Spatiotemporal Analysis of the 2014 Ebola Epidemic in West Africa

<div><p>In 2014–2016, Guinea, Sierra Leone and Liberia in West Africa experienced the largest and longest Ebola epidemic since the discovery of the virus in 1976. During the epidemic, incidence data were collected and published at increasing resolution. To monitor the epidemic as it spread within and between districts, we develop an analysis method that exploits the full spatiotemporal resolution of the data by combining a local model for time-varying effective reproduction numbers with a gravity-type model for spatial dispersion of the infection. We test this method in simulations and apply it to the weekly incidences of confirmed and probable cases per district up to June 2015, as reported by the World Health Organization. Our results indicate that, of the newly infected cases, only a small percentage, between 4% and 10%, migrates to another district, and a minority of these migrants, between 0% and 23%, leave their country. The epidemics in the three countries are found to be similar in estimated effective reproduction numbers, and in the probability of importing infection into a district. The countries might have played different roles in cross-border transmissions, although a sensitivity analysis suggests that this could also be related to underreporting. The spatiotemporal analysis method can exploit available longitudinal incidence data at different geographical locations to monitor local epidemics, determine the extent of spatial spread, reveal the contribution of local and imported cases, and identify sources of introductions in uninfected areas. With good quality data on incidence, this data-driven method can help to effectively control emerging infections.</p></div

Number of introduced cases per district distributed over possible origin districts, based on median posterior values averaged over ten augmented data sets.

<p>Districts are ordered per country by time of first observed case. District columns add up to the total number of observed introduced cases in that district, which can be higher than 1 due to multiple introductions and due to multiple cases per introduction. Colours indicate distinct categories: 1 or more introduced cases (red), between 0.1 and 1 (dark orange), between 0.01 and 0.1 (orange), between 0.001 and 0.01 (yellow), between 0 and 0.001 (light yellow), and 0 (white). The latter category means that this introduction is impossible, because the destination was never infected or the source was not infected at the time of introduction in the destination.</p

Estimated effective reproduction numbers over time per district.

<p>Effective reproduction numbers <i>R</i>_<i>e</i> in all districts of Guinea (green), Sierra Leone (blue) and Liberia (red) over time; median values (black dots), interquartile credible interval (dark coloured bars), and 95% credible interval (light coloured bars). Districts are ordered by time of first observed case; in six districts in Guinea not a single case has been observed.</p

Expected number of migrated cases between districts as a function of underreporting fraction in Guinea, based on median posterior values, with five repetitions per underreporting level, for Guinea (green), Sierra Leone (blue) and Liberia (red).

<p>Median value (dark symbol), median value averaged over five repetitions (dark line), interquartile range (light line), and 2.5% and 97.5% percentiles (light symbols).</p

Observed incidence over time per district, classified by origin of cases.

<p>Weekly observed incidence in all districts of Guinea (green), Sierra Leone (blue) and Liberia (red), based on median posterior values averaged over ten augmented data sets. A distinction is made between locally generated cases (lightly coloured bar), imported cases from another district in the same country (dark tip of same colour), and imported cases from another country (dark tip of different colour). Districts are ordered by time of first observed case; in six districts in Guinea not a single case has been observed. Note that the y-axis scale depends on the maximal number of observed cases per week in a district, ranging from 1 (Dinguiraye, Guinea) to 618 (Montserrado, Liberia).</p

Additional file 1: of The impact of individual-level heterogeneity on estimated infectious disease burden: a simulation study

Sensitivity analyses. (DOCX 86 kb

Additional file of Variation in loss of immunity shapes influenza epidemics and the impact of vaccination

Text S1 Provides support for the finding that increased attack rates are independent of model details. Figure S1 Attack rates are higher when susceptibility varies. Figure S2 Attack rates increase with increasing variation in susceptibility. Figure S3 No increase in infection attack rates with one year vaccine-induced immunity. Figure S4 The impact of variable duration of immunity in two extended infection disease models. Figure S5 Variable duration of protection results in increased attack rates in the leaky vaccine model. Figure S6 Impact of variable duration of immunity on age-specific infection dynamics. (PDF 185 kb

The simulated spread of MRSA at national level (assuming no interventions and equal effective case reproduction numbers) for England (red) and The Netherlands (blue).

<p>A) Proportion of hospitals with MRSA positive patients, arrows show the number of hospitals in both countries, showing faster dispersal in England as compared to the Netherlands. B) Mean MRSA prevalence among hospitals. C) The distribution of time to 50 hospitals infected. D) The percentage of simulated introductions of MRSA resulting in an epidemic.</p

Correlation between proportion of potentially infectious patients among all admitted patients (infectious relative indegree, IRI) and the MRSA bacteraemia incidence rate at hospital level, in England between 2001 and 2009.

<p>A) The proportion of potentially infectious patients among all admitted patients (log IRI) by hospital category. This proportion increases with hospital category, from small acute care hospitals to teaching hospitals. B) The MRSA bacteraemia incidence rate per hospital, between 2001 and 2009 (thin lines), and the mean per hospital category (Thick Lines), the MRSA incidence rate is highest in acute teaching hospitals. C) Correlation between the hospital log IRI and MRSA bacteraemia incidence rate for all regional hospital clusters. Over the 8 years, 20 times a cluster showed a significant positive correlation, while none showed a significant negative correlation. D) Partial correlation coefficient between the hospital log IRI and MRSA bacteraemia incidence rate for all hospitals, adjusted for incidence differences of regional clusters. Hospitals with a high degree of connectedness show higher MRSA rates than their lesser connected counterparts.</p

Estimates for the Frequencies of Adverse Events after Primary and Re-Vaccination

<p>The expected number of deaths per million primary vaccinations and revaccinations with (A) the NYCBH strain, (B) the Lister strain, and (C) the Bern strain for different age groups. The large error bars (95% CIs) indicate the uncertainty of the estimates.</p