Dengue in Singapore from 2004 to 2016: Cyclical Epidemic Patterns Dominated by Serotypes 1 and 2

Rajarethinam, Jayanthi; Ang, Li Wei; Ong, Janet; Ycasas, Joyce; Hapuarachchi, Hapuarachchige Chanditha; Yap, Grace; Chong, Chin Soon; Lai, Yee-Ling; Cutter, Jeffery; Ho, Derek; Lee, Vernon; Ng, Lee Ching

doi:10.4269/ajtmh.17-0819

Cited by 85 publications

(105 citation statements)

References 34 publications

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“…DENV-3 was used as a reference DENV serotype because it is widespread in South-East Asia, [48][49][50][51] and was kindly provided by the National Public Health Laboratory (NPHL), Singapore. DENV-3 was used as a reference DENV serotype because it is widespread in South-East Asia, [48][49][50][51] and was kindly provided by the National Public Health Laboratory (NPHL), Singapore.…”

Section: Virusesmentioning

confidence: 99%

“…Zika virus Polynesian isolate (H/PF/2013) was obtained from the European Virus Archive (EVA, Marseille, France). DENV-3 was used as a reference DENV serotype because it is widespread in South-East Asia, [48][49][50][51] and was kindly provided by the National Public Health Laboratory (NPHL), Singapore. CHIKV SGP011 was isolated from a patient from Singapore.…”

Section: Virusesmentioning

confidence: 99%

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Novel differential linear B‐cell epitopes to identify Zika and dengue virus infections in patients

et al. 2019

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Objectives Recent Zika virus (ZIKV) outbreaks challenged existing laboratory diagnostic standards, especially for serology‐based methods. Because of the genetic and structural similarity of ZIKV with other flaviviruses, this results in cross‐reactive antibodies, which confounds serological interpretations. Methods Plasma from Singapore ZIKV patients was screened longitudinally for antibody responses and neutralising capacities against ZIKV. Samples from healthy controls, ZIKV patients and DENV patients were further assessed using ZIKV and DENV peptides of precursor membrane (prM), envelope (E) or non‐structural 1 (NS1) viral proteins in a peptide‐based ELISA for epitope identification. Identified epitopes were re‐validated and diagnostically evaluated using sera of patients with DENV, bacteria or unknown infections from Thailand. Results Long‐lasting ZIKV‐neutralising antibodies were elicited during ZIKV infection. Thirteen potential linear B‐cell epitopes were identified, and of these, four common flavivirus, three ZIKV‐specific and one DENV‐specific differential epitopes had more than 50% sensitivity and specificity. Notably, ZIKV‐specific peptide 26 on domain I/II of E protein (amino acid residues 271–288) presented 80% sensitivity and 85.7% specificity. Importantly, the differential epitopes also showed significance in differentiating non‐flavivirus patient samples. Conclusion Linear B‐cell epitope candidates to differentiate between ZIKV and DENV infections were identified, providing the first step towards the design of a much‐needed serology‐based assay.

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Section: Virusesmentioning

confidence: 99%

Section: Virusesmentioning

confidence: 99%

Novel differential linear B‐cell epitopes to identify Zika and dengue virus infections in patients

et al. 2019

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“…We analyzed the genotyped and geo-referenced Aedes aegypti individuals collected in two densely populated areas of Singapore with a homogeneous distribution of high-rise apartment blocks. Aedes aegypti is the primary vector of dengue virus in Singapore that, despite having a low Aedes house index (2%) and an extensive vector surveillance and control program [22], continues to experience regular dengue outbreaks [23,24]. This dataset offered an opportunity to characterize Ae.…”

