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Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa

BACKGROUND: The emergence of COVID-19 as a global pandemic presents a serious health threat to African countries and the livelihoods of its people. To mitigate the impact of this disease, intervention measures including self-isolation, schools and border closures were implemented to varying degrees...

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Autores principales: Wamalwa, Mark, Tonnang, Henri E. Z.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178551/
https://www.ncbi.nlm.nih.gov/pubmed/35681129
http://dx.doi.org/10.1186/s12879-022-07510-3
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author Wamalwa, Mark
Tonnang, Henri E. Z.
author_facet Wamalwa, Mark
Tonnang, Henri E. Z.
author_sort Wamalwa, Mark
collection PubMed
description BACKGROUND: The emergence of COVID-19 as a global pandemic presents a serious health threat to African countries and the livelihoods of its people. To mitigate the impact of this disease, intervention measures including self-isolation, schools and border closures were implemented to varying degrees of success. Moreover, there are a limited number of empirical studies on the effectiveness of non-pharmaceutical interventions (NPIs) to control COVID-19. In this study, we considered two models to inform policy decisions about pandemic planning and the implementation of NPIs based on case-death-recovery counts. METHODS: We applied an extended susceptible-infected-removed (eSIR) model, incorporating quarantine, antibody and vaccination compartments, to time series data in order to assess the transmission dynamics of COVID-19. Additionally, we adopted the susceptible-exposed-infectious-recovered (SEIR) model to investigate the robustness of the eSIR model based on case-death-recovery counts and the reproductive number (R(0)). The prediction accuracy was assessed using the root mean square error and mean absolute error. Moreover, parameter sensitivity analysis was performed by fixing initial parameters in the SEIR model and then estimating R(0), β and γ. RESULTS: We observed an exponential trend of the number of active cases of COVID-19 since March 02 2020, with the pandemic peak occurring around August 2021. The estimated mean R(0) values ranged from 1.32 (95% CI, 1.17–1.49) in Rwanda to 8.52 (95% CI: 3.73–14.10) in Kenya. The predicted case counts by January 16/2022 in Burundi, Ethiopia, Kenya, Rwanda, South Sudan, Tanzania and Uganda were 115,505; 7,072,584; 18,248,566; 410,599; 386,020; 107,265, and 3,145,602 respectively. We show that the low apparent morbidity and mortality observed in EACs, is likely biased by underestimation of the infected and mortality cases. CONCLUSION: The current NPIs can delay the pandemic pea and effectively reduce further spread of COVID-19 and should therefore be strengthened. The observed reduction in R(0) is consistent with the interventions implemented in EACs, in particular, lockdowns and roll-out of vaccination programmes. Future work should account for the negative impact of the interventions on the economy and food systems. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07510-3.
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spelling pubmed-91785512022-06-09 Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa Wamalwa, Mark Tonnang, Henri E. Z. BMC Infect Dis Research BACKGROUND: The emergence of COVID-19 as a global pandemic presents a serious health threat to African countries and the livelihoods of its people. To mitigate the impact of this disease, intervention measures including self-isolation, schools and border closures were implemented to varying degrees of success. Moreover, there are a limited number of empirical studies on the effectiveness of non-pharmaceutical interventions (NPIs) to control COVID-19. In this study, we considered two models to inform policy decisions about pandemic planning and the implementation of NPIs based on case-death-recovery counts. METHODS: We applied an extended susceptible-infected-removed (eSIR) model, incorporating quarantine, antibody and vaccination compartments, to time series data in order to assess the transmission dynamics of COVID-19. Additionally, we adopted the susceptible-exposed-infectious-recovered (SEIR) model to investigate the robustness of the eSIR model based on case-death-recovery counts and the reproductive number (R(0)). The prediction accuracy was assessed using the root mean square error and mean absolute error. Moreover, parameter sensitivity analysis was performed by fixing initial parameters in the SEIR model and then estimating R(0), β and γ. RESULTS: We observed an exponential trend of the number of active cases of COVID-19 since March 02 2020, with the pandemic peak occurring around August 2021. The estimated mean R(0) values ranged from 1.32 (95% CI, 1.17–1.49) in Rwanda to 8.52 (95% CI: 3.73–14.10) in Kenya. The predicted case counts by January 16/2022 in Burundi, Ethiopia, Kenya, Rwanda, South Sudan, Tanzania and Uganda were 115,505; 7,072,584; 18,248,566; 410,599; 386,020; 107,265, and 3,145,602 respectively. We show that the low apparent morbidity and mortality observed in EACs, is likely biased by underestimation of the infected and mortality cases. CONCLUSION: The current NPIs can delay the pandemic pea and effectively reduce further spread of COVID-19 and should therefore be strengthened. The observed reduction in R(0) is consistent with the interventions implemented in EACs, in particular, lockdowns and roll-out of vaccination programmes. Future work should account for the negative impact of the interventions on the economy and food systems. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07510-3. BioMed Central 2022-06-09 /pmc/articles/PMC9178551/ /pubmed/35681129 http://dx.doi.org/10.1186/s12879-022-07510-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wamalwa, Mark
Tonnang, Henri E. Z.
Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa
title Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa
title_full Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa
title_fullStr Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa
title_full_unstemmed Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa
title_short Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa
title_sort using outbreak data to estimate the dynamic covid-19 landscape in eastern africa
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178551/
https://www.ncbi.nlm.nih.gov/pubmed/35681129
http://dx.doi.org/10.1186/s12879-022-07510-3
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