Authors:
, , , , , , , , , , , , , , , , , and
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
Hotez PJ et al., 2014. The Global Burden of Disease Study 2010: Interpretation and implications for the neglected tropical diseases. PLoS Negl Trop Dis 8: e2865.
-
2.
WHO , 2023. Schistosomiasis: Key Facts. Available at: https://www.who.int/news-room/fact-sheets/detail/schistosomiasis. Accessed April 17, 2019.
-
3.
Karagiannis-Voules DA et al., 2015. Spatial and temporal distribution of soil-transmitted helminth infection in sub-Saharan Africa: A systematic review and geostatistical meta-analysis. Lancet Infect Dis 15: 74–84.
-
4.
Gryseels B , Polman K , Clerinx J , Kestens L , 2006. Human schistosomiasis. Lancet 368: 1106–1118.
-
5.
World Health Organization , 2011. Helminth Control in School-Age Children: A Guide for Managers of Control Programmes, 2nd edition. Geneva, Switzerland: WHO.
-
6.
Hürlimann E et al., 2018. Effect of an integrated intervention package of preventive chemotherapy, community-led total sanitation and health education on the prevalence of helminth and intestinal protozoa infections in Côte d’Ivoire. Parasit Vectors 11: 115.
-
7.
Muhumuza S , Olsen A , Katahoire A , Nuwaha F , 2013. Uptake of preventive treatment for intestinal schistosomiasis among school children in Jinja district, Uganda: A cross sectional study. PLoS One 8: e63438.
-
8.
World Health Organization , 2020. Ending the Neglect to Attain the Sustainable Development Goals: A Road Map for Neglected Tropical Diseases 2021–2030. Available at: https://www.who.int/publications/i/item/9789240010352. Accessed April 27, 2024.
-
9.
Lai YS et al., 2015. Spatial distribution of schistosomiasis and treatment needs in sub-Saharan Africa: A systematic review and geostatistical analysis. Lancet Infect Dis 15: 927–940.
-
10
Luo C , Wang Y , Su Q , Zhu J , Tang S , Bergquist R , Zhang Z , Hu Y , 2022. Mapping schistosomiasis risk in Southeast Asia: A systematic review and geospatial analysis. Int J Epidemiol 52: 1137–1149.
-
11.
Hu Y , Xia C , Chen Y , Lynn H , Zhang T , Xiong C , Chen G , He Z , Zhang Z , 2017. Assessment of the national schistosomiasis control program in a typical region along the Yangtze River, China. Int J Parasitol 47: 21–29.
-
12.
Hu Y , Bergquist R , Chen Y , Ke Y , Dai J , He Z , Zhang Z , 2021. Dynamic evolution of schistosomiasis distribution under different control strategies: Results from surveillance covering 1991–2014 in Guichi, China. PLoS Negl Trop Dis 15: e0008976.
-
13.
Ekpo UF et al., 2013. Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling. Geospat Health 7: 355–366.
-
14.
Wiegand RE et al., 2021. Associations between infection intensity categories and morbidity prevalence in school-age children are much stronger for Schistosoma haematobium than for S. mansoni. PLoS Negl Trop Dis 15: e0009444.
-
15.
Lwambo NJ , Savioli L , Kisumku UM , Alawi KS , Bundy DA , 1997. The relationship between prevalence of Schistosoma haematobium infection and different morbidity indicators during the course of a control programme on Pemba Island. Trans R Soc Trop Med Hyg 91: 643–646.
-
16.
Ruan D , Shi Y , Jin L , Yang Q , Yu W , Ren H , Zheng W , Chen Y , Zheng N , Zheng M , 2021. An ultrasound image-based deep multi-scale texture network for liver fibrosis grading in patients with chronic HBV infection. Liver Int 41: 2440–2454.
-
17.
Jiang H et al., 2021. Machine learning algorithms to predict the 1 year unfavourable prognosis for advanced schistosomiasis. Int J Parasitol 51: 959–965.
-
18.
