Advanced Search

Vital Surveillances: Relationship Between Climate Change and Marmot Plague of Marmota himalayana Plague Focus — the Altun Mountains of the Qinghai-Xizang Plateau, China, 2000–2022

View author affiliations
  • Abstract

    Introduction

    Plague is a zoonotic disease that occurs naturally in specific geographic areas. Climate change can influence the populations of the plague host or vector, leading to variations in the occurrence and epidemiology of plague in animals.

    Methods

    In this study, we collected meteorological and plague epidemiological data from the Marmota himalayana plague focus in the Altun Mountains of the Qinghai-Xizang Plateau. The data spanned from 2000 to 2022. We describe the climatic factors and plague epidemic conditions and we describe their analysis by Pearson’s correlation.

    Results

    During the period from 2000 to 2022, the isolation rates of Yersinia pestis (Y.pestis) from marmots and fleas were 9.27% (451/4,864) and 7.17% (118/1,646), respectively. Additionally, we observed a positive rate of F1 antibody of 11.25% (443/3,937) in marmots and 18.16% (142/782) in dogs. With regards to climate, there was little variation, and a decreasing trend in blowing-sand days was observed. The temperature in the previous year showed a negative correlation with the Y. pestis isolation rate in marmots (r=−0.555, P=0.011) and the positive rate of F1 antibody in marmots (r=−0.552, P=0.012) in the current year. The average annual precipitation in the previous two years showed a positive correlation with marmot density (r=0.514, P=0.024), while blowing-sand days showed a negative correlation with marmot density (r=−0.701, P=0.001). Furthermore, the average annual precipitation in the previous three years showed a positive correlation with the isolation rate of Y. pestis from marmots (r=0.666, P=0.003), and blowing-sand days showed a negative correlation with marmot density (r=−0.597, P=0.009).

    Conclusions

    The findings of this study indicate that there is a hysteresis effect of climate change on the prevalence of plague. Therefore, monitoring climate conditions can offer significant insights for implementing timely preventive and control measures to combat plague epidemics.

