Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria

Characterizing mobility patterns and malaria risk factors in semi-nomadic populations of Northern Kenya

While many studies have characterized mobility patterns and disease dynamics of settled populations, few have focused on more mobile populations. Highly mobile groups are often at higher disease risk due to their regular movement that may increase the variability of their environments, reduce their access to health care, and limit the number of intervention strategies suitable for their lifestyles. Quantifying the movements and their associated disease risks will be key to developing interventions more suitable for mobile populations. Turkana, Kenya is an ideal setting to characterize these relationships. While the vast, semi-arid county has a large mobile population (>60%) and was recently shown to have endemic malaria, the relationship between mobility and malaria risk in this region has not yet been defined. Here, we worked with 250 semi-nomadic households from four communities in Central Turkana to 1) characterize mobility patterns of travelers and 2) test the hypothesis that semi-nomadic individuals are at greater risk of malaria exposure when migrating with their herds than when staying at their semi-permanent settlements. Participants provided medical and travel histories, demographics, and a dried blood spot for malaria testing before and after the travel period. Further, a subset of travelers was given GPS loggers to document their routes. Four travel patterns emerged from the logger data, Long Term, Transient, Day trip, and Static, with only Long Term and Transient trips being associated with malaria cases detected in individuals who carried GPS devices. After completing their trips, travelers had a higher prevalence of malaria than those who remained at the household (9.2% vs 4.4%), regardless of gender and age. These findings highlight the need to develop intervention strategies amenable to mobile lifestyles that can ultimately help prevent the transmission of malaria.

Characterizing mobility patterns and malaria risk factors in semi-nomadic populations of Northern Kenya

While many studies have characterized mobility patterns and disease dynamics of individuals from settled populations, few have focused on more mobile populations. Highly mobile groups are often at higher disease risk due to their regular movement that may increase the variability of their environments, reduce their access to health care, and limit the number of intervention strategies suitable for their lifestyles. Quantifying the movements and their associated disease risks will be key to developing intervention strategies more suitable for mobile populations. Here, we worked with four semi-nomadic communities in Central Turkana, Kenya to 1) characterize mobility patterns of travelers from semi-nomadic communities and 2) test the hypothesis that semi-nomadic individuals are at greater risk of exposure to malaria during seasonal migrations than when staying at their semi-permanent settlements. From March-October, 2021, we conducted a study in semi-nomadic households (n=250) where some members traveled with their herd while others remained at the semi-permanent settlement. Participants provided medical and travel histories, demographics, and a dried blood spot for malaria testing before and after the travel period. Further, a subset of travelers was given GPS loggers to document their routes. Four travel patterns emerged from the logger data, Long Term, Transient, Day trip, and Static, with only Long Term and Transient trips being associated with malaria cases detected in individuals who carried GPS devices. After completing their trips, travelers had a higher prevalence of malaria than those who remained at the household (9.2% vs 4.4%), regardless of gender, age group, and catchment area. These findings highlight the need to develop intervention strategies amenable to mobile lifestyles that can ultimately help prevent the transmission of malaria.

Assessing demographic and economic vulnerabilities to sea level rise in Bangladesh via a nighttime light-based cellular automata model

The Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6) forecasts a sea level rise (SLR) of up to 2 m by 2100, which poses significant risks to regional geomorphology. As a country with a rapidly developing economy and substantial population, Bangladesh confronts unique challenges due to its extensive floodplains and 720 km-long Bay of Bengal coastline. This study uses nighttime light data to investigate the demographic repercussions and potential disruptions to economic clusters arising from land inundation attributable to SLR in the Bay of Bengal. By using geographical information system (GIS)-based bathtub modeling, this research scrutinizes potential risk zones under three selected shared socioeconomic pathway (SSP) scenarios. The analysis anticipates that between 0.8 and 2.8 thousand km2 of land may be inundated according to the present elevation profile, affecting 0.5-2.8 million people in Bangladesh by 2150. Moreover, artificial neural network (ANN)-based cellular automata modeling is used to determine economic clusters at risk from SLR impacts. These findings emphasize the urgency for land planners to incorporate modeling and sea inundation projections to tackle the inherent uncertainty in SLR estimations and devise effective coastal flooding mitigation strategies. This study provides valuable insights for policy development and long-term planning in coastal regions, especially for areas with a limited availability of relevant data.

Sex and age as determinants of the seroprevalence of anti-measles IgG among European healthcare workers: A systematic review and meta-analysis

INTRODUCTION

The international literature shows good evidence of a significant rate of measles susceptibility among healthcare workers (HCWs). As such, they are an important public health issue.

