Background and Aim: Anthrax, caused by the soil-borne spore-forming bacteria called Bacillus anthracis, is a zoonotic disease that persists worldwide in livestock and wildlife and infects humans. It is a great hazard to livestock; henceforth, evaluating the global concerns about the disease occurrence in livestock is essential. This study was conducted to estimate the global prevalence of anthrax and predict high-risk regions, which could be an input to veterinarians to take necessary steps to control and avoid the disease. Materials and Methods: A literature review was performed using online databases, namely, PubMed, Google Scholar, Scopus, Biomed Central, and Science Direct, to extract relevant publications worldwide between 1992 and 2020. Initially, 174 articles were selected, and after scrutinizing, 24 articles reporting the prevalence of anthrax were found to be adequate for the final meta-analysis. The statistical study was accompanied by employing fixed effects and random effects models using R. Results: The pooled prevalence of anthrax globally was 28% (95% confidence interval, 26-30%) from 2452 samples through the fixed effects model. Continent-wise subgroup analysis through the random effects model revealed that the pooled prevalence of anthrax was highest in Africa (29%) and least in North America (21%). Conclusion: In these publications, anthrax causes economic loss to farmers and, thus, to the world. Hence, controlling anthrax infections in high-risk regions are essential by implementing appropriate control measures to decrease the effect of the disease, thereby reducing economic loss.

Anthrax is a highly fatal zoonotic disease that affects all species of livestock. The study aims to develop an early warning of epidemiological anthrax using machine learning (ML) models and to study the effect of El Niño and La Niña oscillation, as well as the climate–disease relationship concerning the spatial occurrence and outbreaks in Karnataka. The disease incidence data are divided based on El Niño and La Niña events from 2004–2019 and subjected to climate-disease modeling to understand the disease pattern over the years. Machine learning models were implemented using R statistical software version 3.1.3 with Livestock density, soil profile, and meteorological and remote sensing variables as risk factors associated with anthrax incidence. Model evaluation is performed using statistical indices, viz., Cohen’s kappa, receiver operating characteristic (ROC) curve, true skill statistics (TSS), etc. Models with good predictive power were combined to develop an average prediction model. The predicted results were mapped onto the Risk maps, and the Basic reproduction numbers (R₀) for the districts that are significantly clustered were calculated. Early warning or risk prediction developed with a layer of R₀ superimposed on a risk map helps in the preparedness for the disease occurrence, and precautionary measures before the spread of the disease.

Anthrax, a zoonotic disease affecting both livestock and humans globally, is caused by Bacillus anthracis. The objectives of this study were the following: (1) to identify environmental risk factors for anthrax and use this information to develop an improved predictive risk map, and (2) to estimate spatial variation in basic reproduction number (Ro) and herd immunity threshold at the village level, which can be used to optimize vaccination policies within high-risk regions. Based on the anthrax incidences from 2000–2023 and vaccine administration figures between 2008 and 2022 in Karnataka, this study depicted spatiotemporal pattern analysis to derive a risk map employing machine learning algorithms and estimate Ro and herd immunity threshold for better vaccination coverage. Risk factors considered were key meteorological, remote sensing, soil, and geographical parameters. Spatial autocorrelation and SaTScan analysis revealed the presence of hotspots and clusters predominantly in the southern, central, and uppermost northern districts of Karnataka and temporal cluster distribution between June and September. Factors significantly associated with anthrax were air temperature, surface pressure, land surface temperature (LST), enhanced vegetation index (EVI), potential evapotranspiration (PET), soil temperature, soil moisture, pH, available potassium, sulphur, and boron, elevation, and proximity to waterbodies and waterways. Ensemble technique with random forest and classification tree models were used to improve the prediction accuracy of anthrax. High-risk areas are expected in villages in the southern, central, and extreme northern districts of Karnataka. The estimated Ro revealed 11 high-risk districts with Ro > 1.50 and respective herd immunity thresholds ranging from 11.24% to 55.47%, and the assessment of vaccination coverage at the 70%, 80%, and 90% vaccine efficacy levels, all serving for need-based strategic vaccine allocation. A comparison analysis of vaccinations administered and vaccination coverage estimated in this study is used to illustrate difference in the supply and vaccine force. The findings from the present study may support in planning preventive interventions, resource allocation, especially of vaccines, and other control strategies against anthrax across Karnataka, specifically focusing on predicted high-risk regions.