Tran NK , Albahra S , May L , Waldman S , Crabtree S , Bainbridge S , Rashidi H , 2021. Evolving applications of artificial intelligence and machine learning in infectious diseases testing. Clin Chem 68: 125–133.
-
19.
Peiffer-Smadja N , Rawson TM , Ahmad R , Buchard A , Georgiou P , Lescure F-X , Birgand G , Holmes AH , 2020. Machine learning for clinical decision support in infectious diseases: A narrative review of current applications. Clin Microbiol Infect 26: 584–595.
-
20.
Lynch CM , Abdollahi B , Fuqua JD , de Carlo AR , Bartholomai JA , Balgemann RN , van Berkel VH , Frieboes HB , 2017. Prediction of lung cancer patient survival via supervised machine learning classification techniques. Int J Med Inform 108: 1–8.
-
21.
Zhang Y , Chen K , Weng Y , Chen Z , Zhang J , Hubbard R , 2022. An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US. Expert Syst Appl 198: 116882.
-
22.
Jiang D , Hao M , Ding F , Fu J , Li M , 2018. Mapping the transmission risk of Zika virus using machine learning models. Acta Trop 185: 391–399.
-
23.
Deol AK et al., 2019. Schistosomiasis: Assessing progress toward the 2020 and 2025 global goals. N Engl J Med 381: 2519–2528.
-
24.
Isaaks EH , Srivastava RM , 1989. An Introduction to Applied Geostatistics. New York, NY: Oxford University Press.
-
25.
Mewamba EM et al., 2022. Fine-scale mapping of Schistosoma mansoni infections and infection intensities in sub-districts of Makenene in the Centre region of Cameroon. PLoS Negl Trop Dis 16: e0010852.
-
26.
Clements AC , Moyeed R , Brooker S , 2006. Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa. Parasitology 133: 711–719.
-
27.
Jones IJ et al., 2021. Schistosome infection in Senegal is associated with different spatial extents of risk and ecological drivers for Schistosoma haematobium and S. mansoni. PLoS Negl Trop Dis 15: e0009712.
-
28.
De Boni L , Msimang V , De Voux A , Frean J , 2021. Trends in the prevalence of microscopically-confirmed schistosomiasis in the South African public health sector, 2011–2018. PLoS Negl Trop Dis 15: e0009669.
-
29.
Madinga J , Linsuke S , Mpabanzi L , Meurs L , Kanobana K , Speybroeck N , Lutumba P , Polman K , 2015. Schistosomiasis in the Democratic Republic of Congo: A literature review. Parasit Vectors 8: 601.
-
30.
Donohue RE , Mashoto KO , Mubyazwi GM , Madon S , Malecela MN , Michael E , 2017. Biosocial determinants of persistent schistosomiasis among schoolchildren in Tanzania despite repeated treatment. Trop Med Infect Dis 2: 61.
-
31.
Sacko M , Magnussen P , Deita AD , Traoré S , Landouré A , Doucouré A , Madsen H , Vennervald BJ , 2011. Impact of Schistosoma haematobium infection on urinary tract pathology, nutritional status and anaemia in school-aged children in two different endemic areas of the Niger River Basin, Mali. Acta Trop 120: S142–S150.
-
32.
Mushi V , Zacharia A , Shao M , Mubi M , Tarimo D , 2022. Persistence of Schistosoma haematobium transmission among school children and its implication for the control of urogenital schistosomiasis in Lindi, Tanzania. PLoS One 17: e0263929.
-
33.
Manzella A , Ohtomo K , Monzawa S , Lim JH , 2008. Schistosomiasis of the liver. Abdom Imaging 33: 144–150.
-
34.
Ayabina DV , Clark J , Bayley H , Lamberton PHL , Toor J , Hollingsworth TD , 2021. Gender-related differences in prevalence, intensity and associated risk factors of Schistosoma infections in Africa: A systematic review and meta-analysis. PLoS Negl Trop Dis 15: e0009083.
-
35.
