Predicting COVID-19 Spread in Pakistan using the SIR Model

The global pandemic of COVID-19 has raised several questions and attracted researchers from all of the disciplines of scientific research. Regardless of advances in science and technology, equipped laboratories of virology, high literacy rates, and medical resources in developed countries, several nations and their health care systems completely failed to overcome the disaster. The fast spread is caused by frequent air travel for business, tourism, education, etc. COVID-19 can infect third world countries severely. United States of America has the highest per capita spending of health still 1/3rd of the global burden of COVID-19 has consumed existing resources. The WHO has declared COVID-19 as a pandemic. More than 200 countries and territories have reported infected cases. The quarantine is the most effective way to slow the spread of disease and “Flatting of Curve” is a phenomenon to tackle the surge by health systems. To achieve good results from existing Medical Health Care Systems (MHCS), an accurate prediction for the spread of disease is crucial. This study utilizes the generalized method of SIR to accurately predict the spread of COVID-19 associated infection, recoveries, and deaths in Pakistan. The data from the National Command and Control of Pakistan (NCCP) is utilized. Through multiple cases applied on currently available data, the proposed mathematical models predict that by the end of April about more than 14553 infected and about 310 deaths are in Pakistan. The recovery rate is highest in the region up to 99.87 %

Group Teaching Optimization Algorithm Based MPPT Control of PV Systems under Partial Shading and Complex Partial Shading

The most cost-effective electrical energy is produced by photovoltaic (PV) systems, and with the smallest carbon footprint, making it a sustainable renewable energy. They provide an excellent alternative to the existing fossil fuel-based energy systems, while providing 4% of global electricity demand. PV system efficiency is significantly reduced by the intrinsic non-linear model, maximum power point (MPP), and partial shading (PS) effects. These two problems cause major power loss. To devise the maximum power point tracking (MPPT) control of the PV system, a novel group teaching optimization algorithm (GTOA) based controller is presented, which effectively deals with the PS and complex partial shading (CPS) conditions. Four case studies were employed that included fast-changing irradiance, PS, and CPS to test the robustness of the proposed MPPT technique. The performance of the GTOA was compared with the latest bio-inspired techniques, i.e., dragon fly optimization (DFO), cuckoo search (CS), particle swarm optimization (PSO), particle swarm optimization gravitational search (PSOGS), and conventional perturb and observe (P&O). The GTOA tracked global MPP with the highest 99.9% efficiency, while maintaining the magnitude of the oscillation <0.5 W at global maxima (GM). Moreover, 13–35% faster tracking times, and 54% settling times were achieved, compared to existing techniques. Statistical analysis was carried out to validate the robustness and effectiveness of the GTOA. Comprehensive analytical and statistical analysis solidified the superior performance of the proposed GTOA based MPPT technique