Using population-level data in Thailand, we 1) revealed out-of-phase coseasonality of chickenpox transmission and shingles reactivation, 2) determined that chickenpox transmission and shingles reactivation are driven by separate processes, and 3) found a strong correlation between seasonal UV radiation and shingles incidence.
Thailand had seasonal peaks in both chickenpox and shingles, with a 3-month lag in peak incidence (Figures 1 and 2, Web Figure 8). Previous studies of shingles reactivation at smaller scales (i.e., hospitals and towns) indicated a lack of seasonality in reactivation.[26–32] The absence of observed shingles seasonality in previous studies, and in the southern region in our study, may be due to multiple factors. There could be regional differences in seasonality, with some regions lacking seasonal variation in the reactivation rate. For example, if an environmental exposure, such as UV radiation, is the mechanism driving shingles reactivation, the exposure may not vary seasonally in all locations, and/or it may not affect a sufficiently large portion of the population. So if UV radiation was important but most people worked indoors, the seasonal variation in UV radiation would not have the same impact as it would in a population that primarily worked outdoors. Alternatively, it could be that seasonal variation in reactivation exists across regions but because of the low amplitude of the variation it is difficult to distinguish from noise in locations with low incidence, thus reducing the statistical power to detect seasonality. In our study, it became more difficult to identify seasonality as we broke the data into regions (Web Figures 1, 7, 9, 10, 13, and 14), which may have been due to demographic stochasticity.
Shingles seasonality displayed a latitudinal gradient (Web Figures 1, 7, 9, and 10), similar to other human diseases.[6,33–37] We identified a strong correlation between shingles reactivation and UV radiation (both lagged and unlagged), providing evidence that UV radiation may have a biological impact on shingles reactivation, similar to the effect it has on herpes simplex virus (Figures 1 and 2, Web Figures 8–10, Web Table 1). If the positive correlation between UV radiation and shingles is due to UV effects on the immune system, then the observed lag could be due to the time it takes for UV radiation exposure to effect immunity (either systemically or locally in the skin and peripheral neurons where the virus is latent) and/or the time between the start of the cellular reactivation pathway and the appearance and reporting of symptomatic illness (Figure 3D). There may also be a reporting lag between the time of reactivation, symptom appearance, and a clinic/doctor visit.
To better understand the dynamics of the VZV system, we fitted multiple transmission-reactivation models to the data from Thailand. The best-fitting model revealed distinct drivers of chickenpox transmission and shingles reactivation (Figure 3, Web Table 1). The fitted model had a major peak in VZV transmission from December to February and a smaller peak in August–September, both of which mirrored Thailand's school terms. Low transmission was estimated in April–June and October–November, both of which were preceded by weeks when students were on vacation (Figure 3B). However, implementing the school terms as a step function for transmission did not improve model fits (Web Table 1). This is probably because the transmission rate varies among school sessions (i.e., the May–September session has lower transmission than the November–February session). There is also heterogeneity in school terms across Thailand that we were unable to capture. Students in many private international schools go to school year-round or have term breaks in different months than public schools. Additionally, school terms may not accurately reflect contact patterns in Thailand because of heterogeneous social mixing patterns across this culturally and geographically diverse country.
Note that the best-fitting model did not include boosting of immunity by VZV transmission. This does not rule out the possibility that immunity-boosting exists; it simply shows that in our susceptible-infected-latent-reactivated model it was not required in order to capture the dynamics of chickenpox and shingles in Thailand. In fact, immunity-boosting may exist in our system and be captured by the model in the B-spline used to parameterize the reactivation rate. Thus, we must be careful when interpreting how VZV transmission affects shingles. Previous epidemiologic studies have shown a decrease in immunity-boosting due to demographic shifts in the population structure via decreased birth rates.[40,41] In Thailand, a massive demographic shift has been occurring over the last 4 decades through a decrease in births, which has previously been shown to affect the epidemiology of other infectious diseases in the country. With a proven, safe, and effective VZV vaccine that protects against chickenpox and shingles and with clinical trials for other herpesvirus vaccines currently under way, an understanding of the biology underpinning herpesvirus reactivation may aid in the control of these diseases. We believe reactivation seasonality is an important phenomenon, because infectious disease transmission is largely assumed to be the driver of infectious disease seasonality. For shingles, however, the disease is not tied to a transmission event; it is due to reactivation of latent virus acquired decades in the past. We hypothesize that shingles seasonality is tied to an underlying seasonal susceptibility that may be important for public health more generally; but at the very least, these findings should inform shingles vaccination policy by identifying the "shingles season" as a time of vulnerability in the population.
UV, ultraviolet; VZV, varicella zoster virus.
The work of K.M.B. was funded by National Institutes of Health (NIH) awards F32AI134016, KL2TR002241, and UL1TR002240. The work of M.E.M. was funded by the NIH Director's Early Independence Award (award DP5OD023l00). Computational resources were provided under NIH awards U01GM110712 and U24GM110707 and National Science Foundation awards ACI-1548562 and ACI-1445606.
All data and software code are freely available at https://www.kevinmbakker.com/data.html.
Am J Epidemiol. 2021;190(9):1814-1820. © 2021 Oxford University Press