Geodesic
COVID-19 basic reproduction number and assessment of initial suppression policies in Costa Rica
Luis Fernando Chaves1 ; Lisbeth A. Hurtado2 ; Melissa Ramírez Rojas1 ; Mariel D. Friberg3 ; Rodrigo Marín Rodríguez1 ; María L. Avila-Aguero4, 5
1 Vigilancia de la Salud, Ministerio de Salud, San José, San José, Costa Rica.
2 Unidad de Análisis Epidemiolόgico y Bioestadística, Instituto Conmemorativo Gorgas de Estudios de la Salud, Ciudad de Panamá, Panamá.
3 Universities Space Research Association, Columbia, MD 21046, USA.
4 Servicio de Infectología, Hospital Nacional de Niños, San José, Costa Rica.
5 Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA.
Mathematical modelling of natural phenomena, Tome 15 (2020), article no. 32.

Voir la notice de l'article provenant de la source EDP Sciences

SARS-COV-2 is the most recent from a series of emerging pathogens stressing national health systems. Initially restricted to Hubei province in China, COVID-19, the disease caused by SARS-COV-2 has become pandemic, reaching almost every nation on our planet. Here, we present an estimate of the Basic Reproduction Number (R0) for this disease based on confirmed cases recorded during the initial 30 days of transmission. Based on local transmission data for the six initial days of transmission, we estimated (mean ± SE) R0 = 2.58 ± 2.43. R0 was reduced by up to 56% to R0 = 1.12 ± 0.02 following suppression measures in place by April 4th, 2020. Independent estimates for the time-varying reproduction number (Rt) based on the serial interval distribution estimated for China showed that after 30 days, Rt decreased reaching a value of 0.914 ± 0.104 on April 4th, 2020. In this study, we also describe the suppression strategies in place in Costa Rica and compare their impacts with those implemented in Panamá and Uruguay, provided these three middle-income countries have similar demographic and economic indicators. However, these three countries have structurally different health systems and have resorted to different suppression measures against COVID-19. We compare the early epidemic growth curves in the three countries using an exponential deceleration model. We found the lowest epidemic growth rate in Costa Rica, followed by Panamá and then Uruguay, while the highest deceleration was observed in Uruguay, followed by Costa Rica and Panamá. We discuss how the unified, universal healthcare system of Costa Rica has been vital to successfully manage the early stage of the COVID-19 epidemic and call for the developments of precision public health tools to further improve epidemic health surveillance in Costa Rica.
DOI : 10.1051/mmnp/2020019

Luis Fernando Chaves 1 ; Lisbeth A. Hurtado 2 ; Melissa Ramírez Rojas 1 ; Mariel D. Friberg 3 ; Rodrigo Marín Rodríguez 1 ; María L. Avila-Aguero 4, 5

