Quantos poderiam ter sido salvos? Efeitos do distanciamento social na COVID-19
Conteúdo do artigo principal
Resumo
Qual o efeito das políticas de distanciamento social na disseminação do novo coronavírus? As políticas de distanciamento social ganharam destaque como as mais capazes de conter contágio e salvar vidas. Nosso objetivo neste artigo é identificar o efeito causal das políticas de distanciamento social no número de casos confirmados da COVID-19 e na velocidade de contágio. Alinhamos nosso argumento principal com o consenso científico existente: políticas de distanciamento social afetam negativamente o número de casos de contaminação. Para testar esta hipótese, construímos um banco de dados com informações diárias sobre 78 países afetados no mundo. Calculamos várias medidas relevantes a partir de informações publicamente disponíveis sobre o número de casos de infectados e mortes, a fim de estimar efeitos causais para efeitos em curto prazo e cumulativos de políticas de distanciamento social. Usamos uma abordagem de time-series cross-sectional matching a fim de parear históricos observáveis dos países. Efeitos causais (ATTs e ATEs) podem ser extraídos através de um estimador dif-in-dif. Resultados mostram que as políticas de distanciamento social reduzem o número agregado de pessoas contaminadas em 4.832 em média (ou 17,5/100 mil), mas apenas quando medidas rigorosas são adotadas. Esse efeito parece se manifestar a partir da terceira semana.
Downloads
Detalhes do artigo
A Revista de Administração Pública (RAP) compromete-se a contribuir com a proteção dos direitos intelectuais do autor. Nesse sentido:
- Adota a licença Creative Commoms BY (CC-BY) em todos os textos que publica, exceto quando houver indicação de específicos detentores dos direitos autorais e patrimoniais;
- Adota software de verificação de similaridade de conteúdo - plagiarismo (Crossref Similarity Check);
- Adota ações de combate ao plagio e má conduta ética, alinhada às diretrizes do Committee on Publication Ethics (COPE).
Mais detalhes do Código de Ética adotado pela RAP podem ser visualizados em Normas Éticas e Código de Conduta.
Referências
Ainslie, K. E., Walters, C. E., Fu, H., Bhatia, S., Wang, H., Xi, X. ... Cattarino, L. (2020). Evidence of initial success for China exiting COVID-19 social distancing policy after achieving containment. Wellcome Open Research, 81(5). Retrieved from https://wellcomeopenresearch.org/articles/5-81
Allcott, H., Boxell, L., Conway, J., Gentzkow, M., Thaler, M. … Yang, D. Y. (2020). Polarization and public health: Partisan differences in social distancing during the Coronavirus pandemic (NBER Working Paper, w26946). Cambridge, MA: National Bureau of Economic Research.
Batista, M., & Domingos, A. (2017). Mais que boas intenções: técnicas quantitativas e qualitativas na avaliação de impacto de políticas públicas. Revista Brasileira de Ciências Sociais, 32(94), 1-24.
Boin, A. (2019). The Transboundary Crisis: Why we are unprepared and the road ahead. Journal of Contingencies and Crisis Management, 27(1), 94-99.
Boin, A., & McConnell, A. (2007). Preparing for critical infrastructure breakdowns: the limits of crisis management and the need for resilience. Journal of contingencies and crisis management, 15(1), 50-59.
Chen, H., Cohen, P., & Chen, S. (2010). How big is a big odds ratio? Interpreting the magnitudes of odds ratios in epidemiological studies. Communications in Statistics -simulation and Computation, 39(4), 860-864.
Cole, S., Healy, A., & Werker, E. (2012). Do voters demand responsive governments? Evidence from Indian disaster relief. Journal of Development Economics, 97(2), 167-181.
Fredrickson, B. L., & Kahneman, D. (1993). Duration neglect in retrospective evaluations of affective episodes. Journal of personality and social psychology, 65(1), 45.
Gasper, J. T., & Reeves, A. (2011). Make it rain? Retrospection and the attentive electorate in the context of natural disasters. American Journal of Political Science, 55(2), 340-355.
Grint, K. (2020). Leadership, Management and Command in the time of the Coronavirus. Leadership, 16(3), 314-319.
Hall, P. A. (2016). Politics as a process structured in space and time. In O. Fioretos, J Lynch & Ad Steinhouse (Eds.), The Oxford Handbook of Historical Institutionalism (pp. 31-50). New York, NY: Oxford University Press.
