Exploring the Seasonal Drivers of Varicella Zoster Virus Transmission and Reactivation

Kevin M. Bakker; Marisa C. Eisenberg; Robert Woods; Micaela E. Martinez


Am J Epidemiol. 2021;190(9):1814-1820. 

In This Article


We collated clinical case reports of chickenpox and shingles from Thailand[14] to examine varicella transmission and reactivation (see Figures 1 and 2 and Web Appendix 1 (available at https://doi.org/10.1093/aje/kwab073)). Provincially resolved monthly chickenpox and shingles case reports were downloaded and grouped into 4 regions spanning 2003–2010 (Web Figure 1). Nationwide age-specific annual incidence from this time period was also examined (Web Figures 2–6). We observed seasonal variation in the shingles data and tested whether there was a significant seasonal cycle using Morlet wavelet analyses[20] for each region (Web Figures 7 and 8). To test for potential relationships between ambient UV radiation levels and shingles, we also collated complementary UV covariate data from the US National Center for Atmospheric Research (Web Figures 8–10).[18,19] Thailand demographic data were interpolated from annual population estimates.[6] Case data and covariates were coupled with mechanistic transmission-reactivation models to test hypotheses regarding seasonality of VZV transmission and reactivation, as well as immunological interactions between chickenpox and shingles.

Figure 1.

Numbers of chickenpox and shingles cases in Thailand, 2003–2010. A) Monthly log-detrended numbers of cases of chickenpox (dotted line) and shingles (solid line); B) ultraviolet (UV) radiation levels for the corresponding time period.

Figure 2.

Numbers of chickenpox and shingles cases in Thailand and number of shingles cases according to ambient ultraviolet (UV) radiation levels, Thailand, 2003–2010. A) Box plot of monthly log-detrended numbers of chickenpox cases; B) box plot of monthly log-detrended numbers of shingles cases; C) correlation between monthly log-detrended shingles cases and monthly UV radiation levels (R 2 = 0.467, P = 1.66e−14). Box plots: black line, median value; box borders, interquartile range; bars with dashed lines, 5th and 95th percentiles; open circles, outliers.

Since the mechanisms driving the transmission and reactivation of herpesviruses are not fully understood, multiple biological hypotheses were considered (Web Figure 11 and Web Table 1). We developed a modular compartmental model by redeveloping the classical susceptible-infected-recovered model (Web Figure 11, Web Appendices 2–4).[21] Since the majority of VZV infections are symptomatic with classic chickenpox symptoms, in order to model the manifestation of symptoms, clinical visits, and subsequent reporting to the notification system, we assumed that reported cases were the number of infected individuals scaled by an estimated reporting rate and drawn from a normal distribution.

The model included 3 modular components: component 1, the seasonal driver for transmission/chickenpox; component 2, the seasonal driver for reactivation/shingles; and component 3, immunity-boosting. For components 1 and 2, we tested models with flexible seasonal transmission and/or reactivation using B-splines to capture seasonal variation due to an unspecified driver. We also alternatively tested seasonality driven by the timing of school terms, UV radiation exposure, and past UV radiation exposure. For component 3, we tested the presence/absence and amount of immunity-boosting. The combinations of these options resulted in 14 different model variants (detailed in Web Figure 11). Model variants included those that assumed chickenpox transmission and shingles reactivation had identical or independent seasonal drivers. For the models with B-splines, it is important to note that they were a semiparametric representation of seasonal forcing that could account for flexible seasonal patterns; however, because we implemented the B-splines for seasonal effects, they were unable to account for interannual variation in seasonality.[22] All models were implemented in the R package "pomp" and fitted via maximization by iterated particle filtering[23,24] using national-level chickenpox and shingles data from 2003–2010. Although the chickenpox data were available for 1985–2019, shingles data were only available for 2003–2010.