_2015_grijalva_peru_participant_common (
  "part_id" BIGINT,
  "hh_id" BIGINT,
  "part_age" BIGINT,
  "part_gender" VARCHAR
);

N 2015 Grijalva Peru Sday

@kaggle.thedevastator_peruvian_social_contact_network.n_2015_grijalva_peru_sday

9.68 kB
588 rows
6 columns

CREATE TABLE n_2015_grijalva_peru_sday (
  "part_id" BIGINT,
  "sday_id" DOUBLE,
  "day" DOUBLE,
  "month" DOUBLE,
  "year" DOUBLE,
  "dayofweek" DOUBLE
);