Mazigo HD , Nuwaha F , Dunne DW , Kaatano GM , Angelo T , Kepha S , Kinung’hi SM , 2017. Schistosoma mansoni infection and its related morbidity among adults living in selected villages of Mara Region, North-Western Tanzania: A cross-sectional exploratory study. Korean J Parasitol 55: 533–540.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 1641 | 1641 | 197 |
| Full Text Views | 76 | 76 | 27 |
| PDF Downloads | 72 | 72 | 13 |
Predicting Schistosomiasis Intensity in Africa: A Machine Learning Approach to Evaluate the Progress of WHO Roadmap 2030
Xinyue Chen
Xinyue Chen
Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
Search for other papers by Xinyue Chen in
Current site
Google Scholar
PubMed
Search for other papers by Xinyue Chen in
Current site
Google Scholar
PubMed
Jiaxu Le
Jiaxu Le
Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
Search for other papers by Jiaxu Le in
Current site
Google Scholar
PubMed
Search for other papers by Jiaxu Le in
Current site
Google Scholar
PubMed
Yi Hu
Yi Hu
Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
Search for other papers by Yi Hu in
Current site
Google Scholar
PubMed
Search for other papers by Yi Hu in
Current site
Google Scholar
PubMed
ABSTRACT.
The World Health Organization (WHO) 2030 Roadmap aims to eliminate schistosomiasis as a public health issue, targeting reductions in the heavy intensity of infections. Previous studies, however, have predominantly used prevalence as the primary indicator of schistosomiasis. We introduce several machine learning (ML) algorithms to predict infection intensity categories, using morbidity prevalence, with the aim of assessing the elimination of schistosomiasis in Africa, as outlined by the WHO. We obtained morbidity prevalence and infection intensity data from the Expanded Special Project to Eliminate Neglected Tropical Diseases, which spans 12 countries in sub-Saharan Africa. We then used a series of ML algorithms to predict the prevalence of infection intensity categories for Schistosoma haematobium and Schistosoma mansoni, with morbidity prevalence and several relevant environmental and demographic covariates from remote-sensing sources. The optimal model had high accuracy and stability; it achieved a mean absolute error (MAE) of 0.02, a root mean square error (RMSE) of 0.05, and a coefficient of determination (R2) of 0.84 in predicting heavy-intensity prevalence for S. mansoni; and an MAE of 0.02, an RMSE of 0.04, and an R2 value of 0.81 for S. haematobium. Based on this optimal model, we found that most areas in the surveyed countries have not achieved the target of the WHO road map for 2030. The ML algorithms used in our analysis showed a high overall predictive power in estimating infection intensity for each species, and our methods provided a low-cost, effective approach to evaluating the disease target in Africa set in the WHO road map for 2030.
- Supplemental Materials (PDF 2488.0 KB)
Author Notes
Financial support: This work is primarily being funded by the
Disclosure: This study used surveys of Schistosoma infection derived from the ESPEN database. Because the data are aggregated and do not contain identifiable individual-level information, no specific ethics approval was needed for this study.
Authors’ contributions: Y. Hu conceived and designed the experiments, X. Chen analyzed the data, X. Chen and J. Le drafted the manuscript, and Y. Hu critically revised the manuscript for intellectual content. All authors read and approved the final version of the manuscript.
Data availability: All data used in this study are available on request from the corresponding author.
Authors’ addresses: Xinyue Chen, Jiaxu Le, and Yi Hu, Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China, Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China. E-mails: 22211020119@m.edu.fudan.cn, ljx9756@163.com, and huyi@fudan.edu.cn.
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
Hotez PJ et al., 2014. The Global Burden of Disease Study 2010: Interpretation and implications for the neglected tropical diseases. PLoS Negl Trop Dis 8: e2865.
-
2.
WHO , 2023. Schistosomiasis: Key Facts. Available at: https://www.who.int/news-room/fact-sheets/detail/schistosomiasis. Accessed April 17, 2019.
-
3.
