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Geospatial analysis of household spread of Ebola virus in a quarantined village – Sierra Leone, 2014

  • B. L. GLEASON (a1), S. FOSTER (a2), G. E. WILT (a2), B. MILES (a2), B. LEWIS (a2), K. CAUTHEN (a3), M. KING (a4), F. BAYOR (a5), S. CONTEH (a5), T. SESAY (a5), S. I. KAMARA (a5), G. LAMBERT (a3), P. FINLEY (a3), W. BEYELER (a3), T. MOORE (a3), J. GAUDIOSO (a3), P. H. KILMARX (a1) and J. T. REDD (a1)...

Summary

We performed a spatial-temporal analysis to assess household risk factors for Ebola virus disease (Ebola) in a remote, severely-affected village. We defined a household as a family's shared living space and a case-household as a household with at least one resident who became a suspect, probable, or confirmed Ebola case from 1 August 2014 to 10 October 2014. We used Geographic Information System (GIS) software to calculate inter-household distances, performed space-time cluster analyses, and developed Generalized Estimating Equations (GEE). Village X consisted of 64 households; 42% of households became case-households over the observation period. Two significant space-time clusters occurred among households in the village; temporal effects outweighed spatial effects. GEE demonstrated that the odds of becoming a case-household increased by 4·0% for each additional person per household (P < 0·02) and 2·6% per day (P < 0·07). An increasing number of persons per household, and to a lesser extent, the passage of time after onset of the outbreak were risk factors for household Ebola acquisition, emphasizing the importance of prompt public health interventions that prioritize the most populated households. Using GIS with GEE can reveal complex spatial-temporal risk factors, which can inform prioritization of response activities in future outbreaks.

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Copyright

Corresponding author

*Author for correspondence: B. L. Gleason, 2160 Freetown Place, Dulles, VA 20189, USA (Email: yer7@cdc.gov)

References

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