Precision mapping of snail habitat in lake and marshland areas: integrating environmental and textural indicators using Random Forest modeling

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Март 6, 2024

Abstract Background Schistosomiasis japonica poses a significant health issue in China, largely due to the spatial distribution of Oncomelania hupensis , only intermediate host Schistosoma which directly affects schistosomiasis incidence. This study therefore aimed address limitations existing remote sensing studies, particularly oversight scale and seasonal variations snail habitats by introducing multi-source data-driven Random Forest approach. Methods method effectively integrates bottomland ground-surface texture data with traditional environmental variables for more comprehensive accurate habitat analysis. Four distinct models focusing on lakes marshlands Guichi, were developed: baseline model, including texture, variables, variables; Model 1, 2, 3, variables. Results The model outperformed others, achieving true skill statistic 0.93, accuracy 0.97, kappa 0.94, area under curve 0.98. findings identified key high-risk habitats, along major rivers belt-like distribution, near Yangtze River, Qiu Pu surrounding areas Shengjin Lake, Jiuhua Qingtong River. Conclusions providing vital effective monitoring, control strategies, prevention. approach may also be applicable locating other epidemic hosts similar survival ecological characteristics.

Язык: Английский

---

Predicating Risk Habitats of Oncomelania hupensis, the Intermediate Host of Schistosoma japonium Under Multiple Environmental Drivers

Опубликована: Янв. 1, 2024

Background: Oncomelania hupensis (O. hupensis), the unique intermediate host for Schistosoma japonicum, exerts a substantial influence on risk of schistosomiasis. Being amphibious freshwater snails, growth, development, and reproductive distribution O. are intricately tied to climatic environmental variables. This study aims predict habitat risks along Yangtze River in China, considering multiple factors.Methods: Data pertaining hupensis, including both presence absence records, with Jiangsu section basin period 2017-2021, were retrieved from Schistosomiasis Control Information Platform. Ten machine learning algorithms an ensemble model used explore drivers. Three datasets (Snail_CLIM, Snail_TOPO, Snail_ALL) incorporating topographic variables examined their impact accuracy. Evaluation was based AUC TSS.Results: The findings elucidate that snail_ALL, which incorporates variables, exhibits superior performance (ensemble model: sensitivity =98.000, specificity =95.960, =0.994). Among ten algorithms, Random Forest (RF) exhibited highest degree accuracy stability (Snail_ALL: AUC=1.000±0.000, TSS=0.985±0.005). key factors affecting snail included distance nearest river, elevation, annual precipitation, average pressure. High-risk areas manifested as two distinct concentrations: downstream Luhe District Nanjing at confluence Zhenjiang Yangzhou.Conclusion: By judiciously selecting pertinent employing modeling techniques, we can accurately habitats. resulting map habitats not only provides valuable insights but also serves guiding tool targeted monitoring control measures. holds particular significance within contest protection restoration projects.

Язык: Английский

---

Predicating risk habitats of Oncomelania hupensis, the intermediate host of schistosoma japonicum under multiple environmental drivers

Ecological Indicators, Год журнала: 2024, Номер 166, С. 112252 - 112252

Опубликована: Июнь 16, 2024

Язык: Английский

---

Precision mapping of snail habitat in lake and marshland areas: integrating environmental and textural indicators using Random Forest modeling

Heliyon, Год журнала: 2024, Номер 10(16), С. e36300 - e36300

Опубликована: Авг. 1, 2024

Schistosomiasis japonica continues to pose a significant public health challenge in China, primarily due the widespread distribution of Oncomelania hupensis, sole intermediate host Schistosoma. This study aims address constraints existing remote sensing analyses for identifying snail habitats, which frequently neglect spatial scale and seasonal variations. To this end, we adopt multi-source data-driven Random Forest approach that integrates bottomland ground-surface texture data with traditional environmental variables, enhancing accuracy habitat assessments. We developed four distinct models lake marshland areas Guichi, China: baseline model incorporating texture, variables; Model 1 only 2 adding 3 integrating variables. The outperformed others, achieving true skill statistic 0.93, an 0.97, kappa 0.94, area under curve 0.99. Our analysis pinpointed critical high-risk habitats distributed belt-like pattern along major water bodies, near Yangtze River, QiuPu around Shengjin Lake, Jiuhua Qingtong River. These insights can aid local authorities more efficiently allocating limited resources, developing effective surveillance control strategies combat schistosomiasis. Additionally, be adapted localize other endemic hosts similar ecological characteristics.

Язык: Английский

---

Three Gorges Dam: The changing trend of the density of Oncomelania hupensis, the intermediate host for Schistosoma japonicum, in reaches of the Yangtze River, 1990-2019

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Янв. 13, 2023

Abstract Background: Oncomelania hupensis ( O. ) snail is the sole intermediate host of Schistosoma japonicum . Environmental changes caused by Three Gorges Dam (TGD), flood and drought, affect distribution population, better understanding dynamics spatial density critical for schistosomiasis risk assessment control affected areas. Methods: Data survey between 1990 2019 were collected from previous studies in four electronic databases (CNKI, Wanfang, Pubmed, SCI) national surveillance. Meta-analysis was conducted to estimate overall annual densities their 95% confidence intervals (CIs). Joinpoint model used identify changing trend inflection point 2019. Inverse distance weighted (IDW) interpolation determine recent density. Results: A total 3777 sites (872 upstream area 2905 downstream TGD) with a precise location village or beach identified. For TGD, peaked 1998 (1.635/0.11m2, CI: 1.220–2.189) fluctuated at relatively high level before 2003, declined steadily 2003 (1.143/0.11m 2 , 0.905–1.397) 2012 (0.127/0.11m 0.081–0.199). The maintained lower than 0.150/0.11m identified that statistically significant showed downward an APC -20.56% (95% -24.15 -16.80). Upstream 2005 (0.760/0.11m2, 0.479–1.207) generally greater 0.300/0.11m2 2005. since 2006 0.150/0.11m2 after 2011. No -6.05% -7.97 -7.09). areas mainly distributed Poyang Lake, Dongting Jianghan Plain, Anhui branch Yangtze River. Conclusion: snails fluctuating River basin In area, decline accelerated operation then low level. Infected higher Jianhan

Язык: Английский

---