Measuring Australia’s Health Security Preparedness: Calculation and Application of the National Health Security Preparedness Index for Australia (preprint)

Background With on-going efforts to strengthen public health security, there is a need to identify areas for improvement. Existing tools designed to measure health security have limitations and the COVID-19 pandemic has revealed their limited predicative capabilities. The National Health Security Preparedness Index (NHSPI) developed in the United States (US) uses an expanded set of indicators beyond the health sector to quantify health security preparedness. The NHSPI has not been applied outside the US, so we aimed to calculate the NHSPI for Australia and compare it to the US to evaluate its predictive ability.Methods The NHSPI for Australia was calculated using the 140 indicators across 6 domains and 19 sub-domains described in the 2019 US release [1]. Data for each indicator was collected through grey literature searches for analogous Australian datasets. Sub-domain, domain, and national scores were computed using the formula described in the US methodology.Results The overall NHSPI score for Australia was 7.3 (99% Cl, 7.2–7.4), which is significantly higher than the score for the US during the same period 6.8 (99% Cl, 6.6–6.9). There was minimal variation between the overall scores for each Australian State and Territory, and for each of the domains.Conclusion The interpretation of the NHSPI should not be used as a predicator for population health outcomes. The greater NHSPI score for Australia than the US suggests greater homogeneity between likely demonstrates greater capacity to implement consistent of public health legislation and capacity. Further work to improve overall interpretability is needed and can be done so by refining the core NHSPI methodology to incorporate a global perspective to facilitate uptake in other national contexts.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research.

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

Analysis of the COVID-19 Pandemic: Lessons for a Contextualized and Effective Response to Public Health Emergencies (preprint)

Background: The pandemic of Coronavirus Disease 2019 (COVID-19) is a timely reminder of the nature and impact of Public Health Emergencies of International Concern. As of 27 May 2021, there were over 169 million cases and over 3.5 million deaths notified since the start of the pandemic. The COVID-19 pandemic takes variable shapes and forms in different regions and countries of the world. The objective of this study is to analyse the COVID-19 pandemic so that lessons can be learned towards an effective public health emergency response. Methods: : We conducted a mixed-methods study to understand the heterogeneity of the COVID-19 pandemic. Correlation analysis and scatter plot were employed for the quantitative data. We used Spearman’s correlation analysis. Thematic analysis was conducted on the qualitative data to explain the findings from the quantitative data. Results: : We have found that regions and countries with high human development index are most affected by COVID-19 due to international connectedness and mobility of their population related to trade and tourism, and their vulnerability related to older populations and higher rates of non-communicable diseases. The pattern of the pandemic is also variable among high- and middle-income countries due to differences in the governance of the pandemic, fragmentation of health systems, and socio-economic inequities. Conclusion: The aspiration towards a healthier and safer society requires that countries develop and implement a coherent and context-specific national strategy, improve governance of public health emergencies, build the capacity of their (public) health systems, minimize fragmentation, and tackle upstream structural issues, including socio-economic inequities. This is possible through a primary health care approach, which ensures provision of universal and equitable promotive, preventive and curative services, through whole-of-government and whole-of-society approaches.

Undiagnosed SARS-CoV-2 seropositivity during the first 6 months of the COVID-19 pandemic in the United States.

Asymptomatic SARS-CoV-2 infection and delayed implementation of diagnostics have led to poorly defined viral prevalence rates in the United States and elsewhere. To address this, we analyzed seropositivity in 9089 adults in the United States who had not been diagnosed previously with COVID-19. Individuals with characteristics that reflected the U.S. population (n = 27,716) were selected by quota sampling from 462,949 volunteers. Enrolled participants (n = 11,382) provided medical, geographic, demographic, and socioeconomic information and dried blood samples. Survey questions coincident with the Behavioral Risk Factor Surveillance System survey, a large probability-based national survey, were used to adjust for selection bias. Most blood samples (88.7%) were collected between 10 May and 31 July 2020 and were processed using ELISA to measure seropositivity (IgG and IgM antibodies against SARS-CoV-2 spike protein and the spike protein receptor binding domain). The overall weighted undiagnosed seropositivity estimate was 4.6% (95% CI, 2.6 to 6.5%), with race, age, sex, ethnicity, and urban/rural subgroup estimates ranging from 1.1% to 14.2%. The highest seropositivity estimates were in African American participants; younger, female, and Hispanic participants; and residents of urban centers. These data indicate that there were 4.8 undiagnosed SARS-CoV-2 infections for every diagnosed case of COVID-19, and an estimated 16.8 million infections were undiagnosed by mid-July 2020 in the United States.