Force of Infection and True Infection Rate of Dengue in Singapore: Implications for Dengue Control and Management

Tan, Li; Low, Swee Ling; Sun, Haoyang; Shi, Yuan; Liu, Lilac; Lam, Sally; Tan, Hwee Huang; Ang, Li Wei; Wong, W. Y.; Chua, Rachel; Teo, Diana; Ng, Lee Ching; Cook, Alex R.

doi:10.1093/aje/kwz110

Cited by 34 publications

(50 citation statements)

References 34 publications

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“…The considerable increase in the relative proportion of 36-55 years-old with DENV1 antibody titres from 2009 to 2013 is consistent and indicative of the occurrence of a DENV1 epidemic in 2013 [17]. The fact that DENV1 seroprevalence is high among older age-classes in 2013 is line with the relatively low force of infection of DENV1 in Singapore compared to other transmission settings and with the declining transmission intensity observed over the years and with population aging [20]. Similarly, the 10% increase in the proportion of the youngest age groups in cluster 1 between 2009 and 2013 (from 23 to 26.5%) is also indicative of the occurrence of a DENV2 epidemic between 2009 and 2013.…”

Section: Discussionsupporting

confidence: 52%

Using cluster analysis to reconstruct dengue exposure patterns from cross-sectional serological studies in Singapore

Sangkaew

Tan

et al. 2020

Parasites Vectors

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Background: Dengue is a mosquito-borne viral disease caused by one of four serotypes (DENV1-4). Infection provides long-term homologous immunity against reinfection with the same serotype. Plaque reduction neutralization test (PRNT) is the gold standard to assess serotype-specific antibody levels. We analysed serotype-specific antibody levels obtained by PRNT in two serological surveys conducted in Singapore in 2009 and 2013 using cluster analysis, a machine learning technique that was used to identify the most common histories of DENV exposure. Methods: We explored the use of five distinct clustering methods (i.e. agglomerative hierarchical, divisive hierarchical, K-means, K-medoids and model-based clustering) with varying number (from 4 to 10) of clusters for each method. Weighted rank aggregation, an evaluating technique for a set of internal validity metrics, was adopted to determine the optimal algorithm, comprising the optimal clustering method and the optimal number of clusters. Results: The K-means algorithm with six clusters was selected as the algorithm with the highest weighted rank aggregation. The six clusters were characterised by (i) dominant DENV2 PRNT titres; (ii) co-dominant DENV1 and DENV2 titres with average DENV2 titre > average DENV1 titre; (iii) co-dominant DENV1 and DENV2 titres with average DENV1 titre > average DENV2 titre; (iv) low PRNT titres against DENV1-4; (v) intermediate PRNT titres against DENV1-4; and (vi) dominant DENV1-3 titres. Analyses of the relative size and age-stratification of the clusters by year of sample collection and the application of cluster analysis to the 2009 and 2013 datasets considered separately revealed the epidemic circulation of DENV2 and DENV3 between 2009 and 2013. Conclusion: Cluster analysis is an unsupervised machine learning technique that can be applied to analyse PRNT antibody titres (without pre-established cutoff thresholds to indicate protection) to explore common patterns of DENV infection and infer the likely history of dengue exposure in a population.

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Section: Discussionsupporting

confidence: 52%

Using cluster analysis to reconstruct dengue exposure patterns from cross-sectional serological studies in Singapore

Sangkaew

Tan

et al. 2020

Parasites Vectors

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“…In 2019, Singapore also experienced a large dengue outbreak with more than 16,000 cases [27]. There is low herd immunity, particularly among younger generations, due to decades of low dengue transmission [28][29][30]. The primary dengue vector is A. aegypti, and although Aedes albopictus is also present, it is considered a weak secondary vector [31].…”

Section: The Singapore Contextmentioning

confidence: 99%

“…Entomological indices and dengue case numbers are not reliable measures for assessing the long-term impact of vector control programmes because of changes in surveillance and diagnostic capabilities over time [30]. Instead, NEA uses blood bank IgG seroprevalence to estimate the force of infection (FOI) over time [30].…”

Section: Vector Surveillance and Monitoring And Evaluation Of Intervementioning

confidence: 99%

“…Given the high-risk dengue outlook for 2020, NEA worked at the whole-of-government level to include vector control activities as an essential service to What continues to drive transmission in Singapore, where there is a well-resourced and effective dengue control programme? The dengue incidence rate in Singapore has increased dramatically in the last 25 years, but this is likely because of improved diagnostics, increased referral for testing by medical practitioners, and increased awareness among the public [30]. A better indicator of the true infection rate is dengue FOI: between the 1960s (when Singapore first implemented environmental management and vector control programmes) and the 1990s, the dengue FOI in Singapore dropped 10-fold to approximately 0.01 (10 per 1,000 individuals per year) and has since held steady at this low level [30].…”

