Identification of prior dengue-naïve Dengvaxia recipients with an increased risk for symptomatic dengue during fever surveillance in the Philippines.

Introduction: Dengue virus (DENV) is the leading cause of mosquito-borne viral diseases in humans. Dengvaxia, the first licensed dengue vaccine, is recommended for DENV-seropositive individuals aged 9-45 years. In the Philippines, Dengvaxia was administered to more than 830,000 children without prior serological testing in 2016-2017. Subsequently, it was revealed that DENV-seronegative children who received Dengvaxia developed severe disease following breakthrough DENV infection. As a result, thousands of children participating in the mass vaccination campaign were at higher risk of severe dengue disease. It is vital that an assay that identifies baseline DENV-naïve Dengvaxia recipients be developed and validated. This would permit more frequent and extensive assessments and timely treatment of breakthrough DENV infections. Methods: We evaluated the performance of a candidate assay, the DENV1-4 nonstructural protein 1 (NS1) IgG enzyme-linked immunosorbent assay (ELISA), developed by the University of Hawaii (UH), using well-documented serum/plasma samples including those >20 years post-DENV infection, and tested samples from 199 study participants including 100 Dengvaxia recipients from the fever surveillance programs in the Philippines. Results: The sensitivity and specificity of the assay were 96.6% and 99.4%, respectively, which are higher than those reported for pre-vaccination screening. A significantly higher rate of symptomatic breakthrough DENV infection was found among children that were DENV-naïve (10/23) than among those that were DENV-immune (7/53) when vaccinated with Dengvaxia (p=0.004, Fisher's exact test), demonstrating the feasibility of the assay and algorithms in clinical practice. Conclusion: The UH DENV1-4 NS1 IgG ELISA can determine baseline DENV serostatus among Dengvaxia recipients not only during non-acute dengue but also during breakthrough DENV infection, and has implications for assessing the long-term safety and effectiveness of Dengvaxia in the post-licensure period.

Genetic Diversity and Dispersal of DENGUE Virus among Three Main Island Groups of the Philippines during 2015-2017.

Dengue has been one of the major public health concerns in the Philippines for more than a century. The annual dengue case burden has been increasing in recent years, exceeding 200,000 in 2015 and 2019. However, there is limited information on the molecular epidemiology of dengue in the Philippines. We, therefore, conducted a study to understand the genetic composition and dispersal of DENV in the Philippines from 2015 to 2017 under UNITEDengue. Our analyses included 377 envelope (E) gene sequences of all 4 serotypes obtained from infections in 3 main island groups (Luzon, Visayas, and Mindanao) of the Philippines. The findings showed that the overall diversity of DENV was generally low. DENV-1 was relatively more diverse than the other serotypes. Virus dispersal was evident among the three main island groups, but each island group demonstrated a distinct genotype composition. These observations suggested that the intensity of virus dispersal was not substantive enough to maintain a uniform heterogeneity among island groups so that each island group behaved as an independent epidemiological unit. The analyses suggested Luzon as one of the major sources of DENV emergence and CAR, Calabarzon, and CARAGA as important hubs of virus dispersal in the Philippines. Our findings highlight the importance of virus surveillance and molecular epidemiological analyses to gain deep insights into virus diversity, lineage dominance, and dispersal patterns that could assist in understanding the epidemiology and transmission risk of dengue in endemic regions.

Combining rapid diagnostic tests to estimate primary and post-primary dengue immune status at the point of care.

