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What Can We Learn from Burkina Faso COVID-19 Data? Using Phenomenological Models to Characterize the Initial Growth Dynamic of the Outbreak and to Generate Short-Term Forecasts
Version 1
: Received: 17 April 2020 / Approved: 19 April 2020 / Online: 19 April 2020 (05:23:40 CEST)
How to cite:
Rouamba, T.; Samadoulougou, S.; Bonnechère, B.; Chiêm, B.; Kirakoya-Samadoulougou, F. What Can We Learn from Burkina Faso COVID-19 Data? Using Phenomenological Models to Characterize the Initial Growth Dynamic of the Outbreak and to Generate Short-Term Forecasts. Preprints2020, 2020040328. https://doi.org/10.20944/preprints202004.0328.v1
Rouamba, T.; Samadoulougou, S.; Bonnechère, B.; Chiêm, B.; Kirakoya-Samadoulougou, F. What Can We Learn from Burkina Faso COVID-19 Data? Using Phenomenological Models to Characterize the Initial Growth Dynamic of the Outbreak and to Generate Short-Term Forecasts. Preprints 2020, 2020040328. https://doi.org/10.20944/preprints202004.0328.v1
Rouamba, T.; Samadoulougou, S.; Bonnechère, B.; Chiêm, B.; Kirakoya-Samadoulougou, F. What Can We Learn from Burkina Faso COVID-19 Data? Using Phenomenological Models to Characterize the Initial Growth Dynamic of the Outbreak and to Generate Short-Term Forecasts. Preprints2020, 2020040328. https://doi.org/10.20944/preprints202004.0328.v1
APA Style
Rouamba, T., Samadoulougou, S., Bonnechère, B., Chiêm, B., & Kirakoya-Samadoulougou, F. (2020). What Can We Learn from Burkina Faso COVID-19 Data? Using Phenomenological Models to Characterize the Initial Growth Dynamic of the Outbreak and to Generate Short-Term Forecasts. Preprints. https://doi.org/10.20944/preprints202004.0328.v1
Chicago/Turabian Style
Rouamba, T., Benjamin Chiêm and Fati Kirakoya-Samadoulougou. 2020 "What Can We Learn from Burkina Faso COVID-19 Data? Using Phenomenological Models to Characterize the Initial Growth Dynamic of the Outbreak and to Generate Short-Term Forecasts" Preprints. https://doi.org/10.20944/preprints202004.0328.v1
Abstract
On 9 March 2020, two cases of COVID-19 were reported in Burkina Faso. As of 10 April 2020, a total number of 484 cases (404 cases in the Kadiogo province) were reported nationwide. Real-time forecasts of COVID-19 are important to inform decision-making in the country. Here, we propose an approach that tests the performance of four models (Exponential Growth model, the Generalized Growth model (GGM), the Generalized Logistic Growth, and Richards Growth model) to select the model that best fit data and to generate short-term forecasting (5-, 10-, and 15-day forecasts from 11 to 25 April 2020) in Kadiogo, the epicenter of the outbreak. Using daily number of confirmed COVID-19 cases, the results suggests that GGM performed the best out of the 4 models. Overall, our GGM predictions suggested an average total number of cumulative cases of 514 (95% CI, 464–559), 629 (95% CI, 559–691), and 750 (95% CI, 661–840) between 11 to 15 April, 16 to 20 April, and 20 to 25 April 2020, respectively. COVID-19 in this province was best approximated by sub exponential growth rather than exponential or logistic growth. Current data suggest that COVID-19 cases would continue to increase over the next 15-days.
Medicine and Pharmacology, Epidemiology and Infectious Diseases
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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