Spatiotemporal variability in dengue transmission intensity in Jakarta, Indonesia

O’Driscoll, Megan; Imai, Natsuko; Ferguson, Neil; Hadinegoro, Sri Rezeki; Satari, Hindra Irawan; Tam, Clarence C.; Dorigatti, Ilaria

doi:10.1371/journal.pntd.0008102

Cited by 18 publications

(23 citation statements)

References 23 publications

(30 reference statements)

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“…Typically median, or mean, FoI estimates are used as the dependent variable [ 6 , 7 , 9 ]; however, ideally, the uncertainty in estimated FoIs should be accounted for when fitting the models and evaluating their predictive ability. To assess how this uncertainty impacted predictions, we compared three approaches incorporating a different level of uncertainty: Central estimates of FoI are used, i.e.…”

Section: Methodsmentioning

confidence: 99%

“…Where this framework has been applied, given the complexity of the inference and relative scarcity of gound-truth data, it is common to assume that exposure has been constant over time. Although this may hold for FoI estimates for dengue [ 6 , 8 , 9 ], yellow fever [ 7 ], rubella [ 10 , 11 ] and malaria [ 12 ], it is more challenging for Chagas disease, as its protracted nature and substantial spatial and temporal heterogeneities in the implementation of control measures lead to temporal and spatial heterogeneities in exposure.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, predicting FoI spatial patterns relies upon point estimates of FoI, with geostatistical models smoothing the central estimates [ 6 , 7 , 9 ], often neglecting their uncertainty. This may generate over-confidence in FoI estimates and ultimately burden.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study

Ledien

Cucunubá²,

Parra-Henao³

et al. 2022

BMC Med Res Methodol

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Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty.In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia.A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions.Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study

Ledien

Cucunubá²,

Parra-Henao³

et al. 2022

BMC Med Res Methodol

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“…Therefore, our estimates are likely a slight overestimation of true dengue transmission intensity across the Philippines. Moreover, spatio-temporal heterogeneity in dengue transmission intensity [38][39][40][41] infers this national estimate is unlikely to be representative of lower administrative areas in the Philippines. Among those reporting with active dengue, dengue immune status remained temporally stable across the country yet spatially heterogeneous in northern Luzon.…”

Section: Discussionmentioning

confidence: 92%

A serological framework to investigate acute primary and post-primary dengue cases reporting across the Philippines

Biggs

Brady

et al. 2020

BMC Med

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Background In dengue-endemic countries, targeting limited control interventions to populations at risk of severe disease could enable increased efficiency. Individuals who have had their first (primary) dengue infection are at risk of developing more severe secondary disease, thus could be targeted for disease prevention. Currently, there is no reliable algorithm for determining primary and post-primary (infection with more than one flavivirus) status from a single serum sample. In this study, we developed and validated an immune status algorithm using single acute serum samples from reporting patients and investigated dengue immuno-epidemiological patterns across the Philippines. Methods During 2015/2016, a cross-sectional sample of 10,137 dengue case reports provided serum for molecular (anti-DENV PCR) and serological (anti-DENV IgM/G capture ELISA) assay. Using mixture modelling, we re-assessed IgM/G seroprevalence and estimated functional, disease day-specific, IgG:IgM ratios that categorised the reporting population as negative, historical, primary and post-primary for dengue. We validated our algorithm against WHO gold standard criteria and investigated cross-reactivity with Zika by assaying a random subset for anti-ZIKV IgM and IgG. Lastly, using our algorithm, we explored immuno-epidemiological patterns of dengue across the Philippines. Results Our modelled IgM and IgG seroprevalence thresholds were lower than kit-provided thresholds. Individuals anti-DENV PCR+ or IgM+ were classified as active dengue infections (83.1%, 6998/8425). IgG− and IgG+ active dengue infections on disease days 1 and 2 were categorised as primary and post-primary, respectively, while those on disease days 3 to 5 with IgG:IgM ratios below and above 0.45 were classified as primary and post-primary, respectively. A significant proportion of post-primary dengue infections had elevated anti-ZIKV IgG inferring previous Zika exposure. Our algorithm achieved 90.5% serological agreement with WHO standard practice. Post-primary dengue infections were more likely to be older and present with severe symptoms. Finally, we identified a spatio-temporal cluster of primary dengue case reporting in northern Luzon during 2016. Conclusions Our dengue immune status algorithm can equip surveillance operations with the means to target dengue control efforts. The algorithm accurately identified primary dengue infections who are at risk of future severe disease.

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“…Studies in the past have utilized spatial and temporal epidemiological tools (such as models and maps) to support local health authorities to implement timely and targeted public health interventions and make better planning for dengue control [ 17 , 18 , 19 , 20 ]. These studies have unravelled high-risk areas and a clustered pattern of dengue incidence.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Patterns of Dengue Incidence in Medan City, North Sumatera, Indonesia

et al. 2021

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Dengue has been a perennial public health problem in Medan city, North Sumatera, despite the widespread implementation of dengue control. Understanding the spatial and temporal pattern of dengue is critical for effective implementation of dengue control strategies. This study aimed to characterize the epidemiology and spatio-temporal patterns of dengue in Medan City, Indonesia. Data on dengue incidence were obtained from January 2016 to December 2019. Kulldorff’s space-time scan statistic was used to identify dengue clusters. The Getis-Ord Gi* and Anselin Local Moran’s I statistics were used for further characterisation of dengue hotspots and cold spots. Results: A total of 5556 cases were reported from 151 villages across 21 districts in Medan City. Annual incidence in villages varied from zero to 439.32 per 100,000 inhabitants. According to Kulldorf’s space-time scan statistic, the most likely cluster was located in 27 villages in the south-west of Medan between January 2016 and February 2017, with a relative risk (RR) of 2.47. Getis-Ord Gi* and LISA statistics also identified these villages as hotpot areas. Significant space-time dengue clusters were identified during the study period. These clusters could be prioritized for resource allocation for more efficient prevention and control of dengue.

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Spatiotemporal variability in dengue transmission intensity in Jakarta, Indonesia

Cited by 18 publications

References 23 publications

Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study

Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study

A serological framework to investigate acute primary and post-primary dengue cases reporting across the Philippines

Spatio-Temporal Patterns of Dengue Incidence in Medan City, North Sumatera, Indonesia

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