Rubella metapopulation dynamics and importance of spatial coupling to the risk of congenital rubella syndrome in Peru

Abstract

Rubella is generally a mild childhood disease, but infection during early pregnancy may cause spontaneous abortion or congenital rubella syndrome (CRS), which may entail a variety of birth defects. Consequently, understanding the age-structured dynamics of this infection has considerable public health value. Vaccination short of the threshold for local elimination of transmission will increase the average age of infection. Accordingly, the classic concern for this infection is the potential for vaccination to increase incidence in individuals of childbearing age. A neglected aspect of rubella dynamics is how age incidence patterns may be moulded by the spatial dynamics inherent to epidemic metapopulations. Here, we use a uniquely detailed dataset from Peru to explore the implications of this for the burden of CRS. Our results show that the risk of CRS may be particularly severe in small remote regions, a prediction at odds with expectations in the endemic situation, and with implications for the outcome of vaccination. This outcome results directly from the metapopulation context: specifically, extinction–re-colonization dynamics are crucial because they allow for significant leakage of susceptible individuals into the older age classes during inter-epidemic periods with the potential to increase CRS risk by as much as fivefold.

Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

“…1 A and B and Figs. S1 and S2) (15,(22)(23)(24)(25). Similar patterns have been reported in other countries in East Africa, suggesting regional consistency (26).…”

“…To infer the unknown parameters (β s , I 0 and S 0 , and m) we can link this to data by the relationship I ðrÞ T = BinomialðI t + I t+1 , ρÞ, where ρ is the reporting rate and I ðrÞ T is the reported number of infected cases at a monthly time scale; the sum is taken over the previous 2 wk. We have previously found that low reporting rates result in strongly downward biased estimates of m, which result in unrealistic dynamics (15); this also proved to be the case here. For this analysis, we therefore fixed m at a consensus value of 0.97.…”

“…Rubella incidence is frequently underreported, as a result of the mild, even asymptomatic manifestation of the infection (15). Evidence that mobile phone data can capture population fluctuations that relate to rubella transmission provides grounds for optimism that patterns of incidence might be inferred indirectly.…”

“…Direct measures of population travel are rare (2, 4, 12, 13). As a result, proxy measures such as school terms and rainfall patterns have been used (1,9,(13)(14)(15). Term time forcing, where school-driven aggregation leads to seasonal peaks of transmission for directly transmitted immunizing infections such as measles, mumps, and rubella, has been observed in many high-income countries (8,16,17) [England and Wales (8), Peru (15), and Denmark (17)].…”

“…As a result, proxy measures such as school terms and rainfall patterns have been used (1,9,(13)(14)(15). Term time forcing, where school-driven aggregation leads to seasonal peaks of transmission for directly transmitted immunizing infections such as measles, mumps, and rubella, has been observed in many high-income countries (8,16,17) [England and Wales (8), Peru (15), and Denmark (17)]. On the other end of the spectrum in the low-income, predominantly agricultural context of Niger (13), analysis of night lights indicates that peaks in transmission reflect population changes resulting from annual mass migrations of individuals between agricultural areas to cities in the dry season (1).…”