Section: Introductionmentioning

confidence: 99%

Using spatial genetics to quantify mosquito dispersal for control programs

Filipović

Hapuarachchi

Tien

et al. 2020

Preprint

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Background: Hundreds of millions of people get a mosquito-borne disease every year, of which nearly one million die. Mosquito-borne diseases are primarily controlled and mitigated through the control of mosquito vectors. Accurately quantified mosquito dispersal in a given landscape is critical for the design and optimization of the control programs, yet the field experiments that measure dispersal of mosquitoes recaptured at certain distances from the release point (markrelease-recapture MRR studies) are challenging for such small insects and often unrepresentative of the insect's true field behavior. Using Singapore as a study site, we show how mosquito dispersal patterns can be characterized from the spatial analyses of genetic relatedness among individuals sampled over a short time span without interruption of their natural behaviors. Methods and Findings:We captured ovipositing females of Aedes aegypti, a major arboviral disease vector, across floors of high-rise apartment blocks and genotyped them using thousands of genome-wide SNP markers. We developed a methodology that produces a dispersal kernel for distance that results from one generation of successful breeding (effective dispersal), using the distances separating full siblings, 2 nd and 3 rd degree relatives (close kin). In Singapore, the estimated dispersal distance kernel was exponential (Laplacian), giving the mean effective dispersal distance (and dispersal kernel spread σ) of 45.2 m (95%CI: 39.7-51.3 m), and 10% probability of dispersal >100 m (95%CI: 92-117 m). Our genetic-based estimates matched the parametrized dispersal kernels from the previously reported MRR experiments. If few close-kin are captured, a conventional genetic isolation-by-distance analysis can be used, and we show that it can produce σ estimates congruent with the close-kin method, conditioned on the accurate estimation of effective population density. We also show that genetic patch size, estimated with the spatial autocorrelation analysis, reflects the spatial extent of the dispersal kernel 'tail' that influences e.g. predictions of critical radii of release zones and Wolbachia wave speed in mosquito replacement programs. Conclusions:We demonstrate that spatial genetics (the newly developed close-kin analysis, and conventional IBD and spatial autocorrelation analyses) can provide a detailed and robust characterization of mosquito dispersal that can guide operational vector control decisions. With the decreasing cost of next generation sequencing, acquisition of spatial genetic data will become increasingly accessible, and given the complexities and criticisms of conventional MRR methods, but the central role of dispersal measures in vector control programs, we recommend genetic-based dispersal characterization as the more desirable means of parameterization.

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“…Despite the ongoing baseline vector control programme that targets Aedes mosquitoes in residential areas, the outbreak demonstrates the possibility that human movement may allow the virus to break the 'cordon' of vector control and lead to cross-island transmission. In contrast to dengue, which has been endemic in Singapore since at least the 1960s [10,19], the 2016 Zika outbreak spread from one initial locus and across a population that has heretofore not experienced recorded Zika outbreaks; like dengue, it is expected that there be relatively little seasonal forcing on transmission risk [20]. Together, these characteristics make Singapore an ideal model system to understand Zika transmission.…”

Section: Introductionmentioning

confidence: 99%

Inferring who-infected-whom-where in the 2016 Zika outbreak in Singapore—a spatio-temporal model

Prem

Lau

Tam

et al. 2019

J. R. Soc. Interface.

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Singapore experienced its first known Zika outbreak in 2016. Given the lack of herd immunity, the suitability of the climate for pathogen transmission, and the year-round presence of the vector— Aedes aegypti —Zika had the potential to become endemic, like dengue. Guillain–Barré syndrome and microcephaly are severe complications associated elsewhere with Zika and the risk of these complications makes understanding its spread imperative. We investigated the spatio-temporal spread of locally transmitted Zika in Singapore and assessed the relevance of non-residential transmission of Zika virus infections, by inferring the possible infection tree (i.e. who-infected-whom-where ) and comparing inferences using geographically resolved data on cases' home, their work, or their home and work. We developed a spatio-temporal model using time of onset and both addresses of the Zika-confirmed cases between July and September 2016 to estimate the infection tree using Bayesian data augmentation. Workplaces were involved in a considerable fraction (64.2%) of infections, and homes and workplaces may be distant relative to the scale of transmission, allowing ambulant infected persons may act as the ‘vector’ infecting distant parts of the country. Contact tracing is a challenge for mosquito-borne diseases, but inferring the geographically structured transmission tree sheds light on the spatial transmission of Zika to immunologically naive regions of the country.

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Dengue in Singapore from 2004 to 2016: Cyclical Epidemic Patterns Dominated by Serotypes 1 and 2

Cited by 85 publications

References 34 publications

Novel differential linear B‐cell epitopes to identify Zika and dengue virus infections in patients

Novel differential linear B‐cell epitopes to identify Zika and dengue virus infections in patients

Using spatial genetics to quantify mosquito dispersal for control programs

Inferring who-infected-whom-where in the 2016 Zika outbreak in Singapore—a spatio-temporal model

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