COVID-19 versus seasonal influenza 2019-2020: USA

Understanding the Role of Environmental Transmission on COVID-19 Herd Immunity and Invasion Potential

COVID-19 is caused by the SARS-CoV-2 virus, which is mainly transmitted directly between humans. However, it is observed that this disease can also be transmitted through an indirect route via environmental fomites. The development of appropriate and effective vaccines has allowed us to target and anticipate herd immunity. Understanding of the transmission dynamics and the persistence of the virus on environmental fomites and their resistive role on indirect transmission of the virus is an important scientific and public health challenge because it is essential to consider all possible transmission routes and route specific transmission strength to accurately quantify the herd immunity threshold. In this paper, we present a mathematical model that considers both direct and indirect transmission modes. Our analysis focuses on establishing the disease invasion threshold, investigating its sensitivity to both transmission routes and isolate route-specific transmission rate. Using the tau-leap algorithm, we perform a stochastic model simulation to address the invasion potential of both transmission routes. Our analysis shows that direct transmission has a higher invasion potential than that of the indirect transmission. As a proof of this concept, we fitted our model with early epidemic data from several countries to uniquely estimate the reproduction numbers associated with direct and indirect transmission upon confirming the identifiability of the parameters. As the indirect transmission possess lower invasion potential than direct transmission, proper estimation and necessary steps toward mitigating it would help reduce vaccination requirement.

The Effect of Coronavirus 2019 Disease Control Measures on the Incidence of Respiratory Infectious Disease and Air Pollutant Concentrations in the Yangtze River Delta Region, China

The Yangtze River Delta is one of the top five Chinese regions affected by COVID-19, as it is adjacent to Hubei Province, where COVID-19 first emerged. We investigated the impact of COVID-19 non-pharmaceutical interventions (NPIs) on changes in respiratory infectious diseases (RIDs) incidence and air quality in the Yangtze River Delta by constructing two proportional tests and fitting ARIMA and linear regression models. Compared with the pre-COVID-19 period, the average monthly incidence of seven RIDs decreased by 37.80% (p < 0.001) and 37.11% (p < 0.001) during the COVID-19 period and the post-vaccination period, respectively, in Shanghai, and decreased by 20.39% (p < 0.001) and 22.86% (p < 0.001), respectively, in Zhejiang. Similarly, compared with the pre-COVID-19 period, the monthly overall concentrations of six air pollutants decreased by 12.7% (p = 0.003) and 18.79% (p < 0.001) during the COVID-19 period and the post-vaccination period, respectively, in Shanghai, and decreased by 12.85% (p = 0.008) and 15.26% (p = 0.001), respectively, in Zhejiang. Interestingly, no significant difference in overall incidence of RIDs and concentrations of air quality was shown between the COVID-19 period and the post-vaccination period in either Shanghai or Zhejiang. This study provides additional evidence that the NPIs measures taken to control COVID-19 were effective in improving air quality and reducing the spread of RIDs. However, a direct causal relationship has not been established.

Effects of the Enhanced Public Health Intervention during the COVID‐19 Epidemic on Respiratory and Gastrointestinal Infectious Diseases in China

The public health interventions to mitigate coronavirus disease 2019 (COVID‐19) could also potentially reduce the global activity of influenza. However, this strategy's impact on other common infectious diseases is unknown. We collected data of 10 respiratory infectious (RI) diseases, influenza‐like illnesses (ILIs), and seven gastrointestinal infectious (GI) diseases during 2015–2020 in China and applied two proportional tests to check the differences in the yearly incidence and mortality, and case‐fatality rates (CFRs) over the years 2015–2020. The results showed that the overall RI activity decreased by 7.47%, from 181.64 in 2015–2019 to 168.08 per 100 000 in 2020 (p < 0.001); however, the incidence of influenza was seen to have a 16.08% escalation (p < 0.001). In contrast, the average weekly ILI percentage and positive influenza virus rate decreased by 6.25% and 61.94%, respectively, in 2020 compared to the previous 5 years (all p < 0.001). The overall incidence of GI decreased by 45.28%, from 253.73 in 2015–2019 to 138.84 in 2020 per 100 000 (p < 0.001), and with the greatest decline seen in hand, foot, and mouth disease (HFMD) (64.66%; p < 0.001). The mortality and CFRs from RI increased by 128.49% and 146.95%, respectively, in 2020, compared to 2015–2019 (p < 0.001). However, the mortality rates and CFRs of seven GI decreased by 70.56% and 46.12%, respectively (p < 0.001). In conclusion, China's COVID‐19 elimination/containment strategy is very effective in reducing the incidence rates of RI and GI, and ILI activity, as well as the mortality and CFRs of GI diseases.

