Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching model

Articulo original de la revista Scientific Reports Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching model

Autores:
Garcia Balaguera Cesar
Picinini Freitas Lais
Douwes‑Schultz Dirk
Schmidt Alexandra
Avila Monsalve Brayan
Salazar Flores Jorge Emilio
Restrepo Berta
Jaramillo Ramirez Gloria
Carabali Mabel
Zinzer Kate
Tipo de recurso:
Article of journal
Fecha de publicación:
2024
Institución:
Universidad Cooperativa de Colombia
Repositorio:
Repositorio UCC
Idioma:
eng
OAI Identifier:
oai:repository.ucc.edu.co:20.500.12494/56886
Acceso en línea:
https://hdl.handle.net/20.500.12494/56886
https://doi.org/10.1038/s41598-024-59976-7
Palabra clave:
Zika, Transmisión, Modelos
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
id COOPER2_5a7663ed81bc68b9c8a42965eb0bda9a
oai_identifier_str oai:repository.ucc.edu.co:20.500.12494/56886
network_acronym_str COOPER2
network_name_str Repositorio UCC
repository_id_str
dc.title.eng.fl_str_mv Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching model
title Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching model
spellingShingle Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching model
Zika, Transmisión, Modelos
title_short Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching model
title_full Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching model
title_fullStr Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching model
title_full_unstemmed Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching model
title_sort Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching model
dc.creator.fl_str_mv Garcia Balaguera Cesar
Picinini Freitas Lais
Douwes‑Schultz Dirk
Schmidt Alexandra
Avila Monsalve Brayan
Salazar Flores Jorge Emilio
Restrepo Berta
Jaramillo Ramirez Gloria
Carabali Mabel
Zinzer Kate
dc.contributor.author.none.fl_str_mv Garcia Balaguera Cesar
Picinini Freitas Lais
Douwes‑Schultz Dirk
Schmidt Alexandra
Avila Monsalve Brayan
Salazar Flores Jorge Emilio
Restrepo Berta
Jaramillo Ramirez Gloria
Carabali Mabel
Zinzer Kate
dc.contributor.researcher.none.fl_str_mv Garcia Balaguera Cesar
Picinini Freitas Lais
Douwes‑Schultz Dirk
Schmidt Alexandra
Avila Monsalve Brayan
Salazar Flores Jorge Emilio
Restrepo Berta
Jaramillo Ramirez Gloria
Carabali Mabel
Zinzer Kate
dc.contributor.researchgroup.none.fl_str_mv GRIVI
dc.subject.proposal.spa.fl_str_mv Zika, Transmisión, Modelos
topic Zika, Transmisión, Modelos
description Articulo original de la revista Scientific Reports Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching model
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-08-12T18:41:56Z
dc.date.available.none.fl_str_mv 2024-08-12T18:41:56Z
dc.date.issued.none.fl_str_mv 2024-06-12
dc.type.none.fl_str_mv Artículo de revista
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_6501
format http://purl.org/coar/resource_type/c_6501
dc.identifier.citation.none.fl_str_mv Picinini Freitas, L., Douwes-Schultz, D., Schmidt, A.M. et al. Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space-time Markov switching model. Sci Rep 14, 10003 (2024). https://doi.org/10.1038/s41598-024-59976-7
dc.identifier.issn.none.fl_str_mv 20452322
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12494/56886
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1038/s41598-024-59976-7
dc.identifier.eissn.none.fl_str_mv 2045-2322
identifier_str_mv Picinini Freitas, L., Douwes-Schultz, D., Schmidt, A.M. et al. Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space-time Markov switching model. Sci Rep 14, 10003 (2024). https://doi.org/10.1038/s41598-024-59976-7
20452322
2045-2322
url https://hdl.handle.net/20.500.12494/56886
https://doi.org/10.1038/s41598-024-59976-7
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.citationendpage.none.fl_str_mv 13
dc.relation.citationissue.none.fl_str_mv 10003
dc.relation.citationstartpage.none.fl_str_mv 1
dc.relation.citationvolume.none.fl_str_mv 14
dc.relation.ispartofjournal.none.fl_str_mv Scientific Reports
dc.relation.references.none.fl_str_mv Lowe, R. et al. The zika virus epidemic in brazil: From discovery to future implications. Int. J. Environ. Res. Public Health 15, 96. https:// doi. org/ 10. 3390/ ijerp h1501 0096 (2018).
2. Ferreira-de Brito, A. et al. First detection of natural infection of Aedes aegypti with Zika virus in Brazil and throughout South America. Memórias do Instituto Oswaldo Cruz 111, 655–658, https:// doi. org/ 10. 1590/ 0074- 02760 160332 (2016). Publisher: Instituto Oswaldo Cruz, Ministério da Saüde.
3. Lounibos, L. P. Invasions by insect vectors of human disease. Annu. Rev. Entomol. 47, 233–266. https:// doi. org/ 10. 1146/ annur ev. ento. 47. 091201. 145206 (2002).
4. Powell, J. R. & Tabachnick, W. J. History of domestication and spread of aedes aegypti—A review. Mem. Inst. Oswaldo Cruz 108, 11–17. https:// doi. org/ 10. 1590/ 0074- 02761 30395 (2013).
5. Mordecai, E. A. et al. Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. PLoS Negl. Trop. Dis. 11, e0005568. https:// doi. org/ 10. 1371/ journ al. pntd. 00055 68 (2017).
6. Watts, A. G. et al. Elevation as a proxy for mosquito-borne zika virus transmission in the americas. PLoS One 12, e0178211. https:// doi. org/ 10. 1371/ journ al. pone. 01782 11 (2017).
7. Kraemer, M. U. G. et al. Past and future spread of the arbovirus vectors aedes aegypti and aedes albopictus. Nat. Microbiol. 4, 854–863. https:// doi. org/ 10. 1038/ s41564- 019- 0376-y (2019).
