Hojjat Zeraati, PhD.
Vol 6 No 2 (2020)
Introduction: According to the Oxford Medical Dictionary, Corona virus is the largest known viral RNA genome and causes devastating epizootics in livestock and poultry. Human corona viruses cause upper respiratory tract infections and severe acute respiratory syndrome (SARS). The initiative for this study was the extreme life threatening nature of this virus and the global pandemic it has caused. The responses were taken to be the number of deaths, number of recoveries and the number sick with the disease at a particular point in time, globally and the explanatory variables were climate variables.
Method: This is a survey type of study as the data has been extracted over a short period of time and the sampling method adopted is post cluster sampling. Simple descriptive statistics, clustering and generalized linear mixed models have been used for modelling.
Results: There was a strong regional effect of over three which was highly significant for every Covid 19 response. The air quality and temperature interaction and the air quality and humidity interaction were associated with the count of death at 0.0298 and 0.0027 levels of significance respectively. The count recovered was strongly associated with the temperature and humidity interaction and air quality at significance levels of 0.0002 and <0.0001 respectively. The Count at risk was strongly associated with the temperature, wind speeds and air quality three way interaction and this was significant at 0.0005 level.
Discussion: All four weather parameters effected one or more of the Covid 19 responses. The plots of Student residuals versus fitted values showed well-fitting models. The results of this research is useful in planning health care and allocating resources according to the region and the climate during a particular period.
Background: COVID-19 mortality rates differ across countries. We aimed to construct a model that predicts mortality worldwide, by including only country-level socioeconomic and health system indicators and excluding variables related to short-term measures for pandemic management.
Methods: COVID-19 mortality data was collected from Johns Hopkins University resource center. Additional sources were public reports from the United Nations, the World Bank and the Heritage Foundation. We implemented multiple linear regression with backward elimination on the selected predictors.
Results: The final model constructed on seven Independent variables, significantly predicted COVID-19 mortality rate by country (F-statistic: 29.2, p<0.001). Regression coefficients (95% CI) in descending order of standardized effects: Annual tourist arrivals: 5.43 (4.03, 6.83); health expenditure per capita: 4.43 (2.92, 5.96); GDP (PPP): -4.60 (-6.81, -2.38); specialist surgical workforce per 100000: 2.63 (0.67, 4.59); number of physicians per 1000: -2.32 (-4.3, -0.28); economic freedom score: -1.35 (-2.60, -0.10); and total population: 1.66 (-0.19, 3.52). All VIF values were below 5, showing acceptable collinearity. R-squared (52.65%), adjusted R-squared (50.25%) and predicted R-squared (42.33%) showed strong model fit.
Conclusion: limited country-level socioeconomic and health system indicators can explain COVID-19 mortality worldwide; emphasizing the priority of attending to these fundamental structures when planning for pandemic preparedness.
Background: The Coronavirus 2019-nCOV (COVID-19) epidemic by SARS-CoV-2 is spreading worldwide, and by March 1, 2020, 67 countries, including Iran, have been affected. Many studies are being conducted at home and abroad to predict the outbreak of the disease so that they can make the necessary medical and health decisions in a timely manner.
Methods: we used the SIR model to identify parameters to calculate epidemic features and some estimates of the new coronavirus. Data on the transmission of the novel coronavirus were extracted from the GitHub source in the covid19.analytics software package.
Results: According to our model estimates, the rate of infection β = 1 and the rate of removal γ = 0.667 and index R0 = 1.497 were obtained. Because the value of R0 is more than one, it is still an epidemic disease. Given that tfinal~132 days was estimated, we can expect the transmission of this epidemic to stop in Iran after July 3, 2020, provided that existing quarantine measures and patient isolation rates continue as usual. In comparison with the global SIR model, we reached the peak of the infection earlierthan the global model, but in improved and susceptible cases, we performed better than the global model. The graph of recovered and susceptible cases in Iran earlier than the global model cut off themselves.
Conclusion: Forecasts are set to be a useful guide for deciding whether to transfer COVID-19. According to the predictions and estimates made, more attention should be paid to control measures
Aim: The aim of this study was to investigate the association between circadian rhythm with resting metabolic rate (RMR) in overweight\obese women
Methods: This cross-sectional study included 232 obese and overweight women. Morningness-Eveningness Questionnaire (MEQ) was used to assess the level of circadian rhythm. RMR was measured by indirect calorimetry after a 10-12 hour overnight fasting period by a trained nutritionist. We assessed body composition using multi-frequency bioelectrical impedance analyzer (BIA).