1 Vigilancia de la Salud, Ministerio de Salud, San José, San José, Costa Rica.
2 Unidad de Análisis Epidemiolόgico y Bioestadística, Instituto Conmemorativo Gorgas de Estudios de la Salud, Ciudad de Panamá, Panamá.
3 Universities Space Research Association, Columbia, MD 21046, USA.
4 Servicio de Infectología, Hospital Nacional de Niños, San José, Costa Rica.
5 Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA. 
@article{MMNP_2020_15_a7,
     author = {Luis Fernando Chaves and Lisbeth A. Hurtado and Melissa Ram{\'\i}rez Rojas and Mariel D. Friberg and Rodrigo Mar{\'\i}n Rodr{\'\i}guez and Mar{\'\i}a L. Avila-Aguero},
     title = {COVID-19 basic reproduction number and assessment of initial suppression policies in {Costa} {Rica}},
     journal = {Mathematical modelling of natural phenomena},
     eid = {32},
     publisher = {mathdoc},
     volume = {15},
     year = {2020},
     doi = {10.1051/mmnp/2020019},
     language = {en},
     url = {https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020019/}
}
TY  - JOUR
AU  - Luis Fernando Chaves
AU  - Lisbeth A. Hurtado
AU  - Melissa Ramírez Rojas
AU  - Mariel D. Friberg
AU  - Rodrigo Marín Rodríguez
AU  - María L. Avila-Aguero
TI  - COVID-19 basic reproduction number and assessment of initial suppression policies in Costa Rica
JO  - Mathematical modelling of natural phenomena
PY  - 2020
VL  - 15
PB  - mathdoc
UR  - https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020019/
DO  - 10.1051/mmnp/2020019
LA  - en
ID  - MMNP_2020_15_a7
ER  - 
%0 Journal Article
%A Luis Fernando Chaves
%A Lisbeth A. Hurtado
%A Melissa Ramírez Rojas
%A Mariel D. Friberg
%A Rodrigo Marín Rodríguez
%A María L. Avila-Aguero
%T COVID-19 basic reproduction number and assessment of initial suppression policies in Costa Rica
%J Mathematical modelling of natural phenomena
%D 2020
%V 15
%I mathdoc
%U https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020019/
%R 10.1051/mmnp/2020019
%G en
%F MMNP_2020_15_a7
Luis Fernando Chaves; Lisbeth A. Hurtado; Melissa Ramírez Rojas; Mariel D. Friberg; Rodrigo Marín Rodríguez; María L. Avila-Aguero. COVID-19 basic reproduction number and assessment of initial suppression policies in Costa Rica. Mathematical modelling of natural phenomena, Tome 15 (2020), article  no. 32. doi : 10.1051/mmnp/2020019. https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020019/

[1] R.M. Anderson Populations and infectious diseases: ecology or epidemiology? J. Animal Ecol 1991 1 50

[2] R.M. Anderson and R.N. May, Infectious diseases of humans: dynamics and control. Oxford University Press, Oxford (1991).

[3] R.M. Anderson, H. Heesterbeek, D. Klinkenberg, T.D. Hollingsworth How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet 2020 931 934

[4] K.G. Andersen, A. Rambaut, W.I. Lipkin, E.C. Holmes, R.F. Garry The proximal origin of SARS-CoV-2 Nat. Med 2020 1 3

[5] D. Aran, H. Laca Sistema de salud de Uruguay Salud Pública de México 2011 s265 s274

[6] E.D. Belay, S.S. Monroe Low-incidence, high-consequence pathogens Emerg. Infect. Dis 2014 319 321

[7] O.N. Bjørnstad, Epidemics Models and Data using R. Springer, Cham, Switzerland (2018).

[8] B.M. Bolker, Ecological Models and Data in R. Princeton University Press, Princeton (2008).

[9] R.H. Byrd, P. Lu, P. Nocedal, C. Zhu A limited memory algorithm for bound constrained optimization SIAM J. Sci. Comput 1995 1190 1208

[10] J.-P. Carrera, N. Forrester, E. Wang, A.Y. Vittor, A.D. Haddow, S. Lόpez-Vergès, I. Abadía, E. Castaño, N. Sosa, C. Báez Eastern equine encephalitis in Latin America New Eng. J. Med 2013 732 744

[11] Jf.-W. Chan, S. Yuan, K.-H. Kok, Kk.-W. To, H. Chu, J. Yang, F. Xing, J. Liu, Cc.-Y. Yip, Rw.-S. Poon A familial cluster ofpneumonia associated withthe 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster The Lancet 2020 514 523

[12] L.F. Chaves, M. Ramírez Rojas, M. Prado, J.L. Garcés, D. Salas Peraza, R. Marín Rodríguez Health policy impacts on malaria transmission in Costa Rica Parasitology 2020

[13] P.G. Coleman, B.D. Perry, M.E.J. Woolhouse Endemic stability–a veterinary idea applied to human public health Lancet 2001 1284 1286

[14] COVID-19 Testing Trends – Globally Regionally. Available at: https://www.healthpolicy-watch.org/covid-19-testing-trends-globally-regionally/ (2020).