8. Winokur, O. C., Main, B. J., Nicholson, J. & Barker, C. M. Impact of temperature on the extrinsic incubation period of Zika virus in Aedes aegypti. PLoS Negl. Trop. Dis. 14, e0008047. https:// doi. org/ 10. 1371/ journ al. pntd. 00080 47 (2020).
9. Tesla, B. et al. Temperature drives Zika virus transmission: Evidence from empirical and mathematical models. Proc. R. Soc. B: Biol. Sci. 285, 20180795. https:// doi. org/ 10. 1098/ rspb. 2018. 0795 (2018).
10. Freitas, L. P., Schmidt, A. M., Cossich, W., Cruz, O. G. & Carvalho, M. S. Spatio-temporal modelling of the first chikungunya epidemic in an intra-urban setting: The role of socioeconomic status, environment and temperature. PLoS Negl. Trop. Dis. 15, e0009537. https:// doi. org/ 10. 1371/ journ al. pntd. 00095 37 (2021).
11. Xu, Z. et al. Spatiotemporal patterns and climatic drivers of severe dengue in Thailand. Sci. Total Environ. 656, 889–901. https:// doi. org/ 10. 1016/j. scito tenv. 2018. 11. 395 (2019).
12. Hu, W., Clements, A., Williams, G., Tong, S. & Mengersen, K. Spatial patterns and socioecological drivers of dengue fever transmission in Queensland, Australia. Environ. Health Perspect. 120, 260–266. https:// doi. org/ 10. 1289/ ehp. 10032 70 (2012).
13. Lowe, R. et al. Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil. Comput. Geosci. 37, 371–381. https:// doi. org/ 10. 1016/j. cageo. 2010. 01. 008 (2011).
14. Lowe, R. et al. Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study. PLoS Med. 15, e1002613. https:// doi. org/ 10. 1371/ journ al. pmed. 10026 13 (2018).
15. Morin, C. W., Comrie, A. C. & Ernst, K. Climate and dengue transmission: Evidence and implications. Environ. Health Perspect. 121, 1264–1272. https:// doi. org/ 10. 1289/ ehp. 13065 56 (2013).
16. de Almeida Costa, E. A. P., de Mendonça Santos, E. M., Correia, J. C. & de Albuquerque, C. M. R. Impact of small variations in temperature and humidity on the reproductive activity and survival of Aedes aegypti (Diptera, Culicidae). Revista Brasileira de Entomologia 54, 488–493, https:// doi. org/ 10. 1590/ s0085- 56262 01000 03000 21 (2010).
17. Carabali, M. et al. Spatiotemporal distribution and socioeconomic disparities of dengue, chikungunya and Zika in two Latin American cities from 2007 to 2017. Trop. Med. Int. Health 26, 301–315. https:// doi. org/ 10. 1111/ tmi. 13530 (2020).
18. Power, G. M. et al. Socioeconomic risk markers of arthropod-borne virus (arbovirus) infections: A systematic literature review and meta-analysis. BMJ Glob. Health 7, e007735. https:// doi. org/ 10. 1136/ bmjgh- 2021- 007735 (2022).
19. Carrasquilla, M. C. et al. Entomological characterization of aedes mosquitoes and arbovirus detection in ibagué, a colombian city with co-circulation of zika, dengue and chikungunya viruses. Parasites Vect. 14, 1. https:// doi. org/ 10. 1186/ s13071- 021- 04908-x (2021).
20. Romeo-Aznar, V., Picinini Freitas, L., Gonçalves Cruz, O., King, A. A. & Pascual, M. Fine-scale heterogeneity in population density predicts wave dynamics in dengue epidemics. Nat. Commun. 13, 1. https:// doi. org/ 10. 1038/ s41467- 022- 28231-w (2022).
21. Barcellos, C. & Lowe, R. Expansion of the dengue transmission area in Brazil: the role of climate and cities. Trop. Med. Int. Health 19, 159–168. https:// doi. org/ 10. 1111/ tmi. 12227 (2013).
22. Villar, L. A., Rojas, D. P., Besada-Lombana, S. & Sarti, E. Epidemiological trends of dengue disease in Colombia (2000–2011): A systematic review. PLoS Negl. Trop. Dis. 9, e0003499. https:// doi. org/ 10. 1371/ journ al. pntd. 00034 99 (2015).
23. Freitas, L. P. et al. Spatio-temporal clusters and patterns of spread of dengue, chikungunya, and Zika in Colombia. PLoS Negl. Trop. Dis. 16, e0010334. https:// doi. org/ 10. 1371/ journ al. pntd. 00103 34 (2022).
24. Pérez, N. T. Protocolo de vigilancia en salud püblica—Enfermedad por Virus Zika (2017).
25. Ospina, J. et al. Stratifying the potential local transmission of Zika in municipalities of Antioquia, Colombia. Trop. Med. Int. Health 22, 1249–1265. https:// doi. org/ 10. 1111/ tmi. 12924 (2017).
26. Shragai, T. et al. Distance to public transit predicts spatial distribution of dengue virus incidence in Medellín, Colombia. Scientific Reports 12, 1. https:// doi. org/ 10. 1038/ s41598- 022- 12115-6 (2022).
27. Carabali, M., Schmidt, A. M., Restrepo, B. N. & Kaufman, J. S. A joint spatial marked point process model for dengue and severe dengue in Medellin, Colombia. Spat. Spatio-temporal Epidemiol. 41, 100495. https:// doi. org/ 10. 1016/j. sste. 2022. 100495 (2022).
28. Carabali, M., Maheu-Giroux, M. & Kaufman, J. S. Dengue, severity paradox, and socioeconomic distribution among Afro-Colombians. Epidemiology 32, 541–550. https:// doi. org/ 10. 1097/ ede. 00000 00000 001353 (2021).