Results: The percentage of overweight and obese women were 48.7% (113) and 51.3% (119), respectively. The number of participants who were morningness, intermediate and eveningness was 28(12.1%), 135(58.2%) and 69(29.7%) respectively. A significant relationship was found between MEQ and RMR.normal (p=0.011). According to linear regression model non-eveningness participants had 81.92 higher RMR compared to eveningness participants (p=0.027).
Conclusion: We found that non-eveningness participants had higher RMR compared to eveningness participants that can lead to obesity, diabetes type2 and other health disorders.
Background: The rapid outbreak of Coronavirus has led to the worrying situation. Prevention strategies such as a stay at home offer great opportunities for transmission reduction of the virus. Therefore, the purpose of current study has developed a questionnaire to investigate the reasons for not staying at home in Iran.
Methods: In this study a self-administered questionnaire was designed in two Delphi rounds and based on 50 expert and 10 expert opinions from different fields of study.
Results: In the first Delphi round 11 questions were obtained and in the second round 14 questions were confirmed. The mean of CVR and CVI for the questionnaire was 95.33 and 94.67, respectively. A questionnaire was designed and developed according to the purpose.
Conclusion: Using the designed questionnaire, the reasons why some people do not pay attention to home quarantine can be examined and solutions can be considered for them. This can prevent further corona spread.
Background: Based on data from the Ministry of Health, which highlighted the earlier onset of Covid-19 epidemic in Italy, compared with the Europe, we would like to present a statistical elaboration on the impact of measures taken by the Government, during the phase 1 and the start of phase 2.
Methods: After the implementation of a Bayesian changepoint detection method, we looked for a best fit model, based on the first part of time series data, in order to observe the progress of the data in the presence and absence of the restriction measures introduced.
Results: Both the implementation of changepoint detection method and the analysis of the curves showed that the decree that marked the start of lockdown has had the effect of slowing down the epidemic by allowing the start of a plateau between 21 and 25 March. Moreover, the decree that decided the beginning of phase 2 on 4 May did not have a negative impact.
Conclusion: This statistical analysis supports the hypothesis that stringent measures decreased hospitalization, thanks to a slowing down in the evolution of the epidemic compared with what was expected.
Introduction: Respiratory distress syndrome (RDS) is not only the most common respiratory disorder in premature infants but also the main cause of neonatal mortality.
Methods: Competing risk framework was used to examine and identify potential prognostic factors of the health status of preterm infants with respiratory distress syndrome. Preterm infants with RDS admitted to the neonatal intensive care units (NICUs) of selected hospitals in Ethiopia were followed for 28 days and only neonates with complete cases were included in the analysis. The Fine-Gray or sub-distribution hazard model was used to identify significant prognostic factors. Three outcome variables (death due to RDS, death due to other causes and discharged alive) were considered.
Results: The Fine-Gray model fit results revealed that anemia, multiple pregnancies, birth-weight and gestational age were the prognostic factors significantly associated with the death of neonates due to Respiratory distress syndrome problem while Pneumonia, meningitis, anemia and gestational age of neonates were the significant prognostic factors for death of neonates due to other causes. Moreover, pneumonia, birth weight and gestational age were identified as the prognostic factors associated with neonates being discharged alive.
Conclusion: Offering intensive and adequate treatments for neonates with lowest birth-weights and gestational age may be useful to reduce neonatal mortality and increase the incidence of being discharged alive.
Introduction: Neuroinflammation is the inflammatory reaction in the central nervous system (CNS) provoked by diverse insults. This phenomenon results in a cascade of release of inflammatory mediators and intracellular messengers such as reactive oxygen species. The elicited responses are the cause of many neurological and neurodegenerative disorders. Erythropoietin (EPO) has been considered effective in attenuating this inflammatory process in the CNS, yet its administration in COVID-19 needs meticulously designed studies.
Discussion: Neuroinflammation in COVID-19 due to probable contribution of renin-angiotensin system dysregulation resulting in surplus of Ang II and owing to the synergistic interaction between this octapeptide and EPO needs special consideration. Both of these compounds increase intracellular Ca2+ which may induce release of cytokine and inflammatory mediators leading to aggravation of neuroinflammation. In addition, Ang II elevates HIF even in normoxia which by itself increases EPO. It is implicated that EPO and HIF may likely increase in patients with COVID-19 which makes administration of EPO to these patients hazardous. Furthermore, papain-like protease of SARS-CoV2 as a deubiquitinase may also increase HIF.
Conclusion: It is hypothesized that administration of EPO to patients with COVID-19-induced neuroinflammation may not be safe and in case EPO is needed for any reason in this disease adding of losartan may block AT1R-mediated post-receptor harmful effects of Ang II in synergism with EPO. Inhibition of papain-like protease might additionally decrease HIF in this disease. More in vitro, in vivo and clinical studies are needed to validate these hypotheses.
Hojjat Zeraati, PhD.
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