INFORMING SURVEILLANCE FOR THE LOWLAND PLAGUE FOCUS...
Transcript of INFORMING SURVEILLANCE FOR THE LOWLAND PLAGUE FOCUS...
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INFORMING SURVEILLANCE FOR THE LOWLAND PLAGUE FOCUS IN AZERBAIJAN USING A HISTORIC DATASET
By
LILLIAN REBECCA MORRIS
A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
2013
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© 2013 Lillian Rebecca Morris
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To my mom and dad
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ACKNOWLEDGMENTS
This project was funded by the United States Defense Threat Reduction Agency
(DTRA) through the Cooperative Biological Engagement Program under the
Cooperative Biological Research Project AJ-3. Funding was administered by the joint
University Partnership managed by the University of New Mexico.
I thank my family for their constant support, guidance and motivation for all of my
endeavors inside and outside of academia. I would also like to thank the members of
the SEER lab for their help, encouragement and patience throughout this process. I
would like to thank my advisor, Jason Blackburn, for his dedication and enthusiasm. He
has continuously challenged me to learn and grow as a scientist over the past two
years. I would also like to thank the members of my committee, Liang Mao and Mike
Binford, for their participation and guidance in writing this thesis.
Finally, I would like to thank FUEL for providing a much needed and appreciated
outlet throughout my graduate school career.
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TABLE OF CONTENTS
page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
LIST OF ABBREVIATIONS ............................................................................................. 9
ABSTRACT ................................................................................................................... 10
CHAPTER
1 INTRODUCTION .................................................................................................... 12
Plague Background ................................................................................................ 12 Primary Host and Focus ......................................................................................... 15 Plague in Azerbaijan ............................................................................................... 18 STAMP ................................................................................................................... 20 Plague and Climate ................................................................................................ 21 Human Plague Risk ................................................................................................ 23 Objectives ............................................................................................................... 25
2 METHODS .............................................................................................................. 27
Study Area .............................................................................................................. 27 Meriones libycus Data ............................................................................................. 28 Database Development .......................................................................................... 29 STAMP ................................................................................................................... 30 Stable Regions ....................................................................................................... 31 Environmental Data ................................................................................................ 32 Environmental Analysis ........................................................................................... 33 Human Population Analysis .................................................................................... 34
3 RESULTS ............................................................................................................... 44
STAMP ................................................................................................................... 44 Stable Regions ....................................................................................................... 44 Environmental Data ................................................................................................ 45 Environmental Analysis ........................................................................................... 45 Human Population .................................................................................................. 46
4 DISCUSSION ......................................................................................................... 59
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Future Directions .................................................................................................... 66 APPENDIX
A STAMP OUTPUTS ................................................................................................. 67
B POPULATION DISTRIBUTION .............................................................................. 93
LIST OF REFERENCES ............................................................................................... 94
BIOGRAPHICAL SKETCH .......................................................................................... 101
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LIST OF TABLES
Table page 2-1 List of host and vector species found in annual yearbooks ................................ 37
2-2 GlobCover classifications. .................................................................................. 38
3-1 Changes in the area from STAMP analysis ........................................................ 48
3-2 Mann Whitney U test, 5 categories. .................................................................... 49
3-3 Descriptive statistics for environmental variables. .............................................. 50
3-4 Mann Whitney U test, 3 categories ..................................................................... 51
3-5 The percentages of each land cover type ........................................................... 52
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LIST OF FIGURES
Figure page 2-1 Azerbaijan with land cover. ................................................................................. 39
2-2 Plague focus and protected areas in Azerbaijan ................................................ 40
2-3 Extracting data from annual yearbooks .............................................................. 41
2-4 Hand drawn maps .............................................................................................. 42
2-5 Event definitions for STAMP ............................................................................... 43
3-1 STAMP outputs from 1976 to 1977. ................................................................... 53
3-2 Stable regions for each abundance category ..................................................... 54
3-3 The distribution of environmental variables across Azerbaijan ........................... 55
3-4 Regions of stability for each abundance level .................................................... 56
3-5 The percentage of each land cover type ............................................................ 57
3-6 The percentage population change between 1970 and 2010. ............................ 58
A-1 STAMP outputs for very low Meriones libycus abundance ................................. 68
A-2 STAMP outputs for low Meriones libycus abundance ......................................... 71
A-3 STAMP outputs for average Meriones libycus abundance ................................. 77
A-4 STAMP outputs for high Meriones libycus abundance ....................................... 83
A-5 STAMP outputs for very high Meriones libycus abundance ............................... 89
B-1 HYDE population grids for 1970 and 2010. ........................................................ 93
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LIST OF ABBREVIATIONS
APS Republican Anti-Plague Station
APD Anti-Plague Division
CDC Center for Disease Control
CEPF Critical Ecosystems Partnership Fund
GIS Geographic Information System
GlobCover Global composites and land cover maps
HYDE History Database of the Global Environment
RMS root-mean-square error
STAMP Space Time Analysis of Moving Polygons
WHO World Health Organization
WorldClim Global Climate Data
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science
INFORMING SURVEILLANCE FOR THE LOWLAND PLAGUE FOCUS IN
AZERBAIJAN USING A HISTORIC DATASET
By
Lillian Rebecca Morris
May 2013
Chair: Jason Blackburn Major: Geography
Yersinia pestis is a gram-negative, zoonotic pathogen that causes plague.
Plague is maintained in nature through a transmission cycle between partially resistant
rodent hosts and fleas. There are natural reservoirs on almost every continent, and the
number of human plague cases has increased in recent years. Azerbaijan is a country
at the crossroads of Eastern Europe and western Asia that has a history of
environmental plague foci. Informing plague surveillance in this region is imperative
due to the deteriorating public health system that resulted from the collapse of the
Soviet Union. This study aims to prioritize regions for plague surveillance in Azerbaijan.
A 14 year historic data set was employed to analyze the spatio-temporal pattern of the
primary plague host in the country, Meriones libycus, using the Space Time Analysis of
Moving Polygons method (STAMP). This method has the utility to identify areas that
remained stable over time, which is meaningful when analyzing historic patterns. The
relationship between stable M. libycus abundance and environmental variables
including temperature, altitude, land cover type and annual precipitation was explored.
Changes in human population density over the historic period to modern times were
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also analyzed. We were particularly interested in identifying increasing population
trends in the area surrounding regions characterized by historically high M. libycus
abundance, as the risk of human plague increases as humans come into close
proximity with hosts and vectors. We found that there was variation in M. libycus
abundance over the historic period, but regions of stability were identified. There are
significantly different climatic and land cover conditions associated with different levels
of abundance. The population in Azerbaijan has steadily increased over the past 30
years, including regions bordering plague foci. Surveillance should be prioritized for
regions with historically stable high host abundance, regions with climatic conditions
associated with high abundance, and regions with increasing populations surrounding
plague foci.
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CHAPTER 1 INTRODUCTION
Plague Background
Plague is a flea borne zoonosis caused by the gram negative bacterium Yersinia
pestis (Pollitzer 1954, Perry and Fetherston 1997, Gage and Kosoy 2005). Plague is a
notorious disease that has been associated with three massive pandemics throughout
history (Gage and Kosoy 2005). The first plague pandemic spread across the
Mediterranean in the 6th century and has been linked to the weakening of the Byzantine
empire (Perry and Fetherston 1997). The Black Death, which killed almost one third of
Europe’s population in the fourteenth century, has been attributed to plague. There is
evidence that the black death originated from an environmental plague focus, which is a
region on the landscape that maintains the pathogen, in central Asia and spread to the
south and east (Pollitzer 1951). The third pandemic started in China in the 19th century
(Perry and Fetherston 1997, Stenseth et al. 2008). The spread of this pandemic was
facilitated by rat infested steamships travelling to major ports (Dennis 1998). An
estimated 26 million plague cases resulted, with approximately 12 million fatalities
(Dennis 1998). The geographic range of plague has greatly expanded since the onset
of the most recent pandemic (Gage and Kosoy 2005). This expansion includes the
Western United States, portions of India, and Southeast Asia (Dennis 1998, Gage and
Kosoy 2005, Pollitzer 1954). There has also been an increase in human plague cases.
From 1981 to 1995 approximately 20,000 human plague cases from 25 countries were
reported to the World Health Organization (WHO).
The Center for Disease Control (CDC) classifies Y. pestis as a Category A
biological agent (Rotz et al. 2002). Category A agents have the greatest potential for
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adverse public health impacts through mass casualties, which presents a bioterrorism
concern. These agents require preparedness efforts including improved surveillance
(Rotz et al. 2002). The greatest challenge related to plague control and surveillance is
its presence and maintenance in rural, natural cycles (Dennis 1998). Plague has a
history of appearing in new populations or reemerging in a region that has not seen
cases for extended time periods , which is difficult to predict (Dennis 1998). The 1994
pneumonic human outbreak in India exemplifies the necessity of monitoring this
important pathogen because it reiterates the idea that there is a constant risk of human
plague surrounding natural foci (Dennis 1994).
