key: cord-0967011-bw99a9zl
authors: Zachreson, C.; Martino, E.; Tomko, M.; Shearer, F. M.; Bentley, R.; Geard, N.
title: Mapping internet activity in Australian cities during COVID-19 lockdown: how occupational factors drive inequality
date: 2021-02-08
journal: nan
DOI: 10.1101/2021.02.04.21251171
sha: ab2b4d4366ffb30caab042adcce0bee645ffb57b
doc_id: 967011
cord_uid: bw99a9zl

During the COVID-19 pandemic, evidence has accumulated that movement restrictions enacted to combat virus spread produce disparate consequences along socioeconomic lines. One explanation for this differential impact is the distribution of people into occupations that can be performed from home and may provide greater financial security. However, little is known about the nature and scale of shifting home-based work patterns and their geographic distribution, despite this being a likely determinant of the success of geographically defined COVID-19 lockdown strategies. We investigate the hypothesis that people engaged in financially secure employment are better able to adapt to mobility restrictions, due to common occupational factors. In the context of two Australian urban centres, we test this hypothesis by analysing changes to home-based internet usage and quantify the relationship between area-level measures of population income security, the ability to perform job requirements remotely, and the degree of transition to home-based work during COVID-19. Our analysis confirms that financial security in Australia is geographically clustered and concentrated. Income security is also correlated with increased internet traffic during work hours under lockdown, and home-based work patterns that persist post-lockdown. Our findings suggest that geographic diversity in preparedness for government-imposed restrictions should be factored into response planning and provision of social and economic support for residents within lockdown areas.

gaging in written communication, and replacing face-to-face interactions with online streaming activity, work-related tasks are likely to result in measurable increases to home internet traffic.

Population-level data on home internet usage may therefore provide a useful complement to the widely available mobility data typically used to monitor and model the real-time effects of COVID-19 [16, 17, [19] [20] [21] [22] [23] [24] [25] [26] [27] . While mobility data can tell us who is staying home and where people are going when they leave the home, internet data provides critically important information on what is happening within households, particularly in relation to adapting their work arrangements to COVID-19 lockdown requirements.

Here, we demonstrate how relationships between occupational factors and home internet traffic can provide unique insights into the disparities amplified by the pandemic and associated non-pharmaceutical intervention policies. Our results support the hypothesis that occupational factors link the ability to work from home with income security, and clearly show how this link produces strong positive correlations between income security and increases to home internet activity during COVID-19 restrictions. Our results in the Australian context may help explain the observations of other recent studies describing the connection between income, internet, and the ability to self-isolate during COVID-19 [17, 21] . Overall, the results we present contribute to the detailed picture that is currently forming of the impacts of COVID-19 on human behaviour, and will help policy makers to understand the balance between public health and social impact in making future decisions. Furthermore, results such as those presented in this work will contribute to the ability to produce precise, integrated models of epidemic dynamics connected to social and economic phenomena.

We examine the relationships between income security, the ability to work from home, and changes to home internet use volumes during the COVID-19 pandemic in Australia. We utilise a population-scale data set describing home internet use patterns aggregated on the scale of Statistical Area Level 2 (SA2) released by nbn co ltd. (nbn TM ), a Government Business Enterprise providing national, wholesale broadband access in Australia. The SA2 regions are defined by the Australian Bureau of Statistics (ABS), and typically contain between our individual-level interpretation of observed population-scale trends.

The data provided by nbn TM consist of upload and download volume (in bytes) by individual households over 30 minute intervals, spatially aggregated into SA2 regions. Different types of internet usage (such as streaming movies, videoconferencing and online gaming) are associated with different patterns of upload and download volume. The high time resolution and structure of the data allowed us to approximately differentiate between background activity, recreational activity, and work-related activity.

To reveal spatial variations in residents' ability to adapt their working arrangements to home-based activities, we investigate the relationship between internet activity and the characteristics of local occupation distributions. We define several measures to translate local distributions of occupation types into meaningful quantifiers of two properties: a) income security, and b) the ability to work from home. We then assess the relationship between these quantifiers and putative work-related internet activity.

