Pathogen-Host Associations and Predicted Range Shifts of Human Monkeypox in Response to Climate Change in Central Africa

<div><p>Climate change is predicted to result in changes in the geographic ranges and local prevalence of infectious diseases, either through direct effects on the pathogen, or indirectly through range shifts in vector and reservoir species. To better understand the occurrence of monkeypox virus (MPXV), an emerging Orthopoxvirus in humans, under contemporary and future climate conditions, we used ecological niche modeling techniques in conjunction with climate and remote-sensing variables. We first created spatially explicit probability distributions of its candidate reservoir species in Africa's Congo Basin. Reservoir species distributions were subsequently used to model current and projected future distributions of human monkeypox (MPX). Results indicate that forest clearing and climate are significant driving factors of the transmission of MPX from wildlife to humans under current climate conditions. Models under contemporary climate conditions performed well, as indicated by high values for the area under the receiver operator curve (AUC), and tests on spatially randomly and non-randomly omitted test data. Future projections were made on IPCC 4^th Assessment climate change scenarios for 2050 and 2080, ranging from more conservative to more aggressive, and representing the potential variation within which range shifts can be expected to occur. Future projections showed range shifts into regions where MPX has not been recorded previously. Increased suitability for MPX was predicted in eastern Democratic Republic of Congo. Models developed here are useful for identifying areas where environmental conditions may become more suitable for human MPX; targeting candidate reservoir species for future screening efforts; and prioritizing regions for future MPX surveillance efforts.</p></div

MPX hotspots in Sankuru district DRC.

<p>(A) secteurs classified as hotspots by a model that assumes that ecological factors are constant across the district; (B) MPX hotspots according to the model adjusted for spatial heterogeneity in deforestation and climate.</p

Land cover at sites with MPX infections in humans in Sankuru (2005–07).

<p>The land cover data are from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066071#pone.0066071-FACET1" target="_blank">[58]</a>.</p

Results of model performance tests using spatial subdivisions.

*<p>The binomial test compares model performance to random performance, whereas the Wilcoxon signed rank test compares the predicted and observed logistic probabilities. Thus, good model performance is indicated by significant test results in the case of the binomial test, but non-significant results in the case of the Wilcoxon signed rank tests.</p

Flowchart of models created using the indicated input data.

<p>We modeled human MPX distributions under current conditions and assessed the concordance among models using the following predictor datasets: 1) reservoir species distributions (based on climate and remote sensing variables) and in addition the climate and remote sensing variables (the full model), which may contain additive information on top of the reservoir species distributions that were also based on both types of data; 2) only reservoir species distributions (based on climate and remote sensing variables); 3) reservoir species distributions (based on climate variables) plus climate variables; and 4) only reservoir species distributions (based on climate variables). To project future human MPX distributions under different climate change scenarios, we projected reservoir species distributions onto future climate variables. We then used the results as input for models of human MPX under approaches 3) and 4) above, since future remote sensing variables are not available.</p

Observed and predicted human MPX occurrence.

<p>(A) Maxent prediction of human MPX occurrence under contemporary climate conditions, using reservoir species as predictor variables, with only linear ‘features’ (i.e. only linear coefficients are used for each predictor) allowed in the model. Colors indicate the probability of MPX occurrence, with cooler colors indicating lower probabilities and warmer colors higher probabilities (see color bar). Crosses indicate the reduced set of observed cases of MPX in humans (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066071#s4" target="_blank">Materials and Methods</a>). (B) Study area. (C) Average projected change in probability of human MPX occurrence for eight climate change scenarios for 2050. (D) Average projected change in probability of human MPX occurrence under eight climate change scenarios for 2080. Colors in (C) and (D) indicate the change in probability of occurrence, with cooler colors indicating a decrease, and warmer colors an increase. (E) Projected human population growth from 1990–2015. The growth of the human population in the eastern DRC and Uganda, which borders North Kivu province in the eastern DRC, will be among the greatest of any region in central Africa. For areas shown in red, there has been a large increase in the human population since 1990 and further growth is forecast in the imminent future. Projections for later in the 21 century are qualitatively similar. For example, the population density of Uganda is forecast to increase 171% by 2050 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066071#pone.0066071-United1" target="_blank">[56]</a>. A potential epicenter of future MPX outbreaks in the eastern DRC could arise from the confluence of population growth and increased MPX prevalence under climate change.</p

Results of Maxent runs for reservoir species.

<p>Results of Maxent runs for reservoir species.</p

Climatic and land cover variables used to analyze MPX transmission.

<p>AMO = Atlantic Multidecadal Oscillation, MWD = maximum water deficit, NDVI = normalized difference vegetation index.</p

Variable importance of Maxent models of MPXV occurrence.

<p>Results are shown for a model that included reservoir species based on climate and remote sensing variables with all ‘features’ allowed (auto features, i.e. linear and quadratic coefficients can be used for each predictor, as well as step functions and interactions) (top panel), and a model that included only the reservoir species, based on climate variables, and with only linear ‘features’ (i.e. only linear coefficients are used for each predictor) allowed in the model (bottom panel). Dark blue bars indicate test results in which only the variable in question was entered into the model, and light blue bars in which all variables except the one in question were entered. Longer dark blue bars and shorter light blue bars indicate higher variable importance.</p

Results of Maxent runs for human MPX.

<p>Results are shown for models with different sets of input predictor variables. CLIM = climate variables; RS = remote sensing variables; Res() indicate reservoir species based on the variables in brackets; linear features = only linear features allowed in the Maxent model.</p