The epidemiology of rubella in Mexico: seasonality, stochasticity and regional variation

Metcalf, C. Jessica E.; Bjørnstad, Ottar N.; Ferrari, Matt J.; Klepac, Petra; Bharti, Nita; López‐Gatell, Hugo; Grenfell, Bryan T.

doi:10.1017/s0950268810002165

Cited by 44 publications

(65 citation statements)

References 29 publications

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“…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).…”

Section: Resultssupporting

confidence: 79%

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

Wesolowski

Metcalf

Eagle

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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130

116

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Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or crosssectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.rubella | mobile phones | population mobility | Kenya | seasonality S easonal variation in infectious disease incidence is a ubiquitous phenomenon observed for a range of pathogens such as malaria, measles, and influenza (1-7). Understanding and quantifying key mechanisms that drive seasonal variability such as climatic conditions (malaria and influenza) or patterns of human aggregation (measles and influenza) contribute to our fundamental understanding of epidemic dynamics; they also have important implications for evaluating public health measures that may reduce transmission such as vaccination and school closures (8-11).The effectiveness of any public health measure designed to reduce seasonal transmission by modifying patterns of human aggregation and travel will depend on the degree to which transmission depends on population density and movement. 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)]. 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). School terms and holidays versus agricultural movements likely represent the extremes in terms of density-related drivers of transmiss...

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“…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).…”

Section: Resultssupporting

confidence: 79%

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

Wesolowski

Metcalf

Eagle

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

130

116

View full text Add to dashboard Cite

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“…Seasonality in transmission often play an important role in the dynamics of childhood infections (Ferrari et al, 2008; Schenzle, 1984), and is generally observed for rubella (Metcalf et al, 2011a; Metcalf et al, 2011b). In the absence of detailed data, we chose to model seasonal fluctuations as a trigonometric function ( e.g.…”

Section: Model Frameworkmentioning

confidence: 99%

“…However, given no substantial evidence for large variation in transmission for other countries in the region (Metcalf et al, 2011a; Metcalf et al, 2011b), and evidence for transmission predominantly in school children (Fig. 3b), we set β 2 =0.2.…”

Section: Parameterizing the Modelmentioning

confidence: 99%

Structured models of infectious disease: Inference with discrete data

Metcalf

Lessler

Klepac

et al. 2012

Theoretical Population Biology

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The usage of structured population models can make substantial contributions to public health, particularly for infections where clinical outcomes vary over age. There are three theoretical challenges in implementing such analyses: i) developing an appropriate framework that models both demographic and epidemiological transitions; ii) parameterizing the framework, where parameters may be based on data ranging from the biological course of infection, basic patterns of human demography, specific characteristics of population growth, and details of vaccination regimes implemented; and iii) evaluating public health strategies in the face of changing human demography. We illustrate the general approach by developing a model of rubella in Costa Rica. The demographic profile of this infection is a crucial aspect of its public health impact, and we use a transient perturbation analysis to explore the impact of changing human demography on immunization strategies implemented.

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“…5 Data on these effects are limited for Vietnam. Studies using TSIR (Time-series SusceptibleInfectious-Recovered) models applied to district-level rubella data from Mexico, 21 found regional variation in the amount of transmission and the extent to which transmission varied seasonally. As shown in Figures 2-3, the amount of transmission influences both the impact of vaccination and the CRS incidence.…”

Section: Discussionmentioning

confidence: 99%

“…Following previous work, 20 the CRS incidence per 100,000 live births was calculated for 5-year age groups (15)(16)(17)(18)(19)(20)(21)(22)(23)(24), 25-29, 30-34, 35-39 and 40-44 years) for each year during 2000-2050, assuming a 65% risk of a child being born with CRS if infection occurs during the first 16 weeks of pregnancy. The CRS incidence per live birth for each year in the model for mothers in each 5 year age group was then multiplied by the estimated number of live births to calculate the number of CRS cases born each year.…”

Section: Calculating the Crs Incidencementioning

confidence: 99%

Modeling the impact of rubella vaccination in Vietnam

Vynnycky

Yoshida

Huyen

et al. 2015

Human Vaccines & Immunotherapeutics

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Supported by GAVI Alliance, measles-rubella vaccination was introduced in Vietnam in 2014, involving a mass campaign among 1-14 year olds and routine immunization of children aged 9 months. We explore the impact on the incidence of Congenital Rubella Syndrome (CRS) during 2013-2050 of this strategy and variants involving women aged 15-35 years. We use an age and sex-structured dynamic transmission model, set up using recently-collected seroprevalence data from Central Vietnam, and also consider different levels of transmission and contact patterns. If the serological profile resembles that in Central Vietnam, the planned vaccination strategy could potentially prevent 125,000 CRS cases by 2050 in Vietnam, despite outbreaks predicted in the meantime. Targeting the initial campaign at 15-35 year old women with or without children aged 9 months-14 years led to sustained reductions in incidence, unless levels of ongoing transmission were medium-high before vaccination started. Assumptions about contact greatly influenced predictions if the initial campaign just targeted 15-35 year old women and/or levels of ongoing transmission were medium-high. Given increased interest in rubella vaccination, resulting from GAVI Alliance funding, the findings are relevant for many countries.

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The epidemiology of rubella in Mexico: seasonality, stochasticity and regional variation

Cited by 44 publications

References 29 publications

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

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

Structured models of infectious disease: Inference with discrete data

Modeling the impact of rubella vaccination in Vietnam

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