We focus on the urban areas of Sydney and Melbourne during a pre-COVID period (which we use as a baseline), during the first pandemic wave (with Australia-wide transmission and restrictions), and during the second wave (with substantial transmission and restrictions in Melbourne only). We identify positive correlations between income security and changes to internet activity during COVID-19. These correlations are consistent with the assertion that higher income security is associated with more people working from home during lockdown.

This assertion is further supported by individual-level data from the CARE survey. We observe that in Sydney this trend persists after the release of lockdown restrictions, indicating the possibility of a "new normal" of remote working conditions, particularly for occupations associated with higher income security. In Melbourne, we find that the role of children conducting their studies online disrupts these correlations due to an inverse relationship between income security and the proportion of families with children.

To quantify the salient features of the complex distributions of employment characteristics in Sydney and Melbourne, we constructed an income security index using data on employ- is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint After computing income security values for each occupation, we used occupation distributions from the ABS National Census of 2016 to compute the weighted average income security for each region on the scale of SA2 to match the spatial granularity of the internet traffic data used in this study. The result is a geospatial distribution of average income security for Sydney and Melbourne, as estimated from independent distributions of income security by occupation type, and occupation by region.

Income security is distributed spatially according to distinct patterns, with high values in the central and northeast suburbs of both Sydney and Melbourne (Figure 1a and 1b) .

The upper 50% income security quantile (Figure 1c ) favours managerial and office-based occupations, while the lower 50% quantile (Figure 1d ) contains more service staff and other socially-oriented occupations. The distributions of average income security among SA2s in Sydney and Melbourne (respectively) are provided in the Supporting Information Figure S4 , which demonstrates that the distributions in the two regions are not significantly different (two sample t-test, p = 0.552).

To examine the qualitative association between income security and the ability to work from home indicated by the distributions in Figures 1c and 1d , we apply the occupation classification method developed by Dingel and Neiman [32] . This results in a binary (0 or 1) value indicating whether or not a particular occupation type can be performed from home. We found a strong association between income security and the ability to work from home ( Figure   2 ). This association was observed both by occupation ( is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted February 8, 2021. ; https://doi.org/10.1101/2021.02.04.21251171 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint 

To quantify changes to home internet use during COVID-19 restrictions, we aggregated internet activity data reported for the SA2 regions within Greater Sydney and Greater Melbourne. For the pre-COVID baseline, we chose the period spanning from October through November, 2019. Over this period, we averaged the per-user upload and download rates from the hours of 9am to 12pm in order to capture putative work-related activity (see Methods).

During the first and second waves of COVID-19 in Australia, peaks in case incidence coincided with the implementation of the most restrictive policies, and were followed by increases in total internet use, which peaked approximately one to three weeks after implementation of the tightest level of restrictions ( Figure 3 ). The grey bands in Figure 3 show the periods over which nbn TM data was averaged in order to quantify changes to internet activity during first and second waves of COVID-19.

We found that during restrictions associated with the first wave, areas with higher income security tended to exhibit larger absolute and relative changes to download volume ( Figure   4a , 4b, and 4c). Examination of absolute download volumes during the baseline and firstwave periods shows that increasing absolute and relative changes to download volume with income security corresponds to a shift from decreasing, to uncorrelated absolute download levels ( Figure 4a ). However, absolute upload volume shows the opposite trend ( Figure 4d ).

For upload activity, baseline rates are uncorrelated with income security, while first-wave rates demonstrate an increasing trend. This produces relative and absolute changes with income security similar to those observed for downloads (Figure 4e and 4f). The strong spatial correspondence between income security and relative increases in internet traffic during the first-wave lockdown is apparent in heat maps of these quantities for Sydney and Melbourne is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted February 8, 2021. Daily case incidence is shown as black dots (the black line is the 7 day average), and dates on which restriction policies were modified are shown as vertical dashed lines for increasing (red) and decreasing (green) restriction levels. The grey bands indicate the dates over which nbn TM data was averaged for our analysis of first-and second-wave changes. See the Supporting Information Figure S1 for an equivalent timeseries presenting average downloads rather than uploads.

