Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

# Socio-demographic correlates of wildlife consumption during early stages of the COVID-19 pandemic

## Abstract

To inform efforts at preventing future pandemics, we assessed how socio-demographic attributes correlated with wildlife consumption as COVID-19 (coronavirus disease 2019) first spread across Asia. Self-reported wildlife consumption was most strongly related to COVID-19 awareness; those with greater awareness were 11–24% less likely to buy wildlife products. A hypothetical intervention targeting increased awareness, support for wildlife market closures and reduced medical impacts of COVID-19 could halve future wildlife consumption rates across several countries and demographics.

## Main

The global COVID-19 pandemic has killed over four million people around the world and caused trillions of dollars of economic damage, but it did not arise unexpectedly. Indeed, experts had warned of this type of large-scale outbreak in the wake of other recent emerging zoonotic diseases1. While uncertainty remains regarding the specific origin of COVID-192, a key driving force of emerging infectious diseases of zoonotic origin is the trade and consumption of wildlife, in particular of high-risk taxa3, or of species sold in high-risk market conditions4. While the global costs of pandemics such as COVID-19 drastically exceed the benefits of the global wildlife trade5, it has nevertheless proven difficult to address large-scale wildlife consumption at local or regional scales. This is especially true in certain Asian countries where demand for wildlife used in various traditional, cultural and economic contexts is high6, and where attempts to curb illegal trade are sometimes hampered by weak wildlife trade laws, low enforcement rates and/or corruption7.

The global conservation community is debating the best long-term response to COVID-19, in particular on how to reduce wildlife consumption and habitat destruction so that the probability of future pandemic emergence is reduced8,9,10. Regulatory approaches such as the closing of wildlife markets—especially those deemed high-risk—are a popular demand8; however, previous examples have shown that rendering the consumption of certain goods illegal (for example, alcohol, recreational drugs) can drive existing demand underground to black markets11. Closing markets or otherwise restricting access to wildlife in situations where trade is highly localized, and/or where wildlife use is imperative for livelihoods or subsistence, also poses ethical dilemmas and trade-offs that are not easily answered8,12.

A complement to regulatory approaches are demand reduction efforts, which seek to influence consumer preferences so that demand for wildlife is reduced, leading to lower consumption rates. Reducing consumer demand may be a more comprehensive approach to lessening wildlife consumption13, but is beset by many complications, including limited investment in research to understand what drives individuals to consume wildlife14. Non-governmental organizations and academics are increasingly cognizant of the need for a solid research foundation to feed into behaviour change campaigns to reduce demand. Recent studies have made advances in identifying motivations for wildlife purchasing, as well as in developing consumer surveys that can help target specific groups of interest rather than whole populations15,16. The increasing popularity of demand reduction campaigns13,17 can be usefully bolstered by empirical studies that provide evidence-based justification for targeting and messaging strategies18,19, which would ultimately allow these interventions to realize their full potential within a comprehensive ‘One Health’ approach to zoonotic disease regulation20.

To address this empirical aspect of wildlife demand reduction efforts, we surveyed a total of 5,000 respondents among the general public in five countries and territories in Asia (Hong Kong SAR, Japan, Myanmar, Thailand and Vietnam), eliciting their self-reported wildlife consumption patterns, their awareness of and attitudes towards wildlife markets and COVID-19, and a variety of socio-demographic information (Methods). We built Bayesian hierarchical regression models on the basis of respondent socio-demographic attributes for (1) self-reported wildlife consumption in the previous 12 months, (2) change in consumption as a result of COVID-19 and (3) anticipated future wildlife consumption (Methods and Fig. 1a). Wildlife consumption in our case referred specifically to the purchase of terrestrial wild animals or their derived products in open, in-country markets such as ‘wet’ markets (see Supplementary Methods for all questions used in our modelling). We then used insights from these models to develop a simulated behaviour change intervention and assessed the impact this intervention could have on future wildlife consumption.

