ArticleScientific reports2017
A comparative analysis of three vector-borne diseases across Australia using seasonal and meteorological models.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Investigating seasonal patterns in enteric infections: a systematic review of time series methods.Epidemiology and infection · 2022Pooled it
- Epidemiological models for predicting Ross River virus in Australia: A systematic review.PLoS neglected tropical diseases · 2020Pooled it
- Ross River virus transmission, infection, and disease: two and a half decades of research progress-an updated cross-disciplinary review.Clinical microbiology reviews · 2026Review
- Seasonality and risk factors of relapse to acute malnutrition: a multi-country prospective cohort study in Mali, South Sudan, and Somalia, 2021-2022.Journal of public health policy · 2026Article
- The impact of dengue viruses: Surveillance, response, and public health implications in Queensland, Australia.Public health in practice (Oxford, England) · 2024Review
- Arbovirus Transmission in Australia from 2002 to 2017.Biology · 2024Article
- Application of improved harmonic Poisson segmented regression model in evaluating the effectiveness of Kala-Azar intervention in Yangquan City, China.Frontiers in public health · 2024Article
- Profile and dynamics of infectious diseases: a population-based observational study using multi-source big data.BMC infectious diseases · 2022Observational
- How Seasonality of Malnutrition Is Measured and Analyzed.International journal of environmental research and public health · 2021Review
- An analecta of visualizations for foodborne illness trends and seasonality.Scientific data · 2020Article
- Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore.PLoS neglected tropical diseases · 2020Article
- Effects of Data Aggregation on Time Series Analysis of Seasonal Infections.International journal of environmental research and public health · 2020Article
- Review
- Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates.International journal of environmental research and public health · 2019Article
- Ecological and life history traits are associated with Ross River virus infection among sylvatic mammals in Australia.BMC ecology · 2019Article
- Spatial and temporal analysis of dengue infections in Queensland, Australia: Recent trend and perspectives.PloS one · 2019Article
- Climate drivers of vector-borne diseases in Africa and their relevance to control programmes.Infectious diseases of poverty · 2018Article
Corrections and comments
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale. We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50-65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue. We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases.
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