Spatiotemporal modeling of schistosomiasis in Ghana: linking remote sensing data to infectious disease

More than 90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. The use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. The transmission of schistosomiasis, a disease acquired from contact with contaminated surface water, requires specific environmental conditions to sustain freshwater snails. If a connection between schistosomiasis and remotely sensed environmental variables can be established, then cost effective and current disease risk predictions can be made available. Schistosomiasis transmission has unknown seasonality, and the disease is difficult to study due to a long lag between infection and clinical symptoms. To overcome these challenges, we employed a comprehensive 15-year time-series built from remote sensing feeds, which is the longest environmental dataset to be used in the application of remote sensing to schistosomiasis. The following environmental variables will be used in the model: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique. This technique, improves upon the conventional Köppen-Geiger method, which has been the primary climate classification system in use the past 100 years. These predictor variables will be regressed against 8 years of national health data in Ghana, the largest health dataset of its kind to be used in this context, and acquired from freely available satellite imagery data. A benefit of remote sensing processing is that it only requires training and time in terms of resources. The results of a fixed effects model can be used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.Published versio

The use of remotely sensed environmental parameters for spatial and temporal schistosomiasis prediction across climate zones in Ghana

Schistosomiasis control in sub-Saharan Africa is enacted primarily through preventive chemotherapy. Predictive models can play an important role in filling knowledge gaps in the distribution of the disease and help guide the allocation of limited resources. Previous modeling approaches have used localized cross-sectional survey data and environmental data typically collected at a discrete point in time. In this analysis, 8 years (2008-2015) of monthly schistosomiasis cases reported into Ghana's national surveillance system were used to assess temporal and spatial relationships between disease rates and three remotely sensed environmental variables: land surface temperature (LST), normalized difference vegetation index (NDVI), and accumulated precipitation (AP). Furthermore, the analysis was stratified by three major and nine minor climate zones, defined using a new climate classification method. Results showed a downward trend in reported disease rates (~ 1% per month) for all climate zones. Seasonality was present in the north with two peaks (March and September), and in the middle of the country with a single peak (July). Lowest disease rates were observed in December/January across climate zones. Seasonal patterns in the environmental variables and their associations with reported schistosomiasis infection rates varied across climate zones. Precipitation consistently demonstrated a positive association with disease outcome, with a 1-cm increase in rainfall contributing a 0.3-1.6% increase in monthly reported schistosomiasis infection rates. Generally, surveillance of neglected tropical diseases (NTDs) in low-income countries continues to suffer from data quality issues. However, with systematic improvements, our approach demonstrates a way for health departments to use routine surveillance data in combination with publicly available remote sensing data to analyze disease patterns with wide geographic coverage and varying levels of spatial and temporal aggregation.Accepted manuscrip

Exploring the Association Between Remotely Sensed Environmental Parameters and Surveillance Disease Data: An Application to the Spatiotemporal Modelling of Schistosomiasis in Ghana

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

LINKING SATELLITE REMOTE SENSING BASED ENVIRONMENTAL PREDICTORS TO DISEASE: AN APPLICATION TO THE SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA

90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R2 as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys

LINKING SATELLITE REMOTE SENSING BASED ENVIRONMENTAL PREDICTORS TO DISEASE: AN APPLICATION TO THE SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA

90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R2 as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys