Feb 13, 2025

Public workspaceAnthrax Surveillance and Analysis Protocol

DOI
dx.doi.org/10.17504/protocols.io.x54v92n81l3e/v1
  • 1University of Zimbabwe;
  • 2Central veterinary laboratory
Tawanda A Chari
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DOI: dx.doi.org/10.17504/protocols.io.x54v92n81l3e/v1
Protocol Citation: Tawanda A Chari, claudious Gufe 2025. Anthrax Surveillance and Analysis Protocol. protocols.io https://dx.doi.org/10.17504/protocols.io.x54v92n81l3e/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: September 17, 2024
Last Modified: February 13, 2025
Protocol Integer ID: 107746
Keywords: Anthrax epidemiology, Transboundary disease control, Zoonotic diseases, One Health approach, Livestock-wildlife-human interface
Disclaimer

  • Potential underreporting of anthrax cases in grey data sources.

  • Variability in surveillance intensity across different regions.

Abstract
This study employs a systematic review and retrospective analysis to assess anthrax incidence, prevalence, and risk factors in Zimbabwe. Data were sourced from peer-reviewed literature, grey veterinary records from the Central Veterinary Laboratory (CVL), and geospatial analysis using GIS. Anthrax prevalence was estimated using weighted prevalence analysis, with a calculated regional prevalence of 17.33% across Zimbabwe, Kenya, Botswana, and Namibia. Logistic regression modelling was applied to examine climate influences, revealing a positive correlation (OR = 2.41, p < 0.05) between outbreaks and post-drought rainfall events. Spatial analysis identified high-risk zones in Midlands, Mashonaland Central, and Matabeleland North using the Getis-Ord Gi hotspot detection method*. This methodology provides a reproducible framework for anthrax surveillance, incorporating epidemiological, statistical, and GIS-based approaches to inform climate-adaptive risk mapping and targeted intervention strategies.
Guidelines
1. Intended Use and Scope
‱ This protocol is designed for researchers, epidemiologists, veterinarians, and public health officials working on anthrax surveillance and analysis. ‱ It provides step-by-step instructions for data collection, statistical modeling, GIS-based risk mapping, and climate-anthrax correlation analysis. ‱ The methodology is applicable to Zimbabwe and other anthrax-endemic regions with similar ecological and epidemiological conditions.


2. Experimental Controls and Quality Assurance

‱ Data Validation: Cross-check anthrax incidence data from peer-reviewed sources and grey literature reports to ensure consistency. ‱ GIS Accuracy: Validate hotspot mapping results by comparing them with historical outbreak records. ‱ Statistical Robustness: Test logistic regression model sensitivity using bootstrap resampling. ‱ Reproducibility Check: Ensure that different analysts using the same dataset obtain the same spatial and statistical results.

3. Key Considerations Before Running the Protocol

‱ Ethical and Data Access Considerations
  • If using laboratory-confirmed anthrax data, ensure institutional review board (IRB) approvals where required.
  • Some datasets, such as national disease surveillance reports, may require permission from government agencies before access.
‱ Common Pitfalls to Avoid
  • GIS Mapping Errors: Ensure that spatial coordinate systems are correctly set before overlaying anthrax case data onto base maps.
  • Statistical Model Assumptions: Verify that data meets logistic regression assumptions (e.g., no perfect multicollinearity, balanced classes).
  • Climate Data Handling: Climate raster data may need pre-processing (e.g., resampling to uniform grid resolution) before analysis.

4. Troubleshooting & FAQ

Q1: What if my GIS mapping outputs do not align with known anthrax outbreak locations? ‱ Check that your spatial reference system (SRS) is set correctly (use WGS 84 projection for global datasets).
Q2: How do I validate my statistical results? ‱ Run bootstrap resampling to confirm logistic regression output consistency.
Q3: How do I modify this protocol for another region? ‱ Replace Zimbabwe-specific datasets with your local anthrax case records, rainfall/temperature data, and land-use maps.

