Mapping partner drug resistance to guide antimalarial combination therapy policies in sub-Saharan Africa

Resistance to artemisinin-based combination therapies (ACTs) threatens the global control of Plasmodium falciparum malaria. ACTs combine artemisinin-derived compounds with partner drugs to enable multiple mechanisms of clearance. Although ACTs remain widely effective in sub-Saharan Africa, long-standing circulation of parasite alleles associated with reduced partner drug susceptibility may contribute to the development of clinical resistance. We fitted a hierarchical Bayesian spatial model to data from over 500 molecular surveys to predict the prevalence and frequency of four key markers in transporter genes (pfcrt 76T and pfmdr1 86Y, 184F, and 1246Y) in first-level administrative divisions in sub-Saharan Africa from the uptake of ACTs (2004 to 2009) to their widespread usage (2010 to 2018). Our models estimated that the pfcrt 76T mutation decreased in prevalence in 90% of regions; the pfmdr1 N86 and D1246 wild-type genotypes increased in prevalence in 96% and 82% of regions, respectively; and there was no significant directional selection at the pfmdr1 Y184F locus. Rainfall seasonality was the strongest predictor of the prevalence of wild-type genotypes, with other covariates, including first-line drug policy and transmission intensity more weakly associated. We lastly identified regions of high priority for enhanced surveillance that could signify decreased susceptibility to the local first-line ACT. Our results can be used to infer the degree of molecular resistance and magnitude of wild-type reversion in regions without survey data to inform therapeutic policy decisions.

Community factors and excess mortality in first wave of the COVID-19 pandemic in England

Risk factors for increased risk of death from COVID-19 have been identified, but less is known on characteristics that make communities resilient or vulnerable to the mortality impacts of the pandemic. We applied a two-stage Bayesian spatial model to quantify inequalities in excess mortality in people aged 40 years and older at the community level during the first wave of the pandemic in England, March-May 2020 compared with 2015–2019. Here we show that communities with an increased risk of excess mortality had a high density of care homes, and/or high proportion of residents on income support, living in overcrowded homes and/or with a non-white ethnicity. We found no association between population density or air pollution and excess mortality. Effective and timely public health and healthcare measures that target the communities at greatest risk are urgently needed to avoid further widening of inequalities in mortality patterns as the pandemic progresses.

Economic Growth Modelling in West Nusa Tenggara Using Bayesian Spatial Model Approach

Economic growth is a measure of the welfare of the people in an area. Economic movement is characterized by the number of goods and services produced. The high amount of goods produced and the services used are of course strongly influenced by the amount of available capital, the labor involved, and the level of technology used. The measuring instrument or a reflection of economic growth is the Gross Regional Domestic Product (GRDP). The purpose of this study is to model economic growth in NTB in 2018. In this study, GRDP modeling was carried out using the Bayesian Spatial approach. Based on the results of testing the spatial dependency and spatial heterogeneity, it shows that there is a spatial dependence on the GRDP of districts / cities in NTB Province.. From the analysis conducted, it was found that was positive and insignificant at the 10% level. The parameter estimation results show that the number of workers, the value of capital and the number of workers weighed are variables that have a significant effect on the model. Thus the GRDP of an area in West Nusa Tenggara is influenced by the number of workers, the value of capital and the total labor weight and the GRDP of other surrounding areas.

Elevated temperatures drive the evolution of armor loss in the threespine stickleback Gasterosteus aculeatus

While there is evidence of genetic and phenotypic responses to climate change, few studies have demonstrated change in functional traits with a known genetic basis. Here we present evidence for an evolutionary adaptive response to elevated temperatures in freshwater populations of the threespine stickleback (Gasterosteus aculeatus). Using a unique set of historical data and museum specimens, in combination with contemporary samples, we fitted a Bayesian spatial model to identify a population-level decline in the number of lateral bony plates, comprising anti-predator armor, in multiple populations of sticklebacks over the last 90 years in Poland. Armor loss was predicted by elevated temperatures and is proposed to be a correlated response to selection for reduced body size. This study demonstrates a change in a functional trait of known genetic basis in response to elevated temperature, and illustrates the utility of the threespine stickleback as a model for measuring the evolutionary and ecological impacts of environmental change across the northern hemisphere.