The plot at the end of the previous post has suggested a link between nominal Gross Domestic Product (nominal GDP) and the growth rate of the Ebola outbreak during the exponential stage. I had used the nominal GDP as a proxy for poverty. Thus, the argument was: the poorer the population, the faster the spread of Ebola. Caitlin Rivers had commented that it would be interesting to see such an analysis at the level of counties.

Caitlin and colleagues provide Ebola data at the county or district level. Hence, we need only to find the corresponding socio-economic data at the same level. In fact, The World Bank and the government of Sierra Leone have compiled a report (“A poverty profile for Sierra Leone”, PDF). They derive a handy aggregated poverty measure:

… welfare is measured by aggregate household consumption over the last twelve months. The aggregate incorporates food consumption, non-food consumption, housing, and benefit derived from durable goods. (p.27)

The report presents in Table A2 on p. 36 the number of poor people in each district based on the above poverty measure. I divide this number by the total number of inhabitants of each district to obtain the fraction of poor people as a measure of the poverty of the respective district.

Now let us bring the two sets of data together. First a look at Ebola at the level of districts (Figure 1).

Since we are interested in the growth rate during the exponential stage of the outbreak, we have to exclude four districts: Kailahun and Kenema are the two districts with the greatest numbers of confirmed cases, but, obviously, the growth there is no longer exponential but the number has levelled off (top two curves in Figure 1). At the other end of the spectrum are districts Bonthe and Koinadugu, both having (almost) no confirmed cases and thus no exponential growth (bottom two curves). All the other districts show a marked, and approximately exponential growth in September, perhaps with the exception of district Kambia where the first cases occur only in mid-September and the number stays relatively small; hence, I also exclude Kambia.

For the remaining districts I fit exponentials to the points and determine the growth rates. When I first plotted these growth rates against the district poverties, I saw a clear positive trend, but also a remarkable outlier (Figure 2): the Western Area Urban, with the capital Freetown.

In Sierra Leone, Freetown is unique in several respects. For instance, the fraction of poor people is by far the smallest of all districts, while the population density is by far the largest of all districts (Figure 3).

Figure 2 suggests a relatively fast spread of Ebola in the Western Area Urban despite the relatively low poverty. This seems to contradict our hypothesis of “greater poverty means faster spread of Ebola”. But it is also obvious that the Western Area Urban with the dominating capital Freetown has an exceedingly high population density, which of course supports the spread of infections. Including this high-population density point in our analysis will partially mask the effect of poverty. Hence, I drop this point, too. This brings us then to our final analysis (Figure 4).

Ebola growth rate and poverty are linearly correlated with an adjusted . The correlation is significantly different from zero with . Although we have not proven a causal connection of poverty and Ebola growth rate, this result is compatible with Ebola feeding on poverty.