Policy and Science for Global Health Security: Shaping the Course of International Health

Managing the Risk of COVID-19 Using Model Based Predictors: The Case of North Dakota

OBJECTIVE

North Dakota (ND) had the highest coronavirus disease 2019 (COVID-19) case and mortality rate in the United States for nearly 2 mo. This study aims to compare 3 metrics ND used to guide public health action across its 53 counties.

METHODS

Daily COVID-19 case and death totals in North Dakota were evaluated using data from the COVID-tracker website provided by the North Department of Health (NDDoH). It was reported as: active cases per 10,000, tests administered per 10,000, and test positivity rate (the North Dakota health metric). The COVID-19 Response press conferences provided data for the Governor's metric. The Harvard model used daily new cases per 100,000. A chi-squared test was used to compare differences in these 3 metrics on July 1, August 26, September 23, and November 13, 2020.

RESULTS

On July 1, no significant difference between the metrics was found. By September 23, Harvard's health metric indicated critical risk while ND's health metric was moderate risk, and the Governor's metric was still low risk.

CONCLUSIONS

ND's and the Governor's metric underrepresented the risk of the COVID-19 outbreak in North Dakota. The Harvard metric reflected North Dakota's increasing risk; it should be considered as a national standard in future pandemics.

PUBLIC HEALTH IMPLICATIONS

Model-based predictors could guide policy-makers to effectively control spread of infectious disease; proactive models could reduce risk of disease as it progresses in vulnerable communities.

Surveillance of emerging infectious diseases for biosecurity

Emerging infectious diseases, such as COVID-19, continue to pose significant threats to human beings and their surroundings. In addition, biological warfare, bioterrorism, biological accidents, and harmful consequences arising from dual-use biotechnology also pose a challenge for global biosecurity. Improving the early surveillance capabilities is necessary for building a common biosecurity shield for the global community of health for all. Furthermore, surveillance could provide early warning and situational awareness of biosecurity risks. However, current surveillance systems face enormous challenges, including technical shortages, fragmented management, and limited international cooperation. Detecting emerging biological risks caused by unknown or novel pathogens is of particular concern. Surveillance systems must be enhanced to effectively mitigate biosecurity risks. Thus, a global strategy of meaningful cooperation based on efficient integration of surveillance at all levels, including interdisciplinary integration of techniques and interdepartmental integration for effective management, is urgently needed. In this paper, we review the biosecurity risks by analyzing potential factors at all levels globally. In addition to describing biosecurity risks and their impact on global security, we also focus on analyzing the challenges to traditional surveillance and propose suggestions on how to integrate current technologies and resources to conduct effective global surveillance.

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.

From Climate Change to Pandemics: Decision Science Can Help Scientists Have Impact

Scientific knowledge and advances are a cornerstone of modern society. They improve our understanding of the world we live in and help us navigate global challenges including emerging infectious diseases, climate change and the biodiversity crisis. However, there is a perpetual challenge in translating scientific insight into policy. Many articles explain how to better bridge the gap through improved communication and engagement, but we believe that communication and engagement are only one part of the puzzle. There is a fundamental tension between science and policy because scientific endeavors are rightfully grounded in discovery, but policymakers formulate problems in terms of objectives, actions and outcomes. Decision science provides a solution by framing scientific questions in a way that is beneficial to policy development, facilitating scientists’ contribution to public discussion and policy. At its core, decision science is a field that aims to pinpoint evidence-based management strategies by focussing on those objectives, actions, and outcomes defined through the policy process. The importance of scientific discovery here is in linking actions to outcomes, helping decision-makers determine which actions best meet their objectives. In this paper we explain how problems can be formulated through the structured decision-making process. We give our vision for what decision science may grow to be, describing current gaps in methodology and application. By better understanding and engaging with the decision-making processes, scientists can have greater impact and make stronger contributions to important societal problems.

