COVID-19 was declared a global pandemic by the World Health Organization in March 2020, and has infected more than 4 million people worldwide with over 300,000 deaths by early May 2020. Many researchers around the world incorporated various prediction techniques such as Susceptible–Infected–Recovered model, Susceptible–Exposed–Infected–Recovered model, and Auto Regressive Integrated Moving Average model (ARIMA) to forecast the spread of this pandemic. The ARIMA technique was not heavily used in forecasting COVID-19 by researchers due to the claim that it is not suitable for use in complex and dynamic contexts. The aim of this study is to test how accurate the ARIMA best-fit model predictions were with the actual values reported after the entire time of the prediction had elapsed. We investigate and validate the accuracy of an ARIMA model over a relatively long period of time using Kuwait as a case study. We started by optimizing the parameters of our model to find a best-fit through examining auto-correlation function and partial auto correlation function charts, as well as different accuracy measures. We then used the best-fit model to forecast confirmed and recovered cases of COVID-19 throughout the different phases of Kuwait’s gradual preventive plan. The results show that despite the dynamic nature of the disease and constant revisions made by the Kuwaiti government, the actual values for most of the time period observed were well within bounds of our selected ARIMA model prediction at 95% confidence interval. Pearson’s correlation coefficient for the forecast points with the actual recorded data was found to be 0.996. This indicates that the two sets are highly correlated. The accuracy of the prediction provided by our ARIMA model is both appropriate and satisfactory.

Microbial water quality and concentrations of faecal sterols in sediment have been used to assess the degree of sewage contamination in Kuwait's marine environment. A review of microbial (faecal coliform, faecal streptococci and Escherichia coli) water quality data identified temporal and spatial sources of pollution around the coastline. Results indicated that bacterial counts regularly breach regional water quality guidelines. Sediments collected from a total of 29 sites contained detectable levels of coprostanol with values ranging from 29 to 2420 ng g(-1) (dry weight). Hot spots based on faecal sterol sediment contamination were identified in Doha Bay and Sulaibikhat Bay, which are both smaller embayments of Kuwait Bay. The ratio of epicoprostanol/coprostanol indicates that a proportion of the contamination was from raw or partially treated sewage. Sewage pollution in these areas are thought to result from illegal connections and discharges from storm drains, such as that sited at Al-Ghazali.

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This paper investigates the technical, allocative and economic efficiency of public schools in Kuwait over four levels of schooling (kindergartens, primary, intermediate and secondary) and two periods (1999/00 and 2004/05) using data envelopment analysis (DEA). Mean pure technical efficiency varies between 0.695 and 0.852 across all levels of education; the majority of schools at kindergarten, primary and intermediate levels are operating at a point where returns to scale are increasing; and there are considerable cost efficiencies to be gained. In a second stage analysis of the determinants of efficiency, teacher salary and the proportion of teaching staff who are Kuwaiti are highly significant in explaining school efficiency at all levels. The former has a positive effect and the latter a negative effect. Allgirls schools have significantly higher efficiency than all-boys schools. There is limited evidence that geographical location affects efficiency, and this may be a consequence of differences between regions in terms of affluence or density of population.

Kuwait is a country with low rainfall and highly concentrated industrial and domestic effluents entering its coastal waters. These can be both treated and untreated. In this study we sampled a series of coastal and open-sea sites and used a variety of analyses to identify those sites requiring the most attention. We used a high throughput GC-MS screen to look for over 1000 chemicals in the samples. Estrogen and androgen screens assessed the potential to disrupt endocrine activity. An oyster embryo development screen was used to assess biological effect potential. The chemical screen identified sites which had high numbers of identified industrial and domestic chemicals. The oyster screen showed that these sites had also caused high levels of developmental abnormalities with 100% of embryos affected at some sites. The yeast screen showed that estrogenic chemicals were present in outfalls at 2-3 ng/l E2 equivalent, and detectable even in some open water sites.