Spatio-Temporal Projection of Covid-19 in Pereira

Keywords: Spatio-temporal projection, spread of COVID-19, Markov Microscopic Chain Approach, heat maps, Coronavirus

Abstract

The current outbreak of coronavirus disease (COVID-19), which was first reported in Wuhan, China on December 31, 2019, has left a balance as of April 19, 2020 of more than 3500000 infected and 160000 deaths in 185 countries. In this work we use a model based on the Markov Microscopic Chain Approach (MMCA) to estimate the spread of COVID-19 in the city of Pereira (Risaralda-Colombia). This model incorporates important aspects of the population related to spatial location within the city which is discretized by communes, mobility between communes, stratification by age groups and separation of individuals into seven epidemiological compartments. This model is used to predict, in a timeline, the incidence of epidemics in geolocated populations, which translates into an indicator tool to take control measures. The data referring to COVID-19, from the municipality of Pereira, until April 20, 2020 are used to feed the model and obtain the spatio-temporal projections. The results presented consider multiple mobility scenarios, so that the flattening of the curves of the different epidemiological compartments can be visualized according to different confinement strategies. As it is a spatio-temporal model, the results of the model can easily be presented as heat over each of the epidemiological compartments, in order to facilitate decision-making processes.

Author Biographies

Mauricio Granada-Echeverri*, Universidad Tecnológica de Pereira, Colombia

PhD. en Ingeniería Eléctrica, Facultad de Ingenierías, Programa de Ingeniería Eléctrica, Universidad Tecnológica de Pereira, Pereira-Colombia, magra@utp.edu.co

Alexander Molina-Cabrera, Universidad Tecnológica de Pereira, Colombia

PhD. en Ingeniería Eléctrica, Facultad de Ingenierías, Programa de Ingeniería Eléctrica, Universidad Tecnológica de Pereira, Pereira-Colombia, almo@utp.edu.co

Patricia Granada-Echeverri, Universidad Tecnológica de Pereira, Colombia

PhD. en Ciencias Sociales Niñez y Juventud, Facultad Ciencias de la Salud, Departamento de Ciencias Clínicas, área Maternoinfantil, Universidad Tecnológica de Pereira, Pereira-Colombia, patriciagranada@utp.edu.co

References

S. Banisch, “Markov Chain Aggregation for Agent-Based Models” en Understanding Complex Systems, Springer, Switzerland, 2016. https://doi.org/10.1007/978-3-319-24877-6

A. Arenas et al., “A mathematical model for the spatiotemporal epidemic spreading of COVID19,” medRxiv, pp. 1-13, Mar. 2020. https://doi.org/10.1101/2020.03.21.20040022

S. Gómez; A. Arenas; J. Borge-Holthoefer; S. Meloni; Y. Moreno, “Discrete-time Markov chain approach to contact-based disease spreading in complex networks”. EPL (Europhysics Letters), vol. 89, no. 3, pp. 1-6, Feb. 2010. https://doi.org/10.1209/0295-5075/89/38009

Y. Chen; J. Cheng; Y. Jian; K. Liu, “A time delay dynamical model for outbreak of 2019-nCoV and the parameter identification”. Journal of Inverse and Ill-posed Problems, vol. 28, no. 2, pp. 243-250. Mar. 2020. https://doi.org/10.1515/jiip-2020-0010

B. Cantó; C. Coll; E. Sánchez, “Estimation of parameters in a structured SIR model”. Advances in Difference Equations, vol. 1, no. 33, pp. 33, Jan. 2017. https://doi.org/10.1186/s13662-017-1078-5

S. Ma. Stefan; Y. Xia, “Mathematical Understanding of Infectious Disease Dynamics” en Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore: vol. 16. 2008. https://doi.org/10.1142/7020

C. Liu; G. Ding; J. Gong; L. Wang; K. Cheng; D. Zhang, “Studies on mathematical models for SARS outbreak prediction and warning” Chinese Sci Bull, vol. 49, no. 21, pp. 2245-2251, 2014. https://doi.org/10.1360/csb2004-49-21-2245.

R. E. González, “Different scenarios in the Dynamics of SARS-Cov-2 Infection: an adapted ODE model”. Populations and Evolution, 2020. https://arxiv.org/abs/2004.01295

Observatorio de movilidad vial en Pereira, Universidad Tecnológica de Pereira (UTP), Pereira, Risaralda, 2019. https://www.researchgate.net/profile/Mauricio_Granada-Echeverri/publication/342183390_PROYECCION_ESPACIO-TEMPORAL_DEL_COVID-19_EN_PEREIRA/data/5ee7caa692851ce9e7e47622/DatosCovidPereira.zip

Departamento Administrativo Nacional de estadísticas, “Resultados Censo Nacional de población y Vivienda 2018”, 2019. https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/censo-nacional-de-poblacion-y-vivenda-2018

Q. Li, et al., “Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia”, The New Engl J Med., no. 382, pp. 1199-1207, Mar. 2020. https://doi.org/10.1056/NEJMoa2001316

J. M. Read; J. R. Bridgen; D. A. Cummings; A. Ho; C. P. Jewell, “Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions,” MedRxiv, Jan. 2020. https://doi.org/10.1101/2020.01.23.20018549

L. Danon; E. Brooks-Pollock; M. Bailey; M. J. Keeling. “A spatial model of CoVID-19 transmission in England and Wales: early spread and peak timing,” MedRxiv, pp. 1- 10, Feb. 2020. https://doi.org/10.1101/2020.02.12.20022566

N. Wilson; A. Kvalsvig; L. T. Barnard; M. G. Baker. “Case-fatality risk estimates for covid-19 calculated by using a lag time for fatality,” Emerging Infectious Diseases, vol. 26, no. 6, Jun. 2020. https://dx.doi.org/10.3201/eid2606.200320

H. Nishiura; N. M. Linton; A. R. Akhmetzhanov, “Serial interval of novel coronavirus (covid-19) infections,” International Journal of Infectious Diseases., vol. 93, pp. 284-286, Apr. 2020. https://doi.org/10.1016/j.ijid.2020.02.060

How to Cite
Granada-Echeverri, M., Molina-Cabrera, A., & Granada-Echeverri, P. (2020). Spatio-Temporal Projection of Covid-19 in Pereira. TecnoLógicas, 23(49), 129-146. https://doi.org/10.22430/22565337.1655

Downloads

Download data is not yet available.
Published
2020-09-15
Section
Research Papers

Most read articles by the same author(s)