Project

After consulting with the De Leo Lab, I have been able to narrow down on my approaches to produce my project. The project involves exploring the relationship between two different parasites that cause schistosomiasis in humans: Schistosoma mansoni and Schistosoma haematobium, with respect to spatial data, time, 19 climate predictor variables, and 9 terrain predictor variables. So far, I have not been told where the lab got the dataset from despite asking. I plan on asking again in the week and including it in my Week 3 report.

Key Definitions

According to the CDC, schistosomiasis is a disease caused by parasitic worms in humans, which is second only to malaria in terms of its devastating effect 11 CDC. (2021, January 13). CDC - Schistosomiasis. Www.cdc.gov. https://www.cdc.gov/parasites/schistosomiasis/index.html#:~:text=Schistosomiasis%2C%20also%20known%20as%20bilharzia. The three main species infecting humans are Schistosoma haematobium, Schistosoma japonicum, and Schistosoma mansoni. Risk factors for the disease include regions with poor sanitation and freshwater bodies.

Due to the devastating impacts of this disease, understanding the environments that help it spread can contribute to a more focused and effective disease-spread mitigation strategy. Currently, such investigations are common in the literatureto try and determine more knowledgeabout schistosomiasis. There have been geostatistical studies to understand schistosomiasis prevalence 22 Schur, N., Hürlimann, E., Garba, A., Traoré, M. S., Ndir, O., Ratard, R. C., Tchuem Tchuenté, L. A., Kristensen, T. K., Utzinger, J., & Vounatsou, P. (2011). Geostatistical model-based estimates of Schistosomiasis prevalence among individuals aged ≤ 20 years in West Africa. PLoS neglected tropical diseases, 5(6), e1194. https://doi.org/10.1371/journal.pntd.0001194, and spatial mapping of Schistosoma infection riskin east Africa 33 Schur, N., Hürlimann, E., Stensgaard, A. S., Chimfwembe, K., Mushinge, G., Simoonga, C., Kabatereine, N. B., Kristensen, T. K., Utzinger, J., & Vounatsou, P. (2013). Spatially explicit Schistosoma infection risk in eastern Africa using Bayesian geostatistical modelling. Acta tropica, 128(2), 365–377. https://doi.org/10.1016/j.actatropica.2011.10.006, but there is less knowledge on the seasonal and environmental influence on the spread of the disease. This project aims to address this by examining the relationship of biological climate and encironmental indicators.

At this point, I am not entirely sure what my overall research question is, but right now I am brainstorming something along the lines of “what climate and terrain features promote the spread of schistosomiasis?”

Over the course of meetings in the coming week, I hope to finalize my research question with the De Leo Lab and include it in my report for Week 3.

Dataset Description

My dataset was obtained directly from the De Leo Lab. It contains 35 variables and 11,331 observations.

The variables and their description are as follows:

• percent_pos: aggregated schistosomiasis parasitology study percentage positivity of parasite presence at each “loc_spp” (described below),
• longitude: longitude coordinates,
• latitude: latitude coordinates,
• lat-long: longitude-latitude coordinates (in one column),
• loc_spp: unique location and species - even though there are three main species of parasite, this dataset and project only compares two of them: Schistosoma haematobium and Schistosoma mansoni,
• parasite_s: parasite species,
• yearof_pub: year of study,
• bio_01 to bio_19: biological climate indicators,
• hnd: hydrologically adjusted elevation,
• upa: Upstream drainage area (flow accumulation area),
• slope: slope of terrain (digital elevation model [DEM]-derived),
• gHM: global human modification,
• elevation: elevation of terrain (DEM-derived),
• pH: metric to measure acidity or basicity - this data had to be transformed by scaling the values by 0.1 to represent a regular pH scale which I did through Excel,
• aspect: aspect of terrain (DEM-derived),
• clay: how much of the soil is clay,
• sand: how much of the soil is sand.

Other than the pH column, no further cleaning is required. I haven’t made a decision about handling the NaN values, so I am not including it in this report.

Overall Approach: Tufte’s Principles

In my investigation, my hypothesis will revolve around there being a higher percent_pos (percentage positivity of parasite presence) in conditions that represent an environment closer to water (eg. pH close to 7 for water). As for the biological climate indicators, I feel like it is difficult to make a specific claim about them, but I would assume that not every indicator has the same weight in terms of prediction and that each of the two species have a different set of biological climate indicators that are responsible for them. The main comparison I will be making involves the different features that correspond to the presence of one type of schistosomiasis versus the other. The mechanisms of cause and effect will revolve around understanding how each of the biological climate and terrain predictors promote the growth of these parasites, as well as searching for alternative explanations for their growth, such as temperature.