Risk Factors for Post-Traumatic Stress Disorder (PTSD) in COVID Survivors A Cross-Sectional Study (preprint)

The study aimed to investigate the impact of demographic, socio-economic, health, and lifestyle variables on the development of PTSD symptoms in COVID survivors. The study used a cross-sectional design, and data were collected via a standard set of questionnaires from 228 COVID survivors, who required oxygen support and were admitted to Damak COVID hospital from April to October 2021. Descriptive statistics such as frequency and percentage were used to summarize the data and inferential statistics such as chi-square test, Fishers exact test, and Binary logistic regression were used to analyze the data and to infer the overall result from the taken sample. The study found that only three variables, i.e., gender, diabetes, and chronic obstructive pulmonary disorder (COPD), were significant factors that posed a higher threat of PTSD in COVID survivors. Additionally, the study uses model adequacy tests such as Pseudo R2 test, Reliability test and Hosmer and Lemeshow test to validate the model fitted. The study found that only three variables had significant impact PTSD symptoms in COVID survivors. Male patients were more likely to have PTSD symptoms than female patients. The presence of diabetes before or after the infection increased the risk of PTSD. The patients with high blood pressure before COVID and those who developed chronic obstructive pulmonary disorder (COPD) after COVID were more likely to experience PTSD symptoms. The study provides valuable information on the risk factors for developing PTSD symptoms in COVID survivors. This study can contribute to the understanding and growing body of research on the psychological impact of COVID and help healthcare professionals identify patients who are at risk of developing PTSD and provide them with appropriate interventions to prevent or treat PTSD.

Trend analysis, modelling and impact assessment of COVID-19 in Nepal (preprint)

With continued global expansion of COVID-19 transmission and mounting threat of the disease, the timely analysis of its trend in Nepal and forecasting the potential situation in the country has been deemed necessary. We analyzed the trend, modelling and impact assessment of COVID-19 cases of Nepal from 23rd January 2020 to 30th April 2020 to portray the scenario of COVID-19 after the first phase of lockdown. Exponential smoothing state-space and autoregressive integrated moving average (ARIMA) models were constructed to forecast the cases. Susceptible-infectious-recovered (SIR) model was fit to estimate the basic reproduction number (Ro) of COVID-19 in Nepal. There has been increase in the number of cases but the overall growth in COVID-19 was not high. Statistical modelling has shown that COVID-19 cases may continue to increase exponentially in Nepal. The basic reproduction number in Nepal being maintained at low level of 1.08 for the period of 23rd January to 30th April 2020 is an indication of effectiveness of lockdown in containing the COVID-19 spread. The models further suggest that COVID-19 might persist until December 2020 with peak cases in August 2020. On the other hand, basic reproduction number of 1.25 was computed for total cases reported for the 22nd March to 30th April 2020 period implying that COVID-19 may remain for at least for a year in the country. Thus, maintaining social distance and stay home policy with an implementation of strict lockdown in COVID-19 affected district is highly recommended.