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Household immunity and individual risk of infection with dengue virus in a prospective, longitudinal cohort study

Nature Microbiology volume 9, pages 274–283 (2024)Cite this article

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Abstract

Although it is known that household infections drive the transmission of dengue virus (DENV), it is unclear how household composition and the immune status of inhabitants affect the individual risk of infection. Most population-based studies to date have focused on paediatric cohorts because more severe forms of dengue mainly occur in children, and the role of adults in dengue transmission is understudied. Here we analysed data from a multigenerational cohort study of 470 households, comprising 2,860 individuals, in Kamphaeng Phet, Thailand, to evaluate risk factors for DENV infection. Using a gradient-boosted regression model trained on annual haemagglutination inhibition antibody titre inputs, we identified 1,049 infections, 90% of which were subclinical. By analysing imputed infections, we found that individual antibody titres, household composition and antibody titres of other members in the same household affect an individual’s risk of DENV infection. Those individuals living in households with high average antibody titres, or households with more adults, had a reduced risk of infection. We propose that herd immunity to dengue acts at the household level and may provide insight into the drivers of the recent change in the shifting age distribution of dengue cases in Thailand.

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Fig. 1: Cohort data summary (n = 11,131).
Fig. 2: Model performance and fit.
Fig. 3: Incidence, proportion of primary infections and ratio of subclinical to symptomatic cases.
Fig. 4: Household composition and risk of infection across n = 11,131 intervals.
Fig. 5: Impact of pre-interval DENV titres and probability of infection and symptoms across n = 11,131 intervals.

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Data availability

The dataset analysed in this study is available at https://github.com/marcohamins/role-of-HH-immunity.

Code availability

All code associated with the work is available at https://github.com/marcohamins/role-of-HH-immunity.

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Acknowledgements

We thank the data collection team as well as the children and adults involved in the study for all their efforts. We were supported in this work by the following: the National Institutes of Health (NIH) Grant 5P01AI034533-22: entire team; Military Infectious Disease Research Program (MIDRP): D.B., S.F., A.F. and K.B.A.; NIH 1R01AI175941-01: entire team; European Research Council 804744: H.S.; and NIH 1R35GM138361-01: M.H.-P. and I.R.-B. The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the paper.

Author information

Author notes

  1. These authors jointly supervised this work: Isabel Rodriguez-Barraquer, Kathryn B. Anderson.

Authors and Affiliations

  1. Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

    Marco Hamins-Puértolas & Isabel Rodriguez-Barraquer

  2. Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand

    Darunee Buddhari, Stefan Fernandez & Aaron Farmer

  3. Department of Genetics, University of Cambridge, Cambridge, UK

    Henrik Salje

  4. Department of Biology, University of Florida, Gainesville, FL, USA

    Henrik Salje & Derek A. T. Cummings

  5. Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA

    Derek A. T. Cummings

  6. Ministry of Public Health, Nonthaburi, Thailand

    Surachai Kaewhiran, Direk Khampaen & Sopon Iamsirithaworn

  7. Institute for Immunology and Informatics, Department of Cell and Molecular Biology, University of Rhode Island, Providence, RI, USA

    Anon Srikiatkhachorn & Alan L. Rothman

  8. Faculty of Medicine, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Anon Srikiatkhachorn

  9. Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, NY, USA

    Adam Waickman, Stephen J. Thomas, Timothy Endy & Kathryn B. Anderson

  10. Institute for Global Health and Translational Sciences, SUNY Upstate Medical University, Syracuse, NY, USA

    Stephen J. Thomas & Kathryn B. Anderson

  11. Coalition for Epidemic Preparedness Innovations (CEPI), Washington DC, USA

    Timothy Endy

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Contributions

The study was conceived and designed by D.B., H.S., D.A.T.C., S.F., A.F., A.L.R., T.E., I.R.-B. and K.B.A. The data were collected by D.B., S.F., S.K., D.K., S.I. and A.S. The analysis and interpretation of results was performed by M.H.-P., H.S., D.A.T.C., A.F., S.J.T., A.W., A.L.R., T.E., I.R.-B. and K.B.A. The draft manuscript was prepared by M.H.-P., I.R.-B. and K.B.A. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Kathryn B. Anderson.

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Extended data

Extended Data Fig. 1 Cohort age distribution and various characteristics broken apart by sex.