Yersinia pestis is maintained in nature through a transmission cycle between
partially resistant rodent hosts and adult hematophagous fleas (Gage and Kosoy 2005,
Meyer 1942). Yersinia pestis is a successful vector borne pathogen because it is able to
survive in its host and also spreads from the site of a flea bite, which serves as a source
of infection for feeding fleas (Anisimov 2002). The infected flea spreads the bacteria to
other rodent hosts. It is believed that the bacteria can be maintained indefinitely in these
enzootic or maintenance cycles (Gage and Kosoy 2005, Beran 1994, Gage, Ostfeld and
Olson 1995). The enzootic or primary host is able to maintain plague foci without the
involvement of other hosts as long as sufficient numbers of flea vectors are present
(Gage and Kosoy 2005). When Y. pestis spreads from enzootic hosts to more highly
susceptible or secondary hosts then rapid die offs take place (Gage and Kosoy 2005).
Classically it is hypothesized that plague transmission relies on the flea vector.
High numbers of fleas tend to be correlated with a higher prevalence of plague (Conrad,
LeCocq and Krain 1968). There are more than 200 potential species of plague vectors
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that are specific to rodent hosts (Gage and Kosoy 2005). Fleas are efficient vectors of
Y. pestis as a result of a blocking process that takes place during the transmission
cycles (Bacot and Martin 1914). Once a flea has fed on an infected host the bacteria
rapidly replicates in the flea’s gut (Bacot and Martin 1914, Bibikova 1977). The bacterial
colonies grow large enough to block the movement of blood from the midgut to the
foregut, which essentially starves the flea. The starving flea feeds rapidly by using
pharyngeal muscles to draw blood, but eventually has to relax those muscles, which
leads to contaminated blood being flushed back to the feeding site infecting the host
(Webb et al. 2006, Bacot and Martin 1914).
There is ongoing research on the role of partially blocked and unblocked fleas in
plague transmission (Gage and Kosoy 2005). It has been suggested that partially
blocked fleas could play an important, short term role in transmission that helps sustain
outbreaks (Webb et al. 2006). Webb et al. (2006) found that blocked fleas cannot
exclusively drive epizootics in prairie dogs. Short term reservoirs including other small
mammals, infectious carcasses, and transmission from unblocked fleas likely drive
disease spread during an outbreak.
Over 200 species of rodents have been identified as playing a role in plague
transmission cycles (Gratz 1999). A successful host must have enough resistance to Y.
pestis to allow bacteria to cycle through the blood and infect fleas (Meyer 1942). The
amount of resistance in a host is related to variables including species, age, breeding
status, immune and physiological status, and season (Meyer 1942, Hubbert and
Goldenberg 1970). A host population tends to contain a combination of susceptible
individuals that die as a result of infection and more resistant individuals that survive
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infection (Pollitzer 1954, Gage and Kosoy 2005). Recovered hosts likely become
chronic carriers, which allows populations to maintain the bacteria between seasons
(Pollitzer 1954). It has been suggested that the impact of host populations on disease
transmission depends on the ratio of resistant to susceptible individuals within the
population (Gage and Kosoy 2005). An effective host population tends to be heavily
infected with multiple flea vectors, and live in burrows with high densities of rodents and
fleas (Pavlovsky, Plous JR and Levine 1966).
Studies have found that there is also a relationship between host abundance and
plague prevalence in host communities. Davis et al. (2004) found that plague
persistence in Kazakhstan’s desert region was related to the total abundance of the
Great Gerbil, Rhombomys opimus, host population. They suggest that the colonization
and extinction dynamic affecting gerbil abundance determines the meta-population
context of plague (Davis et al. 2004). In order to successfully monitor and control
plague on the landscape, it is necessary to understand the distribution and abundance
of hosts (Stenseth et al. 2008, Gage and Kosoy 2005).
Primary Host and Focus
Large, natural foci of plague are active in Asia, part of the Russia Federation, and
Asian republics (Gratz 1999). Over the past twenty years, human plague cases have
reemerged in this region (Pollitzer 1954). Bertherat et al. (2007) reported a human
plague outbreak in Algeria in 2003, which was the first reported instance of plague in
the country for 50 years. Human plague cases have also been reported in Saudi Arabia
(Saeed, Al-Hamdan and Fontaine 2005), Jordan (Arbaji et al. 2005), Afghanistan (Leslie
et al. 2011), and a limited outbreak in Libya (Tarantola et al. 2009) over the past
decade. The reemergence of human plague in the region suggests that there are still
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active environmental reservoirs. The Republic of Azerbaijan is a country at the
crossroads of western Asia and Eastern Europe that has three well documented
historical plague foci (Gurbanov and Akhmedova 2010).
Meriones libycus is a major plague host in this part of the world (Gratz 1999,
Gage and Kosoy 2005, Gurbanov and Akhmedova 2010, Bakanidze et al. 2010).
Meriones libycus has been documented as the primary plague host in the Trans-
continental lowland foothills plague focus in Azerbaijan (Gurbanov and Akhmedova
2010). As early as 1953, M. libycus prevalence was linked to a plague epizootic in the
Apshnon peninsula of Azerbaijan (Bakanidze et al. 2010).
In addition to its established role as a plague host, this ground dwelling gerbil
plays a critical role in the maintenance of leishmaniasis in the greater Mediterranean
area and Western Asia. Leishmanisis is a vector born disease caused by a protozoa
transmitted by the bite of a sand-fly, and presents a critical public health concern in
several countries (González et al. 2009, Desjeux 2001). Meriones libycus is one
species that maintains the zoonotic form of the disease in the environment (Nadim and
Faghih 1968). Human cases can result from humans coming into contact with sandflies
inhabiting M. libycus burrows. In the Republic of Iran, M. libycus is the primary reservoir
for zoonotic cutaneous leishmaniasis (Rassi et al. 2006, Yaghoobi-Ershadi, Akhavan
and Mohebali 1996). Meriones libycus has also been linked to the maintenance of
leishmaniasis in Saudi Arabia (Ibrahim et al. 1994). It is clear that monitoring and
controlling M. libycus populations has important implications for multiple public health
initiatives.
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The natural range of M. libycus spans from the Western Sahara to Western
Xinjiang, China (Nowak and Paradiso 1999). They are found in a range of habitats
including clay and sandy deserts, bush country, arid steppe, low plains, cultivated fields,
grasslands, and mountain valleys (Nowak and Paradiso 1999, Daly and Daly 2009).
Meriones libycus has a rat like appearance characterized by narrow well developed
ears, a tail the length of their head and body, elongated hind legs for leaping, and strong
claws (Nowak and Paradiso 1999). They eat green vegetation, roots, bulbs, seeds,
cereal, fruits, and insects (Nowak and Paradiso 1999). Meriones libycus live in complex
multi entranced burrows, which are approximately 1.5 meters deep, and expand
outward approximately 3 to 4 meters (Nowak and Paradiso 1999). Their burrows
generally have food storage chambers near the top, and nesting chambers deeper in
the ground (Naumov et al. 1973). Individuals store as much as 10 kilograms of seeds at
a time in the northern part of its range (Naumov et al. 1973). Their preferred burrow
location is under bush cover (Daly and Daly 2009), which puts them at close proximity
to flea vectors (Gage and Kosoy 2005). Many individuals burrow in the same vicinity
(Naumov et al. 1973). They spend the majority of their time underground, and are prone
to nocturnal activity (Daly and Daly 2009). Males traverse large overlapping home
ranges, while females prefer smaller more exclusive ranges (Daly and Daly 2009,
Nowak and Paradiso 1999). Females will remain in the same vegetative depression
surrounded by desert for several consecutive months. Home ranges are between 50
and 120 meters in diameter (Nowak and Paradiso 1999). Reproduction occurs year
round, but usually takes place in late winter to early autumn. Each female produces 2
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to 3 litters, and litter sizes range from 1 to 12. The gestation period is 20 to 31 days
(Nowak and Paradiso 1999).
Plague in Azerbaijan
The concern about plague and its hosts has a long history in Azerbaijan. During
the Soviet era, an Anti–plague system (APS) was created to respond to outbreaks of
plague and other bacterial and viral diseases (Ouagrham-Gormley 2006, Zilinskas
2006). These stations addressed disease through surveillance, research production
and training efforts; however, their primary objective was disease control (Ouagrham-
Gormley 2006, Zilinskas 2006). During the Soviet era, the APS grew to include over 100
facilities throughout the region, which was the largest system of its kind the world had
ever seen. Today, The APS in Baku is responsible for surveillance, identification,
documentation and preventive measures against very dangerous infections (10
conditions including plague, cholera and anthrax). It has five regional branches known
as Anti-Plague Divisions (APD) that obtain laboratory samples and perform an initial
diagnosis before referring samples to the APS for confirmation testing. However,
contemporary field sampling for plague has been limited since independence from the
Soviet Union.