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is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint While household internet traffic declines in Sydney during the second wave relative to the first wave, the positive correlation between income security and internet activity relative to baseline remains prominent for both downloads (Figure 6a, 6c) , and uploads (Figure 6d, 6f) . This is despite the absence of formal stay-at-home orders in the Greater Sydney region. The time interval between the first and second waves was long enough to support the assertion that behavioural changes made in response to COVID-19 lockdown policies remain observable 10 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint , while (e) shows absolute changes to upload volume, and (f) shows changes to upload volume relative to baseline. In each subplot, the internet traffic quantifiers are plotted against the income security score for the corresponding SA2 region, and Pearson's correlation coefficients with 95% CI intervals are shown in the legends.

after those policies have been formally relaxed.

Greater Melbourne behaves similarly in both waves with respect to changes in download traffic as a function of income security (compare Figure 4(a, c) to Figure 6(a, b) ). However, changes to upload volumes do not mirror the correlations observed during the first-wave period (compare Figure 4 (d, f) to Figure 6(d, e) ). In fact, there are many areas of Melbourne with high income security that show substantial reductions in upload traffic during the second wave, relative to the first. While our data gives no immediate explanation for this counter-intuitive trend, we speculate that it may be due to alterations in work habits that occurred as the lockdown became protracted. Decreases in upload traffic without corresponding decreases in 11 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint Table S5 ). This suggestion is also consistent with the observation that daytime internet activity increases during school holidays, when children are more likely to be in the home (Figure 7b ).

We hypothesise that schooling in the home had a greater impact on internet volume in general, and upload rates in particular, than working in the home during the second wave of is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted February 8, 2021. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted February 8, 2021. In each subplot, the internet traffic quantifiers are plotted against the income security score for the corresponding SA2 region, and Pearson's correlation coefficients with 95% CI intervals are shown in the legends.

To complement our analysis of spatially-aggregated household-level associations between income security and the ability to work from home, we also analysed individual-level survey data collected by the COVID-19 Attitudes Resilience and Epidemiology (CARE) study. An online cross-sectional survey of 1006 residents of Victoria, Australia. While the CARE study did not collect data on income security per-se, it did record the annual income bracket reported by each respondent.

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is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint One of the survey questions was posed as follows: "Have you personally experienced a change in work environment (working from home) because of COVID-19 and the measures to prevent its spread? (yes or no)". We computed the proportion of respondents who reported income above and below (or within) the median income bracket for the sample (sample median annual income was $AUD 60,000 to 69,999) who answered "yes" to this question. We then performed a two-tailed Fisher's exact test to determine the resulting odds ratio between the two groups, and its statistical significance given the response numbers (see Table I ). The results demonstrate a strong positive relationship between income and switching to work from home, with an odds ratio of 2.15 (95% CI [1.59, 2.92], p = 6.8×10 −7 ) computed for the abovemedian income group, relative to the median-and-below income group. While the income data tabulated by the CARE survey is not an exact representation of the income security score used in our analysis of internet trends (which incorporated contract classification), this result supports the same conclusion: those with higher financial security have more capacity to change their work environments in response to COVID-19 restrictions.

By combining an analysis of occupational factors and distributions with large-scale, highresolution, real-time data on internet activity, we have broadly characterised the impact of COVID-19 restrictions on two major urban centres in Australia, demonstrating three main results:

1. The occupations associated with greater income security are also associated with the ability to work from home.

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The copyright holder for this this version posted February 8, 2021. ;  2. Internet usage increased during periods in which COVID-19 restrictions were in place.

Increases were greatest in regions with high income security, suggesting that they may be caused by people who were able to adapt to working from home.

3. During the second wave in Melbourne, lower-income regions also displayed increased internet usage, potentially driven by increased levels of home schooling.