Our models of recent wildlife-purchasing behaviour and COVID-related changes in wildlife consumption had excellent in-sample goodness-of-fit, with areas under the receiver operating curve, using posterior predictive probability of models, equal to 0.84 and 0.83, respectively21 (Supplementary Fig. 1). The area under the receiver operating curve for the model for future wildlife product purchases was lower at 0.76, but still at a level considered to provide acceptable classification performance21. The model containing all independent variables had the highest predictive power for recent self-reported wildlife consumption, and was statistically indistinguishable from the best reduced-form models for future wildlife consumption and for COVID-related changes in wildlife consumption (Supplementary Table 2). As has been suggested, we therefore retained the model containing all predictor variables for inference and subsequent predictive modelling across all three response variables22.

For all five countries/territories, awareness of COVID-19 was the strongest predictor of whether someone responded positively to any of the three questions regarding self-reported wildlife consumption (that is, current, future and changes as a result of COVID-19; Fig. 1b–d). For all three questions and across all countries/territories, there was strong evidence for negative associations between the highest level of awareness of COVID-19 and the probability of respondents saying they or someone they know would purchase wildlife. There was also strong evidence of a negative association between having some awareness of COVID-19 and the probability of a respondent reporting yes to each consumption question. The exceptions to this were respondents in Vietnam to the question on changes in wildlife consumption as a result of COVID-19 and in Myanmar to the question on the probability of being a future buyer.

Questions related to potential wildlife market closures had variable associations with wildlife consumption. Respondents in Thailand who viewed wildlife market closures as effective against future pandemics were less likely to say they would consume wildlife in the future. In all countries and territories except Myanmar, respondents who thought wildlife closures would be very effective in preventing future pandemics were actually more likely to have reported wildlife purchases among their social circle in the last 12 months. This may be explained by the fact that the people most familiar with these markets and the conditions wildlife are kept in may also be best placed to understand how closing them may protect public health. Those who were very likely to support government closures of wildlife markets were less likely to say they would consume wildlife in the future in all countries except for Vietnam, where those who were extremely worried about a future pandemic were more likely to have increased their wildlife consumption as a result of COVID-19.

We simulated the impacts of a hypothetical intervention package that simultaneously targeted several socio-demographic variables, assessing how future wildlife-purchasing behaviour might change compared to baseline expectations of a population with similar attributes to the one we sampled. The intervention included information provisioning to raise awareness on COVID-19, as well as a hypothetical elimination of medical impacts associated with the pandemic and the achievement of universal support for wildlife market closures. There is strong evidence that this hypothetical intervention would result in substantial reductions in the probability of future buying across simulated populations in Myanmar (mean frequency of future buying reduced from 15.5% to 7.3%) and Japan (mean frequency of future buying reduced from 10% to 4.5%). There was also strong evidence for reductions in future wildlife consumption among specific demographic groups in all countries/territories (Fig. 2 and Supplementary Table 3). For example, exposing simulated individuals aged 21–25 in Thailand to the hypothetical intervention resulted in a reduction in the mean probability of future buying from 24.1% to 13.5% (a nearly 50% reduction). And in Hong Kong SAR, our models suggest that targeting wealthier individuals (those earning >US\$135,000 per year) would reduce the mean probability of future buying in that group from 16% to 7% (Fig. 2).

Our results provide clues on how to best approach potential interventions that focus on the demand side of wildlife consumption in parts of Asia, and are particularly relevant for consumption that occurs in high-risk markets where live and/or freshly butchered wildlife and their derived products may be sold for luxury consumption, medicinal use, ornaments or as pets. They show the importance of identifying target groups and target messages before conducting demand reduction campaigns, as results may vary among demographically distinct groups or in different regions. They also suggest areas for follow-up work that should build on the survey we report here. These include further investigation on the drivers of consumer demand for wildlife in Myanmar, Thailand and Vietnam (where consumption levels were highest), as well as surveys in additional countries of importance (for example, China). The opinion poll results we present could also be usefully complemented with experimental survey techniques that address how to elicit information and trade-offs on sensitive topics such as wildlife consumption23,24,25 as well as the psychosocial motivations that may not surface during a traditional survey. Ultimately, basing potential behaviour-change interventions on the best available data and analytical approaches reduces the chance of unintended negative consequences when making policy decisions on wildlife consumption8, and could greatly increase the effectiveness and efficiency of these campaigns26 within a ‘One Health’ approach to confronting zoonotic disease emergence.