29. Adin, A., Martínez-Bello, D. A., López-Quílez, A. & Ugarte, M. D. Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia. PLoS One 13, e0203382. https:// doi. org/ 10. 1371/ journ al. pone. 02033 82 (2018).
30. Martínez-Bello, D. A., López-Quílez, A. & Torres-Prieto, A. Bayesian dynamic modeling of time series of dengue disease case counts. PLoS Negl. Trop. Dis. 11, e0005696. https:// doi. org/ 10. 1371/ journ al. pntd. 00056 96 (2017).
31. Martínez-Bello, D. A., López-Quílez, A. & Prieto, A. T. Relative risk estimation of dengue disease at small spatial scale. Int. J. Health Geogr. 16, https:// doi. org/ 10. 1186/ s12942- 017- 0104-x (2017).
32. Martínez-Bello, D. A., López-Quílez, A. & Prieto, A. T. Joint estimation of relative risk for dengue and Zika infections, Colombia, 2015–2016. Emerg. Infect. Dis. 25, 1118–1126. https:// doi. org/ 10. 3201/ eid25 06. 180392 (2019).
33. Delmelle, E., Hagenlocher, M., Kienberger, S. & Casas, I. A spatial model of socioeconomic and environmental determinants of dengue fever in Cali, Colombia. Acta Tropica 164, 169–176. https:// doi. org/ 10. 1016/j. actat ropica. 2016. 08. 028 (2016).
34. Chien, L.-C., Sy, F. & Pérez, A. Identifying high risk areas of Zika virus infection by meteorological factors in Colombia. BMC Infect. Dis. 19, 1. https:// doi. org/ 10. 1186/ s12879- 019- 4499-9 (2019).
35. Chien, L.-C., Lin, R.-T., Liao, Y., Sy, F. S. & Pérez, A. Surveillance on the endemic of Zika virus infection by meteorological factors in Colombia: a population-based spatial and temporal study. BMC Infect. Dis. 18, 1. https:// doi. org/ 10. 1186/ s12879- 018- 3085-x (2018).
36. Flórez-Lozano, K. et al. Spatial distribution of the relative risk of Zika virus disease in Colombia during the 2015–2016 epidemic from a Bayesian approach. Int. J. Gynecol. Obstet. 148, 55–60. https:// doi. org/ 10. 1002/ ijgo. 13048 (2020).
37. Triana-Vidal, L. E., Morales-García, M. A., Arango-Cárdenas, M. J., Badiel-Ocampo, M. & Cuartas, D. E. Análisis de la distribución espacial y temporal de los virus del Dengue (2006-2017), Zika (2015- 2017) y Chikungunya (2014-2017) en Colombia. Infectio 23, 352. https:// doi. org/ 10. 22354/ in. v23i4. 810 (2019).
38. Desjardins, M., Whiteman, A., Casas, I. & Delmelle, E. Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016. Acta Trop. 185, 77–85. https:// doi. org/ 10. 1016/j. actat ropica. 2018. 04. 023 (2018).
39. Arab, A. Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros. Int. J. Environ. Res. Public Health 12, 10536–10548, https:// doi. org/ 10. 3390/ ijerp h1209 10536 (2015). Number: 9 Publisher: Multidisciplinary Digital Publishing Institute.
40. Lambert, D. Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics 34, 1–14. https:// doi. org/ 10. 2307/ 12695 47 (1992).
41. Chen, C. W. S., Khamthong, K. & Lee, S. Markov switching integer-valued generalized auto-regressive conditional heteroscedastic models for dengue counts. J. R. Stat. Soc.: Ser. C (Appl. Stat.) 68, 963–983. https:// doi. org/ 10. 1111/ rssc. 12344 (2019).
42. Douwes-Schultz, D. & Schmidt, A. M. Zero-state coupled Markov switching count models for spatio-temporal infectious disease spread. J. R. Stat. Soc.: Ser. C (Appl. Stat.) 71, 589–612. https:// doi. org/ 10. 1111/ rssc. 12547 (2022).
43. Coutinho, F. A. B., Burattinia, M. N., Lopeza, L. F. & Massada, E. Threshold conditions for a non-autonomous epidemic system describing the population dynamics of dengue. Bull. Math. Biol. 68, 2263–2282. https:// doi. org/ 10. 1007/ s11538- 006- 9108-6 (2006).
44. National Health Institute of Colombia & Ministry of Health of Colombia. Portal SIVIGILA. http:// porta lsivi gila. ins. gov. co/ (2023). Accessed: 31 Mar 2023.
45. Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439–446. https:// doi. org/ 10. 32614/ RJ- 2018- 009 (2018).
46. Hollister, J., Shah, T., Robitaille, A. L., Beck, M. W. & Johnson, M. Elevatr: Access elevation data from various APIs. https:// doi. org/ 10. 5281/ zenodo. 58096 45 (2021). R package version 0.4.2.
47. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2022).
48. National Administrative Department of Statistics of Colombia (DANE). Geoportal dane. https:// geopo rtal. dane. gov. co/ (2023). Accessed: 31 March 2023.
49. Siraj, A. S. et al. Data from: Spatiotemporal incidence of Zika and associated environmental drivers for the 2015-2016 epidemic in Colombia. https:// doi. org/ 10. 5061/ DRYAD. 83NJ1 (2019). Dataset, Version Number: 2.
50. Siraj, A. S. et al. Spatiotemporal incidence of Zika and associated environmental drivers for the 2015–2016 epidemic in Colombia. Sci. Data 5, 180073. https:// doi. org/ 10. 1038/ sdata. 2018. 73 (2018).