  • loading...
  • Funding: Supported by the National Key Research and Development Program of China (2022YFC2602203)
  • [1] WHO. Plague. Wkly Epidemiol Rec 2005;80(15):138-40. https://pubmed.ncbi.nlm.nih.gov/15875712/.
    [2] Stenseth NC, Samia NI, Viljugrein H, Kausrud KL, Begon M, Davis S, et al. Plague dynamics are driven by climate variation. Proc Natl Acad Sci USA 2006;103(35):13110 − 5. https://doi.org/10.1073/pnas.0602447103CrossRef
    [3] Pham HV, Dang DT, Tran Minh NN, Nguyen ND, Nguyen TV. Correlates of environmental factors and human plague: an ecological study in Vietnam. Int J Epidemiol 2009;38(6):1634 − 41. https://doi.org/10.1093/ije/dyp244CrossRef
    [4] Xu L, Liu QY, Stige LC, Ben Ari T, Fang XY, Chan KS, et al. Nonlinear effect of climate on plague during the third pandemic in China. Proc Natl Acad Sci USA 2011;108(25):10214 − 9. https://doi.org/10.1073/pnas.1019486108CrossRef
    [5] Wang X, Wei XY, Song ZZ, Wang ML, Xi JX, Liang JR, et al. Mechanism study on a plague outbreak driven by the construction of a large reservoir in southwest China (surveillance from 2000-2015). PLoS Negl Trop Dis 2017;11(3):e0005425. https://doi.org/10.1371/journal.pntd.0005425CrossRef
    [6] He ZK, Wei BQ, Zhang YJ, Liu J, Xi JX, Ciren D, et al. Distribution and characteristics of human plague cases and Yersinia pestis isolates from 4 Marmota plague foci, China, 1950-2019. Emerg Infect Dis 2021;27(10):2544 − 53. https://doi.org/10.3201/eid2710.202239CrossRef
    [7] Tang DM, Duan R, Chen YH, Liang JR, Zheng XJ, Qin S, et al. Plague outbreak of a Marmota himalayana family emerging from hibernation. Vector Borne Zoonotic Dis 2022;22(8):410 − 8. https://doi.org/10.1089/vbz.2022.0010CrossRef
    [8] Wang YM, Zhou L, Fan MG, Wang QY, Li JY, Li Q, et al. Isolated cases of plague - Inner Mongolia-Beijing, 2019. China CDC Wkly 2019;1(1):13 − 6. https://doi.org/10.46234/ccdcw2019.005CrossRef
    [9] Abedi AA, Shako JC, Gaudart J, Sudre B, Ilunga BK, Shamamba SKB, et al. Ecologic features of plague outbreak areas, democratic republic of the Congo, 2004-2014. Emerg Infect Dis 2018;24(2):210 − 20. https://doi.org/10.3201/eid2402.160122CrossRef
    [10] Wang DS, Ge PF, Xi JX, Su YQ, Xu DQ, Gai YZ, et al. Non-linear effects of meteorological factors on plague epidemics in the plague foci of Subei and Sunan counties of Gansu Province. Chin J Endemiol 2020;39(1):27 − 32. https://doi.org/10.3760/cma.j.issn.2095-4255.2020.01.006CrossRef
    [11] Xi JX, Duan R, He ZK, Meng L, Xu DQ, Chen YH, et al. First case report of human plague caused by excavation, skinning, and eating of a hibernating marmot ( Marmota himalayana). Front Public Health 2022;10:910872. https://doi.org/10.3389/fpubh.2022.910872CrossRef
    [12] Gage KL. Factors affecting the spread and maintenance of plague. In: De Almeida AMP, Leal NC, editors. Advances in Yersinia research. New York: Springer. 2012; p. 79-94. http://dx.doi.org/10.1007/978-1-4614-3561-7_11.
    [13] Schmid BV, Büntgen U, Easterday WR, Ginzler C, Walløe L, Bramanti B, et al. Climate-driven introduction of the Black Death and successive plague reintroductions into Europe. Proc Natl Acad Sci USA 2015;112(10):3020 − 5. https://doi.org/10.1073/pnas.1412887112CrossRef
    [14] Xu L, Schmid BV, Liu J, Si XY, Stenseth NC, Zhang ZB. The trophic responses of two different rodent-vector-plague systems to climate change. Proc Biol Sci 2015;282(1800):20141846. https://doi.org/10.1098/rspb.2014.1846CrossRef
    [15] Ju C, Liu ZC, Zhang GJ, Yao XH, Xu C, Duan TY, et al. Relationship between human plague epidemic and meteorological factors in China. Chin J Endemiol 2014;33(5):488 − 91. https://doi.org/10.3760/cma.j.issn.2095-4255.2014.05.005CrossRef
  • FIGURE 1.  Epidemiological characteristics of plague in M. himalayana plague foci in the Altun Mountains on the Qinghai-Xizang Plateau, China, 2000−2022.

    FIGURE 2.  Climate change in M. himalayana plague foci in the Altun Mountains on the Qinghai-Xizang Plateau, China, 2000−2022. (A) The average annual number of unusual weather days; (B) The average annual temperature; (C) The average annual precipitation; (D) The average annual sunshine hours; (E) The average annual relative humidity.

    TABLE 1.  Correlation analysis of climate factors and plague epidemic indicators in the Altun Mountains on the Qinghai-Xizang Plateau, China, 2000−2022.