Section: Dengue Control In the Time Of Covid-19mentioning

confidence: 99%

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A greener vision for vector control: The example of the Singapore dengue control programme

Sim

Lindsay

et al. 2020

PLoS Negl Trop Dis

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Vector-borne diseases are a major cause of morbidity and mortality worldwide. Aedesborne diseases, in particular, including dengue, chikungunya, yellow fever, and Zika, are increasing at an alarming rate due to urbanisation, population movement, weak vector control programmes, and climate change. The World Health Organization calls for strengthening of vector control programmes in line with the Global Vector Control Response (GVCR) strategy, and many vector control programmes are transitioning to this new approach. The Singapore dengue control programme, situated within the country's larger vision of a clean, green, and sustainable environment for the health and well-being of its citizens, provides an excellent example of the GVCR approach in action. Since establishing vector control operations in the 1960s, the Singapore dengue control programme succeeded in reducing the dengue force of infection 10-fold by the 1990s and has maintained it at low levels ever since. Key to this success is consideration of dengue as an environmental disease, with a strong focus on source reduction and other environmental management methods as the dominant vector control strategy. The programme collaborates closely with other government ministries, as well as town councils, communities, the private sector, and academic and research institutions. Community engagement programmes encourage source reduction, and house-to-house inspections accompanied by a strong legislative framework with monetary penalties help to support compliance. Strong vector and epidemiological surveillance means that routine control activities can be heightened to specifically target dengue clusters. Despite its success, the programme continues to innovate to tackle challenges such as climate change, low herd immunity, and manpower constraints. Initiatives include development of novel vector controls such as Wolbachia-infected males and spatiotemporal models for dengue risk assessment. Lessons learnt from the Singapore programme can be applied to other settings, even those less well-resourced than Singapore, for more effective vector control.

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“…Primarily, vector control is used to mitigate dengue transmissions in Singapore and its success is evidenced in the decreasing seroprevalence nationally for the past two decades [ 3 – 5 ]. However, this low seroprevalence complicates the implementation of vaccination using the tetravalent Dengvaxia (CYD-TDV) [ 6 , 7 ] vaccine on the national scale [ 8 ] due to potentially longer-term risks of severe dengue in vaccinated but seronegative individuals [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Time varying methods to infer extremes in dengue transmission dynamics

et al. 2020

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Dengue is an arbovirus affecting global populations. Frequent outbreaks occur, especially in equatorial cities such as Singapore, where year-round tropical climate, large daily influx of travelers and population density provide the ideal conditions for dengue to transmit. Little work has, however, quantified the peaks of dengue outbreaks, when health systems are likely to be most stretched. Nor have methods been developed to infer differences in exogenous factors which lead to the rise and fall of dengue case counts across extreme and non-extreme periods. In this paper, we developed time varying extreme mixture (tvEM) methods to account for the temporal dependence of dengue case counts across extreme and non-extreme periods. This approach permits inference of differences in climatic forcing across non-extreme and extreme periods of dengue case counts, quantification of their temporal dependence as well as estimation of thresholds with associated uncertainties to determine dengue case count extremities. Using tvEM, we found no evidence that weather affects dengue case counts in the near term for non-extreme periods, but that it has non-linear and mixed signals in influencing dengue through tvEM parameters in the extreme periods. Using the most appropriate tvEM specification, we found that a threshold at the 70 th (95% credible interval 41.1, 83.8) quantile is optimal, with extreme events of 526.6, 1052.2 and 1183.6 weekly case counts expected at return periods of 5, 50 and 75 years. Weather parameters at a 1% scaled increase was found to decrease the long-run expected case counts, but larger increases would lead to a drastic expected rise from the baseline correspondingly. The tvEM approach can provide valuable inference on the extremes of time series, which in the case of infectious disease notifications, allows public health officials to understand the likely scale of outbreaks in the long run.

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Force of Infection and True Infection Rate of Dengue in Singapore: Implications for Dengue Control and Management

Cited by 34 publications

References 34 publications

Using cluster analysis to reconstruct dengue exposure patterns from cross-sectional serological studies in Singapore

Using cluster analysis to reconstruct dengue exposure patterns from cross-sectional serological studies in Singapore

A greener vision for vector control: The example of the Singapore dengue control programme

Time varying methods to infer extremes in dengue transmission dynamics

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