51. National Administrative Department of Statistics of Colombia (DANE). Departamento Administrativo Nacional de Estadística. https:// www. dane. gov. co/ (2023). Accessed: 31 March 2023.
52. National Administrative Department of Statistics of Colombia (DANE). Necesidades Básicas Insatisfechas (NBI). https:// www. dane. gov. co/ index. php/ estad istic as- por- tema/ pobre za-y- condi ciones- de- vida/ neces idades- basic as- insat isfec has- nbi (2018). Accessed 31 March 2023.
53. Feres, J. C. & Mancero, X. El método de las necesidades básicas insatisfechas (NBI) y sus aplicaciones en América Latina. No. 7 in Serie estudios estadísticos y prospectivos (Naciones Unidas, CEPAL, Div. de Estadística y Proyecciones Económicas, Santiago de Chile, 2001).
54. Bauer, C. & Wakefield, J. Stratified space-time infectious disease modelling, with an application to hand, foot and mouth disease in China. J. R. Stat. Soc. Ser. C 67, 1379–1398 (2018).
55. Fourié, T., Grard, G., Leparc-Goffart, I., Briolant, S. & Fontaine, A. Variability of Zika Virus Incubation Period in Humans. Open Forum Infectious Diseases 5, Ofy261. https:// doi. org/ 10. 1093/ ofid/ ofy261 (2018).
56. da Cruz Ferreira, D. A. et al. Meteorological variables and mosquito monitoring are good predictors for infestation trends of Aedes aegypti, the vector of dengue, chikungunya and Zika. Parasit. Vect. 10, 1. https:// doi. org/ 10. 1186/ s13071- 017- 2025-8 (2017).
57. Nelson, M. J. Aedes aegypti: Biology and Ecology (Pan American Health Organization, 1986).
58. Krow-Lucal, E. R., Biggerstaff, B. J. & Staples, J. E. Estimated incubation period for Zika virus disease. Emerg. Infect. Dis. 23, 841–845. https:// doi. org/ 10. 3201/ eid23 05. 161715 (2017).
59. Zhao, L.-Z. et al. Kinetics of antigen-specific IgM/IgG/IgA antibody responses during Zika virus natural infection in two patients. J. Med. Virol. 91, 872–876. https:// doi. org/ 10. 1002/ jmv. 25366 (2018).
60. de Valpine, P. et al. Programming with models: writing statistical algorithms for general model structures with NIMBLE. J. Comput. Gr. Stat. 26, 403–413. https:// doi. org/ 10. 1080/ 10618 600. 2016. 11724 87 (2017).
61. de Valpine, P. et al. NIMBLE: MCMC, Particle Filtering, and Programmable Hierarchical Modeling, https:// doi. org/ 10. 5281/ zenodo. 12111 90 (2022). R package version 0.13.1.
62. de Valpine, P. et al. NIMBLE User Manual, https:// doi. org/ 10. 5281/ zenodo. 12111 90 (2022). R package manual version 0.13.1.
63. Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence diagnosis and output analysis for MCMC. R News 6, 7–11 (2006).
64. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2016).
65. Zeileis, A. et al. colorspace: A toolbox for manipulating and assessing colors and palettes. J. Stat. Softw. 96, 1–49. https:// doi. org/ 10. 18637/ jss. v096. i01 (2020).
66. Overgaard, H. J. et al. A cross-sectional survey of Aedes aegypti immature abundance in urban and rural household containers in central Colombia. Parasit. Vect. 10, 1. https:// doi. org/ 10. 1186/ s13071- 017- 2295-1 (2017).
67. Caminade, C. et al. Global risk model for vector-borne transmission of Zika virus reveals the role of El Niño 2015. Proc. Natl. Acad. Sci. 114, 119–124. https:// doi. org/ 10. 1073/ pnas. 16143 03114 (2016).
68. Muñoz, E., Poveda, G., Arbeláez, M. P. & Vélez, I. D. Spatiotemporal dynamics of dengue in Colombia in relation to the combined effects of local climate and ENSO. Acta Trop. 224, 106136. https:// doi. org/ 10. 1016/j. actat ropica. 2021. 106136 (2021).
69. Lowe, R. et al. Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: A spatiotemporal modelling study. Lancet Planet. Health 5, e209–e219. https:// doi. org/ 10. 1016/ s2542- 5196(20) 30292-8 (2021).
70. Carabali, M. et al. Assessing the reporting of Dengue, Chikungunya and Zika to the National Surveillance System in Colombia from 2014–2017: A capture-recapture analysis accounting for misclassification of arboviral diagnostics. PLoS Negl. Trop. Dis. 15, e0009014. https:// doi. org/ 10. 1371/ journ al. pntd. 00090 14 (2021).
71. Reyes, A. J. R. Informe de evento Enfermedad por virus Zika, Colombia, 2017. https:// www. ins. gov. co/ busca dor- event os/ Infor mesde evento/ ZIKA% 202017. pdf (2017). Accessed 13 February 2024.
72. Reyes, A. J. R. Informe de evento Chikungunya, Colombia, 2017. https:// www. ins. gov. co/ busca dor- event os/ Infor mesde evento/ CHIKU NGUNYA% 202017. pdf (2017). Accessed 13 February 2024.
73. Romero, S. E. G. Informe de evento Dengue, Colombia, 2017. https:// www. ins. gov. co/ busca dor- event os/ Infor mesde evento/ DENGUE% 202017. pdf (2017). Accessed 13 February 2024.
74. Lowe, R. et al. Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study. PLoS Med. 15, e1002613. https:// doi. org/ 10. 1371/ journ al. pmed. 10026 13 (2018).
75. Colón-González, F. J. et al. Projecting the risk of mosquito-borne diseases in a warmer and more populated world: A multi-model, multi-scenario intercomparison modelling study. Lancet Planet. Health 5, e404–e414. https:// doi. org/ 10. 1016/ s2542- 5196(21) 00132-7 (2021).
