AID  Vol.10 No.3 , December 2020
Temporal Dynamics in COVID-19 Transmission: Case of Some African Countries
Abstract: Coronavirus disease 2019 (COVID-19) has become a global threat to public health and economy. The potential burden of this pandemic in developing world, particularly the African countries, is much concerning. With the aim of providing supporting evidence for decision making, this paper studies the dynamics of COVID-19 transmission through time in selected African countries. Time-dependent reproduction number (Rt) is one of the tools employed to quantify temporal dynamics of the disease. Pattern of the estimated reproduction numbers showed that transmissibility of the disease has been fluctuating through time in most of the countries included in this study. In few countries such as South Africa and Democratic Republic of Congo (DRC), these estimates dropped quickly and stayed stable, but greater than 1, for months. Regardless of their variability through time, the estimated reproduction numbers remain greater than or nearly equal to 1 in all countries. Another Statistical model used in this study, namely Autoregressive Conditional Poisson (ACP) model, showed that expected (mean) number of new cases is significantly dependent on short range change in new cases in all countries. In countries where there is no persistent trend in new cases, current mean number of new cases (on day t) depend on both previous observation and previous mean (day t - 1). In countries where there is continued trend in new cases, current mean is more affected by number of new cases on preceding day.
Cite this paper: Turasie, A. (2020) Temporal Dynamics in COVID-19 Transmission: Case of Some African Countries. Advances in Infectious Diseases, 10, 110-122. doi: 10.4236/aid.2020.103011.

[1]   World Health Organization (2020) WHO Director-General’s Opening Remarks at the Media Briefing on COVID-19 11 March 2020.

[2]   Kapata, N., Ihekweazu, C., Ntoumi, F., Raji, T., Chanda-Kapata, P., Mwaba, P., et al. (2020) Is Africa Prepared for Tackling the COVID-19 (SARS-CoV-2) Epidemic. Lessons from Past Outbreaks, Ongoing Pan-African Public Health Efforts, and Implications for the Future. International Journal of Infectious Diseases, 93, 233-236.

[3]   World Health Organization (2020) Coronavirus Disease (COVID-2019) Situation Reports.

[4]   Boëlle, P.Y., Ansart, S., Cori, A. and Valleron, A.J. (2011) Transmission Parameters of the A/H1N1 (2009) Influenza Virus Pandemic: A Review. Influenza and Other Respiratory Viruses, 5, 306-316.

[5]   Howard, S. and Donnelly, C. (2000) Estimation of a Time-Varying Force of Infection and Basic Reproduction Number with Application to an Outbreak of Classical Swine Fever. Journal of Epidemiology and Biostatistics, 5, 161-168.

[6]   Riley, S., Fraser, C., Donnelly, C.A., Ghani, A.C., Abu-Raddad, L.J., Hedley, A.J., et al. (2003) Transmission Dynamics of the Etiological Agent of SARS in Hong Kong: Impact of Public Health Interventions. Science, 300, 1961-1966.

[7]   Amundsen, E., Stigum, H., Røttingen, J.A. and Aalen, O. (2004) Definition and Estimation of an Actual Reproduction Number Describing Past Infectious Disease Transmission: Application to HIV Epidemics among Homosexual Men in Denmark, Norway and Sweden. Epidemiology & Infection, 132, 1139-1149.

[8]   Cori, A., Ferguson, N.M., Fraser, C. and Cauchemez, S. (2013) A New Framework and Software to Estimate Time-Varying Reproduction Numbers during Epidemics. American Journal of Epidemiology, 178, 1505-1512.

[9]   He, X., Lau, E.H., Wu, P., Deng, X., Wang, J., Hao, X., et al. (2020) Temporal Dynamics in Viral Shedding and Transmissibility of COVID-19. Nature Medicine, 26, 672-675.

[10]   Wei, W.E., Li, Z., Chiew, C.J., Yong, S.E., Toh, M.P. and Lee, V.J. (2020) Presymptomatic Transmission of SARS-CoV-2 Singapore, January 23-March 16, 2020. Morbidity and Mortality Weekly Report, 69, 411.

[11]   Wu, J.T., Leung, K., Bushman, M., Kishore, N., Niehus, R., de Salazar, P.M., et al. (2020) Estimating Clinical Severity of COVID-19 from the Transmission Dynamics in Wuhan, China. Nature Medicine, 26, 506-510.

[12]   Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., et al. (2020) Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. New England Journal of Medicine, 382, 1199-1207.

[13]   Du, Z., Xu, X., Wu, Y., Wang, L., Cowling, B. and Meyers, L. (2020) The Serial Interval of COVID-19 from Publicly Reported Confirmed Cases.

[14]   Zhao, S., Gao, D., Zhuang, Z., Chong, M., Cai, Y., Ran, J., et al. (2020) Estimating the Serial Interval of the Novel Coronavirus Disease (COVID-19): A Statistical Analysis Using the Public Data in Hong Kong from January 16 to February 15, 2020.

[15]   Bi, Q., Wu, Y., Mei, S., Ye, C., Zou, X., Zhang, Z., et al. (2020) Epidemiology and Transmission of COVID-19 in 391 Cases and 1286 of Their Close Contacts in Shenzhen, China: A Retrospective Cohort Study. The Lancet Infectious Diseases.

[16]   Ferguson, N.M., Cummings, D.A., Fraser, C., Cajka, J.C., Cooley, P.C. and Burke, D.S. (2006) Strategies for Mitigating an Influenza Pandemic. Nature, 442, 448-452.

[17]   Roy, M., Son, A. and May, R.M. (1982) Directly Transmitted Infectious Diseases: Control by Vaccination. Science, 215, 1053-1060.

[18]   Biggerstaff, M., Cauchemez, S., Reed, C., Gambhir, M. and Finelli, L. (2014) Estimates of the Reproduction Number for Seasonal, Pandemic, and Zoonotic Influenza: A Systematic Review of the Literature. BMC Infectious Diseases, 14, Article No. 480.

[19]   Heinen, A. (2003) Modelling Time Series Count Data: An Autoregressive Conditional Poisson Model.

[20]   Ferland, R., Latour, A. and Oraichi, D. (2006) Integer-Valued GARCH Process. Journal of Time Series Analysis, 27, 923-942.

[21]   Fokianos, K., Rahbek, A. and Tjøstheim, D. (2009) Poisson Autoregression. Journal of the American Statistical Association, 104, 1430-1439.

[22]   Fokianos, K. and Tjøstheim, D. (2011) Log-Linear Poisson Autoregression. Journal of Multivariate Analysis, 102, 563-578.

[23]   Agosto, A. and Giudici, P. (2020) A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics.

[24]   Kharroubi, S. (2020) Modeling and Predicting the Spread of COVID-19 in Lebanon: A Bayesian Perspective.