ArticlePLoS neglected tropical diseases2014
Seasonal drivers of the epidemiology of arthropod-borne viruses in Australia.
Article in PLoS neglected tropical diseases, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Review
- Article
- The Dynamic Transmission of Simbu Group Viruses in New South Wales, Australia.Transboundary and emerging diseases · 2026Article
- Molecular detection of novel Jingmen tick virus in hard ticks from diverse hosts in Guangxi, southwestern China.Virology journal · 2025Article
- Review
- Article
- Predictive modelling of Ross River virus using climate data in the Darling Downs.Epidemiology and infection · 2023Article
- Role of cervids in the epidemiology of bovine ephemeral fever virus infection in the Republic of Korea: A cross-sectional retrospective study.Veterinary medicine and science · 2023Article
- Interactions between seasonal temperature variation and temporal synchrony drive increased arbovirus co-infection incidence.Royal Society open science · 2022Article
- Effect of Constant Temperatures onInsects · 2022Article
- Identification and Genetic Characterization of Viral Pathogens in Ruminant Gestation Abnormalities, Israel, 2015-2019.Viruses · 2021Article
- Differentiation of Antibodies against Selected Simbu Serogroup Viruses by a Glycoprotein Gc-Based Triplex ELISA.Veterinary sciences · 2021Article
- Investigation of bovine ephemeral fever virus transmission by putative dipteran vectors under experimental conditions.Parasites & vectors · 2020Article
- Review
- Identification and genomic characterization of the first isolate of bluetongue virus serotype 5 detected in Australia.Veterinary medicine and science · 2019Article
- Review
- Livestock host composition rather than land use or climate explains spatial patterns in bluetongue disease in South India.Scientific reports · 2019Article
- International proficiency trial demonstrates reliable Schmallenberg virus infection diagnosis in endemic and non-affected countries.PloS one · 2019Article
- Development of within-herd immunity and long-term persistence of antibodies against Schmallenberg virus in naturally infected cattle.BMC veterinary research · 2018Article
- Review of Diagnostic Procedures and Approaches to Infectious Causes of Reproductive Failures of Cattle in Australia and New Zealand.Frontiers in veterinary science · 2018Review
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Arthropod-borne viruses are a major cause of emerging disease with significant public health and economic impacts. However, the factors that determine their activity and seasonality are not well understood. In Australia, a network of sentinel cattle herds is used to monitor the distribution of several such viruses and to define virus-free regions. Herein, we utilize these serological data to describe the seasonality, and its drivers, of three economically important animal arboviruses: bluetongue virus, Akabane virus and bovine ephemeral fever virus. Through epidemiological time-series analyses of sero-surveillance data of 180 sentinel herds between 2004-2012, we compared seasonal parameters across latitudes, ranging from the tropical north (-10°S) to the more temperate south (-40°S). This analysis revealed marked differences in seasonality between distinct geographic regions and climates: seasonality was most pronounced in southern regions and gradually decreased as latitude decreased toward the Equator. Further, we show that both the timing of epidemics and the average number of seroconversions have a strong geographical component, which likely reflect patterns of vector abundance through co-varying climatic factors, especially temperature and rainfall. Notably, despite their differences in biology, including insect vector species, all three viruses exhibited very similar seasonality. By revealing the factors that shape spatial and temporal distributions, our study provides a more complete understanding of arbovirus seasonality that will enable better risk predictions.
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