These findings confirm and elaborate on the general observation that COVID-19 and the associated restrictions on human activity produce diverse distortions to normal life activities, largely determined by occupational and demographic distributions [33] [34] [35] [36] . Due to the nature of the data we analysed, our study has several limitations. With the exception of the CARE survey results, all of the data analysed in this work is aggregated to sub-populations. Therefore, a direct behavioural interpretation of the correlations we report is contingent on the assumption that the variables we investigate are independently distributed within these sub-populations. While there are likely to be exceptions, the spatial aggregation of areas by income security (Figure 1a and 1b) , suggests that the spatial resolution of SA2 regions is sufficient to sample within the boundaries that define salient heterogeneity of the population for the purposes of our study. Another inherent limitation is introduced through the use of household internet data in quantifying behaviour across the income spectrum: home internet connections have financial requirements including usage fees and installation costs that may be prohibitive for those at the low end of the income spectrum. For example, a study in United States recently determined that household internet speeds typically increase with 16 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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The copyright holder for this this version posted February 8, 2021. income, and the combination of both high income and high-speed internet is associated with an enhanced ability to self-isolate during the pandemic [21] . Using Australian data from the 2016 ABS Census, we computed a correlation of ρ = 0.50, 95% CI [0.44, 0.56], associating the fraction of households with an internet connection (as of 2016) with the income security measure computed here (see Supporting Information Table S1 ). Therefore, our use of home internet traffic data to estimate the ability of workers with differing income security to adapt to COVID-19 restrictions may omit the behaviour of the most impacted demographics. As such, the relationship between income security and increased internet traffic is likely to represent a conservative estimate for the effects of occupational factors on behaviour during the crisis.

In conclusion, the COVID-19 pandemic has highlighted occupational disparities in urban Australia. Historically, the spatial concentration of income insecurity has produced inequality in the risk that entire urban districts face during crisis situations, such as pandemics [33, 37] .

So far, the immediate economic impacts of COVID-19 in Australia have been delayed and suppressed by extensive government economic support measures. As these measures are reduced and eliminated, careful practical planning for pandemic preparedness must take into account inequality with respect to resilience. This work demonstrates that such inequalities derive from a confluence of occupational factors and suggests that mechanisms designed to promote equity in resilience to crisis must target these factors. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint 

All processed data necessary for reproducing the figures and results of this paper is available in the GitHub repository located here: https://github.com/cjzachreson/Internet_ Income_and_COVID19_in_Australia. Access to raw data from the HILDA survey, nbn TM , and CARE survey was obtained under restricted access agreements and cannot be made directly available. However, arrangements for access may be made for eligible researchers. All ABS data can be accessed by following the instructions provided here: https://www.abs.

gov.au/websitedbs/D3310114.nsf/home/How+to+Apply+for+Microdata.

All authors contributed to study design, data interpretation, and manuscript preparation.

CZ composed the manuscript and figures, and performed all statistical analysis. MT performed pre-processing of nbn TM data. EM performed pre-processing of HILDA survey data and computed work-from-home classifications for each occupation. FMS provided CARE survey data and assisted in its interpretation. NG, MT, and RB conceived of and initialised the study.

The income security index was constructed using data on employment security and income characteristics linked to the Australian and New Zealand Standard Classification of Occupations (ANZSCO). For income, we used Australian Bureau of Statistics average weekly earnings by occupation [38] . To calculate the employment security associated with an occupation, we used the most recent iteration (2018) of the nationally-representative Household, Income and Labour Dynamics in Australia (HILDA) survey. Occupation security status was developed as an individual categorical variable with two levels: 0-secure employment, 1-insecure employment. An individual was classified as 'secure' if they had a fixed-term or permanent job.

Those on a fixed-term contract were deemed securely employed, because the associated conditions with this type of employment are more similar those on permanent contracts rather than casual employees and they are shown to be more socio-demographically similar to those employed permanently than casual employees [39, 40] . We computed the employment security 18 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted February 8, 2021. ; https://doi.org/10.1101/2021.02.04.21251171 doi: medRxiv preprint score associated with each occupation as the proportion of respondents in each occupation who were securely employed. We then computed the index of income security by occupation as the product of the employment security score and average weekly earnings (rescaled to the sample maximum). Distributions of the resulting income security values and the component measures of income and proportion securely employed are shown in Fig. S3 .

