Efficacy and Safety of COVID-19 Treatment Using Convalescent Plasma Transfusion: Updated Systematic Review and Meta-Analysis of Randomized Controlled Trials.

This study investigated the efficacy and safety of convalescent plasma (CP) transfusion against the coronavirus disease 2019 (COVID-19) via a systematic review and meta-analysis of randomized controlled trials (RCTs). A total of 5467 articles obtained from electronic databases were assessed; however, only 34 RCTs were eligible after manually screening and eliminating unnecessary studies. The beneficial effect was addressed by assessing the risk ratio (RR) and standardized mean differences (SMDs) of the meta-analysis. It was demonstrated that CP therapy is not effective in improving clinical outcomes, including reducing mortality with an RR of 0.88 [0.76; 1.03] (I2 = 68% and p = 0.10) and length of hospitalization with SMD of -0.47 [-0.95; 0.00] (I2 = 99% and p = 0.05). Subgroup analysis provided strong evidence that CP transfusion does not significantly reduce all-cause mortality compared to standard of care (SOC) with an RR of 1.01 [0.99; 1.03] (I2 = 70% and p = 0.33). In addition, CP was found to be safe for and well-tolerated by COVID-19 patients as was the SOC in healthcare settings. Overall, the results suggest that CP should not be applied outside of randomized trials because of less benefit in improving clinical outcomes for COVID-19 treatment.

Understanding chaos in COVID-19 and its relationship to stringency index: Applications to large-scale and granular level prediction models.

Understanding the underlying and unpredictable dynamics of the COVID-19 pandemic is important. We supplemented the findings of Jones and Strigul (2020) and described the chaotic behavior of COVID-19 using state space plots which depicted the changes in asymptotic behavior and trajectory brought about by the increase or decrease in the number of cases which resulted from the easing or tightening of restrictions and other non-pharmaceutical interventions instituted by governments as represented by the country's stringency index (SI). We used COVID-19 country-wide case count data and analyzed it using convergent cross-mapping (CCM) and found that the SI influence on COVID-19 case counts is high in almost all the countries considered. When we utilized finer granular geographical data ('barangay' or village level COVID-19 case counts in the Philippines), the effects of SI were reduced as the population density increased. The authors believe that the knowledge of the chaotic behavior of COVID-19 and the effects of population density as applied to finer granular geographical data has the potential to generate more accurate COVID-19 non-linear prediction models. This could be used at the local government level to guide strategic and highly targeted COVID-19 policies which are favorable to public health systems but with limited impact to the economy.