Joseph R Biggs1, Ava Kristy Sy2,3, Oliver J Brady4,5, Adam J Kucharski4,5, Sebastian Funk4,5, Mary Anne Joy Reyes2,3, Mary Ann Quinones2,3, William Jones-Warner6, Yun-Hung Tu6, Ferchito L Avelino7, Nemia L Sucaldito7, Huynh Kim Mai8, Le Thuy Lien8, Hung Do Thai8, Hien Anh Thi Nguyen9, Dang Duc Anh9, Chihiro Iwasaki10, Noriko Kitamura10, Lay-Myint Yoshida10, Amado O Tandoc2, Eva Cutiongco-de la Paz11,12, Maria Rosario Z Capeding3,11, Carmencita D Padilla11,12, Julius Clemence R Hafalla6, Martin L Hibberd6,11,12. 1. Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK. Joseph.Biggs1@lshtm.ac.uk. 2. Department of Virology, Research Institute for Tropical Medicine, Manila, Philippines. 3. Dengue Study Group, Research Institute for Tropical Medicine, Manila, Philippines. 4. Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK. 5. Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK. 6. Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK. 7. Philippine Epidemiology Bureau, Department of Health, Manila, Philippines. 8. Pasteur Institute of Nha Trang, Nha Trang, Vietnam. 9. National Institute of Hygiene and Epidemiology, Hanoi, Vietnam. 10. Paediatric Infectious Diseases Department, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan. 11. Institute of Human Genetics, National Institute of Health, University of the Philippines, Manila, Philippines. 12. Philippine Genome Centre, University of the Philippines, Manila, Philippines.
Abstract
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.
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-DENVIgM/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-ZIKVIgM 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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Authors: Joseph R Biggs; Ava Kristy Sy; Oliver J Brady; Adam J Kucharski; Sebastian Funk; Yun-Hung Tu; Mary Anne Joy Reyes; Mary Ann Quinones; William Jones-Warner; James Ashall; Ferchito L Avelino; Nemia L Sucaldito; Amado O Tandoc; Eva Cutiongco-de la Paz; Maria Rosario Z Capeding; Carmencita D Padilla; Martin L Hibberd; Julius Clemence R Hafalla Journal: Viruses Date: 2021-07-23 Impact factor: 5.048
Authors: Joseph R Biggs; Ava Kristy Sy; James Ashall; Marsha S Santoso; Oliver J Brady; Mary Anne Joy Reyes; Mary Ann Quinones; William Jones-Warner; Amadou O Tandoc; Nemia L Sucaldito; Huynh Kim Mai; Le Thuy Lien; Hung Do Thai; Hien Anh Thi Nguyen; Dang Duc Anh; Chihiro Iwasaki; Noriko Kitamura; Marnix Van Loock; Guillermo Herrera-Taracena; Joris Menten; Freya Rasschaert; Liesbeth Van Wesenbeeck; Sri Masyeni; Sotianingsih Haryanto; Benediktus Yohan; Eva Cutiongco-de la Paz; Lay-Myint Yoshida; Stephane Hue; Maria Rosario Z Capeding; Carmencita D Padilla; R Tedjo Sasmono; Julius Clemence R Hafalla; Martin L Hibberd Journal: PLoS Negl Trop Dis Date: 2022-05-04