Seasonality and susceptibility from measles time series
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910449169072128 |
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| author | Thakkar, Niket Jindal, Sonia Rosenfeld, Katherine |
| author_facet | Thakkar, Niket Jindal, Sonia Rosenfeld, Katherine |
| contents | This paper develops mathematical tools to estimate seasonal changes in measles transmission rates and corresponding variation in population susceptibility. The tools are designed to leverage times series of cases in the absence of demographic data. In particular, we focus on publicly available suspected case reports from the World Health Organization (WHO), which routinely publishes country-level, monthly aggregated time series. With that as input, we show that measles epidemiologies can be characterized efficiently at global-scale, and we use our estimates to recommend context-specific, future supplementary immunization times. Throughout the paper, comparisons with more data-informed models illustrate that the approach captures the essential dynamics, and broadly speaking, the tools we describe represent a scalable intermediate between conventional empirical approaches and more intricate disease models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09664 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Seasonality and susceptibility from measles time series Thakkar, Niket Jindal, Sonia Rosenfeld, Katherine Populations and Evolution This paper develops mathematical tools to estimate seasonal changes in measles transmission rates and corresponding variation in population susceptibility. The tools are designed to leverage times series of cases in the absence of demographic data. In particular, we focus on publicly available suspected case reports from the World Health Organization (WHO), which routinely publishes country-level, monthly aggregated time series. With that as input, we show that measles epidemiologies can be characterized efficiently at global-scale, and we use our estimates to recommend context-specific, future supplementary immunization times. Throughout the paper, comparisons with more data-informed models illustrate that the approach captures the essential dynamics, and broadly speaking, the tools we describe represent a scalable intermediate between conventional empirical approaches and more intricate disease models. |
| title | Seasonality and susceptibility from measles time series |
| topic | Populations and Evolution |
| url | https://arxiv.org/abs/2405.09664 |