    Plague epidemic indicators Current year   Last year   Previous two years   Previous three years
    Temperature Number of sand-blowing days Temperature Number of sunshine hours Temperature Precipitation Number of sand-blowingdays Number of sunshine hours Precipitation Number of sand-blowing days Number of sunshine hours
    Number of self-dead marmota r 0.430 0.103 0.030 0.266 0.052 0.070 −0.287 0.135 0.699 −0.676 0.122
    P 0.125 0.725 0.919 0.359 0.860 0.813 0.320 0.645 0.005* 0.008* 0.679
    Marmot density r −0.017 −0.314 −0.017 −0.273 −0.379 0.514 −0.701 −0.593 0.436 −0.597 −0.611
    P 0.943 0.177 0.944 0.245 0.110 0.024* 0.001* 0.007* 0.071 0.009* 0.007*
    Isolation rate of Y. pestis from marmots r −0.147 −0.300 −0.555 −0.019 −0.387 0.380 0.001 0.007 0.666 −0.505 −0.009
    P 0.536 0.198 0.011* 0.935 0.101 0.108 −0.433 0.023 0.003* 0.032* 0.973
    Isolation rate of Y. pestis from fleas r −0.189 0.463 −0.135 0.132 −0.253 −0.175 0.064 0.925 −0.190 0.186 0.521
    P 0.424 0.040* 0.572 0.579 0.297 0.473 0.262 0.271 0.449 0.460 0.027*
    Positive rate of F1 antibody in marmots r −0.027 −0.015 −0.552 0.195 −0.167 0.208 0.279 0.261 0.214 −0.216 0.181
    P 0.911 0.950 0.012* 0.411 0.494 0.392 −0.182 0.042 0.394 0.388 0.471
    Positive rate of F1 antibody in dogs r 0.575 −0.069 −0.283 0.550 0.278 0.233 0.455 0.864 0.452 −0.527 0.117
    P 0.025* 0.807 0.306 0.034* 0.316 0.404 −0.119 0.220 0.091 0.043* 0.677
    Marmot flea index r −0.399 0.283 −0.255 0.018 −0.577 −0.187 0.672 0.430 0.051 0.229 0.401
    P 0.082 0.227   0.278 0.940   0.010* 0.444 0.205 0.290   0.841 0.361 0.099
    Note: r=pearson correlation coefficient; P=P value.
    * Indicates statistical significance; grey base color indicates correlation.
    Download: CSV

Citation:

通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Turn off MathJax
Article Contents

Article Metrics

Article views(1805) PDF downloads(25) Cited by()

Share

Related

Relationship Between Climate Change and Marmot Plague of Marmota himalayana Plague Focus — the Altun Mountains of the Qinghai-Xizang Plateau, China, 2000–2022

View author affiliations

Abstract

Introduction

Plague is a zoonotic disease that occurs naturally in specific geographic areas. Climate change can influence the populations of the plague host or vector, leading to variations in the occurrence and epidemiology of plague in animals.

Methods

In this study, we collected meteorological and plague epidemiological data from the Marmota himalayana plague focus in the Altun Mountains of the Qinghai-Xizang Plateau. The data spanned from 2000 to 2022. We describe the climatic factors and plague epidemic conditions and we describe their analysis by Pearson’s correlation.

Results

During the period from 2000 to 2022, the isolation rates of Yersinia pestis (Y.pestis) from marmots and fleas were 9.27% (451/4,864) and 7.17% (118/1,646), respectively. Additionally, we observed a positive rate of F1 antibody of 11.25% (443/3,937) in marmots and 18.16% (142/782) in dogs. With regards to climate, there was little variation, and a decreasing trend in blowing-sand days was observed. The temperature in the previous year showed a negative correlation with the Y. pestis isolation rate in marmots (r=−0.555, P=0.011) and the positive rate of F1 antibody in marmots (r=−0.552, P=0.012) in the current year. The average annual precipitation in the previous two years showed a positive correlation with marmot density (r=0.514, P=0.024), while blowing-sand days showed a negative correlation with marmot density (r=−0.701, P=0.001). Furthermore, the average annual precipitation in the previous three years showed a positive correlation with the isolation rate of Y. pestis from marmots (r=0.666, P=0.003), and blowing-sand days showed a negative correlation with marmot density (r=−0.597, P=0.009).

