Temporal Changes in Ebola Transmission in Sierra Leone and Implications for Control Requirements: a Real-time Modelling Study

Background: Between August and November 2014, the incidence of Ebola virus disease (EVD) rose dramatically in several districts of Sierra Leone. As a result, the number of cases exceeded the capacity of Ebola holding and treatment centres. During December, additional beds were introduced, and incidence declined in many areas. We aimed to measure patterns of transmission in different regions, and evaluate whether bed capacity is now sufficient to meet future demand. Methods: We used a mathematical model of EVD infection to estimate how the extent of transmission in the nine worst affected districts of Sierra Leone changed between 10th August 2014 and 18th January 2015. Using the model, we forecast the number of cases that could occur until the end of March 2015, and compared bed requirements with expected future capacity. Results: We found that the reproduction number, R, defined as the average number of secondary cases generated by a typical infectious individual, declined between August and December in all districts. We estimated that R was near the crucial control threshold value of 1 in December. We further estimated that bed capacity has lagged behind demand between August and December for most districts, but as a consequence of the decline in transmission, control measures caught up with the epidemic in early 2015. Conclusions: EVD incidence has exhibited substantial temporal and geographical variation in Sierra Leone, but our results suggest that the epidemic may have now peaked in Sierra Leone, and that current bed capacity appears to be sufficient to keep the epidemic under-control in most districts.

[1]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[2]  References , 1971 .

[3]  S. Ballesteros,et al.  SSM: Inference for time series analysis with State Space Models , 2013, 1307.5626.

[4]  W. Edmunds,et al.  Potential for large outbreaks of Ebola virus disease , 2014, Epidemics.

[5]  J. Hyman,et al.  The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda. , 2004, Journal of theoretical biology.

[6]  C. Althaus Estimating the Reproduction Number of Ebola Virus (EBOV) During the 2014 Outbreak in West Africa , 2014, PLoS currents.

[7]  B. Súdre,et al.  Early transmission dynamics of Ebola virus disease (EVD), West Africa, March to August 2014 - Euro surveillance 17 September 2014. , 2014, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[8]  C. Fraser,et al.  Epidemiology, transmission dynamics and control of SARS: the 2002-2003 epidemic. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[9]  Gareth O. Roberts,et al.  Examples of Adaptive MCMC , 2009 .

[10]  Joseph Dureau,et al.  Capturing the time-varying drivers of an epidemic using stochastic dynamical systems. , 2012, Biostatistics.

[11]  Aaron A. King,et al.  Time series analysis via mechanistic models , 2008, 0802.0021.

[12]  W. Team,et al.  West African Ebola Epidemic after One Year — Slowing but Not Yet under Control , 2015 .

[13]  W. Edmunds,et al.  Evaluation of the Benefits and Risks of Introducing Ebola Community Care Centers, Sierra Leone , 2015, Emerging infectious diseases.

[14]  Cdc Covid- Response Team Update: Ebola Virus Disease Outbreak — West Africa, October 2014 , 2014, MMWR. Morbidity and mortality weekly report.

[15]  A. Doucet,et al.  Particle Markov chain Monte Carlo methods , 2010 .

[16]  Alison P Galvani,et al.  Dynamics and control of Ebola virus transmission in Montserrado, Liberia: a mathematical modelling analysis. , 2014, The Lancet. Infectious diseases.

[17]  Ebola: the power of behaviour change. , 2014, Nature.

[18]  J. Wallinga,et al.  Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures , 2004, American journal of epidemiology.

[19]  A. Gelman,et al.  Weak convergence and optimal scaling of random walk Metropolis algorithms , 1997 .

[20]  Suzanne M. O’Regan,et al.  Ebola Cases and Health System Demand in Liberia , 2014, PLoS biology.