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Its effect on schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers of\n                      <jats:italic>Schistosoma japonicum<\/jats:italic>\n                      infection by (i) bringing all covariates to the same spatial support, (ii) estimating individual-level regression parameters at 30\u00a0m, 90\u00a0m, 250\u00a0m, 500\u00a0m and 1\u00a0km spatial supports, and (iii) quantifying the differences between parameter estimates using five models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We modelled the prevalence of\n                      <jats:italic>Schistosoma japonicum<\/jats:italic>\n                      using sub-provinces health outcome data and pixel-level environmental data. We estimated and compared regression coefficients from convolution models using Bayesian statistics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Increasing the spatial support to 500\u00a0m gradually increased the parameter estimates and their associated uncertainties. Abrupt changes in the parameter estimates occur at 1\u00a0km spatial support, resulting in loss of significance of almost all the covariates. No significant differences were found between the predicted values and their uncertainties from the five models. We provide suggestions to define an appropriate spatial data structure for modelling that gives more reliable parameter estimates and a clear relationship between risk factors and the disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Inclusion of quantified MAUP effects was important in this study on schistosomiasis. This will support helminth control programmes by providing reliable parameter estimates at the same spatial support and suggesting the use of an adequate spatial data structure, to generate reliable maps that could guide efficient mass drug administration campaigns.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13071-020-3987-5","type":"journal-article","created":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T12:13:04Z","timestamp":1583151184000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence"],"prefix":"10.1186","volume":"13","author":[{"given":"Andrea L.","family":"Araujo Navas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Osei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo J.","family":"Soares Magalh\u00e3es","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lydia R.","family":"Leonardo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfred","family":"Stein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,3,2]]},"reference":[{"key":"3987_CR1","doi-asserted-by":"publisher","first-page":"e0004217","DOI":"10.1371\/journal.pntd.0004217","volume":"9","author":"Y Walz","year":"2015","unstructured":"Walz Y, Wegmann M, Dech S, Vounatsou P, Poda J-N, N\u2019Goran EK, et al. 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