Infectious disease pandemic planning and response: Incorporating decision analysis

A comprehensive analysis of non-pharmaceutical interventions and vaccination on Ebolavirus disease outbreak: Stochastic modeling approach

Ebolavirus disease (EVD) outbreaks have intermittently occurred since the first documented case in the 1970s. Due to its transmission characteristics, large outbreaks have not been observed outside Africa. However, within the continent, significant outbreaks have been attributed to factors such as endemic diseases with similar symptoms and inadequate medical infrastructure, which complicate timely diagnosis. In this study, we employed a stochastic modeling approach to analyze the spread of EVD during the early stages of an outbreak, with an emphasis on inherent risks. We developed a model that considers healthcare workers and unreported cases, and assessed the effect of non-pharmaceutical interventions (NPIs) using actual data. Our results indicate that the implementation of NPIs led to a decrease in the transmission rate and infectious period by 30% and 40% respectively, following the declaration of the outbreak. We also investigated the risks associated with delayed outbreak recognition. Our simulations suggest that, when accounting for NPIs and recognition delays, prompt detection could have resulted in a similar outbreak scale, with approximately 50% of the baseline NPIs effect. Finally, we discussed the potential effects of a vaccination strategy as a follow-up measure after the outbreak declaration. Our findings suggest that a vaccination strategy can reduce both the burden of NPIs and the scale of the outbreak.

Ethical frameworks should be applied to computational modelling of infectious disease interventions

This perspective is part of an international effort to improve epidemiological models with the goal of reducing the unintended consequences of infectious disease interventions. The scenarios in which models are applied often involve difficult trade-offs that are well recognised in public health ethics. Unless these trade-offs are explicitly accounted for, models risk overlooking contested ethical choices and values, leading to an increased risk of unintended consequences. We argue that such risks could be reduced if modellers were more aware of ethical frameworks and had the capacity to explicitly account for the relevant values in their models. We propose that public health ethics can provide a conceptual foundation for developing this capacity. After reviewing relevant concepts in public health and clinical ethics, we discuss examples from the COVID-19 pandemic to illustrate the current separation between public health ethics and infectious disease modelling. We conclude by describing practical steps to build the capacity for ethically aware modelling. Developing this capacity constitutes a critical step towards ethical practice in computational modelling of public health interventions, which will require collaboration with experts on public health ethics, decision support, behavioural interventions, and social determinants of health, as well as direct consultation with communities and policy makers.

Having a real say: findings from first nations community panels on pandemic influenza vaccine distribution

BACKGROUND

Recent deliberations by Australian public health researchers and practitioners produced an ethical framework of how decisions should be made to distribute pandemic influenza vaccine. The outcome of the deliberations was that the population should be considered in two categories, Level 1 and Level 2, with Level 1 groups being offered access to the pandemic influenza vaccine before other groups. However, the public health researchers and practitioners recognised the importance of making space for public opinion and sought to understand citizens values and preferences, especially First Nations peoples.

METHODS

We conducted First Nations Community Panels in two Australian locations in 2019 to assess First Nations people's informed views through a deliberative process on pandemic influenza vaccination distribution strategies. Panels were asked to make decisions on priority levels, coverage and vaccine doses.

RESULTS

Two panels were conducted with eighteen First Nations participants from a range of ages who were purposively recruited through local community networks. Panels heard presentations from public health experts, cross-examined expert presenters and deliberated on the issues. Both panels agreed that First Nations peoples be assigned Level 1 priority, be offered pandemic influenza vaccination before other groups, and be offered two doses of vaccine. Reasons for this decision included First Nations people's lives, culture and families are important; are at-risk of severe health outcomes; and experience barriers and challenges to accessing safe, quality and culturally appropriate healthcare. We found that communication strategies, utilising and upskilling the First Nations health workforce, and targeted vaccination strategies are important elements in pandemic preparedness and response with First Nations peoples.

CONCLUSIONS

First Nations Community Panels supported prioritising First Nations peoples for pandemic influenza vaccination distribution and offering greater protection by using a two-dose full course to fewer people if there are initial supply limitations, instead of one dose to more people, during the initial phase of the vaccine roll out. The methodology and findings can help inform efforts in planning for future pandemic vaccination strategies for First Nations peoples in Australia.

