{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T01:45:21Z","timestamp":1791769521801,"version":"4.3.4"},"reference-count":36,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T00:00:00Z","timestamp":1791676800000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000270","name":"Natural Environment Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000270","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ecology and Evolution"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Forecasting infectious disease dynamics is important for ecological management and emerging disease preparedness, yet many systems lack the long\u2010term datasets required to develop reliable predictions. Spatial replication may provide an alternative by allowing information to be shared among populations, but the extent to which this improves forecasting remains unclear. Here, I used epidemic and temperature data from 20 replicated semi\u2010natural\n                    <jats:italic>Daphnia<\/jats:italic>\n                    \u2013parasite pond populations monitored across four seasons (80 epidemics) to test whether information from multiple populations improves forecasts and whether environmental similarity identifies informative populations. I forecasted disease prevalence, infected host density, and healthy host density using a benchmark model, autoregressive integrated moving average (ARIMA) and time\u2010series regression models. Models were trained using focal\u2010population data alone, mean dynamics across other populations, or temperature\u2010weighted mean dynamics from environmentally similar populations. Epidemics showed strong seasonal structure, with prevalence and infected host density generally peaking during the warmest period of the season. However, no forecasting approach consistently outperformed others across all variables. Temperature improved forecasts of prevalence and infected host density but not healthy host density. Multi\u2010population training improved forecasts in some cases, particularly for ARIMA models, whereas temperature\u2010weighted averaging provided no additional benefit over simple averaging. Regression models consistently produced the most accurate forecasts of healthy host density. These results show that borrowing information among populations can improve forecasts under specific conditions but is not universally beneficial, highlighting the importance of matching forecasting approaches and data sources to the ecological processes being predicted.\n                  <\/jats:p>","DOI":"10.1002\/ece3.74458","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T01:09:26Z","timestamp":1791767366000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Borrowing Data From Other Populations to Forecast Epidemic Size"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0303-0929","authenticated-orcid":false,"given":"Sam","family":"Paplauskas","sequence":"first","affiliation":[{"name":"Department of Biological and Environmental Sciences University of Stirling  Stirling UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,11]]},"reference":[{"issue":"4","key":"e_1_2_10_2_1","first-page":"327","article-title":"Use of Time\u2010Series Analysis in Infectious Disease Surveillance","volume":"76","author":"Allard R.","year":"1998","journal-title":"Bulletin of the World Health Organization"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspb.2010.2020"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1461\u20100248.2005.00879.x"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/gcb.13769"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0152560"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.3354\/aei00368"},{"key":"e_1_2_10_8_1","volume-title":"Statistics for Spatio\u2010Temporal Data","author":"Cressie N.","year":"2011"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature06291"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1461\u20100248.2004.00580.x"},{"key":"e_1_2_10_11_1","volume-title":"Ecology, Epidemiology, and Evolution of Parasitism in Daphnia","author":"Ebert D.","year":"2005"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspb.1998.0549"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0043686"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.3354\/meps13902"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1086\/699478"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00442\u2010011\u20101905\u20104"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/20.3.808"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0001908"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolmodel.2006.02.028"},{"key":"e_1_2_10_20_1","volume-title":"Forecasting: Principles and Practice","author":"Hyndman R. 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