| Literature DB >> 36146651 |
Ka Y Yuen1, Joerg Henning1, Melodie D Eng1, Althea S W Wang1, Martin F Lenz2, Karen M Caldwell2, Mitchell P Coyle3, Helle Bielefeldt-Ohmann1,4,5.
Abstract
The increased frequency of extreme weather events due to climate change has complicated the epidemiological pattern of mosquito-borne diseases, as the host and vector dynamics shift to adapt. However, little is known about the seroprevalence of common mosquito-borne virus infections in horses in Australia. In this study, serological surveys for multiple alphaviruses were performed on samples taken from 622 horses across two horse populations (racehorses and horses residing on The University of Queensland (UQ) campus) in Queensland using the gold standard virus neutralization test. As is the case in humans across Australia, Ross River virus (RRV) is the most common arbovirus infection in horses, followed by Barmah Forest virus, with an overall apparent seroprevalence of 48.6% (302/622) and 4.3% (26/607), respectively. Horses aged over 6 years old (OR 1.86, p = 0.01) and residing at UQ (OR 5.8, p < 0.001) were significantly associated with seroconversion to RRV. A significant medium correlation (r = 0.626, p < 0.001) between RRV and Getah virus (GETV) neutralizing antibody titers was identified. Collectively, these results advance the current epidemiological knowledge of arbovirus exposure in a susceptible host in Australia. The potential use of horses as sentinels for arbovirus monitoring should be considered. Furthermore, since GETV is currently exotic to Australia, antibodies cross-reactivity between RRV and GETV should be further investigated for cross-protection, which may also help to inform vaccine developments.Entities:
Keywords: Barmah Forest virus; Ross River virus; Sindbis virus; alphavirus; cross-reactivity; infectious diseases; risk factors; seroprevalence; spatial analysis
Mesh:
Substances:
Year: 2022 PMID: 36146651 PMCID: PMC9504300 DOI: 10.3390/v14091846
Source DB: PubMed Journal: Viruses ISSN: 1999-4915 Impact factor: 5.818
Alphaviruses of concerns in Australia included in this study.
| Examples | Zoonotic | Status in Australia | Type of Clinical Signs (Animals) |
|---|---|---|---|
| Ross River virus | Yes | Endemic | Musculoskeletal |
| Barmah Forest virus | Yes | Endemic | Musculoskeletal |
| Sindbis virus | Yes | Endemic | Musculoskeletal/Neurological |
| Getah virus | No | Exotic | Systemic |
Description of the sample population.
| Category | Level | QRIC | UQ | Total |
|---|---|---|---|---|
|
| Male | 318 | 39 | 357 |
| Female | 196 | 69 | 265 | |
|
| 2–6 | 447 | 27 | 474 |
| >6 | 67 | 81 | 148 | |
|
| Australian Stockhorse | 0 | 25 | 25 |
| Australian Stockhorse × Standardbred | 0 | 7 | 7 | |
| Quarter horse | 0 | 2 | 2 | |
| Standardbred | 0 | 51 | 51 | |
| Thoroughbred | 514 | 17 | 531 | |
| Warmblood | 0 | 6 | 6 |
Outcome summary of alphaviruses serosurvey.
| RRV % ( | BFV % ( | SINV % ( | ||||
|---|---|---|---|---|---|---|
| Seropositive | Seronegative | Seropositive | Seronegative | Seropositive | Seronegative | |
|
| 40.9 (210) | 59.1 (304) | 1.4 (7) | 98.6 (506) | 2.1 (11) | 97.9 (503) |
|
| 85.2 (92) | 14.8 (16) | 20.2 (19) | 79.8 (75) | 1.0 (1) | 99.0 (96) |
|
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* Fisher’s exact test. RRV = Ross River virus; BFV = Barmah Forest virus; SINV = Sindbis virus.
Figure 1Correlation of neutralizing antibody titer (log2 transformed) between RRV and GETV. The random noise effect was added to the same data points to prevent overplotting.
Distribution of QRIC and UQ sample population across QLD.
| QRIC | UQ | |||
|---|---|---|---|---|
| Statistical Local Area 3 (SLA 3) | RRV Seropositive | RRV Seronegative | RRV Seropositive | RRV Seronegative |
| Cleveland–Stradbroke | 4 | 0 | 0 | 0 |
| Nundah | 60 | 110 | 0 | 0 |
| Sandgate | 5 | 5 | 0 | 0 |
| Kenmore–Brookfield–Moggill | 0 | 0 | 9 | 0 |
| Brisbane Inner–North | 0 | 3 | 0 | 0 |
| Cairns–South | 4 | 3 | 0 | 0 |
| Innisfail–Cassowary Coast | 1 | 1 | 0 | 0 |
| Tablelands (East)–Kuranda | 2 | 0 | 0 | 0 |
| Granite Belt | 2 | 2 | 0 | 0 |
| Rockhampton | 8 | 12 | 0 | 0 |
| Gold Coast Hinterland | 39 | 59 | 0 | 0 |
| Ipswich Hinterland | 4 | 7 | 83 | 16 |
| Beaudesert | 5 | 3 | 0 | 0 |
| Mackay | 0 | 2 | 0 | 0 |
| Far North | 0 | 5 | 0 | 0 |
| Sunshine Coast Hinterland | 47 | 39 | 0 | 0 |
| Toowoomba | 26 | 50 | 0 | 0 |
| Townsville | 1 | 3 | 0 | 0 |
| Bundaberg | 1 | 0 | 0 | 0 |
| Gympie–Cooloola | 1 | 0 | 0 | 0 |
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Figure 2Maps of QLD classified by SLA 3 showing (a) distribution of sample population, and (b) apparent seroprevalence of RRV.
Figure 3Maps of southeast QLD classified by SLA 3 showing (a) distribution of sample population; (b) apparent seroprevalence of RRV.
Global spatial autocorrelation (presented as Moran’s I statistics) of overall and season analyses for RRV seropositivity in QLD.
| Dataset | Moran’s | |
|---|---|---|
| Overall | –0.02734297 | 0.437 |
| Spring | 0.04030128 | 0.301 |
| Summer | –0.1685299 | 0.361 |
| Autumn | 0.06044266 | 0.254 |
| Winter | –0.11316486 | 0.549 |
Risk factors analysis using logistic regression models.
| Univariant Analysis | Multivariant Analysis | ||||||
|---|---|---|---|---|---|---|---|
| Risk Factors | Level | RRV Seropositive | RRV Seronegative | Odds Ratio | Odds Ratio | ||
|
| Female | 48.3 (128) | 51.7 (137) | 1 | |||
| Male | 48.7 (174) | 51.3 (183) | 1.02 (0.74–1.40) | ||||
|
| QRIC | 40.9 (210) | 59.1 (304) | 1 | 1 | ||
| UQ | 85.2 (92) | 14.8 (16) | 8.32 (4.76–14.56) | 5.84 (3.16–10.80) | |||
|
| 2–6 | 41.8 (195) | 58.2 (271) | 1 | 1 | ||
| >6 | 72.3 (107) | 27.7 (41) | 3.73 (2.49–5.59) | 1.86 (1.16–2.97) | |||
|
| Spring | 57.6 (95) | 42.4 (70) | 1 | |||
| Summer | 45.9 (61) | 54.1 (72) | 0.62 (0.39–0.99) | ||||
| Autumn | 54.4 (62) | 45.6 (52) | 0.88 (0.54–1.42) | ||||
| Winter | 40.0 (84) | 60.0 (126) | 0.49 (0.32–0.74) | ||||
* Wald-test.