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, vol. 20, pp. e1011714
Abstract
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.
Authors 15
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Giovanni Lo Iacono corresponding
Affiliation as printed
Centre for Mathematical and Computational Biology, University of Surrey, Guilford, United Kingdom
Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
Institute for Sustainability, University of Surrey, Guildford, United Kingdom
People-Centred Artificial Intelligence Institute, University of Surrey, Guilford, United Kingdom
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Affiliation as printed
Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
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Leiden University · University of Surrey
Affiliation as printed
Centre for Mathematical and Computational Biology, University of Surrey, Guilford, United Kingdom
Mathematical Institute, Leiden University, Leiden, the Netherlands
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Affiliation as printed
European Centre for Environment and Human Health, University of Exeter Medical School, Truro, Cornwall, United Kingdom
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New Zealand Food Safety Science and Research Centre · Massey University
Affiliation as printed
New Zealand Food Safety Science & Research Centre, Massey University, Palmerston North, New Zealand
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Affiliation as printed
UK Health Security Agency, Chilton, United Kingdom
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Affiliation as printed
Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
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Affiliation as printed
Institute for Environmental Design and Engineering, University College London, London, United Kingdom
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Affiliation as printed
Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
School of Biosciences, University of Surrey, Guilford, United Kingdom
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London School of Hygiene & Tropical Medicine · UK Health Security Agency
Affiliation as printed
London School of Hygiene and Tropical Medicine, London, United Kingdom
UK Health Security Agency, Chilton, United Kingdom
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Affiliation as printed
Met Office, Exeter, United Kingdom
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Australian National University
Affiliation as printed
Healthy Environments And Lives (HEAL) National Research Network, Australian National University, Canberra, ACT, Australia
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Médecins Sans Frontières · UK Health Security Agency
Affiliation as printed
Médicines Sans Frontièrs, London, United Kingdom
UK Health Security Agency, Porton Down, United Kingdom
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Affiliation as printed
Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
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University of East Anglia · University of Exeter · University of Surrey · UK Health Security Agency
Affiliation as printed
Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
European Centre for Environment and Human Health, University of Exeter Medical School, Truro, Cornwall, United Kingdom
UK Health Security Agency, Chilton, United Kingdom
University of East Anglia, Norwich, United Kingdom
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