5. Citation and Attribution

‱ How to Cite This Protocol: "Researcher(s) Name, Year. 'Anthrax Surveillance and Analysis Protocol: A Statistical and GIS-Based Approach.' Protocols.io, DOI: XXXX-XXXX."
‱ How to Acknowledge Data Sources:
  • If using SILAB laboratory data, cite Zimbabwe Central Veterinary Laboratory (CVL).
  • Climate data must be cited according to ERA5 Reanalysis Data (Copernicus Climate Change Service).

6. Versioning and Updates

‱ This protocol will be updated annually to include new anthrax surveillance techniques and advancements in spatial epidemiology methods. ‱ Users are encouraged to comment and suggest modifications to improve reproducibility and applicability.
Materials
1. Data Sources and Input Datasets
These datasets are essential for conducting epidemiological, statistical, and geospatial analyses.
Anthrax Case Data (2013–2024) Zimbabwe Central Veterinary Laboratory (CVL) SILAB Database CSV, Excel
Historical Anthrax Outbreaks Peer-reviewed studies, Ministry of Health reports CSV, PDF
Climate Data (Rainfall, Temperature, Drought Index) ERA5 Reanalysis, Zimbabwe Meteorological Services NetCDF, CSV
GIS Base Maps (Country Boundaries, Land Use) OpenStreetMap, FAO GeoNetwork SHP (Shapefile), GeoTIFF
Soil & Vegetation Data MODIS NDVI, FAO Soil Database GeoTIFF, CSV
Livestock Density Data FAO, World Bank CSV, Shapefile
Ensure that all datasets are in the correct format before analysis (e.g., coordinate systems in WGS 84 for GIS data).

2. Software & Computational Tools
These tools are used for data processing, statistical modeling, GIS mapping, and climate analysis.
Statistical Analysis R (StatsModels), Python (SciPy, Pandas), SPSS Prevalence calculations, logistic regression
GIS & Mapping QGIS, ArcGIS, Google Earth Pro Hotspot detection, spatial risk mapping
Climate Data Processing ERA5 Climate Toolbox, Python xarray Extracting climate variables for modeling
Data Cleaning & Management Microsoft Excel, OpenRefine Cleaning raw epidemiological and climate data
Remote Sensing Analysis Google Earth Engine (GEE) Processing MODIS NDVI for vegetation mapping
Visualization & Reporting Tableau, Matplotlib, Seaborn Graphing trends, spatial visualizations
Ensure Python and R packages are installed before running scripts (e.g., geopandas, matplotlib, xarray).

3. GIS Data Layers for Spatial Analysis
These spatial datasets are used for risk mapping, outbreak clustering, and environmental correlation analysis.
Administrative Boundaries OpenStreetMap, FAO GeoNetwork SHP, GeoJSON
Population Density Data WorldPop, UN Data GeoTIFF, CSV
Land Cover & Land Use Data MODIS, FAO Land Cover Database GeoTIFF
Road & River Networks OpenStreetMap, Hydrosheds SHP, GeoJSON
Livestock Movement Data FAO Animal Movement Database CSV, Shapefile
Anthrax Case Locations CVL, Ministry of Health CSV, SHP
Ensure all GIS data is correctly georeferenced to avoid misalignment in mapping outputs.

4. Computational Requirements
Due to large datasets and complex GIS processing, ensure the computational system meets these minimum specifications:
Processor Intel i7 or AMD Ryzen 7 (or better)
RAM Minimum 16GB (32GB recommended for GIS analysis)
Storage Minimum 500GB SSD (1TB recommended for large datasets)
Graphics Card (for GIS Processing) NVIDIA GTX 1650 or equivalent
Operating System Windows 10/11, macOS, or Linux (Ubuntu recommended)
For cloud-based processing, use Google Colab (for Python), Google Earth Engine (for remote sensing), or AWS EC2 (for GIS workloads).

