Fig 1.
Map of Makenene showing schools where children were sampled (map obtained from: https://qgis.org/downloads/QGIS-OSGeo4W-3.16.10-1-Setup-x86_64.exe with layers downloaded using the link: https://www.diva-gis.org/gdata; consulted, 14/09/2021). BPB1: Government bilingual primary school of Baloua group 1; BPB2: Government bilingual primary school of Baloua group 2; PSM1: Public school of Makenene group 1; PSM2: Public school of Makenene group 2; PSNn: Public school of Nyokon; PSKD: Public school of Kinding Nde; PSKN: Public school of Kinding Ndjabi; PSMS: Public school of Mock-Sud; PSC: Public school of Carrière; PSNp: Public school of Ngokop.
Table 1.
Prevalence of S. mansoni infections according to age.
Table 2.
Prevalence and infection intensity of S. mansoni infections according to schools.
Table 3.
Prevalence and infection intensity of S. mansoni infections according to sex.
Table 4.
Prevalence and infection intensity of S. mansoni infections according to age groups.
Fig 2.
Distribution per school of the percentage of children with no infection or carrying different infection intensities inferred from Kato-Katz (base layer of the maps were obtained using a free online spatial data software (https://www.diva-gis.org/gdata). BPB1: Government bilingual primary school of Baloua group 1; BPB2: Government bilingual primary school of Baloua group 2; PSM1: Public school of Makenene group 1; PSM2: Public school of Makenene group 2; PSNn: Public school of Nyokon; PSKD: Public school of Kinding Nde; PSKN: Public school of Kinding Ndjabi; PSMS: Public school of Mock-Sud; PSC: Public school of Carrière; PSNp: Public school of Ngokop, *: Schools in the same building. KK+: Number of children carrying S. mansoni eggs in each school; KK-: Number of children without S. mansoni egg in each school.
Fig 3.
Sub-districts distribution of the percentage of children carrying different infection intensities inferred from Kato-Katz (base layer of the maps were obtained using a free online spatial data software (https://www.diva-gis.org/gdata).
Fig 4.
Sub-districts distribution of the percentage of children carrying different infection intensities inferred from POC-CCA (base layer of the maps were obtained using a free online spatial data software (https://www.diva-gis.org/gdata).
Fig 5.
Distribution per school of the percentage of children with no infection or carrying different infection intensities inferred from POC-CCA (base layer of the maps were obtained using a free online spatial data software (https://www.diva-gis.org/gdata). BPB1: Government bilingual primary school of Baloua group 1; BPB2: Government bilingual primary school of Baloua group 2; PSM1: Public school of Makenene group 1; PSM2: Public school of Makenene group 2; PSNn: Public school of Nyokon; PSKD: Public school of Kinding Nde; PSKN: Public school of Kinding Ndjabi; PSMS: Public school of Mock-Sud; PSC: Public school of Carrière; PSNp: Public school of Ngokop, *: Schools in the same building. POC-CCA+: Number of children positive to POC-CCA in each school; POC-CCA-: Number of children with negative POC-CCA in each school.
Fig 6.
Sub-districts distribution of the percentage of children with S. mansoni infections based on POC-CCA and KK results (base layer of the maps were obtained using a free online spatial data software (https://www.diva-gis.org/gdata).
Fig 7.
Global and Local Moran’s I cluster maps showing S. mansoni infection intensities inferred from KK (base layer of the maps were obtained using a free online spatial data software (https://www.diva-gis.org/gdata). High-high cluster indicates significant (p < 0.05) clustering (hot-spot) of houses of children carrying heavy infection intensities of S. mansoni; low-low cluster indicates significant (p < 0.05) clustering (cold spot) of houses of children carrying light infection intensities of S. mansoni; low-high outlier indicates areas where houses of children carrying heavy infection intensities were surrounded by those of children carrying light or moderate infection intensities; high-low outlier indicates areas where houses of children carrying light infection intensities were surrounded by those of children carrying heavy infection intensity.
Fig 8.
Local Moran’s I cluster maps showing S. mansoni infection intensities inferred from POC-CCA (base layer of the maps were obtained using a free online spatial data software (https://www.diva-gis.org/gdata). High-high cluster indicates significant (p < 0.05) clustering (hot-spot) of houses of children carrying heavy infection intensities of S. mansoni; low-low cluster indicates significant (p < 0.05) clustering (cold spot) of houses of children carrying light infection intensities of S. mansoni; low-high outlier indicates areas where houses of children carrying heavy infection intensities were surrounded by those of children carrying light or moderate infection intensities; high-low outlier indicates areas where houses of children carrying light infection intensities were surrounded by those of children carrying heavy infection intensity.
Table 5.
Variations between the mean distances of houses of infected children to the nearest water points according to infection intensities.
Table 6.
Correlation coefficients between the mean distances of schools to the nearest water points and the infection intensities of S. mansoni.