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.

A game changer for vaccine development

A vaccine that prevents transmission of infection is urgently needed in the fight against tuberculosis (TB). Results of clinical trials have been disappointing. Major problems include lack of biomarkers and understanding of the mechanisms of disease and protection. A more fundamental problem is that the scientific community seldom recognizes that primary and post-primary TB are distinct disease entities. Nearly all vaccine candidates have been designed and tested in models of primary TB, while transmission of infection is mediated by post-primary TB. Post-primary TB is seldom studied because no animal develop complete symptoms of the disease as it exists in humans. Nevertheless, mice, guinea pigs and rabbits all develop infections that at certain points appear to be models of human post-primary TB. Slowly progressive pulmonary TB in immunocompetent mice is an example. It is characterized by an alveolitis with infected foamy macrophages that have multiple characteristics of the human disease. We demonstrated that inclusion of an immune modulating agent, lactoferrin, with a BCG vaccine in this model induced a sustained reduction in lung pathology, but not numbers of organisms in tissue. Since the animals die of expanding pathology, this demonstrates the feasibility of using selected animal models for studies of vaccines against post-primary TB.