## Methods

We focused our research on countries/territories in Asia (specifically, Hong Kong SAR, Japan, Myanmar, Thailand and Vietnam) because COVID-19 had not spread much outside Asia at the time of data collection and the global effects were predominantly concentrated in East and Southeast Asia. Our five survey countries/territories were chosen because they all have relatively high levels of wildlife trade but also represent very different forms of trade (for example, the pet trade in Japan versus the wild-meat trade in Vietnam). Surveying respondents from markets with these different forms of trade thus allowed an examination of how the full variety of wildlife consumption types may be impacted by perceived disease risk. Budgetary constraints precluded the inclusion of further countries, although we believe those that were surveyed provide a valid snapshot of the main regional issues and patterns. The exception to this may be the exclusion of China, a key global player in the wildlife trade and the possible origin of the COVID-19 virus. Conducting research in China requires an extensive process to obtain permission that was not consistent with the opportunistic nature of our survey, which was mobilized quickly to target opinions from a snapshot view of an (at that time) emerging disease. Given the time-sensitive nature of the research, we were therefore unable to wait for the necessary permissions to include China in this survey.

Our online survey was conducted between March 3–11, 2020 and surveyed 1,000 respondents in each of the five target countries/territories. We designed and translated our questionnaires with local experts to ensure questions were culturally appropriate, understandable and relevant. The survey was a quantitative data collection instrument that comprised 32 questions, lasted on average 8 minutes, and respondents were offered an incentive for participating. Respondents aged 18+ were invited via email from an online panel of over 2.5 million people in the target countries/territories, and could answer on any internet-capable device (for example smartphone, tablet, laptop) at their convenience. Only respondents aged 18 and over were eligible to take the survey, which was entirely voluntary. Any respondents working in advertising, public relations, marketing, market research or media industries were screened out to prevent possible bias. The email invite that was sent to participants did not specify the exact nature of the survey to avoid skewing the participants towards those that believed they know about the topic. Instead, the invite indicated that the questions would be about ‘consumption and shopping habits’. The panel is maintained by Toluna (https://tolunacorporate.com/), an online data collection group focused on providing high-quality market research data to clients in various business and non-business sectors. Toluna builds and maintains large online consumer panels to collect these data while adhering to stringent global and local guidelines for panel management and data quality, and is a member of the European Society for Opinion and Market Research (https://www.esomar.org).

Toluna respects privacy and is committed to protecting personal data. Their privacy policy (https://tolunacorporate.com/legal/privacy-policy/) provides information on how Toluna collects and processes personal data, explains privacy rights and gives an overview of applicable legislation protecting the handling of personal information. Toluna only uses personal data when the law allows the data to be used.

Respondents were asked demographic questions, and quotas based on the most recent census data for each country/territory were used to ensure the final sample profile was nationally representative of age and gender, except in Myanmar where internet access skewed online panel members to a younger male demographic. Specifically, participants were excluded once quotas on age and gender were filled, and again, participants working in advertising/public relations, marketing research or media were excluded from the survey as we believed these jobs could influence responses. Respondents were asked about societal, economic and environmental concerns, their perception of COVID-19 and their attitudes towards wildlife and wildlife consumption (Supplementary Methods). We also excluded respondents who stated that they were unsure whether they or anyone in their social circle had recently purchased wildlife products (n = 421), as well as an additional n = 39 respondents who were unable to answer survey questions that were later included as covariates in our models.

Because of the potentially sensitive nature of wildlife consumption, we asked about past wildlife purchases indirectly, questioning respondents on whether anyone within their social circle, including themselves, had recently purchased wildlife products. Indirect questions can improve answer rates for questions that people may feel uncomfortable about answering honestly27. During the pandemic, respondents may have felt uncomfortable about revealing wildlife purchases, given links between wildlife consumption and COVID-19. Additionally, although most wildlife consumption is legal (with restrictions) in the markets surveyed, some is not, and researchers can be perceived as having interests contrary to that of the respondent. For less-sensitive questions on future wildlife consumption and changes in consumption resulting from COVID-19, we asked respondents for their own response rather than that of their social group.

