Column

About

This dashboard shows results from Wave 0 of the Berkeley Interpersonal Contact Study (BICS) in Spring 2020.

Newer results are available here

Caveats / underway:

  • except where noted, these results show the national and city samples pooled together

  • the pooled estimates have been weighted to improve sample representativeness. Weights are based on age, sex, race/ethnicity, household size, and urbanicity

  • we are currently collecting data and will post more recent estimates as soon as we can

If you have questions or are interested in funding this study, please contact us at .

Initial support provided by a Berkeley Population Center pilot grant (NICHD P2CHD073964). This project has been approved by the UC Berkeley IRB (Protocol 2020-03-13128).

Updates

Wave 0 results updated 2020-05-07

2020-05-07:

  • replaced this dashboard with a new one focused on Wave 1; find the new one here

2020-04-22:

  • Added an estimated mixing matrix that has symmetrization enforced

2020-04-20:

  • Most plots are now weighted to improve sample representativeness using raking. (These weights have made little difference to the previous estimates)

2020-04-17:

Column

Number of conversational contacts

Number of conversational contacts

Number of conversational contacts outside the household

Conversational contacts by age

Conversational contacts outside household by age

Mixing

NB: please see the ‘data’ tab if you want the numbers behind these mixing estimates

By age/sex

By age/sex - non-hh contacts

By age/sex - non-hh contacts, symmetric

The matrix calculated below uses the symmetrization formula found in the vignette for the socialmixr package.
(We calculate it by hand, since the package was not designed for data collected using our instrument.) We used the 2018 American Community Survey to obtain the national age-distribution.

Comparison - all conversational contacts

# A tibble: 16 x 6
# Groups:   ego_age [4]
   ego_age  alter_age  bics    fb ratio frac_decrease
   <chr>    <chr>     <dbl> <dbl> <dbl>         <dbl>
 1 [25,35)  [25,35)   0.889 7.16  0.124         0.876
 2 [25,35)  [35,45)   0.579 2.36  0.245         0.755
 3 [25,35)  [45,65)   0.397 1.25  0.318         0.682
 4 [25,35)  [65,100]  0.102 0.173 0.591         0.409
 5 [35,45)  [25,35)   0.524 3.29  0.159         0.841
 6 [35,45)  [35,45)   1.11  5.87  0.190         0.810
 7 [35,45)  [45,65)   0.382 1.70  0.224         0.776
 8 [35,45)  [65,100]  0.234 0.528 0.443         0.557
 9 [45,65)  [25,35)   0.315 2.23  0.141         0.859
10 [45,65)  [35,45)   0.401 3.10  0.129         0.871
11 [45,65)  [45,65)   0.910 3.77  0.241         0.759
12 [45,65)  [65,100]  0.281 0.755 0.373         0.627
13 [65,100] [25,35)   0.186 0.737 0.252         0.748
14 [65,100] [35,45)   0.246 2.14  0.115         0.885
15 [65,100] [45,65)   0.528 2.00  0.263         0.737
16 [65,100] [65,100]  0.841 2.17  0.387         0.613

Relationships

Relationships, non-household contacts

Locations

Locations - non-household contacts

Contact durations - by relationship

Contact durations - by respondent age

COVID-19

Awareness

Concern

Behavior change

Cities

Number of interviews

Conversational contacts

Conversational contacts outside of household

Model

Overview

To help summarize patterns in the contact survey data, we fit a negative binomial model, accounting for the right-censoring of reported contacts at 10. These models show relationships among people who have completed the survey; have not been adjusted in any way for sampling. We fit these models using the brms package in R.

We modeled the expected log number of reported contacts as a function of age group, city, gender, and household size. The plots below show posterior means and 95% credible intervals for the estimated coefficients. Estimated coefficients greater than 0 imply that the predictor is associated with higher reported numbers of contacts, while estimated coefficients less than 0 imply that the predictor is associated with lower reported numbers of contacts.

There are two models: one for total number of contacts, and one for the number of non-household contacts.

Negative-binomial model for household contacts

Negative-binomial model for non-household contacts

Technical details

Coming soon.

Respondent characteristics

Weights

The pooled estimates have been weighted using raking to improve sample representativeness. Weights are based on

  • Age
  • Sex
  • Age/sex interaction
  • Race (Black, White, Other)
  • Hispanic status
  • Household size (1/2/3/4/5+)
  • Urbanicity

Population values are taken from the 2018 ACS, estimated from an IPUMS USA extract. We used the R packages ipumsr, leafpeepr and autumn to help perform the raking.

