Animal Foot Prints Vector Free Download ((NEW))
Laveta Nachman
There are several important limitations to this approach that future work should address. First, this study is observational and does not necessarily capture underlying causal mechanisms. It is subject to under-reporting of disease cases (particularly for pathogens that frequently cause asymptomatic infection) and measurement error and autocorrelation among environmental covariates. Second, while human footprint represents an important advance in globally accessible, high-resolution mapping of multidimensional impacts of humans on landscapes, it is currently only available for recent time periods and is updated infrequently. As a result, we had to interpolate between 2013 and 2019 and across two methods (the original computation and a validated machine learning method2,18) to calculate human footprint for each study year. This is limiting because the assumption of a constant, linear change in human footprint from year to year probably biases our results to be conservative because we are not able to catch year-specific shocks in human pressure that could result in rapid changes in VBD occurrence. We also hypothesize that the rate at which a municipality changes in human footprint from one year to the next is important for disease occurrence and, if available, would better define the tipping points in our partial dependence plots (PDPs) and reduce the uncertainty in our variable importance measures. Third, the socio-ecological predictors of disease occurrence may not be the same as the drivers of outbreak size, so the areas that have a high probability of disease occurrence are not necessarily those with the highest disease risk or burden. In particular, disease incidence can vary substantially due to variation in susceptible host population size, vector control measures and access to healthcare and other services. Our primary goal was to capture the land-use niches of multiple VBDs in a comparative approach and to identify critical thresholds across which a more intense human footprint could lead to shifts in disease occurrence. Important directions for future work include conducting causal analyses to understand whether shifts across human footprint thresholds lead to shifts in VBD occurrence (and at what timescales) and investigating how human pressure interacts with socioeconomic variables as drivers of VBD incidence.
The temporal resolution of human footprint and land-cover data limited our analysis to an annual scale. The analysis methods are summarized in Supplementary Fig. 2. Briefly, we used a machine learning approach to assess the relationships between human footprint, climate, land-class categories and vector presence and the occurrence of six VBDs in Brazil. Machine learning approaches are increasingly being applied in disease ecology because they can accommodate complexity and nonlinearity to identify linkages among environmental factors and disease41. To understand the predictors of occurrence for each VBD, we used a random forest model, a versatile machine learning technique that uses randomized recursive partitioning to solve complex prediction, regression and classification problems. These models work by repeatedly drawing bootstrap samples from the original sample and a random selection of predictors to grow a predetermined number of decision trees across which results are pooled42.
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