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therefore, to understand how human risks are affected by the dynamics of these reservoirs and how people interact with them.
The capacity of the plague bacillus to form permanent foci under highly diverse ecological conditions attests to its extraordinary adaptability. During its emergence in central Asia, Y. pestis accumulated copies of insertion sequences rendering its genome highly plastic (Parkhill et al., 2001). The capacity to undergo genomic rearrangements may thus be an efficient means for the plague bacillus to adapt to new ecological niches. Y. pestis was recently shown to be able to acquire antibiotic resistance plasmids under natural conditions (Galimand et al., 1997; Guiyoule et al., 2001), probably during its transit in the flea midgut (Hinnebusch et al., 2002). Obviously, the emergence and spread of multidrug-resistant strains of Y. pestis would represent a major threat to human health.
Although the number of human cases of plague is relatively low, it would be a mistake to overlook its threat to humanity because of the disease's inherent communicability, rapid spread, rapid clinical course, and high mortality if left untreated. A plague outbreak may also cause widespread panic, as occurred in 1994, when a relatively small outbreak, with 50 deaths, was reported in the city of Surat, India (Mudur, 1995), which led to a nationwide collapse in tourism and trade, with an estimated cost of $600 million U.S. dollars (Fritz et al., 1996).
Studying the Plague Dynamics of Central Asia: The Effect of Climate Variation
Together with colleagues, I have been studying the dynamics of the plague ecological system based on long-term monitoring data from the former Soviet Union (specifically from Kazakhstan), some of which have been published (Davis et al., 2004, 2007; Frigessi et al., 2005; Kausrud et al., 2007; Park et al., 2007; Samia et al., 2007; Stenseth et al., 2006) but much more is to come, including information on human plague cases. Currently, we are expanding our geographic area of interest to include China, India, Madagascar, and the United States.
Our core set of monitoring data comes from southeastern Kazakhstan (74-78°E and 44-47°N; see Figure 2-14). Each spring and autumn, between 1949 and 1995, a proportion of inhabited burrows and site-count observations were done at different locations within the PreBalkhash area (see Figure 2-14; for details, see Stenseth et al., 2006).
For monitoring purposes, the area was divided into 10 x 10 km2 sectors. Four sectors constitute a 20 x 20 km2 primary square (PSQ), and four PSQs constitute a large square (LSQ; Figure 2-14). At a given site, the great gerbil population densities were estimated at most twice per year. On approximately 85 percent of these occasions, up to 8,576 gerbils (median = 604) were trapped per LSQ, based on independent plague prevalence data (see Stenseth et al., 2006) and season, and tested for Y. pestis infection. The LSQs chosen had the longest regular and continuous time-series data required by our analysis. We also have access to
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FIGURE 2-14 The field data used in Stenseth et al. (2006) were collected in a natural plague focus in Kazakhstan. The data are plague prevalence in great gerbils, counts of fleas collected from trapped gerbils, and meteorological observations. Left Upper: Kazakhstan on a map of Central Asia with the PreBalkhash focus (between 74 and 78°E and 44 and 47°N) marked as a square. The historic climate (tree-ring) measurement sites are circles marked K (Karakorum) and T (Tien Shan). These sites are located approximately 1,000 and 600 km from the research area, respectively. Lower Right: The LSQ in the PreBalkhash focus from which we have prevalence. The four LSQs (40 x 40 km) circled in red, namely LSQs 78, 83, 91, and 105, represent key sites where collection of samples for testing the presence of plague was more regular and continuous. The Bakanas meteorological station is located in LSQ 117, marked by a red triangle. Upper Right: The time-series plots of the observed prevalence per LSQ. Open and filled circles denote the observed prevalence during the spring and fall, respectively. The time series of the prevalence fitted by using the model defined by the model is shown in red. Using the same model but without any climatic covariates gives the time series shown in gray. Note that owing to the presence of missing values in some covariates (occupancy) and prevalence data, the curves of the fitted values are discontinuous. The fitted values from the model provide a closer fit and reproduce the peaks in prevalence far better than the model without the climatic variables. Lower Left: Time-series plots of the climate variables, spring rainfall, spring temperature, and summer rainfall (from left to right). SOURCE: Stenseth et al. (2006).
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plague prevalence data: gerbils caught were tested for plague through isolation of Y. pestis from blood, spleen, or liver smears.
Spring climatic variables used were the average monthly temperature during the spring (i.e., March and April) and the log average of the spring rainfall. The fall climatic variable used is the log average of summer rainfall over June, July, and August. Incorporating the climatic effects in the model resulted in fitted values that track the peak occurrences in prevalence more closely than the model without the climatic variables.
Climate variability over the past millennium was estimated by using a large data set of 203 Juniperus turkestanica tree-ring width series to reconstruct temperature variations in the Tien Shan Mountains (Kirghizia) (Esper et al., 2003) and a total of 40 stable oxygen isotope (818O) series to reconstruct precipitation variations in the Karakorum Mountains (Pakistan) (Treydte et al., 2006). Climatic variations at these sites are found to be correlated with those in the study area.
We also used the NDVI (Hall et al., 2005; Los et al., 2000; see also Pettorelli et al., 2005), which is based on the difference between near-infrared and visible light reflected from the ground, thereby giving an index of light absorbed by chlorophyll on the ground, an index we also extended through proxy data back in time (see Kausrud et al., 2007).
The following discussion summarizes our findings to date. Davis et al. (2004) demonstrated that plague within an area invades, fades out, and reinvades in response to fluctuations in the abundance of its main reservoir host, the great gerbil. Broadly speaking, they found that infection spreads and persists when total abundance is above a single threshold value and fades out when it is below (see Figure 2-15).
Stenseth et al. (2006) reported that a 1°C increase in spring temperatures is predicted to lead to a >50 percent increase in prevalence (see also Samia et al., 2007). Changes in spring temperature were found to be the most important environmental variable determining the prevalence level, leading to the following scenario: Warmer spring conditions result in an elevated vector-host ratio, which leads to a higher prevalence level in the gerbil host population. Moreover, the climatic conditions that support increased prevalence among gerbils, given unchanged gerbil abundance, also favor increased gerbil abundance (see Kausrud et al., 2007), implying that the threshold density (as found by Davis et al., 2004) condition for plague will be reached more often, thereby increasing the frequency with which plague can occur.
Kausrud et al. (2007), focusing on rodent-host dynamics, drew the following five main conclusions from their analyses:
1. Density fluctuations of the great gerbil, the main host, are highly correlated over large areas, suggesting that climate may be a synchronizing agent. This is probably an important factor causing large-scale plague epizootics in the region.
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