Conclusions

The findings of this study indicate that there is a hysteresis effect of climate change on the prevalence of plague. Therefore, monitoring climate conditions can offer significant insights for implementing timely preventive and control measures to combat plague epidemics.

  • 1. National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
  • 2. Akesai Kazak Autonomous County Center for Disease Control and Prevention, Jiuquan City, Gansu Province, China
  • 3. Jiuquan Center for Disease Control and Prevention, Jiuquan City, Gansu Province, China
  • 4. Gansu Provincial Center for Disease Control and Prevention, Lanzhou City, Gansu Province, China
  • Corresponding author:

    Xin Wang, wangxin@icdc.cn

  • Funding: Supported by the National Key Research and Development Program of China (2022YFC2602203)
  • Online Date: January 26 2024
    Issue Date: January 26 2024
    doi: 10.46234/ccdcw2024.015
    • Plague is a zoonotic disease that can persist in natural foci even without human hosts (1). Climate changes in these foci can disrupt plague hosts and vectors, leading to outbreaks in animals and humans. Studies have demonstrated that climate warming can promote the spread of plague (2). The frequency of plague outbreaks significantly increases during dry seasons (3), and the effects of rainfall on plague intensity differ between northern and southern regions of China (4). Therefore, it is important to conduct detailed studies on local climate factors to better prepare for epidemics. The Altun Mountains region, located in the Marmota himalayana (M. himalayana) plague focus of the Qinghai-Xizang Plateau, has a relatively stable ecological environment with fewer human settlements. This makes it less susceptible to human activities but more vulnerable to ecological changes (5). Consequently, climate change is expected to impact animal plagues in this region.

      In this study, we examined meteorological data and plague monitoring data in the Altun Mountains region of the M. himalayana plague focus of the Qinghai-Xizang Plateau from 2000 to 2022. Our objective was to understand the plague epidemic and climate conditions in this region. We aimed to analyze the relationship between climate change and marmot plague in the Altun Mountains of the Qinghai-Xizang Plateau from 2000 to 2022, in order to provide a theoretical foundation for developing an early warning system for effective plague prevention and control.

    • Meteorological and plague epidemiological data were collected from the M. himalayana plague focus in the Altun Mountains of the Qinghai-Xizang Plateau between the years 2000 and 2022. Meteorological data were obtained from the Meteorological Bureau of Aksai Kazakh Autonomous County, Gansu Province, China. Plague surveillance data were obtained from Aksai CDC, with the exception of the years 2003, 2011, and 2016 due to legitimate reasons.

    • The meteorological data utilized in this study comprise temperature (°C), relative humidity (% RH), precipitation (mm), occurrences of strong wind days (day), floating-dust days (day), blowing-sand days (day), sandstorm days (day), and sunshine hours (h). Plague surveillance data consist of marmot and vector counts, isolation rates of Y. pestis from marmots and fleas, positive rates of F1 antibody in marmots and dogs, as well as the marmot flea index.

    • Statistical analyses were conducted using SPSS software (version 26.0, IBM, Armonk, New York, USA). The study described the prevalence of plague in animals and climate change in the Altun Mountains, which is the natural focus of M. himalayana plague, from 2000 to 2022. A Chi-square test was utilized to compare the isolation rates of Y. pestis from marmots and fleas in different years. Pearson correlation analysis was performed to examine the correlation between climate change and plague prevalence in the current year, as well as the previous year, previous two years, and previous three years (relative to the current year). The previous 1, 2, and 3 years were considered in relation to the current year as a reference. A P value of <0.05 was considered statistically significant.