Previous studies have found a high correlation between an individual’s admission of using a wildlife product and their likelihood of being within a network of individuals who buy such products28, and suggested that this is linked to homophily in social networks, especially in Southeast Asia. The homophily principle states that people’s personal networks are homogeneous with regard to many socio-demographic, behavioural and intrapersonal characteristics29. Research on wildlife consumption in other Southeast Asian contexts suggests that social groups can be a motivator to begin or maintain consumption of wildlife products28,30. Our own previous research supports this, indicating a strong correlation between one’s own tiger and ivory purchases and knowing someone within one’s social circle who has purchased such products. Additionally and recognizing the homophily principle, behaviour change campaigns targeted at social networks rather than individuals per se are likely to achieve better results than non-targeted campaigns. Changing perceptions of acceptability is a key aspect of social marketing and is used in the social mobilization domain of social and behaviour change communications, which has become a popular framework for reducing demand for illegally traded wildlife products31. Influencing people within a wildlife consumer’s social network may therefore have a higher rate of efficacy than attempting to influence the perceptions of individuals who do not know any consumers of wildlife.

We used hierarchical Bayesian regression models to assess relationships between socio-demographic explanators and our three response variables: (1) self-reported recent wildlife consumption, (2) change in wildlife consumption as a result of COVID-19 and (3) anticipated future wildlife consumption. Explanatory variables included 22 non-collinear variables in six categories: basic demographics, awareness and level of worry of COVID-19, COVID-19 personal impacts, support for and effectiveness of wildlife market closures, international travel habits and general attitudes towards global issues (Supplementary Table 1). Aside from household income (measured in US dollars per year), age (midpoint of year categories from the survey question) and education (ordinal, reflecting increasing level of schooling), all other variables were categorical; those with more than two categories were collapsed into dummy variables. Income, age and education were standardized and included to investigate whether a person’s general socio-economic status affects wildlife consumption. General attitudes towards global issues were expected to reflect aspects of respondents’ political tendencies, while travel habits were included to test the hypothesis that those who travel internationally more habitually are, and will be, more frequent consumers of wildlife. Questions regarding awareness and impacts of COVID-19, and concern about future disease epidemics, were asked to determine how the pandemic may be shaping wildlife consumption. Finally, support and perceived effectiveness of wildlife market closures were included as predictor variables since this measure has been suggested as a strong policy lever to reduce wildlife consumption.

The general structure of all three models was as follows:

$$y_{ij}\sim {{{\mathrm{Bernoulli}}}}\left( {\theta _{ij}} \right)$$
(1)
$${\mathrm{logit}}\left( \theta \right) = \alpha + {{u}_1} + {\beta} {\mathbf{X}} + {{u}_2}{\mathbf{Z}}$$
(2)

This model allowed both coefficients and intercepts to vary across countries (that is, a ‘random-slope random-intercept’ model). In equation (1), yij is whether or not individual i in country j reported wildlife consumption, modelled as a Bernoulli trial with probability θij. The logit transformation of θ (equation 2) is a linear function of parameters α and u1 (the fixed intercept term and a vector of the country-specific intercept terms, respectively), as well as a vector of fixed regression coefficients β and a vector of country-specific regression coefficients u2, with X and Z being the corresponding design matrices32. For α and β, we used an improper flat prior over the real numbers, while the group level parameters u1 and u2 were assumed to arise from a multivariate normal distribution with mean 0 and unknown covariance matrix. The covariance matrix was parameterized by a correlation matrix having a Lewandowski–Kurowicka–Joe prior, and a standard deviation with half-Student t prior with three degrees of freedom32.