76. Codeco, C. T. et al. Fast expansion of dengue in Brazil. Lancet Region. Health—Am. 12, 100274. https:// doi. org/ 10. 1016/j. lana. 2022. 100274 (2022).
77. Teixeira, M. G. et al. The epidemic of Zika virus–related microcephaly in Brazil: Detection, control, etiology, and future scenarios. Am. J. Public Health 106, 601–605. https:// doi. org/ 10. 2105/ ajph. 2016. 303113 (2016).
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repository.name.fl_str_mv Repositorio Institucional Universidad Cooperativa de Colombia
repository.mail.fl_str_mv bdigital@metabiblioteca.com
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spelling Garcia Balaguera CesarPicinini Freitas LaisDouwes‑Schultz DirkSchmidt AlexandraAvila Monsalve BrayanSalazar Flores Jorge EmilioRestrepo BertaJaramillo Ramirez GloriaCarabali MabelZinzer KateGarcia Balaguera CesarPicinini Freitas LaisDouwes‑Schultz DirkSchmidt AlexandraAvila Monsalve BrayanSalazar Flores Jorge EmilioRestrepo BertaJaramillo Ramirez GloriaCarabali MabelZinzer KateGRIVI2024-08-12T18:41:56Z2024-08-12T18:41:56Z2024-06-12Picinini Freitas, L., Douwes-Schultz, D., Schmidt, A.M. et al. Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space-time Markov switching model. Sci Rep 14, 10003 (2024). https://doi.org/10.1038/s41598-024-59976-720452322https://hdl.handle.net/20.500.12494/56886https://doi.org/10.1038/s41598-024-59976-72045-2322Articulo original de la revista Scientific Reports Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching modelZika, a viral disease transmitted to humans by Aedes mosquitoes, emerged in the Americas in 2015, causing large-scale epidemics. Colombia alone reported over 72,000 Zika cases between 2015 and 2016. Using national surveillance data from 1121 municipalities over 70 weeks, we identified sociodemographic and environmental factors associated with Zika’s emergence, re-emergence, persistence, and transmission intensity in Colombia. We fitted a zero-state Markov-switching model under the Bayesian framework, assuming Zika switched between periods of presence and absence according to spatially and temporally varying probabilities of emergence/re-emergence (from absence to presence) and persistence (from presence to presence). These probabilities were assumed to follow a series of mixed multiple logistic regressions. When Zika was present, assuming that the cases follow a negative binomial distribution, we estimated the transmission intensity rate. Our results indicate that Zika emerged/re-emerged sooner and that transmission was intensified in municipalities that were more densely populated, at lower altitudes and/or with less vegetation cover. Warmer temperatures and less weekly-accumulated rain were also associated with Zika emergence. Zika cases persisted for longer in more densely populated areas with more cases reported in the previous week. Overall, population density, elevation, and temperature were identified as the main contributors to the first Zika epidemic in Colombia. We also estimated the probability of Zika presence by municipality and week, and the results suggest that the disease circulated undetected by the surveillance system on many occasions. Our results offer insights into priority areas for public health interventions against emerging and re-emerging Aedes-borne diseases.Resumen, introducción, materiales y metodos, resultados, conclusiones, bibliografiaa nationwide application of a space‑time Markov switching model, observational studyEpidemiología y salud públicaIn this ecological study, we analyzed the counts of Zika reported cases by municipality and week obtained from the Colombian National Public Health Surveillance System (Sistema Nacional de Vigilancia en Salud Püblica - SIVIGILA).PDFapplication/pdfengUniversidad Cooperativa de ColombiaVillavicenciohttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://purl.org/coar/access_right/c_abf2https://www.nature.com/articles/s41598-024-59976-7Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space‑time Markov switching modelArtículo de revistahttp://purl.org/coar/resource_type/c_65011310003114Scientific ReportsLowe, R. et al. The zika virus epidemic in brazil: From discovery to future implications. Int. J. Environ. Res. Public Health 15, 96. https:// doi. org/ 10. 3390/ ijerp h1501 0096 (2018).2. Ferreira-de Brito, A. et al. First detection of natural infection of Aedes aegypti with Zika virus in Brazil and throughout South America. Memórias do Instituto Oswaldo Cruz 111, 655–658, https:// doi. org/ 10. 1590/ 0074- 02760 160332 (2016). Publisher: Instituto Oswaldo Cruz, Ministério da Saüde.3. Lounibos, L. P. Invasions by insect vectors of human disease. Annu. Rev. Entomol. 47, 233–266. https:// doi. org/ 10. 1146/ annur ev. ento. 47. 091201. 145206 (2002).4. Powell, J. R. & Tabachnick, W. J. History of domestication and spread of aedes aegypti—A review. Mem. Inst. Oswaldo Cruz 108, 11–17. https:// doi. org/ 10. 1590/ 0074- 02761 30395 (2013).5. Mordecai, E. A. et al. Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. PLoS Negl. Trop. Dis. 11, e0005568. https:// doi. org/ 10. 