Mosquitoes are vectors for numerous pathogens, which are collectively responsible for millions of human deaths each year. As such, it is vital to be able to accurately predict their distributions, particularly in areas where species composition is unknown. Species distribution modeling was used to determine the relationship between environmental, anthropogenic and distance factors on the occurrence of two mosquito genera, Culex Linnaeus and Stegomyia Theobald (syn. Aedes), in the Taita Hills, southeastern Kenya. This study aims to test whether any of the statistical prediction models produced by the Biomod2 package in R can reliably estimate the distributions of mosquitoes 2 in these genera in the Taita Hills; and to examine which factors best explain their presence. Mosquito collections were collected from 122 locations between January-March 2016 along transects throughout the Taita Hills. Environmental-, anthropogenic-and distance-based geospatial data were acquired from the Taita Hills geo-database, satellite-and aerial imagery and processed in GIS software. The Biomod2 package in R, intended for ensemble forecasting of species distributions, was used to generate predictive models. Slope, human population density, normalized difference vegetation index, distance to roads and elevation best estimated Culex distributions by a generalized additive model with an area under the curve (AUC) value of 0.791. Mean radiation, human population density, normalized difference vegetation index, distance to roads and mean temperature resulted in the highest AUC (0.708) value in a random forest model for Stegomyia distributions. We conclude that in the process towards more detailed species-level maps, with our study results, general assumptions can be made about the distribution areas of Culex and Stegomyia mosquitoes in the Taita Hills and the factors which influence their distribution.

Stakeholders’ support is essential for the effective and successful implementation of policies that prioritize enhancing and preserving ocean and coastal ecosystems. However, cross-national studies examining factors influencing stakeholders’ policy support are still lacking. The current study aimed to provide preliminary evidence on factors (e.g., socio-demographic factors, country income levels, and perceived impacts of marine and coastal ecosystems) that affect stakeholders’ endorsement of a policy centered on preserving marine and coastal ecosystems. To conduct the study, we applied the Bayesian Mindsponge Framework (BMF) to a dataset of 709 stakeholders from 42 countries generated by MaCoBioS—a research project funded by the European Commission Horizon 2020. The BMF allowed us to adopt a distinctive and innovative approach to analyzing the data and drawing valuable policy development and implementation insights. The results show no differences in policy endorsement levels across stakeholders with different ages, education, and country income levels. However, female stakeholders tended to support the policy prioritizing ocean protection more than their male counterparts. Stakeholders perceiving the impacts of marine and coastal ecosystem preservation on human wellbeing, climate and weather, and climate change reduction also tended to support the policy more strongly. Meanwhile, the perceived impacts of ocean and coastal ecosystems on global and local economies had an ambiguous effect on stakeholders’ policy support. Based on these findings, we suggest that raising the awareness and knowledge of stakeholders can help improve their support for ocean and coastal preservation policies. Moreover, it is necessary to concentrate more on communicating the adverse consequences induced by the ocean and coastal ecosystems’ loss (e.g., climate change and health) and less on the economic aspects. The study underscores the significance of environmental education and awareness-raising campaigns in disseminating environmental information and cultivating an eco-surplus culture. This culture inspires stakeholders to actively participate in environmental conservation efforts, going beyond mere sustainability and aiming to create positive environmental impacts.

Health insurance is important in disease management, access to quality health care and attaining Universal Health Care. National and regional data on health insurance coverage needed for policy making is mostly obtained from household surveys; however, estimates at lower administrative units like at the county level in Kenya are highly variable due to small sample sizes. Small area estimation combines survey and census data using a model to increases the effective sample size and therefore provides more precise estimates. In this study we estimate the health insurance coverage for Kenyan counties using a binary M‑quantile small area model for women $$(n=14{,}730)$$ ( n = 14,730 ) and men $$(n=12{,}007)$$ ( n = 12,007 ) aged 15 to 49 years old. This has the advantage that we avoid specifying the distribution of the random effects and distributional robustness is automatically achieved. The response variable is derived from the Kenya Demographic and Health Survey 2014 and auxiliary data from the Kenya Population and Housing Census 2009. We estimate the mean squared error using an analytical approach based on Taylor series linearization. The national direct health insurance coverage estimates are $$18\%$$ 18 % and $$21\%$$ 21 % for women and men respectively. With the current health insurance schemes, coverage remains low across the 47 counties. These county-level estimates are helpful in formulating decentralized policies and funding models.

Public health surveillance of overweight prevalence is essential to assess the extent of the problem, identify regions and groups most affected and inform policy-making. However, the needed reliable data at disaggregated levels is lacking in Kenya. The Kenya STEPwise Survey for Non-communicable Diseases and RiskFactors (KSSNDRF) was nationally representative. It was used to obtain various indicators of non-communicable diseases and risk factors including overweight. However, due to small sample sizes at lower levels like at the county, overweight prevalence estimates are statistically imprecise (i.e., high variance). Therefore, to increase the effective sample size we combine data from the KSSNDRF and the Kenya Population and Housing Census by model-based small area methods. In particular, we fit an arcsine square-root transformed Fay–Herriot model. To transform back to the original scale, we use a bias-corrected back transformation. For this model, we smooth the design variance using Generalised Variance Functions. We compute the mean squared error estimates using a bootstrap procedure. We found that counties within urban areas — including the major towns like Nairobi, Nakuru, Nyeri and Mombasa — have a higher prevalence of overweight compared to rural counties. Although the paper focuses on overweight prevalence in Kenya, the presented method can also be applied to other indicators in developing countries with similar data sources.

Crimes have been the most dangerous threat to peace, development, human right, social, political and economic stability in Kenya. There is a great need to eradicate crime to facilitate development and counter all vices that are caused by crime. Efficient management of crime requires an adequate understanding of the patterns in which crime occur to put the appropriate measures in place for crime prevention. Crime has been in existence since the beginning of time hence will remain, and one of the solutions is to identify the pattern in which it occurs to prevent or counter it effectively as it occurs. The main objective of the study was to find out how different crimes are related. The study considered a number of data mining techniques which included; clustering, specifically k-means algorithm, mapping and APRIORI algorithm to analyze how different crimes are related and how often they occur. Crime cases were found to be decreasing over the years under study and counties with a high population reported higher number of crimes as compared to those with low population. The study suggested that these crimes could be controlled by directing more resources in the highly populated counties. The study leaves a research gap where the same crime data could be analyzed using time series methods since observed crime offenses are recorded alongside the time they occur.