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

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.

Drivers and potential distribution of anthrax occurrence and incidence at national and sub-county levels across Kenya from 2006 to 2020 using INLA

“…Random partitioning of the data into training and testing sets can inflate the performance of a model and underestimate the error in the spatial prediction evaluation 38 . Aside from the continental anthrax risk map, two additional livestock anthrax risk mapping studies were done in 2021 in Kenya 16 , 17 . The first article applied Boosted Regression Trees (BRTs) to model the geographical distribution of anthrax in Kenya focusing on the southern parts of the country 17 .…”

“…Although the final ensemble model produced had a mean Area Under the Receiver Operating Characteristic Curve (AUC) of 0.8, the sample size used was smaller (n = 69), and the authors also restricted their model to the southern half of the country thereby limiting interpretation across the whole country 17 . The second article also applied BRTs to model the future distribution of anthrax across Kenya under various climate change scenarios 16 . The final model had a good mean training AUC (0.936; ± 0.0019) and mean testing AUC (0.929; ± 0.0039) under the current scenario 16 .…”

“…The second article also applied BRTs to model the future distribution of anthrax across Kenya under various climate change scenarios 16 . The final model had a good mean training AUC (0.936; ± 0.0019) and mean testing AUC (0.929; ± 0.0039) under the current scenario 16 . However, like the continental study, the occurrence dataset was randomly partitioned by splitting 75% of the data for model training and 25% for model testing which can inflate the performance of a model 16 .…”

“…ENM has been used to model the spatial distribution of B. anthracis suitability across the world 7 , 9 , 10 , 13 – 15 . Although recent studies of B. anthracis suitability in Kenya have applied conventional algorithms that provide useful insights 16 , 17 , these methods are limited due to their inability to cope with random effects such as spatial and temporal pseudo-replication which are common features of most ecological datasets 18 . Without a complete understanding of the true incidence and drivers of anthrax, it is difficult to conduct proper surveillance, diagnosis, prevention, treatment, and control.…”

The potential distribution of Bacillus anthracis suitability across Uganda using INLA

“…Correlative studies of environmental risk factors for anthrax outbreaks suggest that temperature 6 , 29 – 37 , precipitation 6 , 29 – 39 , elevation 6 , 29 , 31 , 32 , 34 , 35 , 37 – 39 , soil (type, calcium concentration, pH, carbon content, and moisture) 6 , 30 , 31 , 33 , 34 , 36 , 37 , 39 – 43 , vegetation 6 , 29 , 31 , 34 , 36 – 40 , 42 , 43 , and hydrology 37 , 44 are some of the major drivers of B. anthracis suitability. A total of 26 environmental predictors (Fig.…”