Evaluation of algal active compounds as potent antibiofilm agent

Biofilm is the syntrophic association of microbial colonies that remain adhered to the biotic and abiotic surfaces with the help of self-secreted polymeric substances also termed extracellular polymeric substances. Chronic pathogenicity caused by biofilm-associated pathogenic microorganisms becomes a significant threat in biomedical research. An extensive search is being made for the antibiofilm agents made from natural sources or their biogenic derivatives due to their effectivity and nontoxicity. Algae being the producer of various biogenic substances are found capable of disintegrating biofilm matrix and eradication of biofilm without exerting any deterrent effect on other biotas in the ecosystem. The current trend in phycological studies includes the exploration of antifouling efficacy among various algal groups. The extracts prepared from about 225 microalgae and cyanobacteria species are found to have antibiofilm activity. Polyunsaturated fatty acids are the most important component in the algal extract with antibacterial and antibiofilm properties. The antibiofilm activity of the sulfated polysaccharides extracted from a marine alga could be effectively used to remove dental biofilm. Algal extracts are also being used for the preparation of different biogenically synthesized nanoparticles, which are being used as potent antibiofilm agents. Genome editing of algal species by CRISPR/Cas9 may make precise modifications in the algal DNA for improving the algal strains and production of a more effective antibiofouling agent.

The Epidemic Failure Cycle hypothesis: Towards understanding the global community's recent failures in responding to an epidemic

BACKGROUND

Within a few years, the global community has failed twice in responding to large viral infection outbreaks: the Ebola epidemic in 2014 and the SARS-Cov-2 pandemic in 2020. There is, however, no systematic approach or research available that analyses the repeated failures with regard to an adequate response to an epidemic.

METHODS

For a better understanding of failing societal responses, we have analysed the available research literature on societal responses to epidemics and we propose a framework called the 'Epidemic Failure Cycle' (EFC).

RESULTS

The EFC consists of four phases: Negligence, Arrogance/Denial, Panic and Analysis/Self-criticism. These phases fit largely with the current World Health Organization pandemic influenza phases: Interpandemic, Alert, Pandemic, Transition. By utilizing the Ebola epidemic and the SARS-Cov-2 pandemic as case studies, we show striking similarities in the response to these outbreaks during both crises. Finally, we suggest three major areas to be of utmost importance for triggering and maintaining the EFC. In terms of ecology, zoonoses, supposed to be the main biological origin for virus epidemics, have been largely neglected by politicians, the media and the scientific community. Socioeconomic and cultural conditions such as harsh living and working conditions as well as conspiracy theories hinder effective preventive and counter measures against epidemics. Lastly, in terms of epistemology, the reliance on knowledge about previous outbreaks has led to slow and inadequate decisions.

CONCLUSIONS

We conclude that any current society has to be aware of the risks of repeating responses to epidemics that will fail. Being aware of the societal mechanisms that trigger inadequate responses may help to get to more appropriate decisions in the face of an epidemic.

The Open-Specimen Movement

The open-science movement seeks to increase transparency, reproducibility, and access to scientific data. As primary data, preserved biological specimens represent records of global biodiversity critical to research, conservation, national security, and public health. However, a recent decrease in specimen preservation in public biorepositories is a major barrier to open biological science. As such, there is an urgent need for a cultural shift in the life sciences that normalizes specimen deposition in museum collections. Museums embody an open-science ethos and provide long-term research infrastructure through curation, data management and security, and community-wide access to samples and data, thereby ensuring scientific reproducibility and extension. We propose that a paradigm shift from specimen ownership to specimen stewardship can be achieved through increased open-data requirements among scientific journals and institutional requirements for specimen deposition by funding and permitting agencies, and through explicit integration of specimens into existing data management plan guidelines and annual reporting.

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

MOTIVATION

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, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy.

RESULTS

Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets 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, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. 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.

AVAILABILITY

https://neo4covid19.ncats.io.