iPAR: A framework for modelling and inferring information about disease spread when the populations at risk are unknown.
We introduce the inference for populations at risk (iPAR) framework which enables modelling and estimation of spatial disease dynamics in scenarios where the population at risk is unknown or poorly mapped. This framework addresses a gap in spatial infectious disease modelling approaches, with curren...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012622 |
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| _version_ | 1849427393875279872 |
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| author | Stephen Catterall Thibaud Porphyre Glenn Marion |
| author_facet | Stephen Catterall Thibaud Porphyre Glenn Marion |
| author_sort | Stephen Catterall |
| collection | DOAJ |
| description | We introduce the inference for populations at risk (iPAR) framework which enables modelling and estimation of spatial disease dynamics in scenarios where the population at risk is unknown or poorly mapped. This framework addresses a gap in spatial infectious disease modelling approaches, with current methods typically requiring data on the spatial distribution of the population at risk. The principles for iPAR are demonstrated in the context of a susceptible-infected disease dynamics model coupled with Bayesian inference implemented via data-augmentation Markov chain Monte Carlo (MCMC). This implementation of iPAR is tested for a range of scenarios using simulated outbreak data. Results indicate that the method can effectively estimate key properties of disease spread from spatio-temporal case reports and make useful predictions of future spread. The method is then applied to a case study exploring the 2014-2019 Estonian outbreak of African Swine Fever (ASF) in wild boar. Estimates of epidemiological parameters reveal evidence for long distance transmission, as well as disease control via reduction of the wild boar population in Estonia. |
| format | Article |
| id | doaj-art-6b495a0699ea4ad98c8be2566a15c321 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-6b495a0699ea4ad98c8be2566a15c3212025-08-20T03:29:03ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-06-01216e101262210.1371/journal.pcbi.1012622iPAR: A framework for modelling and inferring information about disease spread when the populations at risk are unknown.Stephen CatterallThibaud PorphyreGlenn MarionWe introduce the inference for populations at risk (iPAR) framework which enables modelling and estimation of spatial disease dynamics in scenarios where the population at risk is unknown or poorly mapped. This framework addresses a gap in spatial infectious disease modelling approaches, with current methods typically requiring data on the spatial distribution of the population at risk. The principles for iPAR are demonstrated in the context of a susceptible-infected disease dynamics model coupled with Bayesian inference implemented via data-augmentation Markov chain Monte Carlo (MCMC). This implementation of iPAR is tested for a range of scenarios using simulated outbreak data. Results indicate that the method can effectively estimate key properties of disease spread from spatio-temporal case reports and make useful predictions of future spread. The method is then applied to a case study exploring the 2014-2019 Estonian outbreak of African Swine Fever (ASF) in wild boar. Estimates of epidemiological parameters reveal evidence for long distance transmission, as well as disease control via reduction of the wild boar population in Estonia.https://doi.org/10.1371/journal.pcbi.1012622 |
| spellingShingle | Stephen Catterall Thibaud Porphyre Glenn Marion iPAR: A framework for modelling and inferring information about disease spread when the populations at risk are unknown. PLoS Computational Biology |
| title | iPAR: A framework for modelling and inferring information about disease spread when the populations at risk are unknown. |
| title_full | iPAR: A framework for modelling and inferring information about disease spread when the populations at risk are unknown. |
| title_fullStr | iPAR: A framework for modelling and inferring information about disease spread when the populations at risk are unknown. |
| title_full_unstemmed | iPAR: A framework for modelling and inferring information about disease spread when the populations at risk are unknown. |
| title_short | iPAR: A framework for modelling and inferring information about disease spread when the populations at risk are unknown. |
| title_sort | ipar a framework for modelling and inferring information about disease spread when the populations at risk are unknown |
| url | https://doi.org/10.1371/journal.pcbi.1012622 |
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