During the COVID-19 pandemic, reported incidence data played a central role in public health surveillance and in tracking epidemic dynamics, although they provide limited insight into the behavioral, immunological, and socioeconomic drivers of transmission.Population-based seroprevalence studies with linked survey data offer a rich but untapped source of individual-level information that can complement routine surveillance. In this study, we investigate whether aggregated seroprevalence cohort data can be leveraged to predict local COVID-19 incidence and to identify interpretable predictors associated with transmission dynamics. Using data from the Multilocal SeroPrevalence (MuSPAD) study in Germany (2020--2022), we trained multiple machine learning models, including least absolute shrinkage and selection operator (LASSO), vector autoregressive models (VAR), multilayer perceptrons (MLPs), and long short-term memory neural networks (LSTMs), to predict location-specific seven-day incidence rates. Feature importance was assessed using regression coefficients where applicable and model-agnostic explainability methods, including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). Across model classes, cohort-derived features enabled accurate prediction of local incidence, with time-aware models achieving the strongest performance. Consistent predictors included prior infection and testing history, employment-related changes, vaccination status, and mask-wearing behavior, highlighting the importance of behavioral and reporting-related signals. While differential privacy introduced modest degradation in predictive performance under strict privacy budgets, SHAP-based explanations remained stable, and LIME-based explanations were more sensitive to privacy-induced noise. These results demonstrate that aggregated cohort data encode meaningful and interpretable signals of population-level transmission dynamics. Population-based serosurveys therefore provide a complementary source of information for predicting local COVID-19 incidence and identifying key drivers of transmission beyond routine surveillance data. Our findings show that integrating interpretable machine learning with privacy-aware analysis enables actionable insights from sensitive cohort data, supporting their use in digital epidemiology and informing data-driven public health decision-making.

The authors have declared no competing interest.

This work was supported by the Initiative and Networking Fund of the Helmholtz Association, Germany (grant agreement number KA1-Co-08, Project LOKI-Pandemics).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

MuSPAD complies with all relevant laws and declarations (EU Charter of Fundamental Rights, Biomedical Convention of the Council of Europe and additional protocols, the CIOMS guidelines and the Helsinki Declaration), ethical approval was given on 21.06.2020 by the Ethics Committee of the Hannover Medical School (Ethics approval no 9086_BO_S_2020). All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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The data underlying this study are not publicly available due to privacy and ethical restrictions related to individual-level health data. Access can be requested through the MuSPAD data access process via https://serohub.net/, subject to approval by the data custodians and applicable data protection regulations. The code used for the analyses, as well as the data codebooks and top-50 explainability tables are available at https://github.com/JesKre/Using-machine-learning-on-seroprevalence-studies-to-obtain-predictors-for-COVID-19-incidence_code

https://github.com/JesKre/Using-machine-learning-on-seroprevalence-studies-to-obtain-predictors-for-COVID-19-incidence_code