Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Author: search.filters.author.Hayman DTSSubject: search.filters.subject.1108 Medical Microbiology

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    Bat Flight and Zoonotic Viruses
    (Centers for Disease Control and Prevention, 2014) O Shea TJ; Cryan PM; Cunningham AA; Fooks AR; Hayman DTS; Luis AD; Peel AJ; Plowright RK; Wood JLN
    Bats are sources of high viral diversity and high-profile zoonotic viruses worldwide. Although apparently not pathogenic in their reservoir hosts, some viruses from bats severely affect other mammals, including humans. Examples include severe acute respiratory syndrome coronaviruses, Ebola and Marburg viruses, and Nipah and Hendra viruses. Factors underlying high viral diversity in bats are the subject of speculation. We hypothesize that flight, a factor common to all bats but to no other mammals, provides an intensive selective force for coexistence with viral parasites through a daily cycle that elevates metabolism and body temperature analogous to the febrile response in other mammals. On an evolutionary scale, this host–virus interaction might have resulted in the large diversity of zoonotic viruses in bats, possibly through bat viruses adapting to be more tolerant of the fever response and less virulent to their natural hosts.
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    Genomic Analysis of Salmonella enterica Serovar Typhimurium DT160 Associated with a 14-Year Outbreak, New Zealand, 1998-2012.
    (2017-06) Bloomfield SJ; Benschop J; Biggs PJ; Marshall JC; Hayman DTS; Carter PE; Midwinter AC; Mather AE; French NP
    During 1998-2012, an extended outbreak of Salmonella enterica serovar Typhimurium definitive type 160 (DT160) affected >3,000 humans and killed wild birds in New Zealand. However, the relationship between DT160 within these 2 host groups and the origin of the outbreak are unknown. Whole-genome sequencing was used to compare 109 Salmonella Typhimurium DT160 isolates from sources throughout New Zealand. We provide evidence that DT160 was introduced into New Zealand around 1997 and rapidly propagated throughout the country, becoming more genetically diverse over time. The genetic heterogeneity was evenly distributed across multiple predicted functional protein groups, and we found no evidence of host group differentiation between isolates collected from human, poultry, bovid, and wild bird sources, indicating ongoing transmission between these host groups. Our findings demonstrate how a comparative genomic approach can be used to gain insight into outbreaks, disease transmission, and the evolution of a multihost pathogen after a probable point-source introduction.
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    Utility of mosquito surveillance data for spatial prioritization of vector control against dengue viruses in three Brazilian cities
    (BioMed Central, 2015-12) Pepin KM; Leach CB; Marques-Toledo C; Laass KH; Paixao KS; Luis AD; Hayman DTS; Johnson NG; Buhnerkempe MG; Carver S; Grear DA; Tsao K; Eiras AE; Webb CT
    BACKGROUND: Vector control remains the primary defense against dengue fever. Its success relies on the assumption that vector density is related to disease transmission. Two operational issues include the amount by which mosquito density should be reduced to minimize transmission and the spatio-temporal allotment of resources needed to reduce mosquito density in a cost-effective manner. Recently, a novel technology, MI-Dengue, was implemented city-wide in several Brazilian cities to provide real-time mosquito surveillance data for spatial prioritization of vector control resources. We sought to understand the role of city-wide mosquito density data in predicting disease incidence in order to provide guidance for prioritization of vector control work. METHODS: We used hierarchical Bayesian regression modeling to examine the role of city-wide vector surveillance data in predicting human cases of dengue fever in space and time. We used four years of weekly surveillance data from Vitoria city, Brazil, to identify the best model structure. We tested effects of vector density, lagged case data and spatial connectivity. We investigated the generality of the best model using an additional year of data from Vitoria and two years of data from other Brazilian cities: Governador Valadares and Sete Lagoas. RESULTS: We found that city-wide, neighborhood-level averages of household vector density were a poor predictor of dengue-fever cases in the absence of accounting for interactions with human cases. Effects of city-wide spatial patterns were stronger than within-neighborhood or nearest-neighborhood effects. Readily available proxies of spatial relationships between human cases, such as economic status, population density or between-neighborhood roadway distance, did not explain spatial patterns in cases better than unweighted global effects. CONCLUSIONS: For spatial prioritization of vector controls, city-wide spatial effects should be given more weight than within-neighborhood or nearest-neighborhood connections, in order to minimize city-wide cases of dengue fever. More research is needed to determine which data could best inform city-wide connectivity. Once these data become available, MI-dengue may be even more effective if vector control is spatially prioritized by considering city-wide connectivity between cases together with information on the location of mosquito density and infected mosquitos.