We developed an agent-based model to investigate the epidemic dynamics of Ebola virus disease (EVD) in Liberia and Sierra Leone from May 27 to December 21, 2014. The dynamics of the agent-based simulator evolve on small-world transmission networks of sizes equal to the population of each country, with adjustable densities to account for the effects of public health intervention policies and individual behavioral responses to the evolving epidemic. Based on time series of the official case counts from the World Health Organization (WHO), we provide estimates for key epidemiological variables by employing the so-called Equation-Free approach. The underlying transmission networks were characterized by rather random structures in the two countries with densities decreasing by ~19% from the early (May 27-early August) to the last period (mid October-December 21). Our estimates for the values of key epidemiological variables, such as the mean time to death, recovery and the case fatality rate, are very close to the ones reported by the WHO Ebola response team during the early period of the epidemic (until September 14) that were calculated based on clinical data. Specifically, regarding the effective reproductive number Re, our analysis suggests that until mid October, Re was above 2.3 in both countries; from mid October to December 21, Re dropped well below unity in Liberia, indicating a saturation of the epidemic, while in Sierra Leone it was around 1.9, indicating an ongoing epidemic. Accordingly, a ten-week projection from December 21 estimated that the epidemic will fade out in Liberia in early March; in contrast, our results flashed a note of caution for Sierra Leone since the cumulative number of cases could reach as high as 18,000, and the number of deaths might exceed 5,000, by early March 2015. However, by processing the reported data of the very last period (December 21, 2014-January 18, 2015), we obtained more optimistic estimates indicative of a remission of the epidemic in Sierra Leone, as reflected by the derived Re (~0.82, 95% CI: 0.81-0.83).
Background: An EVD outbreak may reduce life expectancy directly (due to high mortality among EVD cases) and indirectly (e.g., due to lower utilization of healthcare and subsequent increases in non-EVD mortality). In this paper, we investigated the direct effects of EVD on life expectancy in Liberia, Sierra Leone and Guinea (LSLG thereafter).
Methods: We used data on EVD cases and deaths published in situation reports by the World Health Organization (WHO), as well as data on the age of EVD cases reported from patient datasets. We used data on non-EVD mortality from the most recent life tables published prior to the EVD outbreak. We then formulated three scenarios based on hypotheses about a) the extent of under-reporting of EVD cases and b) the EVD case fatality ratio. For each scenario, we re-estimated the number of EVD deaths in LSLG and we applied standard life table techniques to calculate life expectancy.
Results: In Liberia, possible reductions in life expectancy resulting from EVD deaths ranged from 1.63 year (low EVD scenario) to 5.56 years (high EVD scenario), whereas in Sierra Leone, possible life expectancy declines ranged from 1.38 to 5.10 years. In Guinea, the direct effects of EVD on life expectancy were more limited (<1.20 year).
Conclusions: Our high EVD scenario suggests that, due to EVD deaths, life expectancy may have declined in Liberia and Sierra Leone to levels these two countries had not experienced since 2001-2003, i.e., approximately the end of their civil wars. The total effects of EVD on life expectancy may however be larger due to possible concomitant increases in non-EVD mortality during the outbreak.
Background:
With the Ebola epidemic raging out of control in West Africa, there has been a flurry of research into the Ebola virus, resulting in the generation of much genomic data.
Methods:
In response to the clear need for tools that integrate multiple strands of research around molecular sequences, we have created the University of California Santa Cruz (UCSC) Ebola Genome Browser, an adaptation of our popular UCSC Genome Browser web tool, which can be used to view the Ebola virus genome sequence from GenBank and nearly 30 annotation tracks generated by mapping external data to the reference sequence. Significant annotations include a multiple alignment comprising 102 Ebola genomes from the current outbreak, 56 from previous outbreaks, and 2 Marburg genomes as an outgroup; a gene track curated by NCBI; protein annotations curated by UniProt and antibody-binding epitopes curated by IEDB. We have extended the Genome Browser’s multiple alignment color-coding scheme to distinguish mutations resulting from non-synonymous coding changes, synonymous changes, or changes in untranslated regions.
Discussion:
Our Ebola Genome portal at http://genome.ucsc.edu/ebolaPortal/ links to the Ebola virus Genome Browser and an aggregate of useful information, including a collection of Ebola antibodies we are curating.
Background: Several monoclonal antibodies (mAb) are being evaluated as treatment options for the current 2014 Ebola outbreak. But they were derived from and tested for protection against the older 1976 Mayinga or 1995 Kikwit Zaire Ebolaviruses (EBOV). The EBOV sequences reported for the current outbreak contain several mutations whose significance remained to be established.
Methods: We analyzed sequence and structural conservation of the Ebolavirus glycoprotein (GP) epitopes for all experimentally identified protective mAbs published to date.
Results: The conservancy analysis of protective mAb epitopes in the Ebolavirus glycoprotein sequences spanning all Ebola virus lineages since 1976 showed that conservancy within the Zaire EBOV lineage was high, with only one immunodominant epitope of mAb 13F6-1-2 acquiring two novel mutations in the 2014 outbreak that might potentially change the antibody specificity and neutralization activity. However, the conservation to other Ebola viruses was unexpectedly low.
Conclusion: Low conservancy of Zaire EBOV mAb epitopes to other EBOV lineages suggests that these epitopes are not indispensable for viral fitness, and that alternative mAbs could be developed to broadly target all EBOV. However, average percent sequence identity of the epitopes for mAbs used in current cocktails to the Zaire EBOV is high with only one epitope differing in the 2014 outbreak. These data bode well for general usefulness of these antibodies in the context of the current outbreak.
Background and Methodology:
The current Ebola virus epidemic in West Africa has been spreading at least since December 2013. The first confirmed case of Ebola virus in Sierra Leone was identified on May 25. Based on viral genetic sequencing data from 72 individuals in Sierra Leone collected between the end of May and mid June, we utilize a range of phylodynamic methods to estimate the basic reproductive number (R0). We additionally estimate the expected lengths of the incubation and infectious periods of the virus. Finally, we use phylogenetic trees to examine the role played by population structure in the epidemic.
Results:
The median estimates of R0 based on sequencing data alone range between 1.65-2.18, with the most plausible model yielding a median R0 of 2.18 (95% HPD 1.24-3.55). Importantly, our results indicate that, at least until mid June, relief efforts in Sierra Leone were ineffective at lowering the effective reproductive number of the virus. We estimate the expected length of the infectious period to be 2.58 days (median; 95% HPD 1.24-6.98). The dataset appears to be too small in order to estimate the incubation period with high certainty (median expected incubation period 4.92 days; 95% HPD 2.11-23.20). While our estimates of the duration of infection tend to be smaller than previously reported, phylodynamic analyses support a previous estimate that 70% of cases were observed and included in the present dataset. The dataset is too small to show a particular population structure with high significance, however our preliminary analyses suggest that half the population is spreading the virus with an R0 well above 2, while the other half of the population is spreading with an R0 below 1.
Conclusions:
Overall we show that sequencing data can robustly infer key epidemiological parameters. Such estimates inform public health officials and help to coordinate effective public health efforts. Thus having more sequencing data available for the ongoing Ebola virus epidemic and at the start of new outbreaks will foster a quick understanding of the dynamics of the pathogen.