To compute the working from home indicator for each occupation classification, we adapted an analysis done by Dingel and Neiman [32] (available on GitHub, https://github.com/ jdingel/DingelNeiman-workathome) to establish which occupations could potentially be performed from home. Dingel and Neiman used the "Work Context" and "Generalized Work Activities" occupational surveys from the O*NET R Database. Drawing on a series of questions, they classified occupations according to whether they were compatible with working from home. For example, occupations with activities that required a workplace -such as needing to operating machinery, handling specialised items, requiring access to the outdoors -where considered to be unsuitable for working from home (for more information in methodology, please refer to Dingel [32] ). To produce international estimates we linked their binary is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted February 8, 2021. ; https://doi.org/10.1101/2021.02.04.21251171 doi: medRxiv preprint 4-Digit Level, by SA2 (UR).

nbn co ltd. (nbn TM ) is a Government Business Enterprise providing national, wholesale broadband access in Australia. nbn TM provided access to aggregated Australian internet usage volume data from household customers. The data were restricted to the total download and upload volume for 30min intervals, per SA2 region, and the corresponding number of active internet connections per time slot that generated these data. Only data generated by at least 50 domestic connections were included, to avoid privacy concerns and to reduce the impact of aberrant individual household behaviours in regions with insufficient service coverage.

Outlier data points beyond three standard deviations from the corresponding time period mean were removed, to limit the impact of outlier usage data points, usually caused by network management or internal infrastructure configuration changes.

We set the data collection interval of October 10th, 2019 until November 29th, 2019 as the baseline period, when life in Australia was not impacted by school holidays or major, longer public holidays, and preceding the major disturbance produced by the bushfire season of summer 2019-2020.

The period representing behaviour during the 1st wave of restrictions was set to the interval of April 18th -April 24th, 2020, while the period representing the second wave of restrictions was set to the interval of August 8th -August 14th, 2020.

The following upload and download volume characteristics per SA2 region were computed for these three periods (baseline and the two COVID-19 intervals): (1) an overall daytime average volume; (2) the daily average minimum volume relating to the minimum internet usage, between 4:30am and 5:30am; (3) the average volume generated during the daytime period (from school start until noon, 9:00am -12:00pm noon); and (4) the average daily maximum usage (8:30pm -10:00pm). See Figure 7 depicting the daily fluctuation of average internet use for the period of September 2019 -September 2020 in Australia, illustrating the relative changes in internet use in these periods.

Our analysis used data from the CARE study's Victoria-wide survey which aimed to address the overall question: How were Victorians thinking, feeling and behaving in response to the 20 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint FIG. 7. Long-term daily fluctuation of average internet activity per active service, per 30min period, for downloads (a), and uploads (b). The daily minimum and maximum, as well as the daytime plateau of activity are clearly visible. The changes between normal work days (school days), weekends and public holidays are salient.

"second wave" of the COVID-19 epidemic and the associated public health measures.

The survey was self-administered online in English to 1006 Victorian residents aged 18 years and over. The survey was based on research developed and conducted by Imperial College in the UK in mid-March 2020 [41] . Some questions in the Australian survey were modified slightly to reflect local response measures and terminology. Additional questions were added 21 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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The copyright holder for this this version posted February 8, 2021. ; https://doi.org/10.1101/2021.02.04.21251171 doi: medRxiv preprint to the Australian survey to measure social and emotional impacts. Data collection in both the UK and Australia was conducted by the online market research agency YouGov.

We used a structured questionnaire addressing the following three domains: perceptions of risk and consequences of COVID-19 infection; measures taken by individuals to protect themselves and others from COVID-19 infection; and social and emotional impact.

The questionnaire was administered online to members of the YouGov Australia panel of individuals who have agreed to take part in surveys of public opinion (over 120,000 Australian adults). Panellists, selected at random from the base sample, received an email inviting them to take part in a survey, which included a survey link. Once a panel member clicked on the link and logged in, they were directed to the survey most relevant to them available on the platform at the time, according to the sample definition and quotas based on census data. A plain language statement appeared on screen and respondents were required to electronically consent prior to the survey questions appearing. Proportional quota sampling was used to ensure that respondents were demographically representative of the Victorian adult population, with quotas based on age, gender, household income, location (state and metropolitan or regional) and whether a language other than English is spoken at home.

The study was by approved by the University of Melbourne Human Research Ethics Committee (2056694).

[1] International Labour Organisation (ILO). COVID-19 and the World of Work: Country Policy Responses. ILO, Geneva, Switzerland; 2020. https://www.ilo.org/global/topics/ coronavirus/regional-country/country-responses/lang--en/index.htm.

[2] Australian Government, The Treasury. The JobKeeper Payment: Three-month review; 2020.

https://treasury.gov.au/publication/jobkeeper-review. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

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