[15] A.P. Dobson, E.R. Carper Infectious diseases and human population history: throughout history the establishment of disease has been a side effect of the growth of civilization BioScience 1996 115 126

[16] S.F. Dowell, D. Blazes, S. Desmond-Hellmann Four steps to precision public health Nature 2016 189

[17] Z. Du, X. Xu, Y. Wu, L. Wang, B.J. Cowling, L.A. Meyers Serial Interval of COVID-19 among Publicly Reported Confirmed Cases Emerg. Infect. Dis. 2020

[18] N. Ferguson, D. Laydon, G. Nedjati Gilani, N. Imai, K. Ainslie, M. Baguelin, S. Bhatia, A. Boonyasiri, Z. Cucunuba Perez and G. Cuomo-Dannenburg, Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand (2020).

[19] B. Gates Responding to Covid-19 – A Once-in-a-Century Pandemic? N. Eng. J. Med. 2020 1677 1679

[20] J. Hopman, B. Allegranzi, S. Mehtar Managing COVID-19 in Low- and Middle-Income Countries JAMA 2020 1549 1550

[21] K. Honjo, L.F. Chaves, A. Satake, A. Kaneko, N. Minakawa When they don't bite, we smell money: Understanding malaria bednet misuse Parasitology 2013 580 586

[22] F.M. Knaul, A. Bhadelia, H.A. Ornelas, L. De Lima, Md.R. Sáenz Madrigal Closing the pain divide: the quest for effective universal health coverage Lancet Global Health 2015 S35

[23] M.U.G. Kraemer, C.-H. Yang, B. Gutierrez, C.-H. Wu, B. Klein, D.M. Pigott, L. Du Plessis, N.R. Faria, R. Li, W.P. Hanage The effect of human mobility and control measures on the COVID-19 epidemic in China Science 2020 eabb4218

[24] M.E. Kruk, A.D. Gage, N.T. Joseph, G. Danaei, S. García-Saisό, J.A. Salomon. Mortality due to low-quality health systems in the universal health coverage era: a systematic analysis of amenable deaths in 137 countries Lancet 2018 2203 2212

[25] M. Kuhn and K. Johnson, Applied Predictive Modeling. Springer, New York (2013).

[26] R. Marín Rodríguez, L.F. Chaves Parasite Removal for Malaria Elimination in Costa Rica Trends Parasitol 2019 585 588

[27] V.J. Lee, C.J. Chiew, W.X. Khong Interrupting transmission of COVID-19: lessons from containment efforts in Singapore J. Travel Med. 2020

[28] R. Levins, Evolution in Changing Environments. Some theoretical explorations. Princeton University Press, Princeton (1968).

[29] R. Levins Toward an integrated epidemiology Trends Ecol. Evol 1995 304

[30] R. Levins, C. Lopez Toward an ecosocial view of health Int. J. Health Serv 1999 261 293

[31] R. Levins, T. Awerbuch, U. Brinkmann, I. Eckardt, P. Epstein, N. Makhoul, C.A. Depossas, C. Puccia, A. Spielman, M.E. Wilson The emergence of new diseases Am. Sci 1994 52 60

[32] R. Lewontin, R. Levins Let the numbers speak Int. J. Health Serv 2000 873 877

[33] R. Li, S. Pei, B. Chen, Y. Song, T. Zhang, W. Yang, J. Shaman Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2) Science 2020 eabb3221

[34] J.A. Nelder, R. Mead A simplex method for function minimization Comput. J 1965 308 313

[35] H. Nishiura, N.M. Linton, A.R. Akhmetzhanov Serial interval of novel coronavirus (COVID-19) infections Int. J. Infect. Dis 2020 284 286

[36] S. Palmer, From Popular Medicine to Medical Populism: Doctors, Healers, and Public Power in Costa Rica, 1800–1940. Duke University Press Books, Duke (2003).

[37] Panamá va un paso por delante en la respuesta al coronavirus. Available at: https://news.un.org/es/story/2020/03/1471462.