The first APD in Azerbaijan was a result of a plague outbreak in the Hadrut
district of Azerbaijan in 1930. This outbreak prompted the establishment of a plague
department within the Institute of microbiology in Baku, the capital city of Azerbaijan
(Ouagrham-Gormley 2006). In 1934 the department became the central Anti – plague
station in Azerbaijan (Ouagrham-Gormley 2006). After several plague epizootic events
in Azerbaijan the source was identified as a natural plague reservoir in Azerbaijan by
Soviet scientists in 1953. Three new field stations were established as a result
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(Ouagrham-Gormley 2006). As part of surveillance efforts in Azerbaijan, a detailed
annual yearbook was published outlining every aspect of the APS effort for that year.
Topics included preventative measures, prophylaxis of plague and other diseases,
epidemiological observations, and epizootic observations. The yearbook also outlined
lab research and field related work from the year. Other details such as the structure,
financial status of the institute, and training efforts that took place at the institute were
also discussed. In Azerbaijan, annual yearbooks were published for more than 40
years. Today, these yearbooks are maintained in the archival library at the Republican
APS in Baku.
APS yearbooks provide a unique and important opportunity to understand the
history of plague in Azerbaijan. This is particularly relevant because surveillance efforts
have been greatly limited since the collapse of the Soviet Union in 1991. The Republic
of Azerbaijan gained its independence at this time; however, this collapse was
characterized by diminished funding and maintenance for the APS system. With fewer
resources available for surveillance, this thorough and detailed historic dataset can be
used to inform modern surveillance efforts.
One of the most thorough and repeated datasets in the yearbook collection
outlined seasonal M. libycus abundance across the country. These hand drawn maps
depicted M. libycus abundance using polygons, and cross hatched patterns to represent
different levels of abundance. Each level of abundance is represented with a polygon of
a particular pattern or color that differentiates it from the other abundance levels and
host species. This is an important dataset to explore because of the continued
significance of M. libycus as a plague host in Azerbaijan. The yearbooks spanned
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several years and represented the entire plague focus, allowing for the analysis of M.
libycus abundance across space and over time.
STAMP
Exploring historical changes in M. libycus spatial patterns and abundance across
the landscape can provide important insight into distribution and ecology of this
important plague host. Identifying explicit regions with extended periods of high or low
abundance can be particularly informative. Specifically, such analyses could provide a
historical baseline of spatio-temporal patterns of host abundance that might inform
contemporary efforts to direct enzootic sampling in Azerbaijan. The Space Time
Analysis of Moving Polygons (STAMP) approach is one method for addressing these
objectives with data such as those available in the APS yearbooks. STAMP uses
polygons representing a phenomenon from two consecutive time periods and describes
the type of change that is taking place between polygons. STAMP overlays polygons
from consecutive time periods, and each incremental time change is categorized based
on the associated spatial change (Robertson et al. 2007). STAMP’s change definitions,
also called event types, are derived from the spatial proximity or overlap between the
two time periods. The analysis includes the identification of regions that do not change,
or that remain stable (Robertson et al. 2007).
STAMP has been applied to a variety of diverse research questions. This
method was initially employed to determine how a wildfire spread over time (Robertson
et al. 2007). Robertson et al. (2007) were able to determine the rate and direction of fire
spread over a three week period using STAMP. This method was also used to
determine differences in the area used by solitary female grizzly bears verses females
with cubs over a two year time period (Smulders et al. 2012). They were particularly
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interested in identifying differences in site fidelity between the two groups, which can be
derived from areas classified as stable during a STAMP analysis (Smulders et al. 2012).
Nelson et al. (2011) employed STAMP to explore changing caribou home ranges. A
home range describes the space an animal uses over a specified time period and is
commonly defined by polygons. A STAMP analysis of changing home ranges revealed
the type of home range drift that occurred over the study interval (Nelson 2011). As
these applications reveal, STAMP can be used to compare two time periods, or can
create a sequence to describe how a polygon changes over multiple time periods
(Robertson et al. 2007).
Our analysis has many similarities to previous applications of STAMP. Our data
consists of polygons to describe M. libycus abundance over a multiyear period. STAMP
has the utility to analyze this type of data, and identify the regions of stability. The
stable areas across years are of particular interest because human plague epidemics,
as well as epizootic events, have been correlated to M. libycus natural foci in the past
(Bakanidze et al. 2010, Daly and Daly 2009). Historical trends might be able to suggest
current regions more likely to support high levels of plague host abundance.
Plague and Climate
Throughout history, links have been made between climatic conditions and
plague prevalence. Precipitation, temperature, and relative humidity have been found
to have the highest impact on the plague cycle (Ben Ari et al. 2011). Seasonal plague
events in India have been related to variations in temperature and humidity (Rogers
1928). A pattern of fewer outbreaks in extreme hot or cold temperatures has been
described for several African countries (Davis 1953). Stenseth et al. (2006) found that
warmer springs and wetter summers increase occurrence and levels of plague
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prevalence. Environmental foci tend to be found in semi-arid to arid regions, and low
humidity forests (Perry and Fetherston 1997). Plague foci do not typically persist for
long periods in humid, tropical lowlands, or the highest, driest deserts (Perry and
Fetherston 1997). Elevation is another environmental variable that has been linked to
plague persistence on the landscape. One study identified elevation as one of the most
important variables in a model for predicting plague risk across the Lushoto district of
Tanzania (Neerinckx et al. 2010). A model of human plague risk in the southwest United
States also identified elevation as a significant predictor (Eisen et al. 2007b).
Important relationships have been described between the plague host and
climatic conditions. Cavanaugh and Marshall (1972) suggest that high intensity rainfall
is detrimental to rodent survival, as burrows become flooded. There appears to be an
association between annual rainfall, rodent densities and seasonal distribution of hosts
(Ben Ari et al. 2011). A recent study in New Mexico presented a trophic cascade
hypothesis where precipitation resulted in increased plant production and rodent food
sources (Parmenter et al. 1999). The subsequent increase in host populations
increases the likelihood of epizootics and human cases (Parmenter et al. 1999). There
is a less apparent direct relationship between hosts and temperature. Though in
temperate regions, low winter temperatures can impact food availability, which
influences rodent densities and distribution (Korslund and Steen 2005).
Climate plays an important role in dictating the presence of plague on the
landscape; therefore, climatic variables should be included when exploring questions
relating to plague and its hosts. This analysis included an exploration of the climatic
conditions associated with rodent abundance over time. We focused on precipitation,
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temperature and elevation because they are known to influence the presence of plague
and plague hosts on the landscape in other regions. Understanding relationships
between environmental factors and host abundance will help us to identify regions that
might be characterized by higher levels of host abundance. For example, if a region
was historically characterized by high levels of rodent abundance we can hypothesize
that stable climate conditions will maintain similar levels of plague and rodents in the
region. Similarly, if there are climatic shifts this analysis can help detect new regions at
risk of increasing host abundance and guide surveillance efforts.
Human Plague Risk
In order to thoroughly explore regions at the highest risk of plague transmission,
it is important to consider human population dynamics, as human plague cases
frequently result from humans coming into contact with flea vectors (Mann et al. 1979,
Saeed et al. 2005). Plague epidemics still occur in developing countries and are
commonly related to large epizootic events. When there is a large die off of host
species, the flea vectors are forced to find another source of food, which causes the
bacteria to spread outside of the enzootic, maintenance cycle (Eskey and Haas 1940,
Kartman 1970, Gage and Kosoy 2005). In California, studies have suggested that
increased serological prevalence of plague in coyotes, Canis latrans, could be an
indicator of a plague epizootic event in the region (Thomas and Hughes 1992).
Carnivores also provide a vehicle for infected fleas to travel to other populations (Perry
and Fetherston 1997). Flea vectors on dogs and cats is one method of facilitating
human infection (Mann et al. 1979). Human outbreaks are sometimes correlated with
unsanitary or rat infested environments, as the hosts, vectors and human populations
are in a close spatial proximity (Gage and Kosoy 2005). A human plague epidemic in
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Vietnam in the winter of 1967 was partially attributed to a rapidly increasing rat
population and a high flea index (Conrad et al. 1968). In the southwest United States,
distance to host habitat is also a significant predictor of high risk human plague regions
(Eisen et al. 2007a). Handling or ingesting wild animal carcasses is another
documented infection pathway. There was one reported human plague outbreak in
Saudi Arabia linked to eating raw camel liver (Saeed et al. 2005). More recently, a large
outbreak with 83 cases, of which 17 were fatal, was attributed to eating camel meat in
Afghanistan (Leslie et al. 2011).
Human cases generally start as the bubonic form of the disease, particularly if
the bacterium was contracted from a flea bite. Symptoms include fever, headache,
chills, and swollen lymph nodes, called buboes (Perry and Fetherston 1997). Symptoms
develop within 2 to 6 days of coming into contact with the bacteria. Septicemic plague
develops as the infection spreads into the bloodstream (Hull, Montes and Mann 1987).
Pneumonic plague results from the lungs becoming infected. This is the most fatal form
of plague. Pneumonic plague can be transmitted from person to person via respiratory
droplets through close contact (Perry and Fetherston 1997).