For the three dependent variables, we evaluated the predictive power of a model containing all 22 variables, as well as six subset models, using Watanabe–Akaike Information Criterion and leave-one-out cross-validation33. Each of these six subset models contained all explanatory variables except for those within one of the six categories described above (for example, all explanatory variables except those relating to international travel habits, all explanatory variables except those relating to support for wildlife market closures). We used this model-comparison approach to test whether any of these categories of explanatory variable were more or less important in explaining wildlife consumption; if particular categories of variable are stronger predictors of wildlife consumption, this could help inform where future conservation interventions should focus on. Watanabe–Akaike Information Criterion and leave-one-out cross-validation are both measures of model predictive accuracy (both use log predictive density as the utility function or comparison metric) and have been suggested as useful metrics for Bayesian model selection33. We interpreted variable coefficients whose 95% Bayesian credible intervals did not contain 0 as providing strong evidence for the impact of that variable on the outcome in each of the three models for self-reported wildlife consumption (that is, recent, future and changes due to COVID-19). Models were estimated using the R statistical computing software34, in particular the package brms32, with four chains of 1,000 iterations each, a 500-iteration warm-up period, and with successful convergence verified by confirming that R-hat statistical values were less than or equal to 1.01 (ref. 22).

We used the Bayesian hierarchical model of anticipated future wildlife consumption and generated predicted probabilities of future consumption for our sample population (Fig. 2, grey bars). We then predicted future consumption probabilities for a hypothetical behaviour-change intervention (Fig. 2, coloured bars). This intervention was simulated by setting the ‘medical impact’ variable to zero for all individuals, and by assigning all individuals into the ‘aware lots’ and ‘support very likely’ categories for questions related to level of awareness of COVID-19 and level of support for government closure of domestic wildlife markets, respectively. All other variables for individuals were held at the levels recorded in the surveys. We considered the difference between these two predicted probabilities as the impact of the hypothetical behaviour-change intervention, which we examined at the level of the country/territory and within education, age, income and gender demographic classes. Strong evidence for the effectiveness of this hypothetical intervention among countries and demographic classes was suggested where Bayesian credible intervals around the mean predicted difference were less than zero (Supplementary Table 3).

### Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

## Data availability

The data analysed in this study are available via the Open Science Framework at https://osf.io/z8kbd/.

## References

1. Morse, S. S. et al. Prediction and prevention of the next pandemic zoonosis. Lancet 380, 1956–1965 (2012).

2. Mallapaty, S. What’s next in the search for COVID’s origins. Nature 592, 337–338 (2021).

3. Gibb, R. et al. Zoonotic host diversity increases in human-dominated ecosystems. Nature 584, 398–402 (2020).

4. Aguirre, A. A., Catherina, R., Frye, H. & Shelley, L. Illicit wildlife trade, wet markets, and COVID-19: preventing future pandemics. World Med. Health Policy 12, 256–265 (2020).

5. Dobson, A. P. et al. Ecology and economics for pandemic prevention. Science 369, 379–381 (2020).

6. Nijman, V. An overview of international wildlife trade from Southeast Asia. Biodivers. Conserv. 19, 1101–1114 (2010).

7. Wyatt, T., Johnson, K., Hunter, L., George, R. & Gunter, R. Corruption and wildlife trafficking: three case studies involving Asia. Asian J. Criminol. 13, 35–55 (2018).

8. Roe, D. et al. Beyond banning wildlife trade: COVID-19, conservation and development. World Dev. 136, 105121 (2020).

9. Lindsey, P. et al. Conserving Africa’s wildlife and wildlands through the COVID-19 crisis and beyond. Nat. Ecol. Evol. 4, 1300–1310 (2020).

10. Hockings, M. et al. Covid-19 and protected and conserved areas. Parks 26, 7–24 (2020).

11. Miron, J. A. & Zwiebel, J. The economic case against drug prohibition. J. Econ. Perspect. 9, 175–192 (1995).

12. Biggs, D. et al. Breaking the deadlock on ivory. Science 358, 1378–1381 (2017).

13. Sas-Rolfes, M. ‘t, Challender, D. W. S., Hinsley, A., Veríssimo, D. & Milner-Gulland, E. J. Illegal wildlife trade: scale, processes, and governance. Annu. Rev. Environ. Resour. 44, 201–228 (2019).

14. Wilkie, D. S. et al. Eating and conserving bushmeat in Africa. Afr. J. Ecol. 54, 402–414 (2016).

15. Bergin, D., Wu, D. & Meijer, W. Response to “The imaginary ‘Asian Super Consumer’: A critique of demand reduction campaigns for the illegal wildlife trade”. Geoforum 107, 216–219 (2020).

