A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales.
Disentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other s...
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2024-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011714&type=printable |
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| author | Giovanni Lo Iacono Alasdair J C Cook Gianne Derks Lora E Fleming Nigel French Emma L Gillingham Laura C Gonzalez Villeta Clare Heaviside Roberto M La Ragione Giovanni Leonardi Christophe E Sarran Sotiris Vardoulakis Francis Senyah Arnoud H M van Vliet Gordon Nichols |
| author_facet | Giovanni Lo Iacono Alasdair J C Cook Gianne Derks Lora E Fleming Nigel French Emma L Gillingham Laura C Gonzalez Villeta Clare Heaviside Roberto M La Ragione Giovanni Leonardi Christophe E Sarran Sotiris Vardoulakis Francis Senyah Arnoud H M van Vliet Gordon Nichols |
| author_sort | Giovanni Lo Iacono |
| collection | DOAJ |
| description | Disentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other seasonal variables using campylobacteriosis as a case study. Campylobacter infections are found worldwide and are the most common bacterial food-borne disease in developed countries, where they exhibit consistent but country specific seasonality. We developed a novel conditional incidence method, based on classical stratification, exploiting the long term, high-resolution, linkage of approximately one-million campylobacteriosis cases over 20 years in England and Wales with local meteorological datasets from diagnostic laboratory locations. The predicted incidence of campylobacteriosis increased by 1 case per million people for every 5° (Celsius) increase in temperature within the range of 8°-15°. Limited association was observed outside that range. There were strong associations with day-length. Cases tended to increase with relative humidity in the region of 75-80%, while the associations with rainfall and wind-speed were weaker. The approach is able to examine multiple factors and model how complex trends arise, e.g. the consistent steep increase in campylobacteriosis in England and Wales in May-June and its spatial variability. This transparent and straightforward approach leads to accurate predictions without relying on regression models and/or postulating specific parameterisations. A key output of the analysis is a thoroughly phenomenological description of the incidence of the disease conditional on specific local weather factors. The study can be crucially important to infer the elusive mechanism of transmission of campylobacteriosis; for instance, by simulating the conditional incidence for a postulated mechanism and compare it with the phenomenological patterns as benchmark. The findings challenge the assumption, commonly made in statistical models, that the transformed mean rate of infection for diseases like campylobacteriosis is a mere additive and combination of the environmental variables. |
| format | Article |
| id | doaj-art-bc56cecd5aee4bb593e9b27e5e58ce5f |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS Computational Biology |
| spelling | doaj-art-bc56cecd5aee4bb593e9b27e5e58ce5f2025-08-20T02:22:26ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-01-01201e101171410.1371/journal.pcbi.1011714A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales.Giovanni Lo IaconoAlasdair J C CookGianne DerksLora E FlemingNigel FrenchEmma L GillinghamLaura C Gonzalez VilletaClare HeavisideRoberto M La RagioneGiovanni LeonardiChristophe E SarranSotiris VardoulakisFrancis SenyahArnoud H M van VlietGordon NicholsDisentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other seasonal variables using campylobacteriosis as a case study. Campylobacter infections are found worldwide and are the most common bacterial food-borne disease in developed countries, where they exhibit consistent but country specific seasonality. We developed a novel conditional incidence method, based on classical stratification, exploiting the long term, high-resolution, linkage of approximately one-million campylobacteriosis cases over 20 years in England and Wales with local meteorological datasets from diagnostic laboratory locations. The predicted incidence of campylobacteriosis increased by 1 case per million people for every 5° (Celsius) increase in temperature within the range of 8°-15°. Limited association was observed outside that range. There were strong associations with day-length. Cases tended to increase with relative humidity in the region of 75-80%, while the associations with rainfall and wind-speed were weaker. The approach is able to examine multiple factors and model how complex trends arise, e.g. the consistent steep increase in campylobacteriosis in England and Wales in May-June and its spatial variability. This transparent and straightforward approach leads to accurate predictions without relying on regression models and/or postulating specific parameterisations. A key output of the analysis is a thoroughly phenomenological description of the incidence of the disease conditional on specific local weather factors. The study can be crucially important to infer the elusive mechanism of transmission of campylobacteriosis; for instance, by simulating the conditional incidence for a postulated mechanism and compare it with the phenomenological patterns as benchmark. The findings challenge the assumption, commonly made in statistical models, that the transformed mean rate of infection for diseases like campylobacteriosis is a mere additive and combination of the environmental variables.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011714&type=printable |
| spellingShingle | Giovanni Lo Iacono Alasdair J C Cook Gianne Derks Lora E Fleming Nigel French Emma L Gillingham Laura C Gonzalez Villeta Clare Heaviside Roberto M La Ragione Giovanni Leonardi Christophe E Sarran Sotiris Vardoulakis Francis Senyah Arnoud H M van Vliet Gordon Nichols A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales. PLoS Computational Biology |
| title | A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales. |
| title_full | A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales. |
| title_fullStr | A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales. |
| title_full_unstemmed | A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales. |
| title_short | A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales. |
| title_sort | mathematical classical stratification modeling approach to disentangling the impact of weather on infectious diseases a case study using spatio temporally disaggregated campylobacter surveillance data for england and wales |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011714&type=printable |
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