Protected lands, such as national parks, create a dynamic that can bring humans
and vectors into close proximity. This dynamic has the potential to effect Azerbaijan
due to its inclusion in the Caucasus Biodiversity Hotspot, which is one of 34 biodiversity
hotspots in the world (Zazanashvili et al. 2009). This designation is a result of the
presence of diverse ecosystems, endangered species, and endangered vegetation
(CEPF 2003). The Caucasus region also has the greatest biodiversity out of any
temperate forest in the world (Zazanashvili et al. 2009). The bezoar goat, goitered
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gazelle, mouflon tar, and Caspian seal are all critically endangered species found in
Azerbaijan (Zazanashvili et al. 2009). Today approximately 10% of the country is
protected lands, which includes national parks, strict nature preserves, and wildlife
sanctuaries. The number of protected lands in Azerbaijan has doubled since the
collapse of the Soviet Union, and there are significant efforts to continue expanding the
system in order to preserve the biodiversity in the country (Zazanashvili et al. 2009).
The presence of non-governmental organizations concerned with conservation, and
organizations such as the World Wildlife Fund, The Critical Ecosystem Partnership
Fund, and the Eco Region Conservation Fund are all contributing to expanding the
protected lands system (Zazanashvili et al. 2009, CEPF 2003). Part of this effort
includes the diversification of protected lands. Traditionally, Azerbaijan has created
strict nature reserves that have minimal access and recreational opportunities for
people. In recent years, the number of national parks with recreational opportunities
and public access has greatly increased (Zazanashvili et al. 2009).
The spatial proximity of human populations to natural reservoirs is clearly a risk
factor for human cases. It is particularly important to monitor changing population
densities in regions neighboring natural plague foci in order to gauge changing risks of
human infection. Prioritizing limited resources for control efforts should incorporate an
evaluation of regions at the greatest risk of human infection.
Objectives
The objective of this study was to inform and identify priorities for plague
surveillance and control in Azerbaijan, which is critically important due to the limited
resources available for public health efforts after the collapse of the Soviet Union. To
achieve this objective, we explored changes in M. libycus abundance over space and
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time, and identified regions of historically stable host abundance. A second objective
was to identify specific climatic factors associated with stable regions of mammal
abundance by category. The relationship between stable host abundance and
changing population levels were also analyzed in order to identify regions characterized
by an increased risk of human infection.
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CHAPTER 2 METHODS
Study Area
Azerbaijan is a small country located in the Caucasus region at the intersection
of Western Asia and Eastern Europe (Figure 2-1). There are 59 administrative districts,
or rayons, in the country. Azerbaijan is bordered by the Caspian Sea to the east,
Armenia and Georgia to the west and Russia to the north. The capital city, Baku, is on
the east coast of Azerbaijan. The Greater and Lesser Caucasus mountain ranges run
through the country. The Greater Caucasus range extends from Sochi Russia on the
northeastern shore of the black sea and ends adjacent to Baku. The lesser Caucasus
range runs parallel to the Greater range, about 100km to the south. In Azerbaijan, the
Greater Caucasus mountain range parallels the northern border. The Lesser Caucasus
range spans the south western border between Armenia and Azerbaijan.
This study focused on one of three plague foci in the country. APS scientists
define the plague focus used for this study as the natural habitat of M. libycus in the
region (Figure 2-2). This area is characterized by constant presence of the Libyan jird,
which was determined through year round perennial investigations. Meriones libycus
natural habitat was employed to define this boundary because it is the primary carrier of
plague in the Transcaucasian Plain-sub Mountain Natural focus.
There are currently 44 protected areas in Azerbaijan, which encompasses
National parks, strict nature reserves, wildlife refuges and wildlife sanctuaries
(Zazanashvili et al. 2009) (Figure 2-2). Spatial data on protected lands was
downloaded from the World Database on Protected Lands (http://www.wdpa.org/). This
database was derived from information from national governments, non-government
28
organizations, academic institutions, and many other sources to provide a
comprehensive global spatial dataset on marine and terrestrial protected areas.
Meriones libycus Data
Annual APS yearbooks thoroughly described every aspect of the stations
activities for the year including lab activities, field work, and control efforts, which were
reported using tables, charts, and maps. Historic yearbooks contained information on
multiple plague hosts and vectors (Table 2-1). Host and vector data was displayed in
hand drawn maps using polygons, colors, and patterns to represent different species, or
abundance levels for the year. These datasets were found in the archival library at the
Republican APS in Baku. Our research team extracted data from yearbooks in order to
convert them to an electronic form more conducive to analysis in a Geographic
Information System (GIS) (Figure 2-3). For this analysis, we use a subset of APS
yearbook data from 1972 to 1985. We limited this analysis to data on M. libycus
because of its history as an important plague host in the country. Additionally, M.
libycus abundance maps were consistently published throughout the fourteen year
period. The historic Azerbaijan dataset included M. libycus data collected during the fall
season across the country.
Meriones libycus themed maps defined abundance using 5 categories from 1972
to 1980 in the historic yearbooks: very low, low, average, high, and very high (Figure 2-
4). Very low was defined as 0 – 1 specimen per hectare. Low was 2-5 specimens per
hectare. Average was defined as 6 – 10 specimens per hectare. 11 – 20 specimens
per hectare was high abundance. Greater than 20 specimens per hectare defined very
high abundance. Each definition was based on APS zoological sampling surveys
conducted in the fall of each year. After 1980, very low and low were collapsed into one
29
category defined as less than or equal to 5 specimens per hectare. Very low was no
longer used due to the inexpediency in identifying areas with close to absent rodent
populations. Instead, the regions of very low abundance were identified by zoologists
based on visual inspection of the landscape for burrows or signs of recent rodent
activity, but were not included in annual abundance maps. This analysis included an
exploration of changes in M. libycus abundance over the historic time period.
Database Development
In order to analyze data available in annual yearbooks we photographed all maps
from all yearbooks between 1972 and 1985; minus 1979 and 1984, as we could not
locate these maps in the collection of yearbooks found in the Baku APS facility. All
photographs were captured as digital *.jpg images and organized by year. A small
Olympus digital camera was used to capture high resolution photos. Each page for
each yearbook was photographed two times to allow for quality comparisons. The
digital camera was held near perpendicular to the map image laid flat on a table. All
maps were cataloged according to translated titles and legends.
Each map image was georeferenced using the georeferencing tools in ArcGIS v
9.3.1 (ESRI, Redlands CA). All maps were referenced back to a 1-m polygon outline of
Azerbaijan from the Geo-Community Geoportal. Each image was georeferenced to the
WGS 1984 geographic projection of the world provided by ESRI using a minimum of
eight control points. Control points are coordinate pairs assigned by the user that match
the map position to real world coordinates. We employed an n+1 control point
experiment to determine the most suitable number of control points from 5 to 25 to
accurately georeferenced the images. There was minimal improvement after eight
control points. We use the root-mean-square error (RMS) and high spatial overlap
30
between known Azerbaijani features (such as political boundaries) from existing GIS
data and features on the images to minimize georeferencing error. Once images were
georeferenced (with quality control), we constructed new GIS data layers to match each
item in the map legend (points, line, and polygons) and then heads-up digitized these
shapes into spatially-referenced shapefiles (Curtis, Blackburn and Sansyzbayev 2006);
the shapefile is an ESRI proprietary data format that is used worldwide. In this way, a
single usable GIS layer was created for each map item.
STAMP
A STAMP analysis was used to explore the spatial and temporal variability of M.
libycus abundance across the landscape from 1972 to 1985. A separate STAMP
analysis was conducted on each of the five abundance categories. Each STAMP
analysis included polygons from every year that data were available for each
abundance category. The very low abundance analysis incorporated 1972 to 1978, as
very low and low abundance were collapsed after this time period. If a year was
unavailable the next available year was used in its place. For example, 1979 was
unavailable for all abundance categories, so polygons representing 1978 were
compared to 1980. STAMP is a freely available toolbar extension for ArcGIS v 9.3
(http://www.geog.uvic.ca/spar/stamp/help/index.html).
STAMP works by overlaying polygons from two consecutive time periods. The
change between intervals is defined based on the type of spatial overlap (Figure 2-5). If
polygons from two consecutive time periods are overlapping that area is classified as
contraction, or expansion. Contraction indicates that the area was only present in the
first time interval, while expansion indicates that a new area was created in the second
time interval. Disappearance and generation events are spatially isolated, which means
31
that they are not connected to any region delineating the opposing time period.
Disappearance is only present in the first time period, while generation is only found in
the second time period. Stable is an area common to both time periods.
STAMP requires spatially unique polygons. To prepare digitized data, the erase
tool was used to create unique polygons for each abundance level for each year.
Georeferenced images of hand drawn maps were consulted to ensure the accurate
categorization of polygons. All polygon layers were then projected
(WGS_1984_UTM_zone_38N). Finally, multipart features were separated into single
part features, which allowed STAMP to evaluate each polygon independently.
