Value of risk-contact data from digital contact monitoring apps in infectious disease modeling
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915216105668608 |
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| author | Uiterkamp, Martijn H. H. Schoot van Dijk, Willian J. Heesterbeek, Hans van der Hofstad, Remco Jong, Jessica C. Kiefte-de Litvak, Nelly |
| author_facet | Uiterkamp, Martijn H. H. Schoot van Dijk, Willian J. Heesterbeek, Hans van der Hofstad, Remco Jong, Jessica C. Kiefte-de Litvak, Nelly |
| contents | In this paper, we present a simple method to integrate risk-contact data, obtained via digital contact monitoring (DCM) apps, in conventional compartmental transmission models. During the recent COVID-19 pandemic, many such data have been collected for the first time via newly developed DCM apps. However, it is unclear what the added value of these data is, unlike that of traditionally collected data via, e.g., surveys during non-epidemic times. The core idea behind our method is to express the number of infectious individuals as a function of the proportion of contacts that were with infected individuals and use this number as a starting point to initialize the remaining compartments of the model. As an important consequence, using our method, we can estimate key indicators such as the effective reproduction number using only two types of daily aggregated contact information, namely the average number of contacts and the average number of those contacts that were with an infected individual. We apply our method to the recent COVID-19 epidemic in the Netherlands, using self-reported data from the health surveillance app COVID RADAR and proximity-based data from the contact tracing app CoronaMelder. For both data sources, our corresponding estimates of the effective reproduction number agree both in time and magnitude with estimates based on other more detailed data sources such as daily numbers of cases and hospitalizations. This suggests that the use of DCM data in transmission models, regardless of the precise data type and for example via our method, offers a promising alternative for estimating the state of an epidemic, especially when more detailed data are not available. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_21228 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Value of risk-contact data from digital contact monitoring apps in infectious disease modeling Uiterkamp, Martijn H. H. Schoot van Dijk, Willian J. Heesterbeek, Hans van der Hofstad, Remco Jong, Jessica C. Kiefte-de Litvak, Nelly Populations and Evolution Computers and Society Physics and Society In this paper, we present a simple method to integrate risk-contact data, obtained via digital contact monitoring (DCM) apps, in conventional compartmental transmission models. During the recent COVID-19 pandemic, many such data have been collected for the first time via newly developed DCM apps. However, it is unclear what the added value of these data is, unlike that of traditionally collected data via, e.g., surveys during non-epidemic times. The core idea behind our method is to express the number of infectious individuals as a function of the proportion of contacts that were with infected individuals and use this number as a starting point to initialize the remaining compartments of the model. As an important consequence, using our method, we can estimate key indicators such as the effective reproduction number using only two types of daily aggregated contact information, namely the average number of contacts and the average number of those contacts that were with an infected individual. We apply our method to the recent COVID-19 epidemic in the Netherlands, using self-reported data from the health surveillance app COVID RADAR and proximity-based data from the contact tracing app CoronaMelder. For both data sources, our corresponding estimates of the effective reproduction number agree both in time and magnitude with estimates based on other more detailed data sources such as daily numbers of cases and hospitalizations. This suggests that the use of DCM data in transmission models, regardless of the precise data type and for example via our method, offers a promising alternative for estimating the state of an epidemic, especially when more detailed data are not available. |
| title | Value of risk-contact data from digital contact monitoring apps in infectious disease modeling |
| topic | Populations and Evolution Computers and Society Physics and Society |
| url | https://arxiv.org/abs/2503.21228 |