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Reliable estimates of subnational vaccination coverage are critical to track progress towards global immunisation targets and ensure equitable health outcomes for all children. However, conflict can limit the reliability of coverage estimates from traditional household-based surveys due to an inability to sample in unsafe and insecure areas and increased uncertainty in underlying population estimates. In these situations, model-based geostatistical (MBG) approaches offer alternative coverage estimates for administrative units affected by conflict. We estimated first- and third-dose diphtheria-tetanus-pertussis vaccine coverage in Borno state, Nigeria, using a spatiotemporal MBG modelling approach, then compared these to estimates from recent conflict-affected, household-based surveys. We compared sampling cluster locations from recent household-based surveys to geolocated data on conflict locations and modelled spatial coverage estimates, while also investigating the importance of reliable population estimates when assessing coverage in conflict settings. These results demonstrate that geospatially-modelled coverage estimates can be a valuable additional tool to understand coverage in locations where conflict prevents representative sampling.

The integration of immunization with other essential health services is among the strategic priorities of the Immunization Agenda 2030 and has the potential to improve the effectiveness, efficiency, and equity of health service delivery. This study aims to evaluate the degree of spatial overlap between the prevalence of children who have never received a dose of the diphtheria–tetanus–pertussis-containing vaccine (no-DTP) and other health-related indicators, to provide insight into the potential for joint geographic targeting of integrated service delivery efforts. Using geospatially modeled estimates of vaccine coverage and comparator indicators, we develop a framework to delineate and compare areas of high overlap across indicators, both within and between countries, and based upon both counts and prevalence. We derive summary metrics of spatial overlap to facilitate comparison between countries and indicators and over time. As an example, we apply this suite of analyses to five countries—Nigeria, Democratic Republic of the Congo (DRC), Indonesia, Ethiopia, and Angola—and five comparator indicators—children with stunting, under-5 mortality, children missing doses of oral rehydration therapy, prevalence of lymphatic filariasis, and insecticide-treated bed net coverage. Our results demonstrate substantial heterogeneity in the geographic overlap both within and between countries. These results provide a framework to assess the potential for joint geographic targeting of interventions, supporting efforts to ensure that all people, regardless of location, can benefit from vaccines and other essential health services.

In many settings, ongoing measles transmission is maintained due to pockets of un- or under-vaccinated individuals even if the critical vaccination threshold is reached nationwide. Therefore, assessing the underlying gaps in measles susceptibility within a population is essential for vaccination programs and measles control efforts. Recently, there have been increased efforts to use geospatial and small area methods to estimate subnational measles vaccination coverage in high-burden settings, such as in Ethiopia. However, the distribution of remaining susceptible individuals, either unvaccinated or having never previously been infected, across age groups and subnational geographies is unknown. In this study, we developed a dynamic transmission model that incorporates geospatial estimates of routine measles vaccination coverage, available data on supplemental immunization activities, and reported cases to estimate measles incidence and susceptibility across time, age, and space. We use gridded population estimates and subnational estimates of routine and supplemental measles vaccination coverage. To account for mixing between age-groups, we used a synthetic contact matrix, and travel times via a friction surface were used in a modified gravity model to account for spatial movement. We explored model fitting using Ethiopia as a case study. To address data-related and statistical challenges, we investigated a range of model parameterization and possible fitting algorithms. The approach with the best performance was a model fitted to case notifications adjusted for case ascertainment by using maximum likelihood estimation with block coordinate descent. This strategy was chosen because many data observations (and likely presence of unquantified uncertainty) yielded a steep likelihood surface, which was challenging to fit using Bayesian approaches. We ran sensitivity analyses to explore variations in vaccine effectiveness and compared patterns of susceptibility across space, time, and age. Substantial heterogeneity in reported measles cases as well as susceptibility persists across ages and second-administrative units. These methods and estimates could contribute towards tailored subnational and local planning to reduce preventable measles burden. However, computational and data challenges would need to be addressed for these methods to be applied on a large scale.

A Correction to this paper has been published: https://doi.org/10.1038/s41591-020-01181-w

I want to thank the chair and the Board for allowing me the indulgence of reading a statement. No one likes being read to. but my purpose is to prevent my rambling and repeating myself throughout the evening in what 1 believe is a reasoned presentation. Although I expect certain statements will be challenged. I sincerely hope we can all restrain emotion and personality clashes on the volatile issue before us.

Super-resolution fluorescence microscopy plays a crucial role in our understanding of cell structure and function by reporting cellular ultrastructure with 20–30 nm resolution. However, this resolution is insufficient to image macro-molecular machinery at work. A path to improve resolution is to image under cryogenic conditions. This substantially increases the brightness of most fluorophores and preserves native ultrastructure much better than chemical fixation. Cryogenic conditions are, however, underutilised because of the lack of compatible high numerical aperture objectives. Here, using a low-cost super-hemispherical solid immersion lens ( super SIL) and a basic set-up we achieve 12 nm resolution under cryogenic conditions, to our knowledge the best yet attained in cells using simple set-ups and/or commercial systems. By also allowing multicolour imaging, and by paving the way to total-internal-reflection fluorescence imaging of mammalian cells under cryogenic conditions, s uper SIL microscopy opens a straightforward route to achieve unmatched resolution on bacterial and mammalian cell samples. Lin Wang et al. present a new super-resolution modality using a super-hemispherical immersion lens. They achieve a 12 nm spatial resolution in cells under cryogenic conditions, which offers the technical means to study bacterial and mammalian cell samples at molecule localisation length-scales.