5. Optional Resources (For Advanced Analysis & Machine Learning)
If applying machine learning models to predict future anthrax outbreaks, the following resources may be required:
Deep Learning for Image Analysis TensorFlow, PyTorch Analyzing remote sensing images for anthrax risk mapping
Climate-Anthrax Prediction Models XGBoost, Random Forest Predicting outbreak likelihood based on climate trends
Big Data Processing Google Cloud, AWS S3 Handling large datasets
Advanced modeling techniques require expertise in machine learning and high-performance computing (HPC) resources.
Safety warnings

1. Risk of Outdated Information

  • Warning: Be cautious of using outdated studies, particularly in rapidly evolving fields such as public health and infectious diseases. Ensure the majority of your sources are from the last 10-20 years, unless historical context is necessary.

2. Reliability of Grey Literature

  • Warning: While grey literature (such as dissertations, reports, and unpublished works) can offer valuable insights, it may lack the rigorous peer-review process found in published journals. Always critically assess the reliability and validity of these sources.Duration00:00:00

Ethics statement
Ethical Considerations: According to Zimbabwean guidelines, ethical approval was not needed for this animal-based review article. However, acknowledge all sources properly and avoid plagiarism. When including unpublished studies or grey literature, ensure appropriate permissions are obtained

Before start
The protocol can be edited to better suit your literature search criteria.
Study Design
Study Design

  • Type: Systematic review and retrospective analysis.
  • Study Period: 2013–2024.
  • Scope: Evaluation of anthrax prevalence in livestock, wildlife, and human cases.
  • Primary Outcomes: Identification of anthrax hotspots, climate-related outbreak patterns, and policy intervention areas.
Materials & Resources
Materials & Resources
Data Sources: Peer-reviewed publications, grey literature from veterinary databases.
Software & Tools:
  • Statistical analysis: R, SPSS, Python (SciPy, StatsModels).
  • GIS mapping: QGIS, ArcGIS.
  • Climate data: ERA5 Reanalysis, Zimbabwe Meteorological Services data.
  • Disease surveillance: SILAB Veterinary Laboratory Database
Data Collection
Data Collection
Literature Review
Databases Used: PubMed, Web of Science, JSTOR, AGRICOLA, HINDARI, Google Scholar, Scopus.
Search Terms: "Anthrax", "Zimbabwe", "livestock anthrax", "wildlife anthrax", "zoonotic diseases", "Bacillus anthracis", "transboundary disease".
Inclusion Criteria: Peer-reviewed studies on anthrax in Zimbabwe (2013–2024). Reports on livestock, wildlife, and human anthrax cases. English language publications.
Exclusion Criteria: Studies without outbreak data. Environmental studies lacking direct epidemiology insights.
Grey Data Collection from the Central Veterinary Laboratory (CVL)
Source: Zimbabwe’s National Veterinary Laboratory (SILAB database).
Data Extracted: Anthrax-positive cases in livestock, wildlife, and humans. Sample types: swabs, spleen, soil, tissue. Geographic and temporal distribution of cases.
Statistical Analysis
Statistical Analysis
Prevalence Calculation: Prevalence = (Positive Cases / Total Cases) x 100
Weighted Prevalence Analysis
To compare anthrax trends in Zimbabwe, Kenya, Botswana, Namibia, a weighted prevalence was calculated: Weighted Prevalence = å (Prevalence x Weight)
Weighting Factor: (Sample size of each region) / (Total sample size across regions).
Confidence Interval Estimation
Wilson Score Interval for 95% CI:



Where:
- X = Positive cases.
- N = Total cases.
- z = 1.96 (for 95% CI).
Logistic Regression for Climate-Anthrax Relationship
Model:




Dependent Variable: Anthrax outbreak occurrence (Binary: 1 = outbreak, 0 = no outbreak).

