RESUMO
Objectives: This paper aims to measure the impact of the implemented nonpharmaceutical interventions (NPIs) in the Kingdom of Saudi Arabia (KSA) during the pandemic using simulation modeling. Methods: To measure the impact of NPI, a hybrid agent-based and system dynamics simulation model was built and validated. Data were collected prospectively on a weekly basis. The core epidemiological model is based on a complex Susceptible-Exposed-Infectious-Recovered and Dead model of epidemic dynamics. Reverse engineering was performed on a weekly basis throughout the study period as a mean for model validation which reported on four outcomes: total cases, active cases, ICU cases, and deaths cases. To measure the impact of each NPI, the observed values of active and total cases were captured and compared to the projected values of active and total cases from the simulation. To measure the impact of each NPI, the study period was divided into rounds of incubation periods (cycles of 14 days each). The behavioral change of the spread of the disease was interpreted as the impact of NPIs that occurred at the beginning of the cycle. The behavioral change was measured by the change in the initial reproduction rate (R0). Results: After 18 weeks of the reverse engineering process, the model achieved a 0.4 % difference in total cases for prediction at the end of the study period. The results estimated that NPIs led to 64 % change in The R0. Our breakdown analysis of the impact of each NPI indicates that banning going to schools had the greatest impact on the infection reproduction rate (24 %). Conclusion: We used hybrid simulation modeling to measure the impact of NPIs taken by the KSA government. The finding further supports the notion that early NPIs adoption can effectively limit the spread of COVID-19. It also supports using simulation for building mathematical modeling for epidemiological scenarios.
RESUMO
Recent national reports have indicated an increasing trend of dental decay among school-aged children. National school-based data are required to guide decision-making to ensure effective public health efforts to manage dental decay. This study aimed to assess the prevalence of dental decay among school-age children in Saudi Arabia and explore the possible link between dental decay and weight status. This was a cross-sectional analysis of a national sample including 1,134,317 Saudi children in the 1st, 4th, 7th, and 10th grades who participated in a national school screening program. Data for weight, height, body mass index (BMI), dental decay prevalence, and decayed, missing, and filled teeth (DMFT) index were analysed. An independent t-test, one-way analysis of variance, and chi-square test were used to compare the means, while Pearson's r correlation and multiple linear regression were used to examine the relationships between the study variables. The prevalence of dental decay was 24.20%, and dental decay was the highest among female students (26.5%), students in primary school (25.9%), students living in the eastern region (35.2%), in the administrative capital (27.6%), and in rural areas (23.4%). Dental decay was the highest among students who were underweight. Female sex and living in rural areas significantly predicted higher DMFT while being overweight/obese significantly predicted lower DMFT. Dental decay is highly prevalent among underweight students, female students, and students living in rural areas and the eastern regions of Saudi Arabia. To reduce the prevalence of dental decay and related health disparities, dental health screening programs should be designed to detect dental decay early among children at high risk due to abnormal BMIs and sociodemographic factors. In addition, dental health screening and management programs should utilize standardized dental decay assessment methods and ethnically representative growth charts.