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Understanding long-term epidemiological patterns and developing low-cost, automated forecasting tools may support timely prevention efforts at the county level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed 26,756 valid cases of 29 statutory infectious diseases reported in Ziyang County (2013\u20132023). Diseases with \u2265\u2009500 cumulative cases were selected for primary analysis. Seasonal autoregressive integrated moving average (SARIMA) models were fitted for tuberculosis (TB), influenza, and hand\u2013foot\u2013mouth disease (HFMD), with two training windows (2013\u20132021 and 2018\u20132021) used for influenza to account for surveillance updates. Model selection was based on the Akaike information criterion, and forecasts were validated using data from 2022\u20132023.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>TB, influenza, and HFMD accounted for more than 55% of the cases. The TB incidence steadily decreased, with spring\u2013summer peaks, whereas the incidence of influenza increased after 2019 and peaked in the winter. Short-horizon forecasts were robust across training windows but less accurate in 2022\u20132023, likely reflecting post-COVID-19 behavioral and policy shifts. HFMD forecasts were unstable because of zero-inflated data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The incidence of tuberculosis decreased consistently, whereas the incidence of influenza rebounded sharply after nonpharmaceutical interventions were lifted. The SARIMA models reproduced seasonal patterns but showed limited accuracy for influenza and HFMD, reflecting the influence of surveillance changes, behavioral shifts, and sparse data. These findings highlight the need for complementary approaches\u2014such as models explicitly accounting for structural breaks\u2014to improve the reliability of local infectious disease forecasts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12889-025-25482-2","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T15:35:05Z","timestamp":1763739305000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Trends in epidemics pertaining to notifiable infectious diseases in China and prediction models for key diseases: a case study of Ziyang County"],"prefix":"10.1186","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0126-2034","authenticated-orcid":false,"given":"Siqi","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5895-9095","authenticated-orcid":false,"given":"Lin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9159-7268","authenticated-orcid":false,"given":"Bosen","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"issue":"17","key":"25482_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17176218","volume":"17","author":"W Yang","year":"2020","unstructured":"Yang W, Zhang J, Ma R. 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