Comparison of spatiotemporal patterns of historic natural Anthrax outbreaks in Minnesota and Kazakhstan

Abstract

Disease spread in populations is a consequence of the interaction between host, pathogen, and environment, i.e. the epidemiological triad. Yet the influences of each triad component may vary dramatically for different settings. Comparison of environmental, demographic, socio-economic, and historical backgrounds may support tailoring site-specific control measures. Because of the long-term survival of Bacillus anthracis , Anthrax is a suitable example for studying the influence of triad components in different endemic settings. We compared the spatiotemporal patterns of historic animal Anthrax records in two endemic areas, located at northern latitudes in the western and eastern hemispheres. Our goal was to compare the spatiotemporal patterns in Anthrax progression, intensity, direction, and recurrence (disease hot spots), in relation to epidemiological factors and potential trigger events. Reported animal cases in Minnesota, USA (n = 289 cases between 1912 and 2014) and Kazakhstan (n = 3,997 cases between 1933 and 2014) were analyzed using the spatiotemporal directionality test and the spatial scan statistic. Over the last century Anthrax occurrence in Minnesota was sporadic whereas Kazakhstan experienced a long-term epidemic. Nevertheless, the seasonality was comparable between sites, with a peak in August. Declining number of cases at both sites was attributed to vaccination and control measures. The spatiotemporal directionality test detected a relative northeastern directionality in disease spread for long-term trends in Minnesota, whereas a southwestern directionality was observed in Kazakhstan. In terms of recurrence, the maximum timespans between cases at the same location were 55 and 60 years for Minnesota and Kazakhstan, respectively. Disease hotspots were recognized in both settings, with spatially overlapping clusters years apart. Distribution of the spatiotemporal cluster radii between study sites supported suggestion of site-specific control zones. Spatiotemporal patterns of Anthrax occurrence in both endemic regions were attributed to multiple potential trigger events including major river floods, changes in land use, agriculture, and susceptible livestock populations. Results here help to understand the long-term epidemiological dynamics of Anthrax while providing suggestions to the design and implementation of prevention and control programs, in endemic settings.

Ecological and Socio-Economic Determinants of Livestock Animal Leptospirosis in the Russian Arctic

“…The cumulative number of leptospirosis cases for the entire observation period per unit area by the districts (G-rate) was chosen as a measure for the intensity of the leptospirosis epidemic within the study area (46)(47)(48)(49). The Forest-based Classification and Regression method was applied to identify the relationships between the log-transformed G-rate and a set of potential explanatory factors (50, 51).…”

An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research

“…Anthrax, caused by a spore-forming bacterium Bacillus anthracis, is characterized by the prolonged survival of the spores on soil and wide range of hosts including wildlife, livestock, and human (201,202). Therefore, the observational study designs on Anthrax are likely to be retrospective based on reported cases (203). Given Anthrax is reportable to the animal and public health authorities, most likely type of data available would be point-referenced in nature (i.e., presence of the disease at farm locations or grazing lands).…”

“…Given the prolonged survival of Anthrax spores in contaminated soils/environment, in addition to the initial testing for spatial dependence, understanding the spatiotemporal dependence and spatiotemporal directionality is the key to understand the extent of past spread of the disease. Testing whether there are space and time clustering in the data would facilitate determining any particular area/s with high relative risk for disease clusters at a specific time [i.e., disease hot-spots; (203)].…”

“…Once geocoded, the primary hypothesis testing stage of the framework and the T2 tools would facilitate the recognition of spatiotemporal dependence between the reported cases and determining any particular area with high relative risk for disease clusters [i.e., disease hot-spots; (203)]. Given the prolonged survival of the Anthrax spores conducting purely spatial and spatiotemporal dependence and directionality is the key to understand the extent of past spread of the disease.…”

“…Improving the quality of spatially explicit health and environmental data through systematic collection of highresolution data and public participation GIS approaches such as "crowdsourcing" or "citizen science data" is increasingly popular in both public and environmental health monitoring efforts (213)(214)(215). Additionally, the use of existing databases as passive surveillance systems and improving systematic data collection are suggested as ways to generate spatially explicit animal health databases (203).…”