The STAMP process is comprised of four steps (Robertson et al. 2007). The first
step is to ensure that polygon’s input layers are spatially exclusive. The second step
creates change layers and assigns them to one of the five event categories: stable,
generation, expansion, disappearance or contraction (Robertson et al. 2007). The third
step uses the change layers and computes the directional relationship between
polygons by creating voronoi polygons (Robertson et al. 2007). The final step, polygon
metrics, calculates the metrics and ratios from the change layers (Robertson et al.
2007). The area of each abundance category for each year was reported in square
kilometers.
Stable Regions
The STAMP analyses identified regions that remained stable between
consecutive years. Those outputs were used to evaluate inter-annual variability for
each category across all consecutive time periods. We were also interested in
identifying areas characterized by long-term persistence. To identify stable regions
spanning more than two years, stable event polygons were extracted from all STAMP
32
outputs for each abundance level. Overlapping stable layers from consecutive years
within each abundance category were identified using the intersect tool in ArcGIS v 10.
The overlapping portion of the polygon was used to create regions of stability. These
stable regions were then used to evaluate environmental conditions associated with
each abundance category.
Environmental Data
STAMP allowed us to identify regions where a particular abundance category
persisted for an extended time interval, and we were interested in determining if there
were climatic conditions associated with different levels of stable M. libycus abundance.
Interpolated world climate surfaces were used to explore potential relationships
between the environment and stable regions. WorldClim climatic data are freely
accessible on the WorldClim website (www.WorldClim.org) (Hijmans et al. 2005).
These data are available at a 1-km spatial resolution. WorldClim data were derived
from a network of ground based weather stations, and reflect data collected between
1950 and 2000 (Hijmans et al. 2005). A smoothing spline algorithm was used for
interpolation using latitude, longitude and elevation as independent variables (Hijmans
et al. 2005). WorldClim includes over 20 variables describing climate in a variety of
ways. Some of these variables include precipitation in the wettest month, precipitation
in the driest month, minimum temperature of coldest month, and mean diurnal range.
Annual mean temperature (BIO1), annual precipitation (BIO12), and altitude were
downloaded for analyses.
Similarly to our climate analysis, we attempted to identify unique land cover types
associated with different levels of stable rodent abundance. We obtained land cover
data from GlobCover v 2.2 (ESA GlobCover project). GlobCover is a land cover dataset
33
at a 300 meter resolution with a thematic resolution of 22 classes
(http://www.ionia1.esrin.esa.int). The data was created from satellite images obtained
from 2005 to 2006. GlobCover has been employed for a variety of recent scientific
analyses. Linard et al. (2011) found that using GlobCover resulted in the most accurate
human population modelling across Africa compared to other available land cover
datasets. Falcucci et al. (2013) incorporated GlobCover data into their model of wolf re-
colonization of the alpine range. The GlobCover dataset identified 16 land cover
classifications found throughout Azerbaijan. We collapsed the land cover categories
comprising a small percentage of Azerbaijan, which resulted in 8 categories used for
analyses: rain-fed cropland, mosaic cropland, deciduous forest, evergreen forest, mixed
forest, shrubland/grassland, sparse/barren, and water (Figure 2-1, Table 2-2).
Environmental Analysis
Climatic data were paired with stable regions of abundance for each category to
explore relationships between climatic conditions and M. libycus abundance. Identifying
unique characteristics related to different levels of abundance could help to predict
abundance levels for modern surveillance efforts. To make WorldClim data compatible
with stable regions, the area of all stable regions from each abundance category were
combined using the merge and dissolve tools in ArcGIS v 10. Temperature,
precipitation, and altitude values were extracted from polygons representing the stable
area for each abundance level using the raster clip tool in ArcGIS v 10. The resulting
attribute tables were imported to the R statistical package for analyses (http://www.r-
project.org/). The maximum value, minimum value, mean, median and range were
calculated for each variable at each abundance level. The Mann Whitney U test was
34
used to determine if climatic conditions were significantly different for each abundance
level.
The Mann Whitney U test is a nonparametric test that compares the median
differences between two distributions (Crichton 2000). This is an appropriate test when
data being compared are naturally linked and do not follow a normal distribution
(Crichton 2000). The null hypothesis of the Mann Whitney U test is that the median
difference between paired values is equal to zero (Wilcoxon 1945, Mann and Whitney
1947). This test, run in R, was used to compare each abundance category with the
other abundance categories for all three environmental variables. The level of
confidence was set at 95%.
To identify distinctive land cover descriptions for each abundance level, the land
cover data set was clipped to each of the polygons representing stability for each level
of abundance to determine if there were unique land cover conditions for different
abundance levels. The total number of raster cells within each polygon was calculated.
The proportion of each land cover type for each abundance category was computed by
dividing the number of cells representing each land cover type by the total number of
raster cells within the polygon.
Human Population Analysis
The risk of human infection should be considered when prioritizing regions on the
landscape for control and preventative measures, as human plague risk is inherently
linked to the proximity of human populations to the pathogen. Toward this, the objective
of this analysis was to inform surveillance of natural plague foci throughout Azerbaijan.
History Database of the Global Environment v 3.1 (HYDE) gridded time series
population data were downloaded (http://themasites.pbl.nl/tridion/en/themasites/hyde/)
35
in an effort to explore the relationship between M. libycus abundance and human
population levels (Klein Goldewijk et al. 2011). HYDE includes georeferenced historical
gridded population data for the past 12,000 years at a 5’ (9.56 km) resolution (Goldewijk
2005). The data were derived from a combination of population statistics and satellite
information with specific allocation algorithms that change over time (Klein Goldewijk et
al. 2011). Available population totals for each country were crosschecked with other
sources, which were primarily local country studies (Goldewijk 2005). The Populstat
database (Lahmeyer 2000), and Gazetteer (2004) were the two primary sources
employed for deriving HYDE population maps from 1700 to 2000 (Goldewijk 2005).
Gaps in data were overcome by interpolating data points (Goldewijk 2005). HYDE data
has a variety of applications including environmental assessments, and biological
diversity analyses. The Global environmental outlook for the United Nations
Environmental Program (UNEP, 1997), have used HYDE data. Gaston et al. (2003)
used HYDE data in their analysis of global avian biodiversity loss.
In an effort to understand how population levels have changed from our historic
study interval to modern times, the population grids for 1970 and 2005 (the most recent
available surface) were downloaded. The 2005 surface was projected to 2010 using the
United Nations, medium variant, inter-censal growth rate by country (UNPD world
population prospects), which is in line with the methods of Hay et al.(2009). The 2010
population surface was derived using
Where is the population within each pixel, represents the population at year x in
the same pixel, r is average growth rate, and t is the number of years between 2010 and
36
x (Hay et al. 2009). The resulting 2010 population grid was used to create a surface
representing the percentage change in population from 1970 to 2010. All calculations
were computed using the raster calculator in ArcGIS v 10.
37
Table 2-1. List of host and vector species found in annual yearbooks
Common Name Genus Species
Hosts
Libyan jird Meriones M. libycus
Persian jird Meriones M. persicus
Tristram's jird, Malaysian jird Meriones M. tristami
Vinogradovi's jird Meriones M. vinogradovi
Common Vole Microtus M. arvalis
Social Vole Microtus M. socialis
Vectors
Fleas Callopsylla C. caspius
Ctenophthalmus C. wladimiri
Nosopsyllus N. consilimis
Nosopsyllus N. iranus
Nosopsyllus N. laericeps
Xenopsylla X. conformis
38
Table 2-2. GlobCover classifications for land cover types found throughout Azerbaijan and definitions of land cover classifications. The definitions report the combined definitions of categories that were collapsed.
Name Definition
Mixed Forest Closed to open (>15%) mixed broadleaved and needleleaved forest (>5m)
Deciduous Forest Closed or open Broadleaved Deciduous Forest (>5m)
Evergreen Forest Closed or open Needleleaved Deciduous or Evergreen Forest (>5m)
Shrubland / Grassland Mosaic grassland, shrubland (broadleaved or needleleaved, evergreen or deciduous, <5m), herbaceous vegetation (grassland, savannas or lichens/mosses)
Rainfed Cropland Rainfed Croplands
Mosaic Cropland and Vegetation 20 - 70% Cropland, 20 - 70% vegetation (grassland/shrubland/forest)
Sparse / Barren Sparse (<15%) vegetation or bare areas
Water Water Bodies
39
Figure 2-1. Azerbaijan with land cover, bordering countries and important rayons identified. The land cover categories were derived from GlobCover v 2.2, and reflect the final eight categories resulting from collapsing the 16 categories identified in Azerbaijan by the GlobCover surface.
40
Figure 2-2. Plague focus and protected areas in Azerbaijan. The focus is defined by M. libycus natural habitat in the area.
The protected areas are depicted based on the year of their designation.
41
Note: Photos courtesy of Jason Blackburn
Figure 2-3. Extracting data from annual yearbooks in the APS archival library in Baku, Azerbaijan
42
bottom map is from 1981.
Note: Photos courtesy of Jason Blackburn Figure 2-4. Hand drawn maps delineating Meriones libycus abundance with translated
legends from annual yearbooks. A) 1972 Meriones libycus abundance. B) 1980 Meriones libycus abundance.
