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...

Seasonal variation in human mobility is globally ubiquitous and affects the spatial spread of infectious diseases, but the ability to measure seasonality in human movement has been limited by data availability. Here, we use mobile phone data to quantify seasonal travel and directional asymmetries in Kenya, Namibia, and Pakistan, across a spectrum from rural nomadic populations to highly urbanized communities. We then model how the geographic spread of several acute pathogens with varying life histories could depend on country-wide connectivity fluctuations through the year. In all three countries, major national holidays are associated with shifts in the scope of travel. Within this broader pattern, the relative importance of particular routes also fluctuates over the course of the year, with increased travel from rural to urban communities after national holidays, for example. These changes in travel impact how fast communities are likely to be reached by an introduced pathogen.

S erologic studies are crucial for understanding current and future dynamics of the coronavirus disease (COVID-19) pandemic. In the past few months, much discussion about serologic studies and key issues with their design and interpretation has occurred. In this article, we discuss the questions that could be answered with these studies at different points in the epidemic and summarize the features and issues regarding study design, implementation of studies during an ongoing epidemic, and interpretation of the results. Discussion on the use of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serologic studies has largely focused on 2 questions: first, what proportion of a population has been infected?; and second, what proportion of a population is immune to disease or infection? First, for infections that elicit detectable antibody responses, serologic studies can detect past infection regardless of clinical symptoms. This capability is useful for understanding the extent of past transmission (Figure 1, panel A). By linking this information with data on symptomatic cases, severe disease, and death in the same population, these studies can provide information on asymptomatic proportion, and the ratio of infections to severe cases and deaths (i.e., infection fatality ratio). Such data are also useful for calibrating mathematical models. Second, if measured antibody responses correlate with protection, serologic studies can be used to measure the proportion of the population that is immune. This information can be used to guide control policies, help identify populations that are still susceptible to epidemics, target treatment or vaccination trials, and target vaccination when available. Although much discussion around use of serologic testing to inform persons of their serologic status has occurred, crucial distinctions exist between the use of serologic information to estimate population-level versus personlevel immunity. Person-level immunity information

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.

SUMMARYChildhood rubella infection in early pregnancy can lead to fetal death or congenital rubella syndrome (CRS) with multiple disabilities. Reduction of transmission via universal vaccination can prevent CRS, but inadequate coverage may increase CRS numbers by increasing the average age at infection. Consequently, many countries do not vaccinate against rubella. The World Health Organization recommends that for safe rubella vaccination, at least 80% coverage of each birth cohort should be sustained. The nonlinear relationship between CRS burden and infection dynamics has been much studied; however, how the complex interaction between epidemic and demographic dynamics affects minimum safe levels of coverage has not been quantitatively evaluated across scales necessary for a global assessment. We modelled 30-year CRS burdens across epidemiological and demographic settings, including the effect of local interruption of transmission via stochastic fadeout. Necessary minimum vaccination coverage increases markedly with birth and transmission rates, independent of amplitude of seasonal fluctuations in transmission. Susceptible build-up in older age groups following local stochastic extinction of rubella increased CRS burden, indicating that spatial context is important. In low birth-rate settings, 80% routine coverage is a conservative guideline, particularly if supplemented with campaigns and vaccination of women of childbearing age. Where birth and transmission rates are high, immunization coverage must be well above 80% and campaigns may be needed. Policy-makers should be aware of the potential negative effect of local extinction of rubella, since heterogeneity in vaccination coverage will shape extinction patterns, potentially increasing CRS burdens.

Highlights First study exploring seasonality and added activities effects on routine vaccine. Routine immunization decreases in months surrounding vaccination campaigns. The majority of missed measles vaccine doses occurred during the rainy season. Specific birth cohorts are at risk to remain unvaccinated. Seasonal variation in health facility organization may shape vaccination gaps.

Summary 1.Demographic rates such as growth and survival may interact directly as a result of allocation constraints, or indirectly through their relationship with structural characteristics. 2. We explored the relationship between growth and survival in a range of rosetteforming species across different habitats, and investigated possible mechanistic explanations for the patterns we found. 3. Results indicated a negative association between growth and survival in small plants across species in different habitats. There was no relationship for large plants. 4. Relative growth rate (RGR) was positively correlated to specific leaf area (SLA), but unrelated to the percentage biomass allocated to roots. This argues against the hypothesized role of allocation to root mass in mediating the growth-survival trade-off. 5. The pattern of biomass partitioning was compared with the predictions of Enquist & Niklas (2002a) Global allocation rules for patterns of biomass partitioning in seed plants. Science 295 , 1517-1520. In agreement with their predictions, the overall relationship between above-and below-ground biomass was isometric. However, after accounting for species-specific effects it was found that allocation to roots varied widely between species and was size-dependent, suggesting that the conventional statistical analysis (double-log regression) may be insensitive to biologically important sources of variation.

Summary Measles vaccine efficacy is higher at 12 months than 9 months because of maternal immunity, but delaying vaccination exposes the children most vulnerable to measles mortality to infection. We explored how this trade-off changes as a function of regionally varying epidemiological drivers, e.g. demography, transmission seasonality, and vaccination coverage. High birth rates and low coverage both favour early vaccination, and initiating vaccination at 9-11 months, then switching to 12-14 months can reduce case numbers. Overall however, increasing the age-window of vaccination decreases case numbers relative to vaccinating within a narrow age-window (e.g. 9-11 months). The width of the age-window that minimizes mortality varies as a function of birth rate, vaccination coverage and patterns of access to care. Our results suggest that locally age-targeted strategies, at both national and sub-national scales, tuned to local variation in birth rate, seasonality, and access to care may substantially decrease case numbers and fatalities for routine vaccination