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On the heterogeneous spread of COVID-19 in Chile

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  • Freire-Flores, Danton
  • Llanovarced-Kawles, Nyna
  • Sanchez-Daza, Anamaria
  • Olivera-Nappa, Álvaro

Abstract

Non-pharmaceutical interventions (NPIs) have played a crucial role in controlling the spread of COVID-19. Nevertheless, NPI efficacy varies enormously between and within countries, mainly because of population and behavioral heterogeneity. In this work, we adapted a multi-group SEIRA model to study the spreading dynamics of COVID-19 in Chile, representing geographically separated regions of the country by different groups. We use national mobilization statistics to estimate the connectivity between regions and data from governmental repositories to obtain COVID-19 spreading and death rates in each region. We then assessed the effectiveness of different NPIs by studying the temporal evolution of the reproduction number Rt. Analysing data-driven and model-based estimates of Rt, we found a strong coupling of different regions, highlighting the necessity of organized and coordinated actions to control the spread of SARS-CoV-2. Finally, we evaluated different scenarios to forecast the evolution of COVID-19 in the most densely populated regions, finding that the early lifting of restriction probably will lead to novel outbreaks.

Suggested Citation

  • Freire-Flores, Danton & Llanovarced-Kawles, Nyna & Sanchez-Daza, Anamaria & Olivera-Nappa, Álvaro, 2021. "On the heterogeneous spread of COVID-19 in Chile," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:chsofr:v:150:y:2021:i:c:s0960077921005105
    DOI: 10.1016/j.chaos.2021.111156
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    References listed on IDEAS

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    1. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Contreras, Sebastián & Biron-Lattes, Juan Pablo & Villavicencio, H. Andrés & Medina-Ortiz, David & Llanovarced-Kawles, Nyna & Olivera-Nappa, Álvaro, 2020. "Statistically-based methodology for revealing real contagion trends and correcting delay-induced errors in the assessment of COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Postnikov, Eugene B., 2020. "Estimation of COVID-19 dynamics “on a back-of-envelope”: Does the simplest SIR model provide quantitative parameters and predictions?," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    5. Contreras, Sebastián & Villavicencio, H. Andrés & Medina-Ortiz, David & Biron-Lattes, Juan Pablo & Olivera-Nappa, Álvaro, 2020. "A multi-group SEIRA model for the spread of COVID-19 among heterogeneous populations," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    6. Sebastian Contreras & Jonas Dehning & Matthias Loidolt & Johannes Zierenberg & F. Paul Spitzner & Jorge H. Urrea-Quintero & Sebastian B. Mohr & Michael Wilczek & Michael Wibral & Viola Priesemann, 2021. "The challenges of containing SARS-CoV-2 via test-trace-and-isolate," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    7. Lalwani, Soniya & Sahni, Gunjan & Mewara, Bhawna & Kumar, Rajesh, 2020. "Predicting optimal lockdown period with parametric approach using three-phase maturation SIRD model for COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
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    Cited by:

    1. Li, Tingting & Guo, Youming, 2022. "Modeling and optimal control of mutated COVID-19 (Delta strain) with imperfect vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
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