How did consumers react to the COVID-19 pandemic over time?

Non-pharmaceutical interventions (NPIs) have been the key policy instrument utilized to contain the impact of the COVID-19 pandemic. This paper disentangles the effects of NPIs from that of the virus and looks at the specific channels through which the virus impacts consumption. Using geo-located transaction data, we find that consumers' behaviour towards the virus has explanatory power for the drop in consumption in the early stages of the pandemic. This effect disappears in the later stages of the pandemic, suggesting that consumers have adapted their behaviour. As the COVID-19 pandemic progressed, consumers tended to make 'safer' consumption decisions, by avoiding crowded places.

Factors Associated With Using the COVID-19 Mobile Contact-Tracing App Among Individuals Diagnosed With SARS-CoV-2 in Amsterdam, the Netherlands: Observational Study.

BACKGROUND: Worldwide, efforts are being made to stop the COVID-19 pandemic caused by SARS-CoV-2. Contact tracing and quarantining are key in limiting SARS-CoV-2 transmission. Mathematical models have shown that the time between infection, isolation of cases, and quarantining of contacts are the most important components that determine whether the pandemic can be controlled. Mobile contact-tracing apps could accelerate the tracing and quarantining of contacts, including anonymous contacts. However, real-world observational data on the uptake and determinants of contact-tracing apps are limited. OBJECTIVE: The aim of this paper is to assess the use of a national Dutch contact-tracing app among notified cases diagnosed with SARS-CoV-2 infection and investigate which characteristics are associated with the use of the app. METHODS: Due to privacy regulations, data from the app could not be used. Instead, we used anonymized SARS-CoV-2 routine contact-tracing data collected between October 28, 2020, and February 26, 2021, in the region of Amsterdam, the Netherlands. Complete case logistic regression analysis was performed to identify which factors (age, gender, country of birth, municipality, number of close contacts, and employment in either health care or education) were associated with using the app. Age and number of close contacts were modelled as B-splines due to their nonlinear relationship. RESULTS: Of 29,766 SARS-CoV-2 positive cases, 4824 (16.2%) reported app use. Median age of cases was 41 (IQR 29-55) years, and 46.7% (n=13,898) were male. In multivariable analysis, males (adjusted odds ratio [AOR] 1.11, 95% CI 1.04-1.18) and residents of municipalities surrounding Amsterdam were more likely to use the app (Aalsmeer AOR 1.34, 95% CI 1.13-1.58; Ouder-Amstel AOR 1.96, 95% CI 1.54-2.50), while people born outside the Netherlands, particularly those born in non-Western countries (AOR 0.33, 95% CI 0.30-0.36), were less likely to use the app. Odds of app use increased with age until the age of 58 years and decreased sharply thereafter (P<.001). Odds of app use increased with number of contacts, peaked at 8 contacts, and then decreased (P<.001). Individuals working in day care, home care, and elderly nursing homes were less likely to use the app. CONCLUSIONS: Contact-tracing app use among people with confirmed SARS-CoV-2 infection was low in the region of Amsterdam. This diminishes the potential impact of the app by hampering the ability to warn contacts. Use was particularly low among older people, people born outside the Netherlands, and people with many contacts. Use of the app was also relatively low compared to those from some other European countries, some of which had additional features beyond contact tracing, making them potentially more appealing. For the Dutch contact-tracing app to have an impact, uptake needs to be higher; therefore, investing more into promotional efforts and additional features could be considered.

Self-reported symptoms as predictors of SARS-CoV-2 infection in the general population living in the Amsterdam region, the Netherlands.

INTRODUCTION: Most COVID-19 symptoms are non-specific and also common in other respiratory infections. We aimed to assess which symptoms are most predictive of a positive test for SARS-CoV-2 in symptomatic people of the general population who were tested. METHODS: We used anonymised data of all SARS-CoV-2 test results from the Public Health Service of Amsterdam from June 1,2020 through August 31, 2021. Symptoms were self-reported at time of requesting a test. Multivariable logistic regression models with generalized estimating equations were used to identify predictors of a positive test. Included symptoms were: cough, fever, loss of smell or taste, muscle ache, runny nose, shortness of breath, and throat ache; adjustments were made for age and gender, and stratification by month. RESULTS: Overall, 12.0% of 773,680 tests in 432,213 unique individuals were positive. All symptoms were significantly associated with a positive test result, the strongest positive associations were: cough (aOR = 1.78, 95%CI = 1.75-1.80), fever (aOR = 2.11, 95%CI = 2.07-2.14), loss of smell or taste (aOR = 2.55, 95%CI = 2.50-2.61), and muscle ache (aOR = 2.38, 95%CI = 2.34-2.43). The adjusted odds ratios for loss of smell or taste slightly declined over time, while that for cough increased. CONCLUSION: Cough, fever, loss of smell or taste, and muscle ache appear to be most strongly associated with a positive SARS-CoV-2 test in symptomatic people of the general population who were tested.