On January 24, 2025, the United States government issued an executive order to freeze all foreign aid programs, including The President's Emergency Plan for AIDS Relief (PEPFAR), for 90 days. A limited waiver option became available, but its implementation remains incomplete. We estimated the impact of these policy changes on HIV deaths and new infections in seven sub-Saharan African (SSA) countries-Ethiopia, Kenya, Malawi, South Africa, Tanzania, Zambia, and Zimbabwe -, which together account for about half of all people living with HIV in SSA. We used STDSIM, an established individual-based simulation model, and previously published quantifications for the seven countries. We predicted changes in HIV deaths and new infections over the period 2025-2030 for four scenarios: (1) Executive order-proportional, where treatment disruption was proportional to the country-specific PEPFAR's share of total HIV funding; (2) Executive order-realistic, assuming near-total system collapse due to program dependencies; and (3-4) Waiver scenarios where treatment was resumed after 4 or after 8 weeks. Resumptions of programs accounted for delays due to organizational and logistical challenges. A 90-day funding freeze would result in 60 thousand [95% UI: 49-71 thousand] excess HIV deaths for the Executive order-proportional scenario. This number would increase to 74 thousand excess HIV deaths [95% UI: 63-89 thousand] for the Executive order-realistic scenario. Under a 4-week and 8-week waiver scenario, projected excess HIV deaths ranged between 21 thousand [95% UI: 15-28 thousand] and 28 thousand [95% UI: 22-36 thousand] respectively. Excess new infections ranged between 35 and 103 thousand for the different scenarios. The sudden cessation of PEPFAR funding likely results in tens of thousands of HIV deaths and new infections. These losses of life and health should compel the United States government to rapidly and fully re-instate one of the most successful health programs in history. None.

This study conducted a comprehensive analysis of multiple supervised machine learning models, regressors and classifiers, to accurately predict diamond prices. Diamond pricing is a complex task due to the non-linear relationships between key features such as carat, cut, clarity, table, and depth. The analysis aimed to develop an accurate predictive model by utilizing both regression and classification approaches. To preprocess the data, the study employed various techniques. The work addressed outliers, standardized the predictors, performed median imputation of missing values, and resolved multicollinearity issues. Equal-width binning on the cut variable was performed to handle class imbalance. Correlation-based feature selection was utilized to eliminate highly correlated variables, ensuring that only relevant features were included in the models. Outliers were handled using the inter-quartile range method, and numerical features were normalized through standardization. Missing values in numerical features were imputed using the median, preserving the integrity of the dataset. Among the models evaluated, the RF regressor exhibited exceptional performance. It achieved the lowest root mean squared error (RMSE) of 523.50, indicating superior accuracy compared to the other models. The RF regressor also obtained a high R-squared ( $$\text {R}^2$$R 2 ) score of 0.985, suggesting it explained a significant portion of the variance in diamond prices. Furthermore, the area under the curve with RF classifier for the test set was 1.00 $$\, (100\%)$$( 100 % ) , indicating perfect classification performance. These results solidify the RF’s position as the best-performing model in terms of accuracy and predictive power, both in regression and classification. The MLP regressor showed promising results with an RMSE of 563.74 and an $$\text {R}^2$$R 2 score of 0.980, demonstrating its ability to capture the complex relationships in the data. Although it achieved slightly higher errors than the RF regressor, further analysis is needed to determine its suitability and potential advantages compared to the RF regressor. The XGBoost Regressor achieved an RMSE of 612.88 and an $$\text {R}^2$$R 2 score of 0.972, indicating its effectiveness in predicting diamond prices but with slightly higher errors compared to the RF regressor. The Boosted Decision Tree Regressor had an RMSE of 711.31 and an $$\text {R}^2$$R 2 score of 0.968, demonstrating its ability to capture some of the underlying patterns but with higher errors than the RF and XGBoost models. In contrast, the KNN regressor yielded a higher RMSE of 1346.65 and a lower $$\text {R}^2$$R 2 score of 0.887, indicating its inferior performance in accurately predicting diamond prices compared to the other models. Similarly, the Linear Regression model performed similarly to the KNN regressor, with an RMSE of 1395.41 and an $$\text {R}^2$$R 2 score of 0.876. The Support Vector Regression model showed the highest RMSE of 3044.49 and the lowest $$\text {R}^2$$R 2 score of 0.421, indicating its limited effectiveness in capturing the complex relationships in the data. Overall, the study demonstrates that the RF outperforms the other models in terms of accuracy and predictive power, as evidenced by its lowest RMSE, highest $$\text {R}^2$$R 2 score, and perfect classification performance. This highlights its suitability for accurately predicting diamond prices. The study not only provides an effective tool for the diamond industry but also emphasizes the importance of considering both regression and classification approaches in developing accurate predictive models. The findings contribute valuable insights for pricing strategies, market trends, and decision-making processes in the diamond industry and related fields.

The control of spread of HIV to reduce its effects on a population is an important role of public health. HIV testing and counselling (HTC) and eventual enrolment of infected individuals on anti retro-viral treatment (ART) as soon as possible to reduce the risk of dying is currently the main intervention against HIV. Mathematical models can be used to study the effects of HIV prevention, testing and treatment with ART on HIV patients. In this study, we employ a deterministic model to provide a quantification of HIV prevention, testing and treatment with ART as public health measurements in the fight against HIV infection. Lyapunov function has been used to derive a condition that ensures that the model system is globally asymptotically stable when R₀ is less than unity. Through sensitivity analysis, we determine the relative importance of model parameters for disease transmission. The sensitivity analysis results suggest that the effective contact rates are mechanisms fuelling HIV epidemic proliferation while ART efficacy reduces the incidence. The model is fitted to HIV surveillance data obtained from world in data website. Although the results show a high proportion of individuals with HIV in Kenya, the incidence curve is indicative of a declining HIV infection and settling at an endemic steady state. The results are suggestive of the need to promote preventive mechanism against the occurrence of new infections. Moreover, the results show that the combination of several control mechanisms would significantly reduce the spread of the disease, if we maintain the level of each control high.

The control of HIV demands different interventions for different age groups. In the present manuscript, we formulate and analyze a mathematical compartmental models of HIV transmission within and between two age groups in Kenya. We fitted the model to data using MCMC technique and inferred the parameters. We also estimate the reproduction numbers, namely within age group transmission and between age groups transmission basic reproduction numbers. The analysis of the data revealed that there is significant difference in mean number of new HIV infections between males and females within the two age groups. More, particularly, females are highly infected with HIV as compared to their male counterparts. Calculation of the reproduction numbers within and between age groups provides insights into control that cannot be deduced simply from observations on the prevalence of infection. More specifically, the analysis showed that the per capita rate of HIV transmission was highest when there is interaction between young adults to adults and most HIV infections occurred in adult population. Furthermore, the sensitivity analysis demonstrated that the reproduction numbers depend mainly on the probabilities of infection. This results can be used to guide HIV interventions, condom distribution and antiretroviral therapy. Precisely, the results can be used to educate the young adults on practicing safe sex with their partners in order to contain the occurrence of new infections.