Explanatory models for tick-borne disease incidence (Astrakhan rickettsial fever and Crimean-Congo hemorrhagic fever)

Introduction. The study focuses on methods providing mathematical substantiation of discrepancies between actual incidence rates of Astrakhan rickettsial fever (ARF) and Crimean-Congo hemorrhagic fever (CCHF) and predicted rates due to the indirect impact of weather conditions during the current epidemic season. The purpose of the study was to develop explanatory models for ARF and CCHF incidence using satellite monitoring (remote sensing) data and to present the results of their practical evaluation in the Stavropol Territory and Astrakhan Region. Materials and methods. The materials included climate data provided by the Space Research Institute of the Russian Academy of Sciences as well as epidemiological data on CCHF and ARF incidence from 2005 to 2021. The explanatory models incorporated the Bayes theorem and Wald sequential analysis. All the calculations were completed using the Microsoft Excel 2010-based program developed by the authors. Results. It has been found that the greatest indirect effect on development of the CCHF epidemiological situation is produced by the normalized difference vegetation index and relative air humidity in June-July in the Stavropol Territory and by the maximum, minimum and average air temperature in October as well as the minimum air temperature in July in the Astrakhan Region. ARF incidence rates depend on the indirect effect of the annual average and average annual maximum temperature, maximum temperature and the normalized difference vegetation index in April-July. The match between explanatory model-based results and prediction model-based results ranged within 46.2-100%. Discussion. In addition to projecting incidence rates, which could be reached with the observed values of climatic factors in the current year, the explanatory models can be used for indirect verification of prediction models and for identification of factors causing differences in results. Conclusion. The practical evaluation of explanatory models confirms the prospects and benefits of the study that should be continued, involving other regions highly endemic for tick-borne infections

Potential corridors and barriers for plague spread in central Asia

Background Plague (Yersinia pestis infection) is a vector-borne disease which caused millions of human deaths in the Middle Ages. The hosts of plague are mostly rodents, and the disease is spread by the fleas that feed on them. Currently, the disease still circulates amongst sylvatic rodent populations all over the world, including great gerbil (Rhombomys opimus) populations in Central Asia. Great gerbils are social desert rodents that live in family groups in burrows, which are visible on satellite images. In great gerbil populations an abundance threshold exists, above which plague can spread causing epizootics. The spatial distribution of the host species is thought to influence the plague dynamics, such as the direction of plague spread, however no detailed analysis exists on the possible functional or structural corridors and barriers that are present in this population and landscape. This study aims to fill that gap. Methods Three 20 by 20 km areas with known great gerbil burrow distributions were used to analyse the spatial distribution of the burrows. Object-based image analysis was used to map the landscape at several scales, and was linked to the burrow maps. A novel object-based method was developed – the mean neighbour absolute burrow density difference (MNABDD) – to identify the optimal scale and evaluate the efficacy of using landscape objects as opposed to square cells. Multiple regression using raster maps was used to identify the landscape-ecological variables that explain burrow density best. Functional corridors and barriers were mapped using burrow density thresholds. Cumulative resistance of the burrow distribution to potential disease spread was evaluated using cost distance analysis. A 46-year plague surveillance dataset was used to evaluate whether plague spread was radially symmetric. Results The burrow distribution was found to be non-random and negatively correlated with Greenness, especially in the floodplain areas. Corridors and barriers showed a mostly NWSE alignment, suggesting easier spreading along this axis. This was confirmed by the analysis of the plague data. Conclusions Plague spread had a predominantly NWSE direction, which is likely due to the NWSE alignment of corridors and barriers in the burrow distribution and the landscape. This finding may improve predictions of plague in the future and emphasizes the importance of including landscape analysis in wildlife disease studies

Spatial distribution patterns of plague hosts : point pattern analysis of the burrows of great gerbils in Kazakhstan

AIM: The spatial structure of a population can strongly influence the dynamics of infectious diseases, yet rarely is the underlying structure quantified. A case in point is plague, an infectious zoonotic disease caused by the bacterium Yersinia pestis. Plague dynamics within the Central Asian desert plague focus have been extensively modelled in recent years, but always with strong uniformity assumptions about the distribution of its primary reservoir host, the great gerbil (Rhombomys opimus). Yet, while clustering of this species' burrows due to social or ecological processes could have potentially significant effects on model outcomes, there is currently nothing known about the spatial distribution of inhabited burrows. Here, we address this knowledge gap by describing key aspects of the spatial patterns of great gerbil burrows in Kazakhstan. LOCATION: Kazakhstan. METHODS: Burrows were classified as either occupied or empty in 98 squares of four different sizes: 200 m (side length), 250 m, 500 m and 590-1020 m. We used Ripley's K statistic to determine whether and at what scale there was clustering of occupied burrows, and semi-variograms to quantify spatial patterns in occupied burrows at scales of 250 m to 9 km. RESULTS: Significant spatial clustering of occupied burrows occurred in 25% and 75% of squares of 500 m and 590-1020 m, respectively, but not in smaller squares. In clustered squares, the clustering criterion peaked around 250 m. Semi-variograms showed that burrow density was auto-correlated up to a distance of 7 km and occupied density up to 2.5 km. MAIN CONCLUSIONS: These results demonstrate that there is statistically significant spatial clustering of occupied burrows and that the uniformity assumptions of previous plague models should be reconsidered to assess its significance for plague transmission. This field evidence will allow for more realistic approaches to disease ecology models for both this system and for other structured host populations

Potential corridors and barriers for plague spread in Central Asia.

Plague (Yersinia pestis infection) is a vector-borne disease which caused millions of human deaths in the Middle Ages. The hosts of plague are mostly rodents, and the disease is spread by the fleas that feed on them. Currently, the disease still circulates amongst sylvatic rodent populations all over the world, including great gerbil (Rhombomys opimus) populations in Central Asia. Great gerbils are social desert rodents that live in family groups in burrows, which are visible on satellite images. In great gerbil populations an abundance threshold exists, above which plague can spread causing epizootics. The spatial distribution of the host species is thought to influence the plague dynamics, such as the direction of plague spread, however no detailed analysis exists on the possible functional or structural corridors and barriers that are present in this population and landscape. This study aims to fill that gap. Three 20 by 20 km areas with known great gerbil burrow distributions were used to analyse the spatial distribution of the burrows. Object-based image analysis was used to map the landscape at several scales, and was linked to the burrow maps. A novel object-based method was developed - the mean neighbour absolute burrow density difference (MNABDD) - to identify the optimal scale and evaluate the efficacy of using landscape objects as opposed to square cells. Multiple regression using raster maps was used to identify the landscape-ecological variables that explain burrow density best. Functional corridors and barriers were mapped using burrow density thresholds. Cumulative resistance of the burrow distribution to potential disease spread was evaluated using cost distance analysis. A 46-year plague surveillance dataset was used to evaluate whether plague spread was radially symmetric. The burrow distribution was found to be non-random and negatively correlated with Greenness, especially in the floodplain areas. Corridors and barriers showed a mostly NWSE alignment, suggesting easier spreading along this axis. This was confirmed by the analysis of the plague data. Plague spread had a predominantly NWSE direction, which is likely due to the NWSE alignment of corridors and barriers in the burrow distribution and the landscape. This finding may improve predictions of plague in the future and emphasizes the importance of including landscape analysis in wildlife disease studies