    • Figure 1 depicts the detection of 4,864 marmots, the primary host animal, from 2000 to 2022 (excluding 2003, 2011, and 2016). During this period, 451 strains of Y. pestis were isolated, resulting in an isolation rate of 9.27% in marmots. A significant difference was observed in the isolation rate of Y. pestis from marmots each year (χ2=103.00, P<0.05). The lowest isolation rate was recorded in 2001 (3.54%, 8/226), while the rate displayed an increasing trend from 2019 (6.47%, 15/232) and reached 22.05% (58/263) in 2022. The isolation rate of Y. pestis from fleas was 7.17% (118/1,646). Similarly, the isolation rate of Y. pestis from fleas exhibited a significant difference each year (χ2=117.17, P<0.05). The isolation rate from fleas increased from 2.73% (2/73) in 2019 to 7.09% (28/395) in 2022, mirroring the findings from marmots, except for a high rate in 2007 (48.78%, 20/41) (Figure 1).

      Figure 1. 

      Epidemiological characteristics of plague in M. himalayana plague foci in the Altun Mountains on the Qinghai-Xizang Plateau, China, 2000−2022.

      The F1 antibody positive rate in marmots was 11.25% (443/3,979). The highest rate was observed in 2008 at 40.06% (59/137). There was a statistically significant difference in the positive rate of F1 antibody in marmots between different years (χ2=350.36, P<0.05). The positive rate of F1 antibody in dogs from 2006 to 2022 was 18.16% (142/782). There was also a statistically significant difference in the positive rate of F1 antibody in dogs between different years (χ2=59.37, P<0.05). Additionally, both the positive rate of F1 antibody in marmots and dogs exhibited zigzag interannual changes.

    • The M. himalayana plague foci in the Altun Mountains on the Qinghai-Xizang Plateau had an average annual temperature ranging from 16.63 °C to 18.51 °C. The RH ranged from 28.10% to 42.86%. The average annual precipitation varied from 18.7 mm to 203.30 mm. Sunshine hours ranged from 1639.30 h to 2063.50 h per year. The number of strong wind days varied from 0 to 7, blowing-sand days ranged from 1 to 20, floating-dust days ranged from 2 to 39, and sandstorm days ranged from 0 to 9 annually. These data exhibited slight variability with minimal fluctuation (Figure 2).

      Figure 2. 

      Climate change in M. himalayana plague foci in the Altun Mountains on the Qinghai-Xizang Plateau, China, 2000−2022. (A) The average annual number of unusual weather days; (B) The average annual temperature; (C) The average annual precipitation; (D) The average annual sunshine hours; (E) The average annual relative humidity.

    • The indicators used for correlation analysis met the criteria of normal distribution (P>0.05), and there were significant linear relationships between climate factors and plague epidemic indicators (P<0.05) (Supplementary Figure S1). In terms of the relationship between current climate change and current plague epidemic, the number of blowing-sand days showed a positive correlation with the isolation rate of Y. pestis from fleas (r=0.463, P=0.04), while temperature was positively correlated with the positive identification of F1 antibodies in dogs (r=0.575, P=0.025). Regarding the relationship between climate change in the previous year and plague prevalence in the current year, there was a negative correlation between temperature and the Y. pestis isolation rate from marmots (r=−0.555, P=0.011), as well as the positive F1 antibody rate in dogs in marmots (r=−0.552, P=0.012). Additionally, there was a positive correlation between average sunshine hours and the positive F1 antibody rate in dogs (r=0.550, P=0.034) (Table 1).