Number of interviews by date

Age/sex

Race/ethnicity

Household size

Contact definition

Respondents to the survey were told to consider someone a contact using this text:

We would like to ask you some questions about people you had in-person conversational contact with yesterday.

By in-person conversational contact, we mean a two-way conversation with three or more words in the physical presence of another person.

You might have conversational contact with family members, friends, co-workers, store clerks, bus drivers, and so forth.

(Please do not count people you contacted exclusively by telephone, text, or online. Only consider people you interacted with face-to-face.)

 

Data

Microdata

We plan to make a version of the data with no identifying information publicly available as soon as we can. If you are a disease modeler who urgently needs to see the microdata, please reach out to us by email.

The estimated mixing matrices are reproduced as tables below. Note that these are the crude estimates, and have not had a symmetry constraint enforced.

All contact mixing estimates

ego_age alter_age weighted_n raw_n num_interviews weighted_num_interviews avg_per_ego
[18,25) [0,10) 37.571151 25 187 173.0706 0.2170857
[18,25) [10,18) 79.644291 52 187 173.0706 0.4601839
[18,25) [18,25) 177.234609 154 187 173.0706 1.0240598
[18,25) [25,35) 70.252132 65 187 173.0706 0.4059161
[18,25) [35,45) 81.103765 49 187 173.0706 0.4686167
[18,25) [45,65) 124.850529 95 187 173.0706 0.7213851
[18,25) [65,100] 7.608073 9 187 173.0706 0.0439594
[25,35) [0,10) 116.352177 60 280 280.9542 0.4141321
[25,35) [10,18) 39.125912 29 280 280.9542 0.1392608
[25,35) [18,25) 110.076026 59 280 280.9542 0.3917934
[25,35) [25,35) 249.689108 243 280 280.9542 0.8887181
[25,35) [35,45) 162.626144 105 280 280.9542 0.5788350
[25,35) [45,65) 111.525268 106 280 280.9542 0.3969517
[25,35) [65,100] 28.742127 27 280 280.9542 0.1023018
[35,45) [0,10) 48.322748 59 300 239.5551 0.2017187
[35,45) [10,18) 94.480736 75 300 239.5551 0.3944009
[35,45) [18,25) 48.123276 37 300 239.5551 0.2008861
[35,45) [25,35) 125.429671 117 300 239.5551 0.5235943
[35,45) [35,45) 266.647299 303 300 239.5551 1.1130939
[35,45) [45,65) 91.532191 92 300 239.5551 0.3820925
[35,45) [65,100] 56.069798 54 300 239.5551 0.2340581
[45,65) [0,10) 55.231023 32 456 452.9296 0.1219417
[45,65) [10,18) 118.638288 91 456 452.9296 0.2619354
[45,65) [18,25) 104.562650 81 456 452.9296 0.2308585
[45,65) [25,35) 142.683316 122 456 452.9296 0.3150232
[45,65) [35,45) 181.844757 156 456 452.9296 0.4014857
[45,65) [45,65) 412.000957 359 456 452.9296 0.9096357
[45,65) [65,100] 127.382691 130 456 452.9296 0.2812417
[65,100] [0,10) 25.721707 12 214 290.4905 0.0885458
[65,100] [10,18) 25.964457 10 214 290.4905 0.0893814
[65,100] [18,25) 28.981090 21 214 290.4905 0.0997660
[65,100] [25,35) 53.956978 44 214 290.4905 0.1857444
[65,100] [35,45) 71.536034 59 214 290.4905 0.2462595
[65,100] [45,65) 153.391066 83 214 290.4905 0.5280416
[65,100] [65,100] 244.200985 148 214 290.4905 0.8406505