(a) Age pyramid of enrolled subjects in five-year bins separated by sex (n = 3539). (b) Seroprevalence curve for subjects enrolled before 2017 in solid line, the analysis was conducted separately by sex. Points are mean seroprevalence in each fifth percentile for non-newborns under 30 years old (n = 6197). Confidence bounds are found using a basic nonparametric bootstrap. (c) Pre-interval DENV titers averaged across all four serotypes and their impact on probability of infection. (d) Relationship between pre-interval DENV titers averaged across all four serotypes and age. Both analyses included n = 11131 intervals with points and intervals representing the mean and 95% confidence interval. Shaded regions represent the 95% confidence intervals of fits.

Extended Data Fig. 2 Model training information and resulting probability of infection distribution.

(a) Confusion matrix for XGBoost model on training data. Reference values are found as outlined in the Methods section titled Training data while prediction values are found using the approach outlined in the Model fit section of the methods section. (b) Histogram of model predictions for probability of infection between sequential blood draws conducted on the full dataset.

Extended Data Fig. 3 Pre and post interval HAI titers by DENV serotype and JEV.

Pre and post interval HAI titers for all DENV serotypes and JEV grouped by age at post interval age and colored by whether the model predicted a DENV infection. Yellow and blue dots represent points that were or were not identified as infections respectively by the model while black and red points represent a similar dichotomy but in laboratory confirmed seroconversions. A four-fold increase in titers between samples is represented by the black diagonal line.

Extended Data Fig. 4 Sensitivity analysis on household related factors when utilizing the four-fold increase in DENV antibodies.

Sensitivity analysis on how household composition (a, b), infection history (c) and immunity (d) impact risk of infection when infections are defined to occur if there is a four-fold increase in antibody levels between paired serological samples (n = 11131). (a) Odds ratio for the number of total individuals in various age bins (newborn [NB], from 1–5 years old, from 5 to 18, and those 18 years or older [GE18]) defined at the time of the post-interval sample. (b) Odds ratio for the number of males and females of various age bins (newborn [NB], from 1–5 years old, from 5 to 18, and those 18 years or older [GE18]) defined at the time of the post-interval sample. (c) Previous interval’s attack rate (AR) and subsequent odds ratio of infection risk relative to having no infections in the previous interval. (d) Geometric mean of DENV HAI titers for the rest of the household and subsequent odds ratio of infection risk relative to having an average household HAI titer under 40. All models are adjusted for household random effects, individual pre-interval titers, as well as the year and month of post-interval sample. The vertical dashed line represents an aOR of 1 (no significant impact on risk).

Extended Data Fig. 5 Sensitivity analysis on household related factors in households with high sampling rates.

Sensitivity analysis on how household composition (a, b), infection history (c) and immunity (d) impact risk of infection in households where more than 80% of samples are recorded (n = 6435). (a) Odds ratio for the number of total individuals in various age bins (newborn [NB], from 1–5 years old, from 5 to 18, and those 18 years or older [GE18]) defined at the time of the post-interval sample. (b) Odds ratio for the number of males and females of various age bins (newborn [NB], from 1–5 years old, from 5 to 18, and those 18 years or older [GE18]) defined at the time of the post-interval sample. (c) Previous interval’s attack rate (AR) and subsequent odds ratio of infection risk relative to having no infections in the previous interval. (d) Geometric mean of DENV HAI titers for the rest of the household and subsequent odds ratio of infection risk relative to having an average household HAI titer under 40. All models are adjusted for household random effects, individual pre-interval titers, as well as the year and month of post-interval sample. The vertical dashed line represents an aOR of 1 (no significant impact on risk).

Extended Data Fig. 6 Sensitivity analysis on household related factors in seronaive individuals.

Sensitivity analysis on how household composition (a, b), infection history (c) and immunity (d) impact risk of infection in individuals who were seronaive at the start of an interval (n = 2066). (a) Odds ratio for the number of total individuals in various age bins (newborn [NB], from 1–5 years old, from 5 to 18, and those 18 years or older [GE18]) defined at the time of the post-interval sample. (b) Odds ratio for the number of males and females of various age bins (newborn [NB], from 1–5 years old, from 5 to 18, and those 18 years or older [GE18]) defined at the time of the post-interval sample. (c) Previous interval’s attack rate (AR) and subsequent odds ratio of infection risk relative to having no infections in the previous interval. (d) Geometric mean of DENV HAI titers for the rest of the household and subsequent odds ratio of infection risk relative to having an average household HAI titer under 40. All models are adjusted for household random effects, individual pre-interval titers, as well as the year and month of post-interval sample. The vertical dashed line represents an aOR of 1 (no significant impact on risk).

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Hamins-Puértolas, M., Buddhari, D., Salje, H. et al. Household immunity and individual risk of infection with dengue virus in a prospective, longitudinal cohort study. Nat Microbiol 9, 274–283 (2024). https://doi.org/10.1038/s41564-023-01543-3

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