METHODS

We conducted a systematic review and meta-analysis to estimate the prevalence of susceptible HCWs in EU/EEA countries and in the UK and to explore the characteristics (sex and age differences) and management of those found to be susceptible.

RESULTS

Nineteen studies were included in the meta-analysis. The prevalence of measles-susceptible HCWs was 13.3% (95 %CI: 10.0-17.0%). In a comparison of serosusceptible female vs. male HCWs, the RR was 0.92 (95 %CI = 0.83-1.03), and in a comparison of age classes (born after vs. before 1980) the RR was 2.78 (95 %CI = 2.20-3.50). The most recent studies proposed the mandatory vaccination of HCWs.

DISCUSSION

According to our meta-analysis, the prevalence of serosusceptible European HCWs is 13%; HCWs born in the post-vaccination era seem to be at higher risk. Healthcare professionals susceptible to measles are a serious epidemiological concern. Greater efforts should therefore be made to identify those who have yet to be vaccinated and actively encourage their vaccination.

Detection of the Seasonally Activated Rural Areas

Rural areas have been usually observed through agriculture; however, today, it is broadened with various activities. In Serbia, it has been characterized by unbalanced development, which has led to a declining vitality and depopulation. The main goal of this research is detection of seasonally activated rural areas in Serbia, expressed through the population fluctuation, temporary settlement, or occasional use of residential and economic facilities, and identification of spatial patterns of seasonal use. This research applied an innovative proxy—nighttime lights (NTL)—as a complementary tool to statistical analyses, which are conducted in the GIS environment. The calculation encompassed two seasonality coefficients: one based on the NTL and the second based on statistical data on tourist turnover. The spatial frame applies settlement level and micro level (pixels), while temporal includes monthly values for the period 2015–2019. The obtained results highlight tourist activity as the main cause of seasonal activation of rural areas. The largest seasonal fluctuations were registered in mountain areas and spa resorts. For mountain areas, the highest seasonality is in the winter months (peak—January/February), and lowest is in the summer season. The seasonal character of spa centers indicates the similar trend, generally less pronounced (peak—January), however, with higher seasonality during the summer.

A review of models applied to the geographic spread of Zika virus

In recent years, Zika virus (ZIKV) has expanded its geographic range and in 2015–2016 caused a substantial epidemic linked to a surge in developmental and neurological complications in newborns. Mathematical models are powerful tools for assessing ZIKV spread and can reveal important information for preventing future outbreaks. We reviewed the literature and retrieved modelling studies that were developed to understand the spatial epidemiology of ZIKV spread and risk. We classified studies by type, scale, aim and applications and discussed their characteristics, strengths and limitations. We examined the main objectives of these models and evaluated the effectiveness of integrating epidemiological and phylogeographic data, along with socioenvironmental risk factors that are known to contribute to vector–human transmission. We also assessed the promising application of human mobility data as a real-time indicator of ZIKV spread. Lastly, we summarised model validation methods used in studies to ensure accuracy in models and modelled outcomes. Models are helpful for understanding ZIKV spread and their characteristics should be carefully considered when developing future modelling studies to improve arbovirus surveillance.

It's risky to wander in September: Modelling the epidemic potential of Rift Valley fever in a Sahelian setting

Estimating the epidemic potential of vector-borne diseases, along with the relative contribution of underlying mechanisms, is crucial for animal and human health worldwide. In West African Sahel, several outbreaks of Rift Valley fever (RVF) have occurred over the last decades, but uncertainty remains about the conditions necessary to trigger these outbreaks. We use the basic reproduction number (R₀) as a measure of RVF epidemic potential in northern Senegal, and map its value in two distinct ecosystems, namely the Ferlo and the Senegal River delta and valley. We consider three consecutive rainy seasons (July-November 2014, 2015 and 2016) and account for several vector and animal species. We parametrize our model with estimates of Aedes vexans arabiensis, Culex poicilipes, Culex tritaeniorhynchus, cattle, sheep and goat abundances. The impact of RVF virus introduction is assessed every week over northern Senegal. We highlight September as the period of highest epidemic potential in northern Senegal, resulting from distinct dynamics in the two study areas. Spatially, in the seasonal environment of the Ferlo, we observe that high-risk locations vary between years. We show that decreased vector densities do not greatly reduce R₀ and that cattle immunity has a greater impact on reducing transmission than small ruminant immunity. The host preferences of vectors and the temperature-dependent time interval between their blood meals are crucial parameters needing further biological investigations.

