Cargando…
Modeling the 2014 Ebola Virus Epidemic – Agent-Based Simulations, Temporal Analysis and Future Predictions for Liberia and Sierra Leone
We developed an agent-based model to investigate the epidemic dynamics of Ebola virus disease (EVD) in Liberia and Sierra Leone from May 27 to December 21, 2014. The dynamics of the agent-based simulator evolve on small-world transmission networks of sizes equal to the population of each country, wi...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4450810/ https://www.ncbi.nlm.nih.gov/pubmed/26064785 http://dx.doi.org/10.1371/currents.outbreaks.8d5984114855fc425e699e1a18cdc6c9 |
_version_ | 1782374055558512640 |
---|---|
author | Siettos, Constantinos Anastassopoulou, Cleo Russo, Lucia Grigoras, Christos Mylonakis, Eleftherios |
author_facet | Siettos, Constantinos Anastassopoulou, Cleo Russo, Lucia Grigoras, Christos Mylonakis, Eleftherios |
author_sort | Siettos, Constantinos |
collection | PubMed |
description | We developed an agent-based model to investigate the epidemic dynamics of Ebola virus disease (EVD) in Liberia and Sierra Leone from May 27 to December 21, 2014. The dynamics of the agent-based simulator evolve on small-world transmission networks of sizes equal to the population of each country, with adjustable densities to account for the effects of public health intervention policies and individual behavioral responses to the evolving epidemic. Based on time series of the official case counts from the World Health Organization (WHO), we provide estimates for key epidemiological variables by employing the so-called Equation-Free approach. The underlying transmission networks were characterized by rather random structures in the two countries with densities decreasing by ~19% from the early (May 27-early August) to the last period (mid October-December 21). Our estimates for the values of key epidemiological variables, such as the mean time to death, recovery and the case fatality rate, are very close to the ones reported by the WHO Ebola response team during the early period of the epidemic (until September 14) that were calculated based on clinical data. Specifically, regarding the effective reproductive number Re, our analysis suggests that until mid October, Re was above 2.3 in both countries; from mid October to December 21, Re dropped well below unity in Liberia, indicating a saturation of the epidemic, while in Sierra Leone it was around 1.9, indicating an ongoing epidemic. Accordingly, a ten-week projection from December 21 estimated that the epidemic will fade out in Liberia in early March; in contrast, our results flashed a note of caution for Sierra Leone since the cumulative number of cases could reach as high as 18,000, and the number of deaths might exceed 5,000, by early March 2015. However, by processing the reported data of the very last period (December 21, 2014-January 18, 2015), we obtained more optimistic estimates indicative of a remission of the epidemic in Sierra Leone, as reflected by the derived Re (~0.82, 95% CI: 0.81-0.83). |
format | Online Article Text |
id | pubmed-4450810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-44508102015-06-09 Modeling the 2014 Ebola Virus Epidemic – Agent-Based Simulations, Temporal Analysis and Future Predictions for Liberia and Sierra Leone Siettos, Constantinos Anastassopoulou, Cleo Russo, Lucia Grigoras, Christos Mylonakis, Eleftherios PLoS Curr Research We developed an agent-based model to investigate the epidemic dynamics of Ebola virus disease (EVD) in Liberia and Sierra Leone from May 27 to December 21, 2014. The dynamics of the agent-based simulator evolve on small-world transmission networks of sizes equal to the population of each country, with adjustable densities to account for the effects of public health intervention policies and individual behavioral responses to the evolving epidemic. Based on time series of the official case counts from the World Health Organization (WHO), we provide estimates for key epidemiological variables by employing the so-called Equation-Free approach. The underlying transmission networks were characterized by rather random structures in the two countries with densities decreasing by ~19% from the early (May 27-early August) to the last period (mid October-December 21). Our estimates for the values of key epidemiological variables, such as the mean time to death, recovery and the case fatality rate, are very close to the ones reported by the WHO Ebola response team during the early period of the epidemic (until September 14) that were calculated based on clinical data. Specifically, regarding the effective reproductive number Re, our analysis suggests that until mid October, Re was above 2.3 in both countries; from mid October to December 21, Re dropped well below unity in Liberia, indicating a saturation of the epidemic, while in Sierra Leone it was around 1.9, indicating an ongoing epidemic. Accordingly, a ten-week projection from December 21 estimated that the epidemic will fade out in Liberia in early March; in contrast, our results flashed a note of caution for Sierra Leone since the cumulative number of cases could reach as high as 18,000, and the number of deaths might exceed 5,000, by early March 2015. However, by processing the reported data of the very last period (December 21, 2014-January 18, 2015), we obtained more optimistic estimates indicative of a remission of the epidemic in Sierra Leone, as reflected by the derived Re (~0.82, 95% CI: 0.81-0.83). Public Library of Science 2015-03-09 /pmc/articles/PMC4450810/ /pubmed/26064785 http://dx.doi.org/10.1371/currents.outbreaks.8d5984114855fc425e699e1a18cdc6c9 Text en https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Siettos, Constantinos Anastassopoulou, Cleo Russo, Lucia Grigoras, Christos Mylonakis, Eleftherios Modeling the 2014 Ebola Virus Epidemic – Agent-Based Simulations, Temporal Analysis and Future Predictions for Liberia and Sierra Leone |
title | Modeling the 2014 Ebola Virus Epidemic – Agent-Based Simulations, Temporal Analysis and Future Predictions for Liberia and Sierra Leone |
title_full | Modeling the 2014 Ebola Virus Epidemic – Agent-Based Simulations, Temporal Analysis and Future Predictions for Liberia and Sierra Leone |
title_fullStr | Modeling the 2014 Ebola Virus Epidemic – Agent-Based Simulations, Temporal Analysis and Future Predictions for Liberia and Sierra Leone |
title_full_unstemmed | Modeling the 2014 Ebola Virus Epidemic – Agent-Based Simulations, Temporal Analysis and Future Predictions for Liberia and Sierra Leone |
title_short | Modeling the 2014 Ebola Virus Epidemic – Agent-Based Simulations, Temporal Analysis and Future Predictions for Liberia and Sierra Leone |
title_sort | modeling the 2014 ebola virus epidemic – agent-based simulations, temporal analysis and future predictions for liberia and sierra leone |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4450810/ https://www.ncbi.nlm.nih.gov/pubmed/26064785 http://dx.doi.org/10.1371/currents.outbreaks.8d5984114855fc425e699e1a18cdc6c9 |
work_keys_str_mv | AT siettosconstantinos modelingthe2014ebolavirusepidemicagentbasedsimulationstemporalanalysisandfuturepredictionsforliberiaandsierraleone AT anastassopouloucleo modelingthe2014ebolavirusepidemicagentbasedsimulationstemporalanalysisandfuturepredictionsforliberiaandsierraleone AT russolucia modelingthe2014ebolavirusepidemicagentbasedsimulationstemporalanalysisandfuturepredictionsforliberiaandsierraleone AT grigoraschristos modelingthe2014ebolavirusepidemicagentbasedsimulationstemporalanalysisandfuturepredictionsforliberiaandsierraleone AT mylonakiseleftherios modelingthe2014ebolavirusepidemicagentbasedsimulationstemporalanalysisandfuturepredictionsforliberiaandsierraleone |