SARS-CoV-2 effective breeding number estimation in Vitória de Santo Antão/PE, Brazil
Keywords:Coronavirus; infectious diseases; pandemic; transmissibility; zoonosis; parameters
COVID-19 is an acute respiratory disease with the SARS-CoV-2 virus as etiological agent, triggering a worldwide pandemic of severe acute respiratory syndrome (SARS) from the year 2019. Effective reproduction number expresses the viral spread potential, being favourable in determining the epidemiological outbreak behaviour; and in obtaining crucial information to identify the disease intensity and which interventions should be conducted. The study objective was to analyze COVID-19 transmissibility, in Vitória de Santo Antão/PE, municipality, Brazil. Therefore, and , indicators were estimated, which reflect the effective number of SARS-CoV-2 infection reproduction among the Vitória de Santo Antão/PE population, within 107 days from the first confirmed case (covering the disease history to date). Results showed that, to date, measures have been sufficient to effectively reduce the epidemic transmissivity. However, even with slower epidemic growth, the population must remain alert and maintain social distance in order to flatten the curve. In addition, estimates can be understood correctly and therefore enable decisions to be made to help more efficiently control pandemic expansion.
Ahmad, T. et al. (2020). COVID-19: Zoonotic aspects. Travel Medicine and Infectious Disease, February, 101607.
Ainslie, K. E. et al. (2020). Evidence of initial success for China exiting COVID-19 social distancing policy after achieving containment. Wellcome Open Research, 5(81).
Biscayart, C. et al. (2020). The next big threat to global health? 2019 novel coronavirus (2019-nCoV): What advice can we give to travellers? – Interim recommendations January 2020, from the Latin-American society for Travel Medicine (SLAMVI). Travel Medicine and Infectious Disease, 33, 101567.
Cao, X. (2020). COVID-19: immunopathology and its implications for therapy. Nature Reviews Immunology, 20(5), 269–270.
Cespedes, M. DA S., & Souza, J. C. R. P. (2020). SARS-CoV-2: A clinical update - II. Revista da Associação Médica Brasileira, 66(4), 547–557.
Cori, A. et al. (2013). A new framework and software to estimate time-varying reproduction numbers during epidemics. American Journal of epidemiology, 178(9), 1505-1512.
Cori, A. (2019). EpiEstim: Estimate Time Varying Reproduction Numbers from Epidemic Curves. R package version 2.2-1. https://CRAN.R-project.org/package=EpiEstim.
Durbin, J., & Koopman, S. J. (2012). Time series analysis by state space methods. 2nd ed. Oxford: Oxford University Press, 346.
Felsenstein, S. et al. (2020). COVID-19: Immunology and treatment options. Clinical Immunology, 215, 108448.
Ganyani, T. et al. (2020). Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Eurosurveillance, 25(17), 2000257.
Gupta, M. et al. (2020). Transmission dynamics of the COVID-19 epidemic in India and modelling optimal lockdown exit strategies. medRxiv.
Heymann, D. L., & Shindo, N. (2020). COVID-19: what is next for public health? The Lancet, 395(10224), 542–545.
Kannan, S. et al. COVID-19 (Novel Coronavirus 2019) – recent trends. European Review for Medical and Pharmacological Sciences, 24(4), 2006–2011.
Kucharski, A. J. et al. (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The lancet infectious diseases, 20(5), 553-558.
Lotka, A. J. (1939). Théorie analytique des associations biologiques. 2. Ed. Hermann, Paris, 149.
MINISTÉRIO DA SAÚDE - BRASIL. (2020). Painel de casos de doença pelo coronavírus 2019 (COVID-19) no Brasil pelo Ministério da Saúde. Secretarias Estaduais de Saúde.
Moirano, G., Schmid, M., & Barone-Adesi, F. (2020). Short-term effects of mitigation measures for the containment of the COVID-19 outbreak: an experience from Northern Italy. Disaster Medicine and Public Health Preparedness, 1-2.
Moon, S. G. et al. (2020). Time variant reproductive number of COVID-19 in Seoul, Korea. Epidemiology and Health, e2020047.
Munoz, C. A. A., Montoya, J. F. A., & Loaiza, A. M. (2016). A Simulation Model with Community Structure for the Dengue Control. Applied Mathematical Sciences, 10(16), 787-794.
Najafi, F. et al. (2020). Serial interval and time-varying reproduction number estimation for COVID-19 in western Iran. New microbes and new infections, 36, 100715.
Nishiura, H., Linton, N. M., Akhmetzhanov, A. R. (2020). Serial interval of novel coronavirus (COVID-19) infections. International journal of infectious diseases, 93, 284-286.
Oliveira, A. C. D., Lucas, T. C., & Iquiapaza, R. (2020). What has the COVID-19 pandemic taught us about adopting preventive measures? Texto & Contexto-Enfermagem, 29.
Prete, C. A. et al. (2020). Serial Interval Distribution of SARS-CoV-2 Infection in Brazil. medRxiv.
Schuchmann, A. Z. et al. (2020). Isolamento social vertical X Isolamento social horizontal: os dilemas sanitários e sociais no enfrentamento da pandemia de COVID-19/Vertical social isolation X Horizontal social isolation: health and social dilemas in copping with the COVID-19 pandemic. Brazilian Journal of Health Review, 3(2), 3556-3576.
Senel, K. et al. (2020). Instantaneous R for COVID-19 in Turkey: Estimation by Bayesian Statistical Inference. Turkiye Klinikleri Journal of Medical Sciences.
Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591-611.
Thompson, R. N. et al. (2019). Improved inference of time-varying reproduction numbers during infectious disease outbreaks. Epidemics, 29, 100356.
Vidale, G., & Senechal, A. Quanto falta para a curva da Covid-19 começar a cair no Brasil. Disponível em: https://veja.abril.com.br/saude/quando-falta-para-a-curva-da-covid-19-comecar-a-cair-no-brasil/. Acesso em: 11 Ago, 2020.
Wallinga, J., & Teunis, P. (2004). Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology, 160(6), 509-516.
WORLD HEALTH ORGANIZATION - WHO. (2020). Clinical management of COVID-19: interim guidance, 01–62, 2020a.
WORLD HEALTH ORGANIZATION - WHO. (2020). WHO Coronavirus Disease (COVID-19) Dashboard. 2020b
Wu, F. et al. (2020). A new coronavirus associated with human respiratory disease in China. Nature, 579(7798), 265–269.
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Copyright (c) 2020 Jucarlos Rufino de Freitas; Denise Stéphanie de Almeida Ferreira; Filipe Mendonça de Lima; Gabriela Isabel Limoeiro Alves Nascimento; Dâmocles Aurélio Nascimento da Silva Alves ; Diego Alves Gomes; André Luiz Pinto dos Santos; João Silva Rocha; Ana Luíza Xavier Cunha; Thaísa Oliveira Folha Piscoya; Renisson Neponuceno de Araújo Filho; Romildo Morant de Holanda; Manoel Vieira de França; Raimundo Mainar de Medeiros; Maria Lindomárcia Leonardo Costa; Victor Casimiro Piscoya; Guilherme Rocha Moreira; Moacyr Cunha Filho
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