Independent Variables: Rainfall (mm), Temperature (°C), Drought Index (SPI)
Geospatial Analysis & GIS Mapping
Geospatial Analysis & GIS Mapping
GIS-Based Spatial Analysis

Software: QGIS, R
Hotspot Detection: Getis-Ord Gi* statistic:




Protocol references
Chari, T. A., Gufe, C., Kayoka, P., Gabriel, R., Manatsa, S., Mbonjani, B., Marumure, J., Makuvara, Z., Makaya, P. V., & Mupungani, C. (2023). Prevalence and control of brucellosis in Zimbabwe, risk factors, and challenges for control: A Review. Tanzania Veterinary Journal, 38(1), 14–31. https://www.ajol.info/index.php/tvj/article/view/262605
Chikanya, E., Macherera, M., & Maviza, A. (2021). An assessment of risk factors for contracting rabies among dog bite cases recorded in Ward 30, Murewa district, Zimbabwe. PLOS Neglected Tropical Diseases, 15(3), e0009305. https://doi.org/10.1371/JOURNAL.PNTD.0009305
Haselbeck, A. H., Rietmann, S., Tadesse, B. T., Kling, K., Kaschubat‐dieudonné, M. E., Marks, F., Wetzker, W., & Thöne‐reineke, C. (2021). Challenges to the Fight against Rabies—The Landscape of Policy and Prevention Strategies in Africa. International Journal of Environmental Research and Public Health 2021, Vol. 18, Page 1736, 18(4), 1736. https://doi.org/10.3390/IJERPH18041736
Matope, G., Gadaga, M. B., Bhebhe, B., Tshabalala, P. T., & Makaya, P. V. (2023). Bovine brucellosis and tuberculosis at a livestock–wildlife interface in Zimbabwe: A nexus for amplification of a zoonosis or a myth? Veterinary Medicine and Science, 9(3), 1327. https://doi.org/10.1002/VMS3.1084
Mukarati, N. L., Matope, G., de Garine-Wichatitsky, M., Ndhlovu, D. N., Caron, A., & Pfukenyi, D. M. (2020). The pattern of anthrax at the wildlife-livestock-human interface in Zimbabwe. PLOS Neglected Tropical Diseases, 14(10), e0008800. https://doi.org/10.1371/JOURNAL.PNTD.0008800
Nyasulu, P. S., Weyer, J., Tschopp, R., Mihret, A., Aseffa, A., Nuvor, S. V., Tamuzi, J. L., Nyakarahuka, L., Helegbe, G. K., Ntinginya, N. E., Gebreyesus, M. T., Doumbia, S., Busse, R., & Drosten, C. (2021). Rabies mortality and morbidity associated with animal bites in Africa: a case for integrated rabies disease surveillance, prevention and control: a scoping review. BMJ Open, 11(12), e048551. https://doi.org/10.1136/BMJOPEN-2020-048551
Acknowledgements
This research and protocol development were made possible through the contributions and support of various institutions, funding bodies, and individuals. We acknowledge the following for their vital roles in ensuring the integrity, credibility, and openness of this scientific work:

Institutional Support

  • Zimbabwe Central Veterinary Laboratory (CVL) – For providing access to national anthrax surveillance data.
  • Ministry of Health and Child Care, Zimbabwe – For epidemiological records and disease control strategies.
  • Zimbabwe Meteorological Services Department – For climate datasets used in the study.


Data and Technical Support

  • Google Earth Engine (GEE) – For remote sensing and vegetation analysis.
  • OpenStreetMap & FAO GeoNetwork – For GIS base maps and administrative boundary data.
  • R & Python Open-Source Communities – For providing statistical modelling and spatial analysis tools.

Acknowledgement of Local Communities & Field Researchers

  • Special thanks to field veterinarians, epidemiologists, and data collectors who contributed to anthrax surveillance in rural Zimbabwe.
  • Gratitude to local livestock farmers and community health workers for reporting cases and providing field data.

Software and Computational Resources

  • QGIS & ArcGIS Teams – For GIS spatial analysis support.
  • Google Colab & AWS EC2 – For computational resources in large-scale climate-anthrax modelling.
  • Scikit-learn & TensorFlow Developers – For machine learning tools used in predictive outbreak modelling.

Final Statement of Integrity

We affirm that all data sources, methodologies, and collaborations have been fully acknowledged to maintain scientific integrity, transparency, and credibility. This protocol follows the principles of open science and FAIR data practices (Findable, Accessible, Interoperable, and Reusable).

For any enquiries or collaborations related to this protocol, please contact [tawandaachari@gmail.comenquiries].