Borrowing data from other populations to forecast epidemic size

Borrowing data from other populations to forecast epidemic size | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 April 2025 V1 Latest version Share on Borrowing data from other populations to forecast epidemic size Author : Sam Paplauskas 0000-0003-0303-0929 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174475403.32807642/v1 201 views 154 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract A key challenge for disease ecology is predicting the size of epidemics. Most models forecast disease in a single population using long-term historical data from that population. However, long-term data is not always available and a possible alternative is to borrow data from multiple similar populations to forecast disease for a population of interest. One step further is to weight the contribution of epidemics to the forecast based on their similarity to the focal population. In this study, we use data from twenty populations of the freshwater crustacean Daphnia magna and its sterilizing bacterial parasite Pasteuria ramosa tracked over four epidemic seasons (a total of 80 epidemics) to predict future epidemics. We evaluate single population, multiple average population and multiple weighted average population approaches for training three suites of forecast model: seasonal naïve, auto-regressive integrated moving average and time series regression models. We found that forecast accuracy depended on both the type of training data and the choice of forecast model, but models trained on data from multiple populations consistently outperformed those trained on single population data. Our study demonstrates the benefit of using a collection of similar populations to forecast disease for a focal population which has limited data. Supplementary Material File (forecasting_manuscript.pdf) Download 923.41 KB Information & Authors Information Version history V1 Version 1 15 April 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords disease ecology epidemic epidemic model forecasting host-parasite interactions Authors Affiliations Sam Paplauskas 0000-0003-0303-0929 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 201 views 154 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sam Paplauskas. Borrowing data from other populations to forecast epidemic size. Authorea . 15 April 2025. DOI: https://doi.org/10.22541/au.174475403.32807642/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Sam Paplauskas, A Conceptual Disease Cycle Model to Link the Size of Past and Future Epidemics, Ecology and Evolution, 15 , 8, (2025). https://doi.org/10.1002/ece3.71868 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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