Tarylee Reddy1,2,3, Ziv Shkedy4, Charl Janse van Rensburg5, Henry Mwambi6, Pravesh Debba7, Khangelani Zuma8, Samuel Manda5,6,9. 1. Biostatistics Research Unit, South African Medical Research Council, Cape Town, South Africa. tarylee.reddy@mrc.ac.za. 2. Censtat, Hasselt University, Hasselt, Belgium. tarylee.reddy@mrc.ac.za. 3. School of Mathematics, Statistics and Computer Science, University of KwaZulu Natal, Durban, South Africa. tarylee.reddy@mrc.ac.za. 4. Censtat, Hasselt University, Hasselt, Belgium. 5. Biostatistics Research Unit, South African Medical Research Council, Cape Town, South Africa. 6. School of Mathematics, Statistics and Computer Science, University of KwaZulu Natal, Durban, South Africa. 7. Smart Places Cluster, Council for Scientific and Industrial Research, Pretoria, South Africa. 8. Human and Social Capabilities Research Division, Human Science Research Council, Pretoria, South Africa. 9. Department of Statistics, University of Pretoria, Pretoria, South Africa.
Abstract
BACKGROUND: The rising burden of the ongoing COVID-19 epidemic in South Africa has motivated the application of modeling strategies to predict the COVID-19 cases and deaths. Reliable and accurate short and long-term forecasts of COVID-19 cases and deaths, both at the national and provincial level, are a key aspect of the strategy to handle the COVID-19 epidemic in the country. METHODS: In this paper we apply the previously validated approach of phenomenological models, fitting several non-linear growth curves (Richards, 3 and 4 parameter logistic, Weibull and Gompertz), to produce short term forecasts of COVID-19 cases and deaths at the national level as well as the provincial level. Using publicly available daily reported cumulative case and death data up until 22 June 2020, we report 5, 10, 15, 20, 25 and 30-day ahead forecasts of cumulative cases and deaths. All predictions are compared to the actual observed values in the forecasting period. RESULTS: We observed that all models for cases provided accurate and similar short-term forecasts for a period of 5 days ahead at the national level, and that the three and four parameter logistic growth models provided more accurate forecasts than that obtained from the Richards model 10 days ahead. However, beyond 10 days all models underestimated the cumulative cases. Our forecasts across the models predict an additional 23,551-26,702 cases in 5 days and an additional 47,449-57,358 cases in 10 days. While the three parameter logistic growth model provided the most accurate forecasts of cumulative deaths within the 10 day period, the Gompertz model was able to better capture the changes in cumulative deaths beyond this period. Our forecasts across the models predict an additional 145-437 COVID-19 deaths in 5 days and an additional 243-947 deaths in 10 days. CONCLUSIONS: By comparing both the predictions of deaths and cases to the observed data in the forecasting period, we found that this modeling approach provides reliable and accurate forecasts for a maximum period of 10 days ahead.
BACKGROUND: The rising burden of the ongoing COVID-19 epidemic in South Africa has motivated the application of modeling strategies to predict the COVID-19 cases and deaths. Reliable and accurate short and long-term forecasts of COVID-19 cases and deaths, both at the national and provincial level, are a key aspect of the strategy to handle the COVID-19 epidemic in the country. METHODS: In this paper we apply the previously validated approach of phenomenological models, fitting several non-linear growth curves (Richards, 3 and 4 parameter logistic, Weibull and Gompertz), to produce short term forecasts of COVID-19 cases and deaths at the national level as well as the provincial level. Using publicly available daily reported cumulative case and death data up until 22 June 2020, we report 5, 10, 15, 20, 25 and 30-day ahead forecasts of cumulative cases and deaths. All predictions are compared to the actual observed values in the forecasting period. RESULTS: We observed that all models for cases provided accurate and similar short-term forecasts for a period of 5 days ahead at the national level, and that the three and four parameter logistic growth models provided more accurate forecasts than that obtained from the Richards model 10 days ahead. However, beyond 10 days all models underestimated the cumulative cases. Our forecasts across the models predict an additional 23,551-26,702 cases in 5 days and an additional 47,449-57,358 cases in 10 days. While the three parameter logistic growth model provided the most accurate forecasts of cumulative deaths within the 10 day period, the Gompertz model was able to better capture the changes in cumulative deaths beyond this period. Our forecasts across the models predict an additional 145-437 COVID-19deaths in 5 days and an additional 243-947 deaths in 10 days. CONCLUSIONS: By comparing both the predictions of deaths and cases to the observed data in the forecasting period, we found that this modeling approach provides reliable and accurate forecasts for a maximum period of 10 days ahead.
Entities:
Keywords:
COVID-19; Logistic growth model; Phenomenological models; Prediction; Richards model
Authors: C R Sebrango-Rodríguez; D A Martínez-Bello; L Sánchez-Valdés; P J Thilakarathne; E Del Fava; P VAN DER Stuyft; A López-Quílez; Z Shkedy Journal: Epidemiol Infect Date: 2017-06-01 Impact factor: 4.434