With COVID-19 surging across the world, understanding the effectiveness of intervention strategies on transmission dynamics is of primary global health importance. Here, we develop and analyze an epidemiological compartmental model using multi-objective genetic algorithm design optimization to compare scenarios related to strategy type, the extent of social distancing, time window, and personal protection levels on the transmission dynamics of COVID-19 in São Paulo, Brazil. The results indicate that the optimal strategy for São Paulo is to reduce social distancing over time with a stepping-down reduction in the magnitude of social distancing every 80-days. Our results also indicate that the ability to reduce social distancing depends on a 5–10% increase in the current percentage of people strictly following protective guidelines, highlighting the importance of protective behavior in controlling the pandemic. Our framework can be extended to model transmission dynamics for other countries, regions, states, cities, and organizations.

Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder characterized by tremors, balance impairments, and mobility limitations. Innovative approaches like combining transcranial direct current stimulation (tDCS) with exercise show promise in addressing these symptoms. This study investigates the effects of exercise combined with tDCS on mobility and tremor management in PD patients. Methods: Twenty-five individuals aged 60−75 (66.6 ± 7.33), diagnosed with PD (Hoehn and Yahr stage 2−3), were assigned to three groups in a randomized controlled design: exercise with active tDCS (n = 8), exercise with sham tDCS (n = 8), and a control group (n = 9). Dual-task training sessions focusing on walking speed, balance, and force control were conducted over ten sessions. Results: No significant differences were detected across the groups for grip strength or force control measures (p > 0.05). Significant improvements were observed in the intervention group: the Timed Up and Go (TUG) test showed a significant reduction in time (mean difference = 2.498 s, p < 0.001, ηp² = 0.331); anterior–posterior displacement significantly increased (mean difference = 21.375 mm, p = 0.0269, ηp² = 0.303); and force-tremor decoupling improved, with coherence in the 1−4 Hz band significantly decreasing (p = 0.0067). Finally, changes in TUG from post- to pre-treatment values were significantly positively correlated with the changes in coherence (R = 0.468, p = 0.018). Conclusions: Combining tDCS with exercise enhances mobility and tremor management in PD patients. These findings support the potential for such interventions to improve functional outcomes and quality of life for individuals with PD.

Background: Accurate differentiation of Parkinson’s disease (PD) from healthy aging is crucial for timely intervention and effective management. Postural sway abnormalities are prominent motor features of PD. Quantitative stabilometry and machine learning (ML) offer a promising avenue for developing objective markers to support the diagnostic process. This study aimed to develop and validate high-performance ML models to classify individuals with PD and age-matched healthy older adults (HOAs) using a comprehensive set of stabilometric parameters. Methods: Thirty-seven HOAs (mean age 70 ± 6.8 years) and 26 individuals with idiopathic PD (Hoehn and Yahr stages 2–3, on medication; mean age 66 years ± 2.9 years), all aged 60–80 years, participated. Stabilometric data were collected using a force platform during quiet stance under eyes-open (EO) and eyes-closed (EC) conditions, from which 34 parameters reflecting the time- and frequency-domain characteristics of center-of-pressure (COP) sway were extracted. After data preprocessing, including mean imputation for missing values and feature scaling, three ML classifiers (Random Forest, Gradient Boosting, and Support Vector Machine) were hyperparameter-tuned using GridSearchCV with three-fold cross-validation. An ensemble voting classifier (soft voting) was constructed from these tuned models. Model performance was rigorously evaluated using 15 iterations of stratified train–test splits (70% train and 30% test) and an additional bootstrap procedure of 1000 iterations to derive reliable 95% confidence intervals (CIs). Results: Our optimized ensemble voting classifier achieved excellent discriminative power, distinguishing PD from HOAs with a mean accuracy of 0.91 (95% CI: 0.81–1.00) and a mean Area Under the ROC Curve (AUC ROC) of 0.97 (95% CI: 0.92–1.00). Importantly, feature analysis revealed that anteroposterior sway velocity with eyes open (V-AP) and total sway path with eyes closed (TOD_EC, calculated using COP displacement vectors from its mean position) are the most robust and non-invasive biomarkers for differentiating the groups. Conclusions: An ensemble ML approach leveraging stabilometric features provides a highly accurate, non-invasive method to distinguish PD from healthy aging and may augment clinical assessment and monitoring.

An epidemiological compartmental model was used to simulate social distancing strategies to contain the COVID-19 pandemic and prevent a second wave in São Paulo, Brazil. Optimization using genetic algorithm was used to determine the optimal solutions. Our results suggest the best-case strategy for São Paulo is to maintain or increase the current magnitude of social distancing for at least 60 more days and increase the current levels of personal protection behaviors by a minimum of 10% (e.g., wearing facemasks, proper hand hygiene and avoid agglomeration). Followed by a long-term oscillatory level of social distancing with a stepping-down approach every 80 days over a period of two years with continued protective behavior.

Transforming subsistence-farming to market-oriented production as a way to increase household's income and reduce poverty in Ethiopia. The objectives are to identify factors determining wheat commercialization in Damot Gale district of Wolaita zone. Multi-stage sampling techniques were employed to select total sample size of 120 households. Firstly, Damot Gale was purposively selected due to its high production potential of cereal crops. Three Kebeles, namely Wandara Boloso, Woshi Gale and Fate were purposively selected. The sample for each kebele was determined by using probability proportional to size using simple random sampling technique. Both primary and secondary data sources were used to generate qualitative and quantitative data types through structured questionnaire, focus group discussion, personal observation and in-depth interview. Data collected were analyzed using household commercialization index and binary logit model. The household commercialization index showed that 45.9% of wheat producing households were commercialized. From sample households, 72.5% participated in wheat output market. Binary logit regression model result revealed that the sex of household head, education level of household head, market-oriented production, credit utilization, extension services use and market information use, number of oxen owned, annual household income, quantity of wheat produced, use of farm inputs and age of household head. Therefore, market orientated production, farm inputs utilization, demonstrative trainings, MFI services, market information dissemination and functional adult literacy can contribute wheat commercialization of households in the study area.