16. Thomas-Walters, L. et al. Motivations for the use and consumption of wildlife products. Conserv. Biol. 35, 483–491 (2021).

17. Greenfield, S. & Verissimo, D. To what extent is social marketing used in demand reduction campaigns for illegal wildlife products? Insights from elephant ivory and rhino horn. Soc. Mar. Q. 25, 40–54 (2019).

18. Veríssimo, D. & Wan, A. K. Y. Characterizing efforts to reduce consumer demand for wildlife products. Conserv. Biol. 33, 623–633 (2019).

19. Olmedo, A., Sharif, V. & Milner-Gulland, E. J. Evaluating the design of behavior change interventions: a case study of rhino horn in Vietnam. Conserv. Lett. 11, e12365 (2018).

20. Cunningham, A. A., Daszak, P. & Wood, J. L. N. One health, emerging infectious diseases and wildlife: two decades of progress? Philos. Trans. R. Soc. B Biol. Sci. 372, 20160167 (2017).

21. Hosmer, D. W., Lemeshow, S. & Sturdivant, R. X. Applied Logistic Regression 3rd edn (John Wiley & Sons, 2013).

22. Gelman, A. et al. Bayesian Data Analysis 3rd edn (CRC Press, 2013).

23. Nuno, A. & St John, F. A. V. How to ask sensitive questions in conservation: a review of specialized questioning techniques. Biol. Conserv. 189, 5–15 (2015).

24. Moro, M. et al. An investigation using the choice experiment method into options for reducing illegal bushmeat hunting in western Serengeti. Conserv. Lett. 6, 37–45 (2013).

25. Nuno, A., Bunnefeld, N., Naiman, L. C. & Milner-Gulland, E. J. A novel approach to assessing the prevalence and drivers of illegal bushmeat hunting in the Serengeti. Conserv. Biol. 27, 1355–1365 (2013).

26. Jacobson, S. K., McDuff, M. D. & Monroe, M. C. Conservation Education and Outreach Techniques (Oxford Univ. Press, 2015).

27. Bergin, D. & Nijman, V. in Evolution, Ecology and Conservation of Lorises and Pottos (eds Nekaris, K. & Burrows, A.) 339–361 (Cambridge Univ. Press, 2020).

28. Davis, E. O., Crudge, B. & Glikman, J. A. The nominative technique: a simple tool for assessing illegal wildlife consumption. Oryx https://doi.org/10.1017/S0030605320000745 (2020).

29. McPherson, M., Smith-Lovin, L. & Cook, J. M. Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001).

30. Davis, E. O. et al. Understanding the prevalence of bear part consumption in Cambodia: a comparison of specialised questioning techniques. PLoS ONE 14, e0211544 (2019).

31. Burgess, G. Monitoring and Evaluating Behaviour Change Amongst Ilegal Wildlife Product Consumers: Good Practice Guidelines for Social and Behavioural Change Communications Practitioners and Communications Professionals (TRAFFIC, 2018).

32. Burkner, P.-C. brms: an R package for Bayesian multilivel models using Stan. J. Stat. Softw. 80, 1–28 (2017).

33. Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).

34. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

## Acknowledgements

We thank A. Nicolas for research support.

## Author information

Authors

### Contributions

J.V. and D.B. conceived the study; D.B. collected the data; R.N. analysed the data; R.N., D.B. and J.V. wrote the paper.

### Corresponding author

Correspondence to Robin Naidoo.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

Peer review information Nature Ecology & Evolution thanks Jarno Vanhatalo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Supplementary information

### Supplementary Information

Supplementary Tables 1–3, Fig. 1 and Methods.

## Rights and permissions

Reprints and Permissions

Naidoo, R., Bergin, D. & Vertefeuille, J. Socio-demographic correlates of wildlife consumption during early stages of the COVID-19 pandemic. Nat Ecol Evol 5, 1361–1366 (2021). https://doi.org/10.1038/s41559-021-01546-5

• Accepted:

• Published:

• Issue Date:

• DOI: https://doi.org/10.1038/s41559-021-01546-5