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.

Rarely have studies focused on the second- and third-order effects of pandemics. Limiting the disruption of critical infrastructures during a pandemic is important for the survival and health of society (i.e., electricity, water, and food) as most medical and public health responses to a pandemic depend on these infrastructures. The studies that have looked at this issue have highlighted alarming gaps in preparedness. This study used a system dynamics model to demonstrate the likely effects of a pandemic on the USA’s food system. The model reveals that a severe pandemic with greater than a 25 % reduction in labor availability can create significant and widespread food shortages. The Ebola epidemic that began in 2014 has caused severe food shortages in West Africa, which are similar to the effects that this model predicts in the USA. The likely effects of the reduction in the amount of available food are difficult to specifically predict; however, it is likely to have severe negative consequences on society. The resilience of the food system must be improved against this hazard and others.

Random networks were generated with the random configuration model with prescribed truncated power-law degree distributions, parameterized by an exponent, an offset, and an exponential rolloff. As a model of an attack, each network had exactly one of its highest degree nodes removed, with the result that in some cases, one or more remaining nodes became congested with the reassignment of the load. The congested nodes were then removed, and the “cascade failure” process continued until all nodes were uncongested. The ratio of the number of nodes of the largest remaining cluster to the number of nodes in the original network was taken to be a measure of the network's resiliency to highest-degree node removal. We found that the resiliency is sensitive to both rolloff and offset (but not to cutoff) in the degree distribution, and that rolloff tends to decrease resiliency while offset tends to increase it.