Summary of analysis variables.

Background
Schistosomiasis and soil-transmitted helminth infections are among the neglected tropical diseases (NTDs) affecting primarily marginalized communities in low- and middle-income countries. Surveillance data for NTDs are typically sparse, and hence, geospatial predictive modeling based on remotely sensed (RS) environmental data is widely used to characterize disease transmission and treatment needs. However, as large-scale preventive chemotherapy has become a widespread practice, resulting in reduced prevalence and intensity of infection, the validity and relevance of these models should be re-assessed.
Methodology
We employed two nationally representative school-based prevalence surveys of Schistosoma haematobium and hookworm infections from Ghana conducted before (2008) and after (2015) the introduction of large-scale preventive chemotherapy. We derived environmental variables from fine-resolution RS data (Landsat 8) and examined a variable distance radius (1–5 km) for aggregating these variables around point-prevalence locations in a non-parametric random forest modeling approach. We used partial dependence and individual conditional expectation plots to improve interpretability of results.
Principal findings
The average school-level S. haematobium prevalence decreased from 23.8% to 3.6% and that of hookworm from 8.6% to 3.1% between 2008 and 2015. However, hotspots of high-prevalence locations persisted for both infections. The models with environmental data extracted from a buffer radius of 2–3 km around the school location where prevalence was measured had the best performance. Model performance (according to the R2 value) was already low and declined further from approximately 0.4 in 2008 to 0.1 in 2015 for S. haematobium and from approximately 0.3 to 0.2 for hookworm. According to the 2008 models, land surface temperature (LST), modified normalized difference water index, elevation, slope, and streams variables were associated with S. haematobium prevalence. LST, slope, and improved water coverage were associated with hookworm prevalence. Associations with the environment in 2015 could not be evaluated due to low model performance.
Conclusions/significance
Our study showed that in the era of preventive chemotherapy, associations between S. haematobium and hookworm infections and the environment weakened, and thus predictive power of environmental models declined. In light of these observations, it is timely to develop new cost-effective passive surveillance methods for NTDs as an alternative to costly surveys, and to focus on persisting hotspots of infection with additional interventions to reduce reinfection. We further question the broad application of RS-based modeling for environmental diseases for which large-scale pharmaceutical interventions are in place.

### 背景
血吸虫病（Schistosomiasis）与土源性蠕虫感染（soil-transmitted helminth infections）属于被忽视的热带病（neglected tropical diseases, NTDs），主要影响中低收入国家中的边缘化社区。此类被忽视的热带病的监测数据通常较为匮乏，因此基于遥感（remotely sensed, RS）环境数据的地理空间预测模型被广泛用于表征疾病传播特征与治疗需求。然而，随着大规模预防性化疗（preventive chemotherapy）的普及，感染率与感染强度均有所下降，这类模型的有效性与相关性亟需重新评估。

### 研究方法
本研究采用两项加纳全国代表性的以学校为基础的感染率调查数据，分别采集于大规模预防性化疗推行前（2008年）与推行后（2015年），针对的是埃及血吸虫（Schistosoma haematobium）与钩虫（hookworm）感染。研究从高分辨率遥感数据（陆地卫星8号，Landsat 8）中提取环境变量，并通过非参数随机森林建模方法，探究在患病率采样点周围聚合变量时的可变距离半径（1~5 km）。此外，本研究使用偏依赖图与个体条件期望图以提升结果的可解释性。

### 主要研究结果
2008年至2015年间，学校层面的埃及血吸虫平均感染率从23.8%降至3.6%，钩虫平均感染率从8.6%降至3.1%。但两类感染均存在持续存在的高患病率热点区域。以采样学校周围2~3 km缓冲半径提取的环境数据构建的模型表现最优。模型的决定系数R²值本就偏低，且进一步下降：埃及血吸虫模型的R²从2008年的约0.4降至2015年的0.1，钩虫模型则从约0.3降至0.2。基于2008年的模型，地表温度（land surface temperature, LST）、修正归一化差异水体指数、海拔、坡度与溪流变量均与埃及血吸虫感染率存在关联；地表温度、坡度与改良水源覆盖则与钩虫感染率相关。由于2015年的模型表现不佳，无法评估其与环境因素的关联。

### 结论与意义
本研究表明，在预防性化疗时代，埃及血吸虫、钩虫感染与环境因素之间的关联有所减弱，因此环境预测模型的预测能力有所下降。基于上述结果，当前亟需开发兼具成本效益的被动监测方法，以替代高成本的现场调查，并针对持续存在的感染热点区域开展额外干预以降低再感染风险。本研究同时对当前广泛应用基于遥感的环境疾病建模方法提出了质疑——这类疾病已推行大规模药物干预措施。