      Plague epidemic indicators Current year   Last year   Previous two years   Previous three years
      Temperature Number of sand-blowing days Temperature Number of sunshine hours Temperature Precipitation Number of sand-blowingdays Number of sunshine hours Precipitation Number of sand-blowing days Number of sunshine hours
      Number of self-dead marmota r 0.430 0.103 0.030 0.266 0.052 0.070 −0.287 0.135 0.699 −0.676 0.122
      P 0.125 0.725 0.919 0.359 0.860 0.813 0.320 0.645 0.005* 0.008* 0.679
      Marmot density r −0.017 −0.314 −0.017 −0.273 −0.379 0.514 −0.701 −0.593 0.436 −0.597 −0.611
      P 0.943 0.177 0.944 0.245 0.110 0.024* 0.001* 0.007* 0.071 0.009* 0.007*
      Isolation rate of Y. pestis from marmots r −0.147 −0.300 −0.555 −0.019 −0.387 0.380 0.001 0.007 0.666 −0.505 −0.009
      P 0.536 0.198 0.011* 0.935 0.101 0.108 −0.433 0.023 0.003* 0.032* 0.973
      Isolation rate of Y. pestis from fleas r −0.189 0.463 −0.135 0.132 −0.253 −0.175 0.064 0.925 −0.190 0.186 0.521
      P 0.424 0.040* 0.572 0.579 0.297 0.473 0.262 0.271 0.449 0.460 0.027*
      Positive rate of F1 antibody in marmots r −0.027 −0.015 −0.552 0.195 −0.167 0.208 0.279 0.261 0.214 −0.216 0.181
      P 0.911 0.950 0.012* 0.411 0.494 0.392 −0.182 0.042 0.394 0.388 0.471
      Positive rate of F1 antibody in dogs r 0.575 −0.069 −0.283 0.550 0.278 0.233 0.455 0.864 0.452 −0.527 0.117
      P 0.025* 0.807 0.306 0.034* 0.316 0.404 −0.119 0.220 0.091 0.043* 0.677
      Marmot flea index r −0.399 0.283 −0.255 0.018 −0.577 −0.187 0.672 0.430 0.051 0.229 0.401
      P 0.082 0.227   0.278 0.940   0.010* 0.444 0.205 0.290   0.841 0.361 0.099
      Note: r=pearson correlation coefficient; P=P value.
      * Indicates statistical significance; grey base color indicates correlation.

      Table 1.  Correlation analysis of climate factors and plague epidemic indicators in the Altun Mountains on the Qinghai-Xizang Plateau, China, 2000−2022.

      The correlation between climate change in the previous two years and the plague epidemic in the current year was examined. The results showed a positive correlation between average annual precipitation and marmot density (r=0.514, P=0.024). On the other hand, there were negative correlations between the number of blowing-sand days (r=−0.701, P=0.001) and sunshine hours (r=−0.593, P=0.007) with marmot density. Additionally, a negative correlation was observed between temperature in the previous two years and flea index in the current year (r=−0.577, P=0.010). Furthermore, the relationship between climate change in the previous three years and plague prevalence in the current year was examined. The findings revealed a positive correlation between average annual precipitation in the previous three years and the number of self-destructed marmots (r=0.699, P=0.005), as well as the isolation rate of Y. pestis in marmots (r=0.666, P=0.003). Conversely, the number of blowing-sand days showed negative correlations with the number of dead marmots (r=−0.676, P=0.008), marmot density (r=−0.597, P=0.009), isolation rate of Y. pestis from marmots (r=−0.505, P=0.032), and positive F1 antibody rate in dogs (r=−0.527, P=0.043). Moreover, average sunshine hours were negatively correlated with marmot density (r=−0.611, P=0.007), and positively correlated with the isolation rate of Y. pestis from fleas (r=0.521, P=0.027) (Table 1).

    • The M. himalayana plague focus in China is a highly prevalent region for animal and human plague. Animal plague occurs every year (6), with occasional transmission to humans (78), highlighting the importance of monitoring animal plague. In recent years, the isolation rate of Y. pestis in marmots has continuously risen from 6.46% in 2019 to 22.05% in 2022. Similarly, the isolation rate of fleas carrying Y. pestis has also increased. Y. pestis was detected in both deceased marmots and the fleas they carried, indicating persistent infection and the widespread presence of plague among animals. The fluctuation in the positive rate of F1 antibody suggests a shift in the active and silent state of the plague foci.

      The Altun Mountains in the M. himalayana plague focus area have undulating basins formed by the foldings of the Qilian Mountains and the Tianshan Mountains. The main types of terrain in this area are deserts and semi-desert grasslands (9). The climate in this region is characterized by high altitude, cold temperatures, large temperature variations, and dryness. From 2000 to 2022, the climate in this region has remained relatively stable in terms of change.

      In analyzing the relationship between climate and plague prevalence in the current year, we observed that an increase in the number of sand-blowing days led to a higher isolation rate of Y. pestis from fleas. This is attributed to reduced marmot activities during blowing-sand days, causing them to spend more time in caves. The cave environment provides favorable conditions for flea parasites on marmots and the propagation of Y. pestis in fleas (10). Consequently, this increases the risk of plague epidemics. Previous sand-raising events in the two to three years prior have adversely affected marmot foraging, resulting in lower marmot density, decreased number of Y. pestis hosts, and reduced isolation rates of Y. pestis from marmots.

      Regarding the analysis of climate and plague prevalence in the current year, we found that higher temperatures created warmer conditions for marmot hibernation. This leads to earlier hibernation periods and increased activities outside, thereby increasing the likelihood of marmots being preyed upon by dogs. Furthermore, marmots may hibernate with bacterial infections (11), and predation of infected marmots by dogs can elevate the positive rate of F1 antibodies in dogs. Rising temperatures also cause drought, resulting in food scarcity for marmots and decreased resistance, making them more susceptible to Y. pestis infection and predation by dogs. Thus, these factors contribute to the outbreak of plague epidemics. Through our study on the relationship between climate and plague prevalence in the previous year, we discovered that increased temperature led to enhanced development and reproductive ability of marmots. As a result, the current year saw a rise in the marmot population, improved healthy survival rates, and decreased isolation rates of Y. pestis and F1 antibody positive rates in marmots. These findings align with previous researches highlighting the impact of temperature on plague host animals (1213).

      Higher levels of sunshine in the previous year are associated with increased detection of F1 antibodies in dogs, indicating a greater likelihood of dogs being exposed to marmots. The increased sunshine in the previous two and three years also promotes vegetation growth, leading to a higher marmot density in the endemic area. This, in turn, results in greater competition for resources and territory, increasing the risk of plague outbreaks. In contrast, a decrease in marmot density in the current year may be attributed to these factors. Additionally, increased precipitation in the previous two to three years provides favorable conditions for marmot survival and reproduction, leading to higher population density in the current year. However, when marmot density exceeds a certain threshold, the risk of plague transmission and self-dead marmots increases. It is important to note that previous studies have reported an association between increased marmot density and the presence of plague in marmots (1415).

      This study focused solely on examining the correlation between climate change and the occurrence of plague epidemics. However, further analysis is necessary to assess the specific impact of individual climate factors on the prevalence of plague. It is important to note that the prevalence of plague is influenced by multiple factors, and this study only explores the role of climate factors. Future research should consider integrating other relevant factors when analyzing the prevalence of plague.

      In this study, we observed variations in the influence of temperature, precipitation, sand-blowing days, and sunshine hours on the prevalence of plague. Specifically, an increase in temperature in the current year increases the risk of plague transmission in the same year. However, temperature increases in the previous one and two years decrease the risk of plague outbreaks in the current year. We also found that an increase in average annual precipitation in the previous two and three years elevates the risk of plague outbreaks in the current year. Moreover, an increase in the number of sand-blowing days in the current year increases the risk of plague outbreaks in the same year, while increases in the previous two and three years reduce the risk in the current year. Additionally, our findings indicate that an increase in average annual sunshine hours in the previous year raises the risk of plague outbreaks in the current year. Conversely, increases in sunshine hours in the previous two and three years decrease the risk in the current year. Generally, the impact of climate on plague epidemics exhibits a hysteresis effect, in which the effects of climate change in a specific year manifest in subsequent years. Consistent with previous researches on the nature focus of Rattus flavipectus plague, we also identified a delayed influence of meteorological factors (15). Long-term climate monitoring can be utilized as a control strategy to provide early warning of future plague epidemics.

    • No conflicts of interest.

    • Charlesworth Author Services (Paper no. 124396) for editing and comments on manuscript.

Reference (15)

Citation:

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return