The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward.

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Appraisal of urbanization trends is limited by the lack of a globally consistent definition of what is meant by urban. This article seeks to identify and explain differences in the definition of “urbanness” as used in two largely distinct research communities. We compare the Global Rural–Urban Mapping Project (GRUMP), which defines urban areas based primarily on satellite imagery of nighttime lights, to the urban classification found in Demographic and Health Surveys (DHS), which relies on the urban definitions of individual countries' national statistical offices. We analyze the distribution of DHS clusters falling within and outside of GRUMP urban extents and examine select characteristics of these clusters (notably, household electrification). Our results show a high degree of agreement between the two data sources on what areas are considered urban; furthermore, when used together, GRUMP and DHS data reveal urban characteristics that are not evident when one data source is used independently. GRUMP urban extents are overwhelmingly medium and large highly electrified localities. DHS clusters that are classified as non‐urban but that fall within GRUMP extents tend to be peri‐urban areas.

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Birth month is broadly predictive of both under-five mortality rates and stunting throughout most of sub-Saharan Africa (SSA). Observed factors, such as mother's age at birth and educational status, are correlated with birth month but are not the main factors underlying the relationship between birth month and child health. Accounting for maternal selection via a fixed-effects model attenuates the relationship between birth month and health in many SSA countries. In the remaining countries, the effect of birth month may be mediated by environmental factors. Birth month effects on mortality typically do not vary across age intervals; the differential mortality rates by birth month were evident in the neonatal period and continued across age intervals. The male-to-female sex-ratio at birth did not vary by birth month, which suggests that in utero exposures are not influencing fetal loss, and therefore, the birth month effects are not likely due to selective survival during the in utero period. In one-third of the sample, the birth month effects on stunting diminished after the age of two years; therefore, some children were able to catch-up. Policies to improve child health should target pregnant women and infants and must take seasonality into account.

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We analyze the impact of birth seasonality (seasonal oscillations in the birth rate) on the dynamics of acute, immunizing childhood infectious diseases. Previous research has explored the effect of human birth seasonality on infectious disease dynamics using parameters appropriate for the developed world. We build on this work by including in our analysis an extended range of baseline birth rates and amplitudes, which correspond to developing world settings. Additionally, our analysis accounts for seasonal forcing both in births and contact rates. We focus in particular on the dynamics of measles. In the absence of seasonal transmission rates or stochastic forcing, for typical measles epidemiological parameters, birth seasonality induces either annual or biennial epidemics. Changes in the magnitude of the birth fluctuations (birth amplitude) can induce significant changes in the size of the epidemic peaks, but have little impact on timing of disease epidemics within the year. In contrast, changes to the birth seasonality phase (location of the peak in birth amplitude within the year) significantly influence the timing of the epidemics. In the presence of seasonality in contact rates, at relatively low birth rates (20 per 1000), birth amplitude has little impact on the dynamics but does have an impact on the magnitude and timing of the epidemics. However, as the mean birth rate increases, both birth amplitude and phase play an important role in driving the dynamics of the epidemic. There are stronger effects at higher birth rates.