Muslims around the world have begun celebrating Eid al-Fitr, one of the biggest celebrations in the Islamic calendar. Eid al-Fitr - which means "festival of the breaking of the fast" - is celebrated at the end of Ramadan, a month of fasting for many adults, as well as spiritual reflection and prayer.ReutersHere in Moscow, worshippers are seen preparing for prayer.ReutersHundreds took part in prayers at Tononoka grounds, in Mombasa, KenyaGetty ImagesPrayers were also observed at a stadium in Port Sudan in the east of the countryGetty ImagesLittle children joined adults at the Moskee Essalam in Rotterdam, NetherlandsGetty ImagesGifts are handed out to Muslim children in Lviv, Ukraine, as Russia's war on the country continuesReuters Palestinians in Jabaliya in the northern Gaza Strip pray amidst the rubble of a mosque destroyed in the current war between Israel and HamasGetty ImagesFamilies gather at al-Aqsa mosque in Jerusalem - the third holiest site in IslamReutersA boy yawns during prayers at a stadium in QatarEPAMuslims greet each-other at Martim Moniz Square in Lisbon, PortugalGetty ImagesWomen worshippers gather in Burgess Park, London, for an outdoor prayerEPAThere were also worshippers gathered outside Plebiscito Square in Naples, ItalyReutersSome women took pictures after attending prayers at the Hagia Sophia Grand Mosque in Istanbul, TurkeyGetty ImagesAfghan refugees pray at a mosque on the outskirts of Peshawar, PakistanMiddle EastEuropeEid al-FitrReligionIslamRelated'I was afraid for my life': At the scene of the attack on Palestinian Oscar winner 5 days agoMiddle EastMore8 hrs ago'In Bradford, families spend thousands on new clothes for Eid' Muslims spend large amounts in Bradford's supermarkets, clothes shops and other services before Eid.8 hrs agoEngland1 day ago The tourist has received an award from the city's mayor after restraining a man during a stabbing.1 day agoEurope1 day ago Another 21 people are injured, as a restaurant and several buildings are set ablaze in the city, local officials say.1 day agoWorld1 day ago Town's successful Ramadan lights project expanded A Scunthorpe community group says it has seen an "amazing" response to its lights display.1 day agoLincolnshire1 day ago Bishop says school that changed Easter events'valued' The BBC is not responsible for the content of external sites.

Prediction is a classic challenge in spatial statistics and the inclusion of spatial covariates can greatly improve predictive performance when incorporated into a model with latent spatial effects. It is desirable to develop flexible regression models that allow for nonlinearities and interactions in the covariate structure. Machine learning models have been suggested in the spatial context, allowing for spatial dependence in the residuals, but fail to provide reliable uncertainty estimates. In this paper, we investigate a novel combination of a Gaussian process spatial model and a Bayesian Additive Regression Tree (BART) model. The computational burden of the approach is reduced by combining Markov chain Monte Carlo (MCMC) with the Integrated Nested Laplace Approximation (INLA) technique. We study the performance of the method via simulations and use the model to predict anthropometric responses, collected via household cluster samples in Kenya.

We've learned how to install R and RStudio, import the dataset, and take care of missing data using the R language. Now I'm going you show you how to encode categorical data in R. If you take a look at our dataset, you'll see that we have two categorical variables. We have the county variables – Nairobi, Kisumu, and Mombasa – and we have the Purchased variables – Yes and No. They're categorical variables, obviously because they have categories. Since machine learning models are based on mathematical/numerical equations, keeping the text in the categorical variables would definitely cause us some problems. We want to have'numbers only' in our equations.

By the end of July 2020, Kenya h ad reported only 341 deaths and ∼20,000 cases of COVID-19. This is in marked contrast to the tens of thousands of deaths reported in many higher-income countries. The true extent of COVID-19 in the community was unknown and likely to be higher than reports indicated. Uyoga et al. found an overall seroprevalence among blood donors of 4.3%, peaking in 35- to 44-year-old individuals (see the Perspective by Maeda and Nkengasong). The low mortality can be partly explained by the steep demographics in Kenya, where less than 4% of the population is 65 or older. These circumstances combine to result in Kenyan hospitals not currently being overwhelmed by patients with respiratory distress. However, the imposition of a strict lockdown in this country has shifted the disease burden to maternal and child deaths as a result of disruption to essential medical services. Science , this issue p. [79][1]; see also p. [27][2] The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Africa is poorly described. The first case of SARS-CoV-2 in Kenya was reported on 12 March 2020, and an overwhelming number of cases and deaths were expected, but by 31 July 2020, there were only 20,636 cases and 341 deaths. However, the extent of SARS-CoV-2 exposure in the community remains unknown. We determined the prevalence of anti–SARS-CoV-2 immunoglobulin G among blood donors in Kenya in April–June 2020. Crude seroprevalence was 5.6% (174 of 3098). Population-weighted, test-performance-adjusted national seroprevalence was 4.3% (95% confidence interval, 2.9 to 5.8%) and was highest in urban counties Mombasa (8.0%), Nairobi (7.3%), and Kisumu (5.5%). SARS-CoV-2 exposure is more extensive than indicated by case-based surveillance, and these results will help guide the pandemic response in Kenya and across Africa. [1]: /lookup/doi/10.1126/science.abe1916 [2]: /lookup/doi/10.1126/science.abf8832