Interactions between serotypes of dengue highlight epidemiological impact of cross-immunity

Abstract

Dengue, a mosquito-borne virus of humans, infects over 50 million people annually. Infection with any of the four dengue serotypes induces protective immunity to that serotype, but does not confer long-term protection against infection by other serotypes. The immunological interactions between serotypes are of central importance in understanding epidemiological dynamics and anticipating the impact of dengue vaccines. We analysed a 38-year time series with 12 197 serotyped dengue infections from a hospital in Bangkok, Thailand. Using novel mechanistic models to represent different hypothesized immune interactions between serotypes, we found strong evidence that infection with dengue provides substantial short-term cross-protection against other serotypes (approx. 1-3 years). This is the first quantitative evidence that short-term cross-protection exists since human experimental infection studies performed in the 1950s. These findings will impact strategies for designing dengue vaccine studies, future multi-strain modelling efforts, and our understanding of evolutionary pressures in multi-strain disease systems.

Keywords: cross-protection; dengue; infectious disease modelling; time-series models.

Figure 1.

The time series of monthly serotype-specific case counts of dengue from Queen Sirikit National Institute of Child Health in Bangkok, Thailand. This facility is a paediatric healthcare facility which serves as a reference hospital for dengue in Bangkok. The counts shown here represent the total number of cases, both primary and secondary infections.

Figure 2.

Estimated parameters and model characteristics for exponential and fixed duration models of cross-protection. Model results shown in bold indicate that the model showed a statistically significant improvement over a null model that included no form of cross-protection and no serotype-specific or seasonal transmission parameters. Based on AIC and likelihood ratio tests, models E_c and F_c showed the most improvement over the null model and also showed significantly better fit to the data than a model that did not include cross-protection but did include seasonal transmission. The point estimates of the average duration of cross-protection are shown by the vertical tick mark and the 95% CIs are shown with horizontal lines.

Figure 3.

Profile likelihood surfaces for the best exponential models (a) and fixed duration models (b). In panel (a), the maximum-likelihood and 95% CI for λ are shown by the dashed vertical lines. In (b), the axes index the two parameters of a fixed duration distribution of cross-protection. The fraction of the population that experiences cross-protection is δ and k is the duration of cross-protection. The two lightest regions represent the 90% and 95% likelihood confidence regions. The confidence region for the average duration (which is calculated as k · δ) represents the range of average durations contained in the respective confidence region. (Online version in colour.)

Figure 4.

Estimated bi-weekly transmission parameters from the best-fitting exponential model of cross-protection. Temperature and rainfall data provide a comparison with the observed seasonal trends. (a) Displays mean-centred estimated bi-weekly transmission parameters. The transmission across all serotypes (from model E_c) is shown in the solid black line with the grey shaded area representing the 95% CI. Serotype-specific transmission parameters (from model E_d) are shown in the thinner coloured lines. Transmission parameters from the corresponding fixed duration models showed similar patterns. (b) Median monthly precipitation in millimetre, at one location in Bangkok between 1981 and 2002. (c) Median monthly temperature in Bangkok between 1983 and 1996. The shaded areas in (b,c) Represent the 10th and 90th percentiles of the observed monthly data. (d) Plots the bi-weekly transmission parameters against the average bi-weekly precipitation and a ‘line of best fit’ is shown. (e) Plots the bi-weekly transmission parameters against the average bi-weekly temperature and a ‘line of best fit’ is shown. For (d,e), Cubic splines were used to interpolate the monthly data in (b,c) into bi-weekly data. In (d,e), the points are the graph are indicated by the corresponding bi-week (numbered 1–26). (Online version in colour.)