Malaria during COVID-19 Travel Restrictions in Makkah, Saudi Arabia.

Malaria is a parasitic infection that may result in an acute, life-threatening illness. It is a major public health problem in the tropical world. The disease is caused by the parasites of the genus Plasmodium and is transmitted by female Anopheles mosquitoes. Saudi Arabia is in the elimination phase of malaria control. Several parts of Saudi Arabia report cases of imported malaria among travelers and visitors. The city of Makkah in Saudi Arabia has a population of about 2.3 million. Moreover, over 6 million religious visitors from different parts of the world visit Makkah annually. During the COVID-19 outbreak, travel restrictions were enforced in Makkah to contain the spread of COVID-19. We compare the total reported cases of malaria in Makkah before, during, and after COVID-19 travel restrictions in this retrospective cross-sectional study. Data on demographics, clinical data, and laboratory parameters were collected from the medical records of the Ministry of Health, Saudi Arabia. The annual malaria incidence rates in Makkah were 29.13/million people (2018), 37.82/million people (2019), 15.65/million people (2020), 12.61/million people (2021), and 48.69/million people (2022). Most of the malaria cases in Makkah were caused by Plasmodium falciparum, followed by P. vivax. Sudan, Nigeria, Yamen, Pakistan, and India are the top five countries contributing to malaria cases in Makkah. Weekly malaria case analyses revealed that COVID-19-related travel restrictions resulted in zero malaria cases in Makkah, indicating the magnitude of the travel-related malaria burden in the city.

Rapid groundwater decline and some cases of recovery in aquifers globally.

Groundwater resources are vital to ecosystems and livelihoods. Excessive groundwater withdrawals can cause groundwater levels to decline1-10, resulting in seawater intrusion11, land subsidence12,13, streamflow depletion14-16 and wells running dry17. However, the global pace and prevalence of local groundwater declines are poorly constrained, because in situ groundwater levels have not been synthesized at the global scale. Here we analyse in situ groundwater-level trends for 170,000 monitoring wells and 1,693 aquifer systems in countries that encompass approximately 75% of global groundwater withdrawals18. We show that rapid groundwater-level declines (>0.5 m year-1) are widespread in the twenty-first century, especially in dry regions with extensive croplands. Critically, we also show that groundwater-level declines have accelerated over the past four decades in 30% of the world's regional aquifers. This widespread acceleration in groundwater-level deepening highlights an urgent need for more effective measures to address groundwater depletion. Our analysis also reveals specific cases in which depletion trends have reversed following policy changes, managed aquifer recharge and surface-water diversions, demonstrating the potential for depleted aquifer systems to recover.

High Seroprevalence of SARS-CoV-2 IgG and RNA among Asymptomatic Blood Donors in Makkah Region, Saudi Arabia.

The gold-standard approach for diagnosing and confirming Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2) infection is reverse transcription-polymerase chain reaction (RT-PCR). This method, however, is inefficient in detecting previous or dormant viral infections. The presence of antigen-specific antibodies is the fingerprint and cardinal sign for diagnosis and determination of exposure to infectious agents including Corona virus disease-2019 (COVID-19). This cross-sectional study examined the presence of SARS-CoV-2 spike-specific immunoglobulin G (IgG) among asymptomatic blood donors in Makkah region. A total of 4368 asymptomatic blood donors were enrolled. They were screened for spike-specific IgG using ELISA and COVID-19 RNA by real-time PCR. COVID-19 IgG was detected among 2248 subjects (51.5%) while COVID-19-RNA was detected among 473 (10.8%) subjects. The IgG frequency was significantly higher among males and non-Saudi residents (p < 0.001 each) with no significant variation in IgG positivity among blood donors with different blood groups. In addition, COVID-19 RNA frequency was significantly higher among donors below 40-years old (p = 0.047, χ2 = 3.95), and non-Saudi residents (p = 0.001, χ2 = 304.5). The COVID-19 IgG levels were significantly higher among the RNA-positive donors (p = 001), and non-Saudi residents (p = 0.041), with no variations with age or blood group (p > 0.05). This study reveals a very high prevalence of COVID-19 IgG and RNA among asymptomatic blood donors in Makkah, Saudi Arabia indicating a high exposure rate of the general population to COVID-19; particularly foreign residents. It sheds light on the spread on COVID-19 among apparently healthy individuals at the beginning of the pandemic and could help in designing various control measures to minimize viral spread.

Factors controlling groundwater radioactivity in arid environments: An automated machine learning approach.

Groundwater resources in the Kingdom of Saudi Arabia (KSA) have high levels of natural radioactivity. Within the northwestern KSA, gross alpha (α) and gross beta (ß) levels exceed national and international drinking-water limits. In this study, we developed and used an automated machine learning (AML) approach to quantify relationships between gross α and gross ß activities and different geological, hydrogeological, and geochemical conditions. Two AML model groups (group I for gross α; group II for gross ß) were constructed, using water samples collected from 360 irrigation and water supply wells, to define a robust model that explains the spatial variability in gross α and gross ß activities, as well as variables that control the gross activities. Each group contained four model families: deep neural network (DNN), gradient boosting machine (GBM), generalized linear model (GLM), and distributed random forest (DRF). Model inputs include chemical compositions as well as geological and hydrogeological conditions. Three performance metrics were used to evaluate the models during training and testing: normalized root mean square error (NRMSE), Pearson's correlation coefficient (r), and Nash-Sutcliff efficiency (NSE) coefficient. Results indicate that (1) the GBM model outperformed (training: NRMSE: 0.37 ± 0.10; r: 0.92 ± 0.05; NSE: 0.85 ± 0.09; testing: NRMSE: 0.71 ± 0.08; r: 0.72 ± 0.08; NSE: 0.49 ± 0.12) the DNN, DRF, and GLM models when modelling gross α activities; (2) gross α activities are controlled by pH, stream density, nitrate, manganese, and vegetation index; (3) the DRF model outperformed (training: NRMSE: 0.41 ± 0.05; r: 0.92 ± 0.02; NSE: 0.83 ± 0.04; testing: NRMSE: 0.67 ± 0.09; r: 0.77 ± 0.07; NSE: 0.54 ± 0.12) the GBM, DNN, and GLM models when modelling gross ß activities; (4) input variables that affect the gross ß actives are pH, temperature, stream density, lithology, and nitrate; and (5) no single model could be used to model both gross α and gross ß activities-instead, a combination of AML models should be used. Our computationally efficient approach provides a framework and insights for using AML techniques in water quality investigations and promotes more and improved use of different geological, hydrogeological, and geochemical datasets by the scientific community and decision makers to develop guidelines for mitigation.