A
Libyan Gerbil
0 – 1 Specimens per ha 1 – 5 Specimens per ha 6 – 10 Specimens per ha 11 – 20 Specimens per ha More than 20 Specimens per ha Settlement type – pesthole
Very High
Low
Average
High
Common Vole
Vinograd’s Gerbil
B
43
Figure 2-5. Event definitions for Space Time Analysis of Moving Polygons (STAMP).
Each circle represents an area in one of two consecutive time periods. Concentration, stable, and expansion are defined by overlapping areas, but contraction is only in time period one while expansion is only in time period two. Disappearance and generation are events that are spatially isolated from other areas.
Close Spatial Proximity
Time 1 Contraction
Time 2 Expansion
Time 1 + Time 2 Stable
Time 1 Disappearance
Time 2 Generation
Spatially Independent
44
CHAPTER 3 RESULTS
STAMP
There were no data available on M. libycus abundance for 1979, or 1984 in any
abundance category. Every year from 1972 to 1985 included M. libycus abundance for
low, average, and high abundance categories. Very low abundance data was available
from 1972 to 1978. Very high abundance was available for every year except 1973,
1976, and 1982 (Table 3-1).
The boundaries for each abundance category changed every year (Table1).
There were no years when the area of any abundance category was completely stable.
Low rodent abundance had the largest average area over the study interval. Very high
abundance had the smallest area over the study area. Very high abundance was
frequently found close to Samukh. Low abundance spanned the entire central portion
of the country. The area calculations for all years and all abundance categories are
shown in Table 2. All inter-annual STAMP outputs are shown in Appendix A.
Stable Regions
Stable regions spanning more than two years were identified for every
abundance category (Figure 3-2). Regions of low stable abundance were the most
widespread across Azerbaijan, and tended to persist longer than other abundance
categories. Stable regions of low abundance were concentrated directly east of Baku,
and bordered the lesser Caucasus region. The longest period of stability was a low
abundance region that persisted from 1976 to 1985. Stable areas associated with
average and high abundance did not consistently persist for more than five years.
Areas of average abundance were more widespread, and tended to be smaller than low
45
and very low abundance stable regions. Average abundance stable regions were the
most prominent in the Samukh region, and the western border of Baku. All stable
regions defining high and very high abundance were in Samukh. There were four
regions associated with stable high abundance, and only one region of stability was
identified for very high abundance.
Environmental Data
Precipitation, temperature, and altitude ranges across Azerbaijan are shown in
Figure 3-3. The two Caucasus mountain ranges in Azerbaijan are characterized by
unique environmental conditions compared to the rest of the country. The highest
altitudes are found in these regions in addition to the highest precipitation levels and
lowest temperatures. The central portion of Azerbaijan, from the Samukh rayon to
south of Baku, showed relatively stable levels of precipitation, temperature, and altitude.
Environmental Analysis
Altitude, temperature, and precipitation variables were extracted and analyzed for
each of the five abundance categories. The Mann Whitney U test revealed that there
was not a significant difference between very high and high for any of the environmental
variables (Table 3-2). Data on very low abundance was not available after 1978. As a
result, we were compelled to collapse very low and low into one category and high and
very high into another category for environmental analyses. This resulted in three
categories: low, average, and high abundance (Figure 3-4). These three abundance
categories were used for all subsequent analyses.
Each abundance category was characterized by significantly different climatic
and land cover conditions. These distinctive characteristics can help identify regions
that should be prioritized for surveillance and control. Stable areas of high abundance
46
had the highest average annual temperature, altitude, and annual precipitation (Table 3-
3). Stable low regions had the lowest average annual temperature and altitude. Stable
average abundance had the highest average precipitation, as a result of its overlap with
the Lesser Caucasus mountain range along the western border of the country. Low
abundance stretched up the northeast border of Azerbaijan and into the Greater
Caucasus region, across a wider range of temperatures, altitudes and precipitation
levels than the other two abundance categories. Stable high abundance was limited to
Samukh, and was characterized by the smallest ranges for all three variables. All
abundance levels were significantly different for each environmental variable except
temperature in high abundance regions compared to temperature in low abundance
regions (Table 3-4).
The region associated with each abundance category had a different land cover
composition. The landscape delineating low rodent abundance was dominated by
mosaic cropland based on GlobCover characterizations (Figure 3-5, Table 3-5). High
abundance regions were dominated by shrubland/grassland and sparse/barren land
cover. The landscape associated with average abundance was approximately 30%
mosaic cropland and 22% grassland / shrubland. Mixed forest, evergreen, and
deciduous all comprised less than 1% of total land cover for each abundance category,
but are the most prominent in low abundance.
Human Population
The highest percentage increase in human populations was along the southern
border of the plague focus in the lesser Caucus region (Figure 3-6, Appendix B). The
area surrounding Samukh, which included the stable region of high abundance, was
characterized by approximately 50% increases in population levels since 1970. The
47
central portion of Azerbaijan also experienced approximately 50% increase in
population levels over the 40 year period. The Greater Caucus region, which borders
stable low abundance, was another region that experienced up to a 100% increase in
human population between 1970 and 2010.
48
Table 3-1. Changes in the area representing each abundance category over time. The total area associated with each
abundance category for each year is reported.
Note: ND = No Data
Year Very Low Abundance Area (Sq KM)
Low Abundance Area (Sq Km)
Average Abundance Area (Sq Km)
High Abundance Area (Sq Km)
Very High Abundance Area (sq KM)
1972 4859.67 15333.38 5419.4 717.91 305.05
1973 8738.92 13904.51 8478.61 6354.78 0
1974 4050.69 25070.44 5659.06 2639.57 290.63
1975 22564.16 9440.97 4178.55 1128.08 235.61
1976 10604.22 6824.64 2370.56 580.41 0
1977 15417.32 4303.45 2072.98 501.02 77.37
1978 7025.66 7687.52 2205.41 1026.39 165.53
1980 ND 27657.74 2853.18 626.12 241.45
1981 ND 21291.7 5033.66 544.52 948.64
1982 ND 28038.79 6114.36 56.77 0
1983 ND 30504.17 3815.76 6.64 160.36
1985 ND 29033.75 2108.9 305.42 172.22
49
Table 3-2. Wilcoxon signed rank test results for temperature, precipitation, and altitude comparing five abundance
categories. The null hypothesis being tested is that there is no significant difference in environmental variables between abundance categories.
* Reject the null hypothesis with 95% confidence ** Reject the null hypothesis with 99% confidence
High v. Very high
High v. Average
High v. Low
High v. Very Low
Very High v.
Average
Very High v.
Low
Very High v. Very Low
Average v. Low
Average v. Very Low
Low v. Very Low
Temperature
Statistic 667 394278 2780926 846997 43117 313012 98752 12856601
9 38356594 196215207
P.value 0.44 0.07 <0.001** <0.001** 0.57 0.04* <0.001** <0.001** <0.001** <0.001**
Precipitation
Statistic 958 2775322 3028050 752339 353292 392333 95538 55356675
6 146705200 173157853
P.value 0.14 <0.001** <0.001** <0.001**
<0.001** <0.001** <0.001** <0.001** <0.001** <0.001**
Altitude
Statistic 840 461061 3234434 712025 50585 360680 79071 13316060
6 29822902 149264722
P.value 0.58 <0.001** <0.001** <0.001** 0.08 <0.001** 0.06 <0.001** <0.001** <0.001**
50
Table 3-3. Descriptive statistics for the distribution of temperature, precipitation, and altitude associated with each of the three abundance levels.
Low Average High
Temperature (Degrees Celsius) Min. 10.1 10.1 13.7 1st Qu. 13.5 14 14.3 Median 14.2 14.6 14.5 Mean 14.03 14.31 14.55 3rd Qu. 14.7 14.8 14.8 Max. 15.4 15.3 15.2 Precipitation (Millimeters) Min. 243 243 337 1st Qu. 348 343 396 Median 373 371 416 Mean 377.9 369.7 406.9 3rd Qu. 398 395 429.5 Max. 672 601 473 Altitude (Meters) Min. -42 -32 70 1st Qu. -7 15 154 Median 41 122 209 Mean 126.2 161.8 202.8 3rd Qu. 204 259 257.5 Max. 1023 993 402
51
Table 3-4. Mann Whitney U test results for temperature, precipitation, and altitude
comparing three abundance categories. The null hypothesis being tested is that there is no significant difference in environmental variables between abundance categories.
* Reject the null hypothesis with 95% confidence ** Reject the null hypothesis with 99% confidence.
High v. Average High v. Low Average v. Low
Temperature (Degrees Celsius)
Statistic 437395.00 4039687.00 167000000.00
P.value 0.05 >0.001** >0.001**
Precipitation (Millimeters)
Statistic 3128614.00 4268260.00 700271956.00
P.value >0.001** >0.001** >0.001**
Altitude (Meters)
Statistic 511645.00 4386209.00 162983507.50
P.value >0.001** >0.001** >0.001**
52
Table 3-5. The percentages of each land cover type that comprises the landscape for
high, medium, and low M. libycus abundance.