Non-household mixing estimates

ego_age alter_age weighted_n raw_n num_interviews weighted_num_interviews ego_acs_N alter_acs_N unadj_avg_per_ego other_unadj_avg_per_ego sym_avg_per_ego
[18,25) [0,10) 3.953890 8 187 173.0706 30.64812 NA 0.0228455 NA 0.0228455
[18,25) [10,18) 13.199912 12 187 173.0706 30.64812 NA 0.0762690 NA 0.0762690
[18,25) [18,25) 77.487869 61 187 173.0706 30.64812 30.64812 0.4477241 0.4477241 0.4477241
[18,25) [25,35) 38.211344 29 187 173.0706 30.64812 45.27702 0.2207848 0.2016708 0.2593583
[18,25) [35,45) 13.229269 13 187 173.0706 30.64812 41.68729 0.0764386 0.0986204 0.1052906
[18,25) [45,65) 7.913155 11 187 173.0706 30.64812 83.87452 0.0457221 0.0875480 0.1426571
[18,25) [65,100] 5.140600 4 187 173.0706 30.64812 52.40755 0.0297023 0.0218956 0.0335717
[25,35) [0,10) 15.203660 4 280 280.9542 45.27702 NA 0.0541144 NA 0.0541144
[25,35) [10,18) 4.637067 4 280 280.9542 45.27702 NA 0.0165047 NA 0.0165047
[25,35) [18,25) 56.660269 25 280 280.9542 45.27702 30.64812 0.2016708 0.2207848 0.1755603
[25,35) [25,35) 88.792135 96 280 280.9542 45.27702 45.27702 0.3160377 0.3160377 0.3160377
[25,35) [35,45) 48.302908 43 280 280.9542 45.27702 41.68729 0.1719245 0.2643685 0.2076664
[25,35) [45,65) 36.261738 41 280 280.9542 45.27702 83.87452 0.1290664 0.1935985 0.2438514
[25,35) [65,100] 11.226470 13 280 280.9542 45.27702 52.40755 0.0399584 0.1393813 0.1006452
[35,45) [0,10) 1.552569 3 300 239.5551 41.68729 NA 0.0064811 NA 0.0064811
[35,45) [10,18) 3.877984 4 300 239.5551 41.68729 NA 0.0161883 NA 0.0161883
[35,45) [18,25) 23.625022 21 300 239.5551 41.68729 30.64812 0.0986204 0.0764386 0.0774087
[35,45) [25,35) 63.330806 55 300 239.5551 41.68729 45.27702 0.2643685 0.1719245 0.2255487
[35,45) [35,45) 81.160015 83 300 239.5551 41.68729 41.68729 0.3387948 0.3387948 0.3387948
[35,45) [45,65) 50.934190 50 300 239.5551 41.68729 83.87452 0.2126200 0.2222265 0.3298690
[35,45) [65,100] 18.719575 20 300 239.5551 41.68729 52.40755 0.0781431 0.1240205 0.1170283
[45,65) [0,10) 4.821513 5 456 452.9296 83.87452 NA 0.0106452 NA 0.0106452
[45,65) [10,18) 9.414275 5 456 452.9296 83.87452 NA 0.0207853 NA 0.0207853
[45,65) [18,25) 39.653093 22 456 452.9296 83.87452 30.64812 0.0875480 0.0457221 0.0521275
[45,65) [25,35) 87.686513 73 456 452.9296 83.87452 45.27702 0.1935985 0.1290664 0.1316355
[45,65) [35,45) 100.652942 91 456 452.9296 83.87452 41.68729 0.2222265 0.2126200 0.1639514
[45,65) [45,65) 142.549816 128 456 452.9296 83.87452 83.87452 0.3147284 0.3147284 0.3147284
[45,65) [65,100] 54.865798 59 456 452.9296 83.87452 52.40755 0.1211354 0.2972861 0.1534447
[65,100] [0,10) 20.758515 8 214 290.4905 52.40755 NA 0.0714602 NA 0.0714602
[65,100] [10,18) 17.492146 7 214 290.4905 52.40755 NA 0.0602159 NA 0.0602159
[65,100] [18,25) 6.360470 9 214 290.4905 52.40755 30.64812 0.0218956 0.0297023 0.0196328
[65,100] [25,35) 40.488956 32 214 290.4905 52.40755 45.27702 0.1393813 0.0399584 0.0869515
[65,100] [35,45) 36.026778 36 214 290.4905 52.40755 41.68729 0.1240205 0.0781431 0.0930895
[65,100] [45,65) 86.358775 46 214 290.4905 52.40755 83.87452 0.2972861 0.1211354 0.2455773
[65,100] [65,100] 74.663290 49 214 290.4905 52.40755 52.40755 0.2570249 0.2570249 0.2570249