Karagiannis-Voules DA et al., 2015. Spatial and temporal distribution of soil-transmitted helminth infection in sub-Saharan Africa: A systematic review and geostatistical meta-analysis. Lancet Infect Dis 15: 74–84.
-
4.
Gryseels B , Polman K , Clerinx J , Kestens L , 2006. Human schistosomiasis. Lancet 368: 1106–1118.
-
5.
World Health Organization , 2011. Helminth Control in School-Age Children: A Guide for Managers of Control Programmes, 2nd edition. Geneva, Switzerland: WHO.
-
6.
Hürlimann E et al., 2018. Effect of an integrated intervention package of preventive chemotherapy, community-led total sanitation and health education on the prevalence of helminth and intestinal protozoa infections in Côte d’Ivoire. Parasit Vectors 11: 115.
-
7.
Muhumuza S , Olsen A , Katahoire A , Nuwaha F , 2013. Uptake of preventive treatment for intestinal schistosomiasis among school children in Jinja district, Uganda: A cross sectional study. PLoS One 8: e63438.
-
8.
World Health Organization , 2020. Ending the Neglect to Attain the Sustainable Development Goals: A Road Map for Neglected Tropical Diseases 2021–2030. Available at: https://www.who.int/publications/i/item/9789240010352. Accessed April 27, 2024.
-
9.
Lai YS et al., 2015. Spatial distribution of schistosomiasis and treatment needs in sub-Saharan Africa: A systematic review and geostatistical analysis. Lancet Infect Dis 15: 927–940.
-
10
Luo C , Wang Y , Su Q , Zhu J , Tang S , Bergquist R , Zhang Z , Hu Y , 2022. Mapping schistosomiasis risk in Southeast Asia: A systematic review and geospatial analysis. Int J Epidemiol 52: 1137–1149.
-
11.
Hu Y , Xia C , Chen Y , Lynn H , Zhang T , Xiong C , Chen G , He Z , Zhang Z , 2017. Assessment of the national schistosomiasis control program in a typical region along the Yangtze River, China. Int J Parasitol 47: 21–29.
-
12.
Hu Y , Bergquist R , Chen Y , Ke Y , Dai J , He Z , Zhang Z , 2021. Dynamic evolution of schistosomiasis distribution under different control strategies: Results from surveillance covering 1991–2014 in Guichi, China. PLoS Negl Trop Dis 15: e0008976.
-
13.
Ekpo UF et al., 2013. Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling. Geospat Health 7: 355–366.
-
14.
Wiegand RE et al., 2021. Associations between infection intensity categories and morbidity prevalence in school-age children are much stronger for Schistosoma haematobium than for S. mansoni. PLoS Negl Trop Dis 15: e0009444.
-
15.
Lwambo NJ , Savioli L , Kisumku UM , Alawi KS , Bundy DA , 1997. The relationship between prevalence of Schistosoma haematobium infection and different morbidity indicators during the course of a control programme on Pemba Island. Trans R Soc Trop Med Hyg 91: 643–646.
-
16.
Ruan D , Shi Y , Jin L , Yang Q , Yu W , Ren H , Zheng W , Chen Y , Zheng N , Zheng M , 2021. An ultrasound image-based deep multi-scale texture network for liver fibrosis grading in patients with chronic HBV infection. Liver Int 41: 2440–2454.
-
17.
Jiang H et al., 2021. Machine learning algorithms to predict the 1 year unfavourable prognosis for advanced schistosomiasis. Int J Parasitol 51: 959–965.
-
18.
Tran NK , Albahra S , May L , Waldman S , Crabtree S , Bainbridge S , Rashidi H , 2021. Evolving applications of artificial intelligence and machine learning in infectious diseases testing. Clin Chem 68: 125–133.
-
19.
Peiffer-Smadja N , Rawson TM , Ahmad R , Buchard A , Georgiou P , Lescure F-X , Birgand G , Holmes AH , 2020. Machine learning for clinical decision support in infectious diseases: A narrative review of current applications. Clin Microbiol Infect 26: 584–595.