Land Cover Type Low Average High
Rainfed Cropland 11.13% 7.49% 7.92%
Mosaic Cropland 55.90% 30.10% 9.43%
Deciduous 0.06% 0.03% 0.00%
Evergreen 0.33% 0.20% 0.00%
Mixed Forest 0.20% 0.03% 0.00%
Grassland / Shrubland 14.60% 22.19% 29.51%
Sparse / Barren 16.30% 36.04% 36.38%
Water 1.48% 3.85% 16.75%
53
Figure 3-1. STAMP outputs from 1976 to 1977. A) is the change in very low abundance from 1976 to 1977; B) is low
abundance from 1976 to 1976. C) is average abundance from 1976 to 1977. D) is high abundance from 1976 to 1977. E) is very high abundance from 1976 to 1977.
54
Figure 3-2. Stable regions for each abundance category representing regions that remained stable for at least 2
consecutive years. A) shows the region of Azerbaijan that each of the subsequent maps focuses on. B) is very low abundance. C) is low regions of stability that persisted for at least 5 years. D) is average abundance stable regions. E) is high abundance stable regions. F) is very high abundance stable regions.
55
Figure 3-3. The distribution of environmental variables across Azerbaijan. A) shows temperature. B) is precipitation. C) is altitude across the country. All climatic data was downloaded from WorldClim.
56
Figure 3-4. Regions of stability for each abundance level. Each polygon represents the
area that was stable for at least 2 consecutive years anytime between 1972 and 1985. A) is low abundance. B) is average abundance. C) is high abundance.
57
Figure 3-5. The percentage of each land cover type associated with regions of high, average, and low rodent abundance
58
Figure 3-6. The percentage population change between 1970 and 2010. Dark red indicates an increase in population. Stable regions for high, average, and low abundance area are also shown.
59
CHAPTER 4 DISCUSSION
Plague presents a pressing public health concern in the developing world.
Plague has a history of devastating pandemics, and there has been an increase in
human cases in recent years. There is also evidence that climate change could further
increase human disease risk. This threat is particularly relevant in the former Soviet
Union, and its bordering countries. The collapse of the Soviet Union was accompanied
by a deteriorating public health system that was left with limited resources for disease
control and surveillance efforts. There is also evidence of active historical foci in
countries surrounding Azerbaijan. In Iran, a recent study found high numbers of host
and vectors in a historically active focus. Yersinia pestis was detected in rodent hosts,
which suggests that plague is being maintained in the environment (Esmaeili 2013).
Continued plague surveillance in these regions is essential.
The goal of this study was to inform plague surveillance in one environmental
focus in Azerbaijan. The first objective was to use a historic data set to evaluate
patterns of persistence in M. libycus abundance in Azerbaijan from 1972 – 1985. It has
been established in the literature that plague prevalence is related to the distribution
and density of hosts on the landscape, and Meriones libycus is an important plague
host in Azerbaijan (Gage and Kosoy 2005, Davis et al. 2004). We were particularly
interested in identifying geographic regions of long-term stability in M. libycus
abundance, as these regions of historical M. libycus persistence should be prioritized.
A second objective was to associate environmental and climatic characteristics with
different levels of M. libycus abundance. Environmental characteristics correlated to
different levels of abundance would provide a way to predict M. libycus across the
60
country. Similarly, stable environmental conditions have the potential to be associated
with stable levels of M. libycus abundance. Finally, we explored changes in human
population densities. Effectively prioritizing regions for surveillance must consider the
risk of human infection.
Overall, this study identified variability in the spatial distribution of M. libycus over
the 13 year study period in all abundance categories. Regions characterized by low M.
libycus abundance were the most stable over time with several regions persisting for
more than five years. For the other abundance categories, regions of stability did not
tend to persist for more than five years, but every abundance category had a stable
region that was maintained for at least two years. Stable regions of low rodent
abundance were the most widespread and continuous across Azerbaijan. Areas of
stable average rodent abundance were found on eastern and western portion of the
country, but were not as continuous. Landscapes consistently characterized by high
abundance were limited to Samukh in the northwest portion of Azerbaijan.
It is important to consider the variability in abundance that the STAMP analysis
established. There were no abundance levels that were spatially or temporally stable
over the entire study period. Abundance levels fluctuated within the boundaries
representing each category; however, those boundaries represent regions that have
been characterized by the persistence of one abundance level for multiple years at
some point between 1972 and 1985. From a surveillance perspective, these historically
stable regions can help inform surveillance by prioritizing regions with a history of higher
abundance levels. If a region was historically characterized by high abundance, modern
surveillance should prioritize that region for control efforts.
61
There were significant differences in environmental characteristics associated
with different rodent abundance categories. Higher counts of M. libycus were correlated
with the greatest amount of annual precipitation, higher mean temperatures, and higher
altitudes. There is little variability in temperature, precipitation, and altitude across the
region of Azerbaijan delineated by M. libycus data employed in this analysis, but these
fine scale differences are important when considered within the context of the entire
plague system. The distinguishing characteristics correlated with each abundance level
can help predict changes or continued stability in M. libycus abundance. If climate
remains stable, the levels of rodent abundance will likely reflect historical levels of M.
libycus abundance. If conditions become warmer and wetter then we might hypothesize
M. libycus abundance is more likely to increase.
It is also important to consider the relationship between plague prevalence and
host abundance. Ideally, control efforts would be prioritized for M. libycus populations
with active Y. pestis. Here we have shown a relationship between relatively warm, wet
climatic conditions and M. libycus host abundance in Azerbaijan. An association
between warm, moist climate and higher levels of flea vectors has also been discussed
in the literature (Davis 1953, Cavanaugh and Marshall 1972). Plague prevalence in
several places worldwide have also been correlated to warm springs and wet summers.
Plague is a complex system with numerous factors affecting prevalence and spread;
however, in general terms, high host abundance, high vector abundance and pathogen
presence have been related to similar, broad scale environmental conditions. It is also
important to consider that plague is more likely to persist in the environment with a
higher abundance of rodent hosts (Gage and Kosoy 2005, Stenseth et al. 2008). These
62
findings suggest that regions associated with stable high rodent abundance are likely at
a higher risk of plague prevalence. Our findings suggest that areas historically
categorized as high abundance should be prioritized for surveillance efforts because
they are the most likely to support plague prevalence.
The landscapes delineating each abundance category had different land cover
compositions. The land cover in Samukh, which contains stable high abundance, is
characterized by higher proportions of sparse/barren landscape, shrubland, and
grassland than the remainder of the country. In contrast, northwestern Azerbaijan has
more forested area and mosaic cropland than Samukh where high abundance stability
occurred. These land cover compositions can be used to prioritize landscapes more
likely to support high levels of plague hosts.
Several studies have suggested that human proximity to plague hosts is a risk
factor for human infection (Schotthoefer et al. 2012, Eisen et al. 2007b, Kartman 1970).
Schotthoefer et al. (2012) found that living close to environmental plague reservoirs in
New Mexico was a significant risk factor for human plague. They also found that
changing socioeconomic demographics were correlated to changes in the location of
plague cases (Schotthoefer et al. 2012). Essentially, changes in human population
dynamics are important to consider when prioritizing surveillance. In Azerbaijan, human
populations have increased in and around the plague focus. This is particularly
concerning in the area surrounding regions of historically high M. libycus abundance.
Increasing human populations, high levels of host abundance, and stable climate could
create an intensified human plague risk in this region. Locations with growing human
63
populations in close proximity to natural plague foci should be prioritized for surveillance
and control efforts.
The increasing number of protected lands, coupled with growing populations has
potential implications for plague risk in Azerbaijan. In other parts of the world, national
parks have been correlated with increased human disease risk. In Kyrgyzstan, the
highest risk region of tick borne encephalitis is in Ala-Archa National Park, which is
close to the capital city (Briggs et al. 2011). The park is a popular attraction for hikers,
climbers, and tourists who come into close proximity with the rodent hosts and tick
vectors that inhabit the park (Briggs et al. 2011). In California, human plague cases
have also been linked to host and vector populations in national parks. One model
predicting plague host distribution identified national parks in California as a probable
location for vector distribution (Adjemian et al. 2006). These predicted distributions also
reflected sites of previous human cases (Adjemian et al. 2006). In the 1970s, California
experienced an increase in human plague cases, and many of those cases were
attributed to host populations in national parks (Nelson 1980). Wilderness areas, and
national monuments were also locations marked as high risk for plague maintenance in
the state (Nelson 1980). The increase in National Parks in Azerbaijan has the potential
to provide an opportunity for plague hosts, such as M. libycus, and flea vectors to come
into contact with people visiting the park. National parks, particularly those in close
proximity to large population centers, should be prioritized for surveillance.
Working with historical data is inherently associated with challenges. Our original
data were in the form of hand drawn maps that were photographed, georeferenced and
hand digitized. It is likely that there were errors associated with this process. As a
64
result of the nature of the original data, the areas of polygons were not exact, and this
analysis was based on comparing the relative size difference in polygons. The historic
M. libycus dataset also contains some sampling bias. It is unclear if the same regions
were sampled consistently over the fourteen year period. We know that sampling efforts
were largely driven by expert opinion of known rodent habitat, which creates a biased
sampling design. It is possible that some of the spatial variation identified in M. libycus
abundance is due to this sampling bias. While it is important to consider the challenges
related to historic datasets, the benefits associated with analyzing historical data are
particularly relevant to the study of disease.