Rapid prototyping of models for COVID-19 outbreak detection in workplaces

Early case detection is critical to preventing onward transmission of COVID-19 by enabling prompt isolation of index infections, and identification and quarantining of contacts. Timeliness and completeness of ascertainment depend on the surveillance strategy employed. This paper presents modelling used to inform workplace testing strategies for the Australian government in early 2021. We use rapid prototype modelling to quickly investigate the effectiveness of testing strategies to aid decision making. Models are developed with a focus on providing relevant results to policy makers, and these models are continually updated and improved as new questions are posed. Developed to support the implementation of testing strategies in high risk workplace settings in Australia, our modelling explores the effects of test frequency and sensitivity on outbreak detection. We start with an exponential growth model, which demonstrates how outbreak detection changes depending on growth rate, test frequency and sensitivity. From the exponential model, we learn that low sensitivity tests can produce high probabilities of detection when testing occurs frequently. We then develop a more complex Agent Based Model, which was used to test the robustness of the results from the exponential model, and extend it to include intermittent workplace scheduling. These models help our fundamental understanding of disease detectability through routine surveillance in workplaces and evaluate the impact of testing strategies and workplace characteristics on the effectiveness of surveillance. This analysis highlights the risks of particular work patterns while also identifying key testing strategies to best improve outbreak detection in high risk workplaces.

Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020

As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast of daily COVID-19 cases for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response.

Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic

Decision analysis (DA) is an explicitly prescriptive discipline that separates beliefs about uncertainties from value preferences in modeling to support decision making. Researchers have been advancing DA tools for the last 60 years to support decision makers handling complex decisions requiring subjective judgments. Recently, some DA researchers and practitioners wondered whether the difficult decisions made during the COVID-19 pandemic regarding testing, masking, closing and reopening businesses, allocating ventilators, and prioritizing vaccines would have been improved with more DA involvement. With its focus on quantifying uncertainties, value trade-offs, and risk attitudes, DA should have been a valuable tool for decision makers during the pandemic. To influence decisions, DA applications require interactions with policymakers and experts to construct formal representations of the decision frame, elicit uncertainties, and assess risk tolerances and trade-offs among competing objectives. Unfortunately, such involvement of decision analysts in the process of decision making and policy setting did not occur during much of the COVID-19 pandemic. This lack of participation may have been partly because many decision makers were unaware of when DA could be valuable in helping with the challenges of the COVID-19 pandemic. In addition, decision analysts were perhaps not sufficiently adept at inserting themselves into the policy process at critical junctures when their expertise could have been helpful.

Funding: This research was partially supported by the U.S. Department of Homeland Security through the Center for Accelerating Operational Efficiency at Arizona State University.

Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review

The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. We selected 109 out of 8,737 studies retrieved from databases and analysed them based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design, as well as computational modelling support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters.

Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology

Probabilistic predictions support public health planning and decision making, especially in infectious disease emergencies. Aggregating outputs from multiple models yields more robust predictions of outcomes and associated uncertainty. While the selection of an aggregation method can be guided by retrospective performance evaluations, this is not always possible. For example, if predictions are conditional on assumptions about how the future will unfold (e.g. possible interventions), these assumptions may never materialize, precluding any direct comparison between predictions and observations. Here, we summarize literature on aggregating probabilistic predictions, illustrate various methods for infectious disease predictions via simulation, and present a strategy for choosing an aggregation method when empirical validation cannot be used. We focus on the linear opinion pool (LOP) and Vincent average, common methods that make different assumptions about between-prediction uncertainty. We contend that assumptions of the aggregation method should align with a hypothesis about how uncertainty is expressed within and between predictions from different sources. The LOP assumes that between-prediction uncertainty is meaningful and should be retained, while the Vincent average assumes that between-prediction uncertainty is akin to sampling error and should not be preserved. We provide an R package for implementation. Given the rising importance of multi-model infectious disease hubs, our work provides useful guidance on aggregation and a deeper understanding of the benefits and risks of different approaches.

