{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T03:04:24Z","timestamp":1792033464475,"version":"4.4.0"},"reference-count":34,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T00:00:00Z","timestamp":1528675200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100004484","name":"Tehran University of Medical Sciences and Health Services","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004484","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Tropical Med Int Health"],"published-print":{"date-parts":[[2018,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>\n                      To predict the occurrence of zoonotic cutaneous leishmaniasis (\n                      <jats:styled-content style=\"fixed-case\">ZCL<\/jats:styled-content>\n                      ) and evaluate the effect of climatic variables on disease incidence in the east of Fars province, Iran using the Seasonal Autoregressive Integrated Moving Average (\n                      <jats:styled-content style=\"fixed-case\">SARIMA<\/jats:styled-content>\n                      ) model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The Box\u2010Jenkins approach was applied to fit the\n                      <jats:styled-content style=\"fixed-case\">SARIMA<\/jats:styled-content>\n                      model for\n                      <jats:styled-content style=\"fixed-case\">ZCL<\/jats:styled-content>\n                      incidence from 2004 to 2015. Then the model was used to predict the number of\n                      <jats:styled-content style=\"fixed-case\">ZCL<\/jats:styled-content>\n                      cases for the year 2016. Finally, we assessed the relation of meteorological variables (rainfall, rainy days, temperature, hours of sunshine and relative humidity) with\n                      <jats:styled-content style=\"fixed-case\">ZCL<\/jats:styled-content>\n                      incidence.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      <jats:styled-content style=\"fixed-case\">SARIMA<\/jats:styled-content>\n                      (2,0,0) (2,1,0)12 was the preferred model for predicting\n                      <jats:styled-content style=\"fixed-case\">ZCL<\/jats:styled-content>\n                      incidence in the east of Fars province (validation Root Mean Square Error,\n                      <jats:styled-content style=\"fixed-case\">RMSE<\/jats:styled-content>\n                      = 0.27). It showed that\n                      <jats:styled-content style=\"fixed-case\">ZCL<\/jats:styled-content>\n                      incidence in a given month can be estimated by the number of cases occurring 1 and 2 months, as well as 12 and 24 months earlier. The predictive power of\n                      <jats:styled-content style=\"fixed-case\">SARIMA<\/jats:styled-content>\n                      models was improved by the inclusion of rainfall at a lag of 2 months (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      = \u22120.02), rainy days at a lag of 2 months (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      = \u22120.09) and relative humidity at a lag of 8 months (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      = 0.13) as external regressors (\n                      <jats:italic>P<\/jats:italic>\n                      \u2010values &lt; 0.05). The latter was the best climatic variable for predicting\n                      <jats:styled-content style=\"fixed-case\">ZCL<\/jats:styled-content>\n                      cases (validation\n                      <jats:styled-content style=\"fixed-case\">RMSE<\/jats:styled-content>\n                      = 0.26).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Time series models can be useful tools to predict the trend of\n                      <jats:styled-content style=\"fixed-case\">ZCL<\/jats:styled-content>\n                      in Fars province, Iran; thus, they can be used in the planning of public health programmes. Introducing meteorological variables into the models may improve their precision.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/tmi.13079","type":"journal-article","created":{"date-parts":[[2018,5,22]],"date-time":"2018-05-22T20:00:08Z","timestamp":1527019208000},"page":"860-869","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a\n                    <scp>SARIMA<\/scp>\n                    analysis"],"prefix":"10.1111","volume":"23","author":[{"given":"Hamid Reza","family":"Tohidinik","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics School of Public Health Tehran University of Medical Sciences Tehran 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