Similarly to the challenges we encountered with our data, the literature has
established sampling bias, or non-uniform reporting as the primary hurdle associated
with employing historic data sets (Cliff et al. 1997). Despite these complications,
historic data still has the potential to provide context for current outbreaks, and should
be used (Cliff, Haggett and Smallman-Raynor 2008). Cliff et al. (1997) liken the use of
historic public health records to the use of long term climate data. In order to effectively
improve short term forecasts, it is necessary to understand the long term patterns (Cliff
et al. 1997). The reemergence and resilience of disease across the globe is another
reason studying past patterns is important (Cliff et al. 1997, Cliff et al. 2008). Using
geographic information systems (GIS) is an effective method for analyzing historic data
because of its capacity to integrate data from different sources and dates (Gregory and
Healey 2007). GIS also provides a framework for exploring change over a historic time
interval (Gregory and Healey 2007). Cunfer (2005) used annual agricultural data and
environmental data sets to explore the cause of the great plains dust storm. Skinner et
65
al. (2000) also used GIS to analyze trends in fertility rates in China’s from the 1960s to
the 1990s.
Some additional limitations associated with this study include the STAMP
toolbar, which is not compatible with ArcGIS v 10, which is the most recent version. The
applicability of STAMP to a wide range of questions calls for an updated version of the
software. The GlobCover dataset employed was derived from satellite images taken in
2005 and 2006, which might not reflect conditions at the time this M. libycus data was
collected. Our research group is currently investigating historical land cover change in
Azerbaijan to determine the degree of land cover stability over time using a remote
sensing approach.
Despite these limitations, valuable information was obtained from this analysis.
Our findings are important and relevant for three primary reasons. Plague still affects
human populations worldwide, and needs to be carefully monitored (Stenseth et al.
2008, Perry and Fetherston 1997). Impending climate change has the capacity to
increase prevalence of gerbil hosts in the future, which can increase the frequency at
which plague can occur (Stenseth et al. 2006). Finally, changes in the public health
system that accompanied the collapse of the Soviet Union have limited resources
available for plague surveillance.
Our results can be used to prioritize limited resources for the surveillance and
control of environmental plague foci in Azerbaijan. Regions of historically high M.
libycus abundance should be prioritized. Regions of the country with climatic conditions
associated with high abundance should also be prioritized. Employing predictive
climate change models is one way to identify regions likely to experience a climatic shift
66
towards favorable plague conditions. Similarly, stable climate is likely to be associated
with stable rodent abundance. Regions with increasing human populations bordering
plague foci should be monitored, as these present a high risk of human infection.
Finally, protected lands with public access should be monitored because of the close
proximity of humans and vectors.
Future Directions
An important future direction will be to analyze M. libycus abundance at a finer
temporal scale. In order to thoroughly understand the impact of climate on rodent
abundance we need to study seasonal variations in M. libycus abundance and the
correlated climatic variation. Ideally, future sampling efforts could take place on a
seasonal basis throughout Azerbaijan. This effort would also require a climatic dataset
at a finer temporal scale to analyze seasonal variations. A modern analysis compared
to historical data would allow us to determine if a shift in abundance levels has taken
place since 1985. A more thorough understanding of stable regions could be obtained.
A modern data set describing rodent abundance throughout Azerbaijan would be a
necessary first step to meet these objectives.
The annual historic yearbooks contain data on multiple other hosts and vectors.
The exploration of these additional data sets could provide a more thorough picture of
how plague hosts and vectors are spread across the landscape. Incorporating
yearbook data that includes lab analysis and spatial information on plague isolates
would provide an informative element to this analysis. In order to evaluate the plague
risk in Azerbaijan, we need to understand the roles of all hosts, vectors, and the
prevalence of the pathogen in the region
67
APPENDIX A STAMP OUTPUTS
All STAMP outputs for all years and all five categories (very low, low, average,
high, and very high) of M. libycus abundance are shown. A-1 includes Very low
abundance STAMP outputs from 1972 to 1978. Each STAMP output reflects the
change between two consecutive years. For example, the change in area representing
very low M. libycus abundance from 1972 to 1973 is one map, and the change in area
from 1973 to 1974 is shown on a separate map. A-2 includes the STAMP outputs for
low abundance from 1972 to 1985, A-3 is the STAMP outputs for average abundance
from 1972 to 1985, A-4 includes STAMP outputs for high abundance from 1972 to 1985,
and A-5 is very high abundance STAMP outputs from 1972 to 1985.
68
Figure A-1. STAMP outputs for very low Meriones libycus abundance. A) through F) shows STAMP outputs from 1973 to 1978.
1972 1973
1973 1974
Contraction
Disappearance Expansion Generation Stable
A
B
69
1974 1975
1975 1976
Contraction
Disappearance Expansion Generation
Stable
C
D
Figure A-1 Continued
70
Figure A-1 Continued
1976 1977
E
1977 1978
Contraction Disappearance Expansion Generation Stable
F
71
Figure A-2. STAMP outputs for low Meriones libycus abundance. A) to K) shows STAMP outputs from 1973 to 1985.
Contraction
Disappearance Expansion Generation
Stable
A
B
1972 1973
1973 1974
72
Contraction
Disappearance Expansion Generation
Stable Figure A-2 Continued
C
D
1974 1975
1975 1976
73
Contraction
Disappearance Expansion Generation
Stable Figure A-2 Continued
E
F
1976 1977
1977 1978
74
Contraction
Disappearance Expansion Generation
Stable Figure A-2 Continued
G
H
1978 1980
1980 1981
75
Contraction
Disappearance Expansion Generation
Stable Figure A-2 Continued
I
J
1981 1982
1982 1983
76
Contraction
Disappearance Expansion Generation
Stable
Figure A-2 Continued
K
1983 1985
77
Figure A-3. STAMP outputs for average Meriones libycus abundance. A) to K) shows STAMP outputs from 1973 to 1985.
Contraction
Disappearance Expansion Generation
Stable
A
B
1972 1973
1973 1974
78
Contraction
Disappearance Expansion Generation
Stable Figure A-3 Continued
C
D
1974 1975
1975 1976
79
Contraction
Disappearance Expansion Generation
Stable Figure A-3 Continued
E
F
1977 1978
1976 1977
80
Contraction
Disappearance Expansion Generation
Stable Figure A-3 Continued
G
H
1978 1980
1980 1981
81
Contraction
Disappearance Expansion Generation
Stable Figure A-3 Continued
I
J
1981 1982
1982 1983
82
Contraction
Disappearance Expansion Generation
Stable
Figure A-3 Continued
K
1983 1985
83
Figure A-4. STAMP outputs for high Meriones libycus abundance. A) to K) shows STAMP outputs from 1973 to 1985.
Contraction
Disappearance Expansion Generation
Stable
A
B
1972 1973
1973 1974
84
Contraction
Disappearance Expansion Generation
Stable Figure A-4 Continued
C
D
1974 1975
1975 1976
85
Contraction
Disappearance Expansion Generation
Stable Figure A-.4 Continued
E
F
1976 1977
1977 1978
86
Contraction
Disappearance Expansion Generation
Stable Figure A-4 Continued
G
H
1978 1980
1980 1981
87
Contraction
Disappearance Expansion Generation
Stable Figure A-4 Continued
I
J
1981 1982
1982 1983
88
Contraction
Disappearance Expansion Generation
Stable
Figure A-4 Continued
K
1983 1985
89
Figure A-5. STAMP outputs for very high Meriones libycus abundance. A) to H) shows STAMP outputs from 1973 to 1985.
Contraction
Disappearance Expansion Generation
Stable
A
B
1972 1974
1974 1975
90
Contraction
Disappearance Expansion Generation
Stable Figure A-5 Continued
C
D
1975 1977
1977 1978
91
Contraction
Disappearance Expansion Generation
Stable Figure A-5 Continued
E
F
1978 1980
1980 1981
92
Contraction
Disappearance Expansion Generation
Stable Figure A-5 Continued
G
H
1981 1983
1983 1985
93
APPENDIX B POPULATION DISTRIBUTION
Figure B-1. HYDE population grids showing the population distribution in Azerbaijan.
A) for 1970. B) 2010.
94
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BIOGRAPHICAL SKETCH
Lillian Morris was born in 1989 in Baltimore, Maryland. She grew up in Baltimore
and graduated from the Bryn Mawr School in 2007. She attended the University of
Colorado in Boulder, and graduated with a Bachelor of Arts in geography in 2011. She
was accepted to the UF Department of Geography master’s program, and was offered a
research assistantship in the Spatial Epidemiology and Ecology Research lab in
conjunction with the Emerging Pathogens Institute in 2011. She received her Master of
Science from the University of Florida in spring 2013.