Canadian Pandemic Influenza Preparedness: Antiviral strategy

Using machine learning to determine the time of exposure to infection by a respiratory pathogen

Given an infected host, estimating the time that has elapsed since initial exposure to the pathogen is an important problem in public health. In this paper we use longitudinal gene expression data from human challenge studies of viral respiratory illnesses for building predictive models to estimate the time elapsed since onset of respiratory infection. We apply sparsity driven machine learning to this time-stamped gene expression data to model the time of exposure by a pathogen and subsequent infection accompanied by the onset of the host immune response. These predictive models exploit the fact that the host gene expression profile evolves in time and its characteristic temporal signature can be effectively modeled using a small number of features. Predicting the time of exposure to infection to be in first 48 h after exposure produces BSR in the range of 80-90% on sequestered test data. A variety of machine learning experiments provide evidence that models developed on one virus can be used to predict exposure time for other viruses, e.g., H1N1, H3N2, and HRV. The interferon [Formula: see text] signaling pathway appears to play a central role in keeping time from onset of infection. Successful prediction of the time of exposure to a pathogen has potential ramifications for patient treatment and contact tracing.

Inhaled Zanamivir versus Oral Oseltamivir to Prevent Influenza-related Hospitalization or Death: a Nationwide Population-based Quasi-experimental Study

BACKGROUND

Individual patient data meta-analyses of randomized controlled trials show that early oseltamivir treatment for influenza cut risk of pneumonia and hospitalization by 44% and 63%, respectively. However, data are lacking for inhaled zanamivir on its effectiveness to prevent hospitalization and death.

METHODS

This nationwide, population-based cohort study included all outpatients treated with inhaled zanamivir or oral oseltamivir, within 48 hours after a clinical diagnosis of influenza, before and after the rollout of inhaled zanamivir as the first-line antiviral in Taiwan. The main outcome is influenza-related hospitalization or death within 14 days, ascertained using Taiwan national health insurance database and national death registry, respectively. Those developed the outcome within 2 days were excluded from analyses. Propensity score stratification was used to control confounding from covariates.

RESULTS

A total of 865,032 eligible influenza outpatients were included in the analysis. The risk of developing the main outcome (adjusted hazard ratio [aHR]: 1.01, 95% confidence interval [CI]: 0.96 to 1.06) did not differ between inhaled zanamivir group (n = 595,897, 68.9%, the reference) and oral oseltamivir group (n = 269,135, 31.1%). Prespecified analysis on high-risk subgroups further showed that inhaled zanamivir is not inferior to oral oseltamivir in either >65 years elderly patients (aHR: 1.14, 95% CI: 1.05 to 1.25) or patients with chronic lung diseases (aHR: 1.23, 95% CI: 1.08 to 1.41).

CONCLUSION

Inhaled zanamivir is not inferior to oral oseltamivir as outpatient treatment to prevent influenza-related hospitalization or death, for patients whose conditions do not require hospitalization within 2 days.

Assessment of biosafety implementation in clinical diagnostic laboratories in pakistan related to the COVID-19 pandemic

Laboratory diagnostic capacity is crucial for an optimal national response to a public health emergency such as the COVID-19 pandemic. Preventing laboratory-acquired infections and the loss of critical human resources, especially during a public health emergency, requires laboratories to have a good biorisk management system in place. In this study, we aimed to evaluate laboratory biosafety and biosecurity in Pakistan during the COVID-19 pandemic. In this cross-sectional study, a self-rated anonymous questionnaire was distributed to laboratory professionals (LPs) working in clinical diagnostic laboratories, including laboratories performing polymerase chain reaction (PCR)-based COVID-19 diagnostic testing in Punjab, Sindh, Khyber Pakhtunkhwa, and Gilgit-Baltistan provinces as well as Islamabad during March 2020 to April 2020. The questionnaire assessed knowledge and perceptions of LPs, resource availability, and commitment by top management in these laboratories. In total, 58.6% of LPs performing COVID-19 testing reported that their laboratory did not conduct a biorisk assessment before starting COVID-19 testing in their facility. Only 31% of LPs were aware that COVID-19 testing could be performed at a biosafety level 2 laboratory, as per the World Health Organization interim biosafety guidelines. A sufficiently high percentage of LPs did not feel confident in their ability to handle COVID-19 samples (32.8%), spills (43.1%), or other accidents (32.8%). These findings demonstrate the need for effective biosafety program implementation, proper training, and establishing competency assessment methods. These findings also suggested that identifying and addressing gaps in existing biorisk management systems through sustainable interventions and preparing LPs for surge capacity is crucial to better address public health emergencies.

Preparing for the Perpetual Challenges of Pandemics of Coronavirus Infections with Special Focus on SARS-CoV-2

COVID-19, arising from novel, zoonotic coronavirus-2, has gripped the world in a pandemic. The present chapter discusses the current internationally implemented pandemic preparedness strategies succeeding/recommended to curb the COVID-19 threat to humankind. The updated phase-wise categorization of a pandemic as recommended by the WHO is described, and associated innovations in surveillance, response, and medical measures/advisory in practice across the globe are elaborated. From a bird’s eye view, the COVID-19 pandemic management relies on revolutionizing the disease surveillance by incorporating artificial intelligence and data analytics, boosting the response strategies—extensive testing, case isolation, contact tracing, and social distancing—and promoting awareness and access to pharmaceutical and non-pharmaceutical interventions, which are discussed in the present chapter. We also preview the economic bearing of the COVID-19 pandemic.

Canadian pandemic influenza preparedness: Public health measures strategy

Public health measures, also known as non-pharmaceutical interventions, are basic actions aimed at slowing the community spread of a communicable disease outbreak. In the event of an influenza pandemic, public health measures and antiviral drugs are the only tools available to mitigate the effects of the pandemic during the months before a vaccine becomes available. The Canadian Pandemic Influenza Preparedness: Planning Guidance for the Health Sector (CPIP) outlines how federal, provincial and territorial governments will work together to ensure a coordinated and consistent health sector approach to pandemic influenza preparedness and response. This article summarizes Canada's pandemic public health measures strategy, as described in the recently updated CPIP Public Health Measures Annex. The strategy builds on lessons learned during the 2009 H1N1 pandemic. Key elements of the public health measures strategy include individual measures (e.g. hand hygiene, self-isolation when ill), community-based measures (e.g. school closures, cancellation of mass gatherings), management of cases and close contacts, travel and border-related actions and public education. Factors that influence the effectiveness of public health measures in a pandemic include the pandemic epidemiology, timing of implementation, how the measures are used (i.e. alone or in combination), the scalability and flexibility of public compliance. The CPIP is an evergreen guidance document and the Annex will be updated as new information warrants.