1371/ journ al. pntd. 00055 68 (2017).6. Watts, A. G. et al. Elevation as a proxy for mosquito-borne zika virus transmission in the americas. PLoS One 12, e0178211. https:// doi. org/ 10. 1371/ journ al. pone. 01782 11 (2017).7. Kraemer, M. U. G. et al. Past and future spread of the arbovirus vectors aedes aegypti and aedes albopictus. Nat. Microbiol. 4, 854–863. https:// doi. org/ 10. 1038/ s41564- 019- 0376-y (2019).8. Winokur, O. C., Main, B. J., Nicholson, J. & Barker, C. M. Impact of temperature on the extrinsic incubation period of Zika virus in Aedes aegypti. PLoS Negl. Trop. Dis. 14, e0008047. https:// doi. org/ 10. 1371/ journ al. pntd. 00080 47 (2020).9. Tesla, B. et al. Temperature drives Zika virus transmission: Evidence from empirical and mathematical models. Proc. R. Soc. B: Biol. Sci. 285, 20180795. https:// doi. org/ 10. 1098/ rspb. 2018. 0795 (2018).10. Freitas, L. P., Schmidt, A. M., Cossich, W., Cruz, O. G. & Carvalho, M. S. Spatio-temporal modelling of the first chikungunya epidemic in an intra-urban setting: The role of socioeconomic status, environment and temperature. PLoS Negl. Trop. Dis. 15, e0009537. https:// doi. org/ 10. 1371/ journ al. pntd. 00095 37 (2021).11. Xu, Z. et al. Spatiotemporal patterns and climatic drivers of severe dengue in Thailand. Sci. Total Environ. 656, 889–901. https:// doi. org/ 10. 1016/j. scito tenv. 2018. 11. 395 (2019).12. Hu, W., Clements, A., Williams, G., Tong, S. & Mengersen, K. Spatial patterns and socioecological drivers of dengue fever transmission in Queensland, Australia. Environ. Health Perspect. 120, 260–266. https:// doi. org/ 10. 1289/ ehp. 10032 70 (2012).13. Lowe, R. et al. Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil. Comput. Geosci. 37, 371–381. https:// doi. org/ 10. 1016/j. cageo. 2010. 01. 008 (2011).14. Lowe, R. et al. Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study. PLoS Med. 15, e1002613. https:// doi. org/ 10. 1371/ journ al. pmed. 10026 13 (2018).15. Morin, C. W., Comrie, A. C. & Ernst, K. Climate and dengue transmission: Evidence and implications. Environ. Health Perspect. 121, 1264–1272. https:// doi. org/ 10. 1289/ ehp. 13065 56 (2013).16. de Almeida Costa, E. A. P., de Mendonça Santos, E. M., Correia, J. C. & de Albuquerque, C. M. R. Impact of small variations in temperature and humidity on the reproductive activity and survival of Aedes aegypti (Diptera, Culicidae). Revista Brasileira de Entomologia 54, 488–493, https:// doi. org/ 10. 1590/ s0085- 56262 01000 03000 21 (2010).17. Carabali, M. et al. Spatiotemporal distribution and socioeconomic disparities of dengue, chikungunya and Zika in two Latin American cities from 2007 to 2017. Trop. Med. Int. Health 26, 301–315. https:// doi. org/ 10. 1111/ tmi. 13530 (2020).18. Power, G. M. et al. Socioeconomic risk markers of arthropod-borne virus (arbovirus) infections: A systematic literature review and meta-analysis. BMJ Glob. Health 7, e007735. https:// doi. org/ 10. 1136/ bmjgh- 2021- 007735 (2022).19. Carrasquilla, M. C. et al. Entomological characterization of aedes mosquitoes and arbovirus detection in ibagué, a colombian city with co-circulation of zika, dengue and chikungunya viruses. Parasites Vect. 14, 1. https:// doi. org/ 10. 1186/ s13071- 021- 04908-x (2021).20. Romeo-Aznar, V., Picinini Freitas, L., Gonçalves Cruz, O., King, A. A. & Pascual, M. Fine-scale heterogeneity in population density predicts wave dynamics in dengue epidemics. Nat. Commun. 13, 1. https:// doi. org/ 10. 1038/ s41467- 022- 28231-w (2022).21. Barcellos, C. & Lowe, R. Expansion of the dengue transmission area in Brazil: the role of climate and cities. Trop. Med. Int. Health 19, 159–168. https:// doi. org/ 10. 1111/ tmi. 12227 (2013).22. Villar, L. A., Rojas, D. P., Besada-Lombana, S. & Sarti, E. Epidemiological trends of dengue disease in Colombia (2000–2011): A systematic review. PLoS Negl. Trop. Dis. 9, e0003499. https:// doi. org/ 10. 1371/ journ al. pntd. 00034 99 (2015).23. Freitas, L. P. et al. Spatio-temporal clusters and patterns of spread of dengue, chikungunya, and Zika in Colombia. PLoS Negl. Trop. Dis. 16, e0010334. https:// doi. org/ 10. 1371/ journ al. pntd. 00103 34 (2022).24. Pérez, N. T. Protocolo de vigilancia en salud püblica—Enfermedad por Virus Zika (2017).25. Ospina, J. et al. Stratifying the potential local transmission of Zika in municipalities of Antioquia, Colombia. Trop. Med. Int. Health 22, 1249–1265. https:// doi. org/ 10. 1111/ tmi. 12924 (2017).26. Shragai, T. et al. Distance to public transit predicts spatial distribution of dengue virus incidence in Medellín, Colombia. Scientific Reports 12, 1. https:// doi. org/ 10. 1038/ s41598- 022- 12115-6 (2022).27. Carabali, M., Schmidt, A. M., Restrepo, B. N. & Kaufman, J. S. A joint spatial marked point process model for dengue and severe dengue in Medellin, Colombia. Spat. Spatio-temporal Epidemiol. 41, 100495. https:// doi. org/ 10. 1016/j. sste. 2022. 100495 (2022).28. Carabali, M., Maheu-Giroux, M. & Kaufman, J. S. Dengue, severity paradox, and socioeconomic distribution among Afro-Colombians. Epidemiology 32, 541–550. https:// doi. org/ 10. 