Peter J. Hudson

Seasonal variations in temperature, rainfall and resource availability are ubiquitous and can exert strong pressures on population dynamics. Infectious diseases provide some of the best-studied examples of the role of seasonality in shaping population fluctuations. In this paper, we review examples from human and wildlife disease systems to illustrate the challenges inherent in understanding the mechanisms and impacts of seasonal environmental drivers. Empirical evidence points to several biologically distinct mechanisms by which seasonality can impact host-pathogen interactions, including seasonal changes in host social behaviour and contact rates, variation in encounters with infective stages in the environment, annual pulses of host births and deaths and changes in host immune defences. Mathematical models and field observations show that the strength and mechanisms of seasonality can alter the spread and persistence of infectious diseases, and that population-level responses can range from simple annual cycles to more complex multiyear fluctuations. From an applied perspective, understanding the timing and causes of seasonality offers important insights into how parasite-host systems operate, how and when parasite control measures should be applied, and how disease risks will respond to anthropogenic climate change and altered patterns of seasonality. Finally, by focusing on well-studied examples of infectious diseases, we hope to highlight general insights that are relevant to other ecological interactions.

BACKGROUND: The Australian Imaging, Biomarkers and Lifestyle (AIBL) flagship study of aging aimed to recruit 1000 individuals aged over 60 to assist with prospective research into Alzheimer's disease (AD). This paper describes the recruitment of the cohort and gives information about the study methodology, baseline demography, diagnoses, medical comorbidities, medication use, and cognitive function of the participants. METHODS: Volunteers underwent a screening interview, had comprehensive cognitive testing, gave 80 ml of blood, and completed health and lifestyle questionnaires. One quarter of the sample also underwent amyloid PET brain imaging with Pittsburgh compound B (PiB PET) and MRI brain imaging, and a subgroup of 10% had ActiGraph activity monitoring and body composition scanning. RESULTS: A total of 1166 volunteers were recruited, 54 of whom were excluded from further study due to comorbid disorders which could affect cognition or because of withdrawal of consent. Participants with AD (211) had neuropsychological profiles which were consistent with AD, and were more impaired than participants with mild cognitive impairment (133) or healthy controls (768), who performed within expected norms for age on neuropsychological testing. PiB PET scans were performed on 287 participants, 100 had DEXA scans and 91 participated in ActiGraph monitoring. CONCLUSION: The participants comprising the AIBL cohort represent a group of highly motivated and well-characterized individuals who represent a unique resource for the study of AD. They will be reassessed at 18-month intervals in order to determine the predictive utility of various biomarkers, cognitive parameters and lifestyle factors as indicators of AD, and as predictors of future cognitive decline.