Investigating persistent measles dynamics in Niger and associations with rainfall

Measles is a major cause of child mortality in sub-Saharan Africa. Current immunization strategies achieve low coverage in areas where transmission drivers differ substantially from those in high-income countries. A better understanding of measles transmission in areas with measles persistence will increase vaccination coverage and reduce ongoing transmission. We analysed weekly reported measles cases at the district level in Niger from 1995 to 2004 to identify underlying transmission mechanisms. We identified dominant periodicities and the associated spatial clustering patterns. We also investigated associations between reported measles cases and environmental drivers associated with human activities, particularly rainfall. The annual and 2-3-year periodicities dominated the reporting data spectrum. The annual periodicity was strong with contiguous spatial clustering, consistent with the latitudinal gradient of population density, and stable over time. The 2-3-year periodicities were weaker, unstable over time and had spatially fragmented clustering. The rainy season was associated with a lower risk of measles case reporting. The annual periodicity likely reflects seasonal agricultural labour migration, whereas the 2-3-year periodicity potentially results from multiple mechanisms such as reintroductions and vaccine coverage heterogeneity. Our findings suggest that improving vaccine coverage in seasonally mobile populations could reduce strong measles seasonality in Niger and across similar settings.

Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications

After many decades of vaccination, measles epidemiology varies greatly between and within countries. National immunization programs are therefore encouraged to conduct regular situation analyses and to leverage models to adapt interventions to local needs. Here, we review applications of models to develop locally tailored interventions to support control and elimination efforts. In general, statistical and semi-mechanistic transmission models can be used to synthesize information from vaccination coverage, measles incidence, demographic, and/or serological data, offering a means to estimate the spatial and age-specific distribution of measles susceptibility. These estimates complete the picture provided by vaccination coverage alone, by accounting for natural immunity. Dynamic transmission models can then be used to evaluate the relative impact of candidate interventions for measles control and elimination and the expected future epidemiology. In most countries, models predict substantial numbers of susceptible individuals outside the age range of routine vaccination, which affects outbreak risk and necessitates additional intervention to achieve elimination. More effective use of models to inform both vaccination program planning and evaluation requires the development of training to enhance broader understanding of models and where feasible, building capacity for modelling in-country, pipelines for rapid evaluation of model predictions using surveillance data, and clear protocols for incorporating model results into decision-making.

On-Orbit Signal-to-Noise Ratio Test Method for Night-Light Camera in Luojia 1-01 Satellite Based on Time-Sequence Imagery

Night-light remote sensing imaging technologies have increasingly attracted attention with the development and application of focal plane arrays. On-orbit signal-to-noise ratio (SNR) test is an important link to evaluate night-light camera’s radiometric performance and the premise for quantitative application of remote sensing imageries. Under night-light illumination conditions, the illuminance of ground objects is very low and varies dramatically, the spatial uniformity of each pixel’s output cannot be guaranteed, and thus the traditional on-orbit test methods represented by variance method are unsuitable for low-resolution night-light cameras. To solve this problem, we proposed an effective on-orbit SNR test method based on consecutive time-sequence images that including the same objects. We analyzed the radiative transfer process between night-light camera and objects, and established a theoretical SNR model based on analysis of the generation and main sources of signal electrons and noise electrons. Finally, we took Luojia 1-01 satellite, the world’s first professional night-light remote sensing satellite, as reference and calculated the theoretical SNR and actual on-orbit SNR using consecutive images captured by Luojia 1-01 satellite. The actual results show the similar characteristics as theoretical results, and are higher than the theoretical results within the reasonable error tolerance, which fully guarantee the detection ability of night-light camera and verify the validity of this time-sequence-based method.

A high-resolution gridded dataset to assess electrification in sub-Saharan Africa

Spatially explicit data on electricity access and use are essential for effective policy-making and infrastructure planning in low-income, data-scarce regions. We present and validate a 1-km resolution electricity access dataset covering sub-Saharan Africa built on gridded nighttime light, population, and land cover data. Using light radiance probability distributions, we define electricity consumption tiers for urban and rural areas and estimate the by-tier split of consumers living in electrified areas. The approach provides new insight into the spatial distribution and temporal evolution of electricity access, and a measure of its quality beyond binary access. We find our estimates to be broadly consistent with recently published province- and national-level statistics. Moreover, we demonstrate consistency between the estimated electricity access quality indicators and survey-based consumption levels defined in accordance with the World Bank Multi-Tier Framework. The dataset is readily reproduced and updated using an open-access scientific computing framework. The data and approach can be applied for improving the assessment of least-cost electrification options, and examining links between electricity access and other sustainable development objectives.

Design Type(s)	modeling and simulation objective • observational design • data integration objective
Measurement Type(s)	Electricity
Technology Type(s)	digital curation
Factor Type(s)	temporal_interval • geographic location
Sample Characteristic(s)	Sub-Saharan Africa • anthropogenic habitat

Machine-accessible metadata file describing the reported data (ISA-Tab format)