Schistosomiasis control in sub-Saharan Africa is enacted primarily through mass drug administration, where predictive modeling plays an important role in filling knowledge gaps in the distribution of disease burden. Remote sensing (RS) satellite imagery is used to predictively model infectious disease transmission in schistosomiasis, since transmission requires environmental conditions to sustain specific freshwater snail species. Surveys are commonly used to obtain health outcome data, and while they provide accurate estimates of disease in a specific time and place, the resources required make performing surveys at large spatiotemporal scales impractical. Ongoing national surveillance data in the form of reported counts from health centers is conceptually better suited to utilizing the full spatiotemporal capabilities of publically available RS data, as most open source satellite products can be utilized as global continuous surfaces with historical (in some cases 40-year) timespans. In addition RS data is often in the public domain and takes at most a few days to order. Therefore, the use of surveillance data as an initial descriptive approach of mapping areas of high disease prevalence (often with large focal variation present) could then be followed up with more resource intensive methods such as health surveys paired with commercial, high spatial resolution imagery. Utilization of datasets and technologies more cost effectively would lead to sustainable control, a precursor to eradication (Rollinson et al. 2013).
In this study, environmental parameters were chosen for their historical use as proxies for climate. They were used as predictors and as inputs to a novel climate classification technique. This allowed for qualitative and quantitative analysis of broad climatic trends, and were regressed on 8 years of Ghanaian national surveillance health data. Mixed effect modeling was used to assess the relationship between reported disease counts and remote sensing data over space and time. A downward trend was observed in the reported disease rates (~1% per month). Seasonality was present, with two peaks (March and September) in the north of the country, a single peak (July) in the middle of the country, and lows consistently observed in December/January. Trend and seasonal patterns of the environmental variables and their associations with reported incidence varied across the defined climate zones. Environmental predictors explained little of the variance and did not improve model fit significantly, unlike district level effects which explained most of the variance. Use of climate zones showed potential and should be explored further. Overall, surveillance of neglected tropical diseases in low-income countries often suffers from incomplete records or missing observations. However, with systematic improvements, these data could potentially offer opportunities to more comprehensively analyze disease patterns by combining wide geographic coverage and varying levels of spatial and temporal aggregation. The approach can serve as a decision support tool and offers the potential for use with other climate-sensitive diseases in low-income settings.
|Advisor:||Gute, David M.|
|Commitee:||Koch, Magaly, Kosinski, Karen C., Naumova, Elena N.|
|School Location:||United States -- Massachusetts|
|Source:||MAI 56/04M(E), Masters Abstracts International|
|Keywords:||Climate classification, Geographic information system, Mixed effects modeling, Remote sensing, Schistosomiasis|
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