COVID‐19 elimination strategy in China decreased the activity of respiratory infectious diseases (RI) and influenza‐like illnesses.

A 16.08% escalation was seen in influenza in 2020 compared to 2015–2019.

The mortality of RI increased slightly in 2020 compared to 2015–2019.

COVID‐19 elimination strategy in China decreased dramatically the activity and mortality of gastrointestinal infectious diseases.

To vaccinate or not to vaccinate; that is the question! (New insights on COVID-19 Vaccination)

AIM

This is a mini-review of the literature; to discuss the obstacles and benefits of vaccination in the era of current pandemic, either the COVID-19 vaccines, which are on their way to be released or the influenza vaccines. There is much debate concerning their effectiveness on ameliorating the severity of the COVID-19 pandemic.

METHODOLOGY

Searching the literature till November 2020 in the PubMed database.

RESULTS

Pathophysiology behind the COVID-19 vaccination obstacles is discussed in detail with future hopes. Influenza vaccination during the debate of the pandemic is also discussed with the most recent guidelines.

CONCLUSIONS

During the COVID-19 pandemic, influenza vaccination is mandatory for all individuals provided no contraindications. Three SARS-CoV-2 vaccines are being released , while FDA approval for monoclonal antibodies for the treatment of at-risk outpatients to lower hospitalization rates is ongoing.

Characteristics of Critically Ill Patients with COVID-19 Compared to Patients with Influenza-A Single Center Experience

Infections with SARS-CoV-2 spread worldwide early in 2020. In previous winters, we had been treating patients with seasonal influenza. While creating a larger impact on the health care systems, comparisons regarding the intensive care unit (ICU) courses of both diseases are lacking. We compared patients with influenza and SARS-CoV-2 infections treated at a tertiary care facility offering treatment for acute respiratory distress syndrome (ARDS) and being a high-volume facility for extracorporeal membrane oxygenation (ECMO). Patients with COVID-19 during the first wave of the pandemic (n = 64) were compared to 64 patients with severe influenza from 2016 to 2020 at our ICU. All patients were treated using a standardized protocol. ECMO was used in cases of severe ARDS. Both groups had similar comorbidities. Time in ICU and mortality were not significantly different, yet mortality with ECMO was high amongst COVID-19 patients with approximately two-thirds not surviving. This is in contrast to a mortality of less than 40% in influenza patients with ECMO. Mortality was higher than estimated by SAPSII score on admission in both groups. Patients with COVID-19 were more likely to be male and non-smokers than those with influenza. The outcomes for patients with severe disease were similar. The study helps to understand similarities and differences between patients treated for severe influenza infections and COVID-19.

Negative impact of the COVID-19 pandemic on the timely diagnosis of tick-borne infections

We describe 3 adult patients who did not have COVID-19 but instead had a treatable tick-borne infection. In each case, however, the duration of time until diagnosis was delayed due to issues that have arisen because of the COVID-19 pandemic. These issues need to be addressed to preserve patient well-being.

Artificial Intelligence Empowers Radiologists to Differentiate Pneumonia Induced by COVID-19 versus Influenza Viruses

Background

Given the current pandemic, differentiation between pneumonia induced by COVID-19 or influenza viruses is of utmost clinical significance in the patients' management. For this purpose, this study was conducted to develop sensitive artificial intelligence (AI) models to assist radiologists to decisively differentiate pneumonia due to COVID-19 versus influenza viruses.

Methods

Cross sectional chest CT images (N=12744) from well-evaluated cases of pneumonias induced by COVID-19 or H1N1 Influenza viruses, and normal individuals were collected. We examined the computer tomographic (CT) chest images from 137 individuals. Various pre-trained convolutional neural network models, such as ResNet-50, InceptionV3, Wide ResNet, SqueezNet, VGG 16 and VGG 19 were fine-tuned on our datasets. The datasets were used for training (60%), validation (20%), and testing (20%) of the final models. Also, the predictive power and means of precision and recall were determined for each model.

Results

Fine-tuned ResNet-50 model differentiated the pneumonia due to COVID-19 or H1N1 influenza virus with accuracies of 96.7% and 92%, respectively This model outperformed all others, i.e., InceptionV3, Wide ResNet, SqueezNet, VGG 16 and VGG 19.

Conclusion

Fine-tuned and pre-trained image classifying models of AI enable radiologists to reliably differentiate the pneumonia induced by COVID-19 versus H1N1 influenza virus. For this purpose, ResNet-50 followed by InceptionV3 models proved more promising than other AI models. Also in the supplements, we share the source codes and our fine-tuned models for use by researchers and clinicians globally toward the critical task of image differentiation of patients infected with COVID-19 versus H1N1 Influenza viruses.