1097/ ede. 00000 00000 001353 (2021).29. Adin, A., Martínez-Bello, D. A., López-Quílez, A. & Ugarte, M. D. Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia. PLoS One 13, e0203382. https:// doi. org/ 10. 1371/ journ al. pone. 02033 82 (2018).30. Martínez-Bello, D. A., López-Quílez, A. & Torres-Prieto, A. Bayesian dynamic modeling of time series of dengue disease case counts. PLoS Negl. Trop. Dis. 11, e0005696. https:// doi. org/ 10. 1371/ journ al. pntd. 00056 96 (2017).31. Martínez-Bello, D. A., López-Quílez, A. & Prieto, A. T. Relative risk estimation of dengue disease at small spatial scale. Int. J. Health Geogr. 16, https:// doi. org/ 10. 1186/ s12942- 017- 0104-x (2017).32. Martínez-Bello, D. A., López-Quílez, A. & Prieto, A. T. Joint estimation of relative risk for dengue and Zika infections, Colombia, 2015–2016. Emerg. Infect. Dis. 25, 1118–1126. https:// doi. org/ 10. 3201/ eid25 06. 180392 (2019).33. Delmelle, E., Hagenlocher, M., Kienberger, S. & Casas, I. A spatial model of socioeconomic and environmental determinants of dengue fever in Cali, Colombia. Acta Tropica 164, 169–176. https:// doi. org/ 10. 1016/j. actat ropica. 2016. 08. 028 (2016).34. Chien, L.-C., Sy, F. & Pérez, A. Identifying high risk areas of Zika virus infection by meteorological factors in Colombia. BMC Infect. Dis. 19, 1. https:// doi. org/ 10. 1186/ s12879- 019- 4499-9 (2019).35. Chien, L.-C., Lin, R.-T., Liao, Y., Sy, F. S. & Pérez, A. Surveillance on the endemic of Zika virus infection by meteorological factors in Colombia: a population-based spatial and temporal study. BMC Infect. Dis. 18, 1. https:// doi. org/ 10. 1186/ s12879- 018- 3085-x (2018).36. Flórez-Lozano, K. et al. Spatial distribution of the relative risk of Zika virus disease in Colombia during the 2015–2016 epidemic from a Bayesian approach. Int. J. Gynecol. Obstet. 148, 55–60. https:// doi. org/ 10. 1002/ ijgo. 13048 (2020).37. Triana-Vidal, L. E., Morales-García, M. A., Arango-Cárdenas, M. J., Badiel-Ocampo, M. & Cuartas, D. E. Análisis de la distribución espacial y temporal de los virus del Dengue (2006-2017), Zika (2015- 2017) y Chikungunya (2014-2017) en Colombia. Infectio 23, 352. https:// doi. org/ 10. 22354/ in. v23i4. 810 (2019).38. Desjardins, M., Whiteman, A., Casas, I. & Delmelle, E. Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016. Acta Trop. 185, 77–85. https:// doi. org/ 10. 1016/j. actat ropica. 2018. 04. 023 (2018).39. Arab, A. Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros. Int. J. Environ. Res. Public Health 12, 10536–10548, https:// doi. org/ 10. 3390/ ijerp h1209 10536 (2015). Number: 9 Publisher: Multidisciplinary Digital Publishing Institute.40. Lambert, D. Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics 34, 1–14. https:// doi. org/ 10. 2307/ 12695 47 (1992).41. Chen, C. W. S., Khamthong, K. & Lee, S. Markov switching integer-valued generalized auto-regressive conditional heteroscedastic models for dengue counts. J. R. Stat. Soc.: Ser. C (Appl. Stat.) 68, 963–983. https:// doi. org/ 10. 1111/ rssc. 12344 (2019).42. Douwes-Schultz, D. & Schmidt, A. M. Zero-state coupled Markov switching count models for spatio-temporal infectious disease spread. J. R. Stat. Soc.: Ser. C (Appl. Stat.) 71, 589–612. https:// doi. org/ 10. 1111/ rssc. 12547 (2022).43. Coutinho, F. A. B., Burattinia, M. N., Lopeza, L. F. & Massada, E. Threshold conditions for a non-autonomous epidemic system describing the population dynamics of dengue. Bull. Math. Biol. 68, 2263–2282. https:// doi. org/ 10. 1007/ s11538- 006- 9108-6 (2006).44. National Health Institute of Colombia & Ministry of Health of Colombia. Portal SIVIGILA. http:// porta lsivi gila. ins. gov. co/ (2023). Accessed: 31 Mar 2023.45. Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439–446. https:// doi. org/ 10. 32614/ RJ- 2018- 009 (2018).46. Hollister, J., Shah, T., Robitaille, A. L., Beck, M. W. & Johnson, M. Elevatr: Access elevation data from various APIs. https:// doi. org/ 10. 5281/ zenodo. 58096 45 (2021). R package version 0.4.2.47. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2022).48. National Administrative Department of Statistics of Colombia (DANE). Geoportal dane. https:// geopo rtal. dane. gov. co/ (2023). Accessed: 31 March 2023.49. Siraj, A. S. et al. Data from: Spatiotemporal incidence of Zika and associated environmental drivers for the 2015-2016 epidemic in Colombia. https:// doi. org/ 10. 5061/ DRYAD. 83NJ1 (2019). Dataset, Version Number: 2.50. Siraj, A. S. et al. Spatiotemporal incidence of Zika and associated environmental drivers for the 2015–2016 epidemic in Colombia. Sci. Data 5, 180073. https:// doi. org/ 10. 1038/ sdata. 2018. 