Healy, A., & Malhotra, N. (2009). Myopic voters and natural disaster policy. American Political Science Review, 103(3), 387-406.
Imai, K., Kim, I. S., & Wang, E. (2020, January). Matching Methods for Causal Inference with Time-Series Cross-Sectional Data Cambridge, MA: Harvard University.
Imai, K., Ratkovic, M. (2014). Covariate Balancing Propensity Score. Royal Statistical Society, 76(1), 243-263.
Jacobs, A. M. (2016). Policy making for the long term in advanced democracies. Annual Review of Political Science, 19(1), 433-454.
Kahn, M. E. (2005). The death toll from natural disasters: the role of income, geography, and institutions. Review of economics and statistics, 87(2), 271-284.
King, G., Lucas, C., & Nielsen, R. A. (2017). The balance sample size frontier in matching methods for causal inference. American Journal of Political Science, 61(2), 473-489.
Langer, T., Sarin, R., & Weber, M. (2005). The retrospective evaluation of payment sequences: duration neglect and peak-and-end effects. Journal of Economic Behavior & Organization, 58(1), 157-175.
Liu, W., Kuramoto, S. J., & Stuart, E. A. (2013). An introduction to sensitivity analysis for unobserved confounding in nonexperimental prevention research. Prevention science, 14(6), 570-580.
Maier, Benjamin F., and Dirk Brockmann. “Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China”. Science, 368(6492), 742-746 2020.
Matrajt, L., & Leung, T. (2020). Evaluating the Effectiveness of Social Distancing Interventions to Delay or Flatten the Epidemic Curve of Coronavirus Disease. Emerging Infectious Diseases, 26(8).
Nature. (2020, March 18). The coronavirus pandemic in five powerful charts: From papers published to carbon emissions to confirmed cases, these data reveal an unprecedented viral outbreak and its impacts around the world Retrieved from https://www.nature.com/articles/d41586-020-00758-2
Neumayer, E., Plümper, T., & Barthel, F. (2014). The political economy of natural disaster damage. Global Environmental Change, 24, 8-19.
O’kane, C. (2020, April 06). These countries have started flattening the curve. Here’s how long it took them. CBS News Retrieved from https://www.cbsnews.com/news/flatten-curve-coronavirus-countries/
Pandey, K. R., Subedee, A., Khanal, B., & Koirala, B. (2020). COVID-19 Control Strategies and Intervention Effects in Resource Limited Settings: A Modeling Study. medRxiv Retrieved from https://www.medrxiv.org/content/10.1101/2020.04.26.20079673v2
Pisano, G. P., Sadun, R., & Zanini, M. (2020, March 27). Lessons from Italy’s Response to Coronavirus Retrieved from https://hbr.org/2020/03/lessons-from-italys-response-to-coronavirus
Prem, K., Liu, Y., Russell, T. W., Kucharski, A. J., Eggo, R. M., Davies, N., ... Abbott, S. (2020). The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. The Lancet Public Health Retrieved from https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(20)30073-6/fulltext
Rafael, R. D. M. R., N Neto, M., Carvalho, M. M. B., David, H. M. S. L., Acioli, S. ... Araujo Faria, M. G. (2020). Epidemiologia, políticas públicas e pandemia de Covid-19: o que esperar no Brasil?. Revista Enfermagem UERJ, 28, 49570. Retrieved from https://www.e-publicacoes.uerj.br/index.php/enfermagemuerj/article/view/49570
Taylor, D. B. (2020, May 12). How the Coronavirus Pandemic Unfolded: a Timeline. The New York Times Retrieved from https://www.nytimes.com/article/coronavirus-timeline.html
Tisdall, S. (2020, May 17). Trump, Putin and Bolsonaro have been complacent. Now the pandemic has made them all vulnerable. The Guardian Retrieved from https://www.theguardian.com/commentisfree/2020/may/17/trump-putin-and-bolsonaro-have-been-complacent-now-the-pandemic-has-made-them-all-vulnerable
Trein, P. (2020). The Paradox of Prevention: Authoritarian Past and Liberal Democracy in Times of Crisis. SSRN paper Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3625523
VanderWeele, T. J., & Arah, O. A. (2011). Unmeasured confounding for general outcomes, treatments, and confounders: bias formulas for sensitivity analysis. Epidemiology, 22(1), 42-52.
Zaller, J. R. (1992). The nature and origins of mass opinion Cambridge, UK: Cambridge University Press.