Modeling the Potential Future Distribution of Anthrax Outbreaks under Multiple Climate Change Scenarios for Kenya.

Samuel A Canfield,Moses Kariuki Njenga,Fredrick Tom Otieno,Bernard Bett,Jason K Blackburn,Harry Oyas,Peter Gikuma-Njuru,John Gachohi,Patrick Kariuki

doi:10.3390/ijerph18084176

Abstract

The climate is changing, and such changes are projected to cause global increase in the prevalence and geographic ranges of infectious diseases such as anthrax. There is limited knowledge in the tropics with regards to expected impacts of climate change on anthrax outbreaks. We determined the future distribution of anthrax in Kenya with representative concentration pathways (RCP) 4.5 and 8.5 for year 2055. Ecological niche modelling (ENM) of boosted regression trees (BRT) was applied in predicting the potential geographic distribution of anthrax for current and future climatic conditions. The models were fitted with presence-only anthrax occurrences (n = 178) from historical archives (2011–2017), sporadic outbreak surveys (2017–2018), and active surveillance (2019–2020). The selected environmental variables in order of importance included rainfall of wettest month, mean precipitation (February, October, December, July), annual temperature range, temperature seasonality, length of longest dry season, potential evapotranspiration and slope. We found a general anthrax risk areal expansion i.e., current, 36,131 km2, RCP 4.5, 40,012 km2, and RCP 8.5, 39,835 km2. The distribution exhibited a northward shift from current to future. This prediction of the potential anthrax distribution under changing climates can inform anticipatory measures to mitigate future anthrax risk.

Highlights

We examined potential spatial shifts in anthrax risk areas from current to present in two ways
The variance inflation factor (VIF) analysis filtered the 71 candidate variables to 10 independent variables (Table 1), which were fitted in the boosted regression trees (BRT) experiments
This study used boosted regression tree modelling to predict the potential spatial distribution of anthrax in Kenya based on current climate conditions (Baseline (1961–1990)

Summary

Introduction

ENMs are modeling approaches aimed at predicting a species’ potential geographic distribution on a selected landscape by pattern matching or statistically correlating species’ presence locations to environmental variables to determine suitable environmental conditions that meet the species’ ecological requirements [30,34]. Those requirements are mapped onto the landscape to predict the areas of relative habitat suitability [35].

Study Area

Anthrax Occurrence Data

Predictive Data and Variable Selection

Model Building and Evaluation

Prediction of Potential Anthrax Distribution Due to Climate Changes

Variable Contribution

Marginal Effect of the Climatic Variables on Anthrax Distribution Predictions

Change Detection

Discussion

Conclusions