73 (2018).51. National Administrative Department of Statistics of Colombia (DANE). Departamento Administrativo Nacional de Estadística. https:// www. dane. gov. co/ (2023). Accessed: 31 March 2023.52. National Administrative Department of Statistics of Colombia (DANE). Necesidades Básicas Insatisfechas (NBI). https:// www. dane. gov. co/ index. php/ estad istic as- por- tema/ pobre za-y- condi ciones- de- vida/ neces idades- basic as- insat isfec has- nbi (2018). Accessed 31 March 2023.53. Feres, J. C. & Mancero, X. El método de las necesidades básicas insatisfechas (NBI) y sus aplicaciones en América Latina. No. 7 in Serie estudios estadísticos y prospectivos (Naciones Unidas, CEPAL, Div. de Estadística y Proyecciones Económicas, Santiago de Chile, 2001).54. Bauer, C. & Wakefield, J. Stratified space-time infectious disease modelling, with an application to hand, foot and mouth disease in China. J. R. Stat. Soc. Ser. C 67, 1379–1398 (2018).55. Fourié, T., Grard, G., Leparc-Goffart, I., Briolant, S. & Fontaine, A. Variability of Zika Virus Incubation Period in Humans. Open Forum Infectious Diseases 5, Ofy261. https:// doi. org/ 10. 1093/ ofid/ ofy261 (2018).56. da Cruz Ferreira, D. A. et al. Meteorological variables and mosquito monitoring are good predictors for infestation trends of Aedes aegypti, the vector of dengue, chikungunya and Zika. Parasit. Vect. 10, 1. https:// doi. org/ 10. 1186/ s13071- 017- 2025-8 (2017).57. Nelson, M. J. Aedes aegypti: Biology and Ecology (Pan American Health Organization, 1986).58. Krow-Lucal, E. R., Biggerstaff, B. J. & Staples, J. E. Estimated incubation period for Zika virus disease. Emerg. Infect. Dis. 23, 841–845. https:// doi. org/ 10. 3201/ eid23 05. 161715 (2017).59. Zhao, L.-Z. et al. Kinetics of antigen-specific IgM/IgG/IgA antibody responses during Zika virus natural infection in two patients. J. Med. Virol. 91, 872–876. https:// doi. org/ 10. 1002/ jmv. 25366 (2018).60. de Valpine, P. et al. Programming with models: writing statistical algorithms for general model structures with NIMBLE. J. Comput. Gr. Stat. 26, 403–413. https:// doi. org/ 10. 1080/ 10618 600. 2016. 11724 87 (2017).61. de Valpine, P. et al. NIMBLE: MCMC, Particle Filtering, and Programmable Hierarchical Modeling, https:// doi. org/ 10. 5281/ zenodo. 12111 90 (2022). R package version 0.13.1.62. de Valpine, P. et al. NIMBLE User Manual, https:// doi. org/ 10. 5281/ zenodo. 12111 90 (2022). R package manual version 0.13.1.63. Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence diagnosis and output analysis for MCMC. R News 6, 7–11 (2006).64. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2016).65. Zeileis, A. et al. colorspace: A toolbox for manipulating and assessing colors and palettes. J. Stat. Softw. 96, 1–49. https:// doi. org/ 10. 18637/ jss. v096. i01 (2020).66. Overgaard, H. J. et al. A cross-sectional survey of Aedes aegypti immature abundance in urban and rural household containers in central Colombia. Parasit. Vect. 10, 1. https:// doi. org/ 10. 1186/ s13071- 017- 2295-1 (2017).67. Caminade, C. et al. Global risk model for vector-borne transmission of Zika virus reveals the role of El Niño 2015. Proc. Natl. Acad. Sci. 114, 119–124. https:// doi. org/ 10. 1073/ pnas. 16143 03114 (2016).68. Muñoz, E., Poveda, G., Arbeláez, M. P. & Vélez, I. D. Spatiotemporal dynamics of dengue in Colombia in relation to the combined effects of local climate and ENSO. Acta Trop. 224, 106136. https:// doi. org/ 10. 1016/j. actat ropica. 2021. 106136 (2021).69. Lowe, R. et al. Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: A spatiotemporal modelling study. Lancet Planet. Health 5, e209–e219. https:// doi. org/ 10. 1016/ s2542- 5196(20) 30292-8 (2021).70. Carabali, M. et al. Assessing the reporting of Dengue, Chikungunya and Zika to the National Surveillance System in Colombia from 2014–2017: A capture-recapture analysis accounting for misclassification of arboviral diagnostics. PLoS Negl. Trop. Dis. 15, e0009014. https:// doi. org/ 10. 1371/ journ al. pntd. 00090 14 (2021).71. Reyes, A. J. R. Informe de evento Enfermedad por virus Zika, Colombia, 2017. https:// www. ins. gov. co/ busca dor- event os/ Infor mesde evento/ ZIKA% 202017. pdf (2017). Accessed 13 February 2024.72. Reyes, A. J. R. Informe de evento Chikungunya, Colombia, 2017. https:// www. ins. gov. co/ busca dor- event os/ Infor mesde evento/ CHIKU NGUNYA% 202017. pdf (2017). Accessed 13 February 2024.73. Romero, S. E. G. Informe de evento Dengue, Colombia, 2017. https:// www. ins. gov. co/ busca dor- event os/ Infor mesde evento/ DENGUE% 202017. pdf (2017). Accessed 13 February 2024.74. Lowe, R. et al. Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study. PLoS Med. 15, e1002613. https:// doi. org/ 10. 1371/ journ al. pmed. 10026 13 (2018).75. Colón-González, F. J. et al. Projecting the risk of mosquito-borne diseases in a warmer and more populated world: A multi-model, multi-scenario intercomparison modelling study. Lancet Planet. Health 5, e404–e414. https:// doi. org/ 10. 1016/ s2542- 5196(21) 00132-7 (2021).76. Codeco, C. T. et al. Fast expansion of dengue in Brazil. Lancet Region. Health—Am. 12, 100274. https:// doi. org/ 10. 1016/j. lana. 2022. 100274 (2022).77. Teixeira, M. G. et al. The epidemic of Zika virus–related microcephaly in Brazil: Detection, control, etiology, and future scenarios. Am. J. 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