-
20.
Lynch CM , Abdollahi B , Fuqua JD , de Carlo AR , Bartholomai JA , Balgemann RN , van Berkel VH , Frieboes HB , 2017. Prediction of lung cancer patient survival via supervised machine learning classification techniques. Int J Med Inform 108: 1–8.
-
21.
Zhang Y , Chen K , Weng Y , Chen Z , Zhang J , Hubbard R , 2022. An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US. Expert Syst Appl 198: 116882.
-
22.
Jiang D , Hao M , Ding F , Fu J , Li M , 2018. Mapping the transmission risk of Zika virus using machine learning models. Acta Trop 185: 391–399.
-
23.
Deol AK et al., 2019. Schistosomiasis: Assessing progress toward the 2020 and 2025 global goals. N Engl J Med 381: 2519–2528.
-
24.
Isaaks EH , Srivastava RM , 1989. An Introduction to Applied Geostatistics. New York, NY: Oxford University Press.
-
25.
Mewamba EM et al., 2022. Fine-scale mapping of Schistosoma mansoni infections and infection intensities in sub-districts of Makenene in the Centre region of Cameroon. PLoS Negl Trop Dis 16: e0010852.
-
26.
Clements AC , Moyeed R , Brooker S , 2006. Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa. Parasitology 133: 711–719.
-
27.
Jones IJ et al., 2021. Schistosome infection in Senegal is associated with different spatial extents of risk and ecological drivers for Schistosoma haematobium and S. mansoni. PLoS Negl Trop Dis 15: e0009712.
-
28.
De Boni L , Msimang V , De Voux A , Frean J , 2021. Trends in the prevalence of microscopically-confirmed schistosomiasis in the South African public health sector, 2011–2018. PLoS Negl Trop Dis 15: e0009669.
-
29.
Madinga J , Linsuke S , Mpabanzi L , Meurs L , Kanobana K , Speybroeck N , Lutumba P , Polman K , 2015. Schistosomiasis in the Democratic Republic of Congo: A literature review. Parasit Vectors 8: 601.
-
30.
Donohue RE , Mashoto KO , Mubyazwi GM , Madon S , Malecela MN , Michael E , 2017. Biosocial determinants of persistent schistosomiasis among schoolchildren in Tanzania despite repeated treatment. Trop Med Infect Dis 2: 61.
-
31.
Sacko M , Magnussen P , Deita AD , Traoré S , Landouré A , Doucouré A , Madsen H , Vennervald BJ , 2011. Impact of Schistosoma haematobium infection on urinary tract pathology, nutritional status and anaemia in school-aged children in two different endemic areas of the Niger River Basin, Mali. Acta Trop 120: S142–S150.
-
32.
Mushi V , Zacharia A , Shao M , Mubi M , Tarimo D , 2022. Persistence of Schistosoma haematobium transmission among school children and its implication for the control of urogenital schistosomiasis in Lindi, Tanzania. PLoS One 17: e0263929.
-
33.
Manzella A , Ohtomo K , Monzawa S , Lim JH , 2008. Schistosomiasis of the liver. Abdom Imaging 33: 144–150.
-
34.
Ayabina DV , Clark J , Bayley H , Lamberton PHL , Toor J , Hollingsworth TD , 2021. Gender-related differences in prevalence, intensity and associated risk factors of Schistosoma infections in Africa: A systematic review and meta-analysis. PLoS Negl Trop Dis 15: e0009083.
-
35.
Mazigo HD , Nuwaha F , Dunne DW , Kaatano GM , Angelo T , Kepha S , Kinung’hi SM , 2017. Schistosoma mansoni infection and its related morbidity among adults living in selected villages of Mara Region, North-Western Tanzania: A cross-sectional exploratory study. Korean J Parasitol 55: 533–540.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 1641 | 1641 | 197 |
| Full Text Views | 76 | 76 | 27 |
| PDF Downloads | 72 | 72 | 13 |