[38] M. Predescu, G. Sirbu, R. Levins, T. Awerbuch-Friedlander On the dynamics of a deterministic and stochastic model for mosquito control Appl. Math. Lett 2007 919 925

[39] R. Pung, C.J. Chiew, B.E. Young, S. Chin, M.I. Chen, H.E. Clapham, A.R. Cook, S. Maurer-Stroh, M. Toh, C. Poh Investigation of three clusters of COVID-19 in Singapore: implications for surveillance and response measures Lancet 2020 1039 1046

[40] L. Rosero-Bixby, W.H. Dow Exploring why Costa Rica outperforms the United States in life expectancy: A tale of two inequality gradients Proc. Natl. Acad. Sci 2016 1130

[41] Md.R. Sáenz, M. Acosta, J. Muiser, J.L. Bermúdez Sistema de salud de Costa Rica Salud Pública de México 2011 s156 s167

[42] P. Sandiford, M. Salvetto Las desigualdades en salud en Panamá Gac. Sanit 2002 70 81

[43] F. Sauma, Análisis de experiencias internacionales sobre sistemas nacionales de salud: el caso de Costa Rica. CEPAL, México (2013), 37.

[44] L. Scrucca GA: A Package for Genetic Algorithms in R J. Stat. Softw 2013 37

[45] H. Tian, Y. Liu, Y. Li, C.-H. Wu, B. Chen, M.U.G. Kraemer, B. Li, J. Cai, B. Xu, Q. Yang An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China Science 2020 eabb6105

[46] R.N. Thompson, J.E. Stockwin, R.D. Van Gaalen, J.A. Polonsky, Z.N. Kamvar, P.A. Demarsh, E. Dahlqwist, S. Li, E. Miguel, T. Jombart Improved inference of time-varying reproduction numbers during infectious disease outbreaks Epidemics 2019 100356

[47] J. Tolle 87.65 Can growth be faster than exponential, and just how slow is the logarithm? Math. Gaz. 2003 522 525

[48] C. Viboud, L. Simonsen, G. Chowell A generalized-growth model to characterize the early ascending phase of infectious disease outbreaks Epidemics 2016 27 37

[49] V. Volpert, M. Banerjee, S. Petrovskii On a quarantine model of coronavirus infection and data analysis Math. Model. Nat. Phenom 2020 24

[50] R.G. Wallace, R. Kock, L. Bergmann, M. Gilbert, L. Hogerwerf, C. Pittiglio, R. Mattioli, R. Wallace Did Neoliberalizing West African Forests Produce a New Niche for Ebola? Int. J. Health Serv. 2016 149 165

[51] R. Wallace, L.F. Chaves, L. Bergmann, Cf.J. Ayres Lopes, L. Hogerwerf, R. Kock and R.G. Wallace, Clear-Cutting Disease Control: Capital-Led Deforestation, Public Health Austerity, and Vector-Borne Infection. Springer, New York (2018).

[52] A. Wilder-Smith, D.O. Freedman Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak J. Travel Med 2020 taaa020

[53] S. Woolhandler, D.U. Himmelstein Intersecting U.S. Epidemics: Covid-19 and lack of health insurance Ann. Intern. Med. https://doi.org/10.7326/M20-1491 2020

[54] Z. Wu, J.M. Mcgoogan Characteristics of and important lessons from the coronavirusdisease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention JAMA 2020 1239 1242

[55] J.T. Wu, K. Leung, M. Bushman, N. Kishore, R. Niehus, P.M. De Salazar, B.J. Cowling, M. Lipsitch, G.M. Leung Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China Nat. Med 2020 506 510

[56] J.T. Wu, K. Leung, G.N. Leung Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study Lancet 2020 689 697

[57] Ministerio de Planificaciόn Nacional y Política Econόmica. Índice de desarrollossocial 2017. San José, CR: MIDEPLAN (2018).

[58] INEC. X Censo Nacional de Poblaciόn y VI de Vivienda 2011: Resultados Generales. San José: Instituto Nacional de Estadísticas y Censos (2012).

Cité par Sources :