Serotype-specific immunity explains the incidence of diseases caused by human enteroviruses

Abstract

Human enteroviruses are a major cause of neurological and other diseases. More than 100 serotypes are known that exhibit unexplained complex patterns of incidence, from regular cycles to more irregular patterns, and new emergences. Using 15 years of surveillance data from Japan (2000-2014) and a stochastic transmission model with accurate demography, we show that acquired serotype-specific immunity can explain the diverse patterns of 18 of the 20 most common serotypes (including Coxsackieviruses, Echoviruses, and Enterovirus-A71). The remaining two serotypes required a change in viral characteristics, including an increase in pathogenicity for Coxsackievirus-A6, which is consistent with its recent global rise in incidence. On the basis of our findings, we are able to predict outbreaks 2 years ahead of time (2015-2016). These results have implications for the impact of vaccines under development.

Fig. 1

Non-polio enterovirus incidence and births in Japan (2000–2014). (A,B) Monthly number of reported enterovirus isolations from January 2000 to December 2014, for: (A) non-polio enteroviruses, and (B) CV-A4. (C) Smoothed annual number of live births. (D) Average wavelet power of the square-root–transformed time-series for CV-A4 showing the emergence of a biennial pattern of incidence.

Fig. 2

Model fit to data. The reported number of monthly isolations (red line) and 10 out of 100 stochastic simulations of the maximum likehood model (gray lines) with the closest match to the data are shown. The estimated number of susceptible individuals (blue lines) is shown in the right y-axis. Maximum likelihood parameter estimation and selection of simulations was based on data for January 2000 to December 2014 (white background), and projections continued out-of-sample for January 2015 to December 2016 (orange background). For CV-A6 and E18, parameter estimation was based on data through to December 2010 and December 2005 respectively, because of the poor fit of the model to the entire period. Parameter values in Table S2.

Fig. 3

Model extensions. Model fit to data for CV-A6 and E18 using the best-fitting model extensions: (A) a five-fold increase in pathogenicity from around 2010 for CV-A6, (B) a change in antigenicity and (C) a 9% increase in transmissibility from around 2006 for E18. Dashed vertical lines indicate the estimated time when the changes occurred. See details in Fig. 2. Parameter values in Table S4.