Analysis of non-pharmaceutical interventions impacts on COVID-19 pandemic in Iran

The prevalence of 17 common respiratory viruses in patients with respiratory illness but negative for COVID-19: A cross-sectional study

Background and Aims

Second to COVID-19 pandemic, other viral respiratory infections are still important causes of human diseases or co-infections. Hence, the present study was carried out to investigate the common respiratory viruses in patients with respiratory illness diagnosed negative for severe acute respiratory syndrome coronavirus-2 in primary screening.

Methods

In a cross-sectional study, a real-time PCR was carried out using HiTeq. 17 Viro Respiratory pathogen One Step RT-PCR Kit (Genova, Bonda Faravar, Bioluence, Tehran, Iran).

Results

A total of 311 individuals (mean age ± SD: 48.2 ± 21.7 years, range: 1-97 years) underwent second PCR. Among these, 161 (51.7%) were female. In total, 55 (17.6%) cases (mean age ± SD: 45.7 ± 18.1 years) were found positive for respiratory viruses panel in the second PCR. The HCoV-OC43/HKU1 was in 5.4% (17/311), Flu A in 4.5% (14/311), HCoV-229E/NL63 in 2.8% (9/311), HMPV in 1.9% (6/311), HPiV 1, 2, 3 in 1.2% (4/311), HRSV in 0.9% (3/311), and HAdV in 0.6% (2/311) of the cases studies. Also, co-infection was detected in 4 samples (1.2%). In addition, sore throat (0.028), headache (p = 0.016), and body pain (p = 0.0001) were statistically the most significant symptoms in studied cases.

Conclusion

According to the findings of our study, respiratory virus infections and co-infections were 17.6% and 1.2% frequent, respectively. Interestingly, nearly half of our positive cases (47.2%) were identified by coronaviruses (ОС43, Е229, NL63, and HKUI), followed by influenza A virus (25.4%). However, for more comprehensive results, we recommend using greater sample size.

Assessing the effectiveness of the intervention measures of COVID-19 in China based on dynamical method

Normalized interventions were implemented in different cities in China to contain the outbreak of COVID-19 before December 2022. However, the differences in the intensity and timeliness of the implementations lead to differences in final size of the infections. Taking the outbreak of COVID-19 in three representative cities Xi'an, Zhengzhou and Yuzhou in January 2022, as examples, we develop a compartmental model to describe the spread of novel coronavirus and implementation of interventions to assess concretely the effectiveness of Chinese interventions and explore their impact on epidemic patterns. After applying reported human confirmed cases to verify the rationality of the model, we apply the model to speculate transmission trend and length of concealed period at the initial spread phase of the epidemic (they are estimated as 10.5, 7.8, 8.2 days, respectively), to estimate the range of basic reproduction number (2.9, 0.7, 1.6), and to define two indexes (transmission rate v _t and controlled rate v _c ) to evaluate the overall effect of the interventions. It is shown that for Zhengzhou, v _c is always more than v _t with regular interventions, and Xi'an take 8 days to achieve v _c  > v _t twice as long as Yuzhou, which can interpret the fact that the epidemic situation in Xi'an was more severe. By carrying out parameter values, it is concluded that in the early stage, strengthening the precision of close contact tracking and frequency of large-scale nucleic acid testing of non-quarantined population are the most effective on controlling the outbreaks and reducing final size. And, if the close contact tracking strategy is sufficiently implemented, at the late stage large-scale nucleic acid testing of non-quarantined population is not essential.