BACKGROUND: Characterising dengue virus (DENV) infection history at the point of care is challenging as it relies on intensive laboratory techniques. We investigated how combining different rapid diagnostic tests (RDTs) can be used to accurately determine the primary and post-primary DENV immune status of reporting patients during diagnosis. METHODS AND FINDINGS: Serum from cross-sectional surveys of acute suspected dengue patients in Indonesia (N:200) and Vietnam (N: 1,217) were assayed using dengue laboratory assays and RDTs. Using logistic regression modelling, we determined the probability of being DENV NS1, IgM and IgG RDT positive according to corresponding laboratory viremia, IgM and IgG ELISA metrics. Laboratory test thresholds for RDT positivity/negativity were calculated using Youden's J index and were utilized to estimate the RDT outcomes in patients from the Philippines, where only data for viremia, IgM and IgG were available (N:28,326). Lastly, the probabilities of being primary or post-primary according to every outcome using all RDTs, by day of fever, were calculated. Combining NS1, IgM and IgG RDTs captured 94.6% (52/55) and 95.4% (104/109) of laboratory-confirmed primary and post-primary DENV cases, respectively, during the first 5 days of fever. Laboratory test predicted, and actual, RDT outcomes had high agreement (79.5% (159/200)). Among patients from the Philippines, different combinations of estimated RDT outcomes were indicative of post-primary and primary immune status. Overall, IgG RDT positive results were confirmatory of post-primary infections. In contrast, IgG RDT negative results were suggestive of both primary and post-primary infections on days 1-2 of fever, yet were confirmatory of primary infections on days 3-5 of fever. CONCLUSION: We demonstrate how the primary and post-primary DENV immune status of reporting patients can be estimated at the point of care by combining NS1, IgM and IgG RDTs and considering the days since symptoms onset. This framework has the potential to strengthen surveillance operations and dengue prognosis, particularly in low resource settings.

Estimating the annual dengue force of infection from the age of reporting primary infections across urban centres in endemic countries.

BACKGROUND: Stratifying dengue risk within endemic countries is crucial for allocating limited control interventions. Current methods of monitoring dengue transmission intensity rely on potentially inaccurate incidence estimates. We investigated whether incidence or alternate metrics obtained from standard, or laboratory, surveillance operations represent accurate surrogate indicators of the burden of dengue and can be used to monitor the force of infection (FOI) across urban centres. METHODS: Among those who reported and resided in 13 cities across the Philippines, we collected epidemiological data from all dengue case reports between 2014 and 2017 (N 80,043) and additional laboratory data from a cross-section of sampled case reports (N 11,906) between 2014 and 2018. At the city level, we estimated the aggregated annual FOI from age-accumulated IgG among the non-dengue reporting population using catalytic modelling. We compared city-aggregated FOI estimates to aggregated incidence and the mean age of clinically and laboratory diagnosed dengue cases using Pearson's Correlation coefficient and generated predicted FOI estimates using regression modelling. RESULTS: We observed spatial heterogeneity in the dengue average annual FOI across sampled cities, ranging from 0.054 [0.036-0.081] to 0.249 [0.223-0.279]. Compared to FOI estimates, the mean age of primary dengue infections had the strongest association (ρ -0.848, p value<0.001) followed by the mean age of those reporting with warning signs (ρ -0.642, p value 0.018). Using regression modelling, we estimated the predicted annual dengue FOI across urban centres from the age of those reporting with primary infections and revealed prominent spatio-temporal heterogeneity in transmission intensity. CONCLUSIONS: We show the mean age of those reporting with their first dengue infection or those reporting with warning signs of dengue represent superior indicators of the dengue FOI compared to crude incidence across urban centres. Our work provides a framework for national dengue surveillance to routinely monitor transmission and target control interventions to populations most in need.

Serological Evidence of Widespread Zika Transmission across the Philippines.

Zika virus (ZIKV) exposure across flavivirus-endemic countries, including the Philippines, remains largely unknown despite sporadic case reporting and environmental suitability for transmission. Using laboratory surveillance data from 2016, 997 serum samples were randomly selected from suspected dengue (DENV) case reports across the Philippines and assayed for serological markers of short-term (IgM) and long-term (IgG) ZIKV exposure. Using mixture models, we re-evaluated ZIKV IgM/G seroprevalence thresholds and used catalytic models to quantify the force of infection (attack rate, AR) from age-accumulated ZIKV exposure. While we observed extensive ZIKV/DENV IgG cross-reactivity, not all individuals with active DENV presented with elevated ZIKV IgG, and a proportion of dengue-negative cases (DENV IgG-) were ZIKV IgG-positive (14.3%, 9/63). We identified evidence of long-term, yet not short-term, ZIKV exposure across Philippine regions (ZIKV IgG+: 31.5%, 314/997) which was geographically uncorrelated with DENV exposure. In contrast to the DENV AR (12.7% (95%CI: 9.1-17.4%)), the ZIKV AR was lower (5.7% (95%CI: 3-11%)) across the country. Our results provide evidence of widespread ZIKV exposure across the Philippines and suggest the need for studies to identify ZIKV infection risk factors over time to better prepare for potential future outbreaks.

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

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.