Experiencing the COVID-19 Emergency: Age-Related Disequilibrating Event for Identity

The experience linked to the COVID-19 emergency constituted a turning point in the biography of most Italians. The suspension of usual activities, the redefinition of life contexts and the restriction of relationships have opened up wide spaces and time for thinking and reflecting on oneself, which may have triggered processes of redefinition of personal identity. The general aim of this study was to explore the impact of pandemic on daily life in the life span, in order to support the hypothesis that the pandemic experience could be considered a disequilibrating life-event and a turning point in the biography of most Italians. A mixed research approach was adopted, with 14 closed and open questions created ad hoc. 41 participants (87% women, average age 40.71), resident in the Campania region, in southern Italy, responded to the online written interview. The data were analyzed by two independent coders, using categorical content analysis with a top-down approach. Membership of the different age groups (young adults, adults, elderly) was assessed as a comparison variable. Findings qualify pandemic-related experiences as a disequilibrating life event, potentially capable of activating, alongside emotionally dense experiences, adaptive and functional resources for identity reconsideration, with differences being age based. The dimensions of change, the affective dimensions, the resources and the areas of risk identified, allowed us to identify three different clusters, showing a differentiation according to age groups, which identifies young adults and the elderly as the subjects most at risk.

When might host heterogeneity drive the evolution of asymptomatic, pandemic coronaviruses?

Controlling many infectious diseases, including SARS-Coronavirus-2 (SARS-CoV-2), requires surveillance followed by isolation, contact-tracing and quarantining. These interventions often begin by identifying symptomatic individuals. However, actively removing pathogen strains causing symptomatic infections may inadvertently select for strains less likely to cause symptomatic infections. Moreover, a pathogen's fitness landscape is structured around a heterogeneous host pool; uneven surveillance efforts and distinct transmission risks across host classes can meaningfully alter selection pressures. Here, we explore this interplay between evolution caused by disease control efforts and the evolutionary consequences of host heterogeneity. Using an evolutionary epidemiology model parameterized for coronaviruses, we show that intense symptoms-driven disease control selects for asymptomatic strains, particularly when these efforts are applied unevenly across host groups. Under these conditions, increasing quarantine efforts have diverging effects. If isolation alone cannot eradicate, intensive quarantine efforts combined with uneven detections of asymptomatic infections (e.g., via neglect of some host classes) can favor the evolution of asymptomatic strains. We further show how, when intervention intensity depends on the prevalence of symptomatic infections, higher removal efforts (and isolating symptomatic cases in particular) more readily select for asymptomatic strains than when these efforts do not depend on prevalence. The selection pressures on pathogens caused by isolation and quarantining likely lie between the extremes of no intervention and thoroughly successful eradication. Thus, analyzing how different public health responses can select for asymptomatic pathogen strains is critical for identifying disease suppression efforts that can effectively manage emerging infectious diseases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11071-022-07548-7.

Evaluating COVID-19 decision-making in a humanitarian setting: The case study of Somalia

The global COVID-19 pandemic is unprecedented in its scope and impact. While a great deal of research has been directed towards the response in high-income countries, relatively little is known about the way in which decision-makers in low-income and crisis-affected countries have contended with the epidemic. Through use of an a priori decision framework, we aimed to evaluate the process of policy and operational decision-making in relation to the COVID-19 response in Somalia, a chronically fragile country, focusing particularly on the use of information and the role of transparency. We undertook a desk review, observed a number of key decision-making fora and conducted a series of key informant and focus group discussions with a range of decision-makers including state authority, civil society, humanitarian and development actors. We found that nearly all actors struggled to make sense of the scale of the epidemic and form an appropriate response. Decisions made during the early months had a large impact on the course of the epidemic response. Decision-makers relied heavily on international norms and were constrained by a number of factors within the political environment including resource limitations, political contestation and low population adherence to response measures. Important aspects of the response suffered from a transparency deficit and would have benefitted from more inclusive decision-making. Development of decision support tools appropriate for crisis-affected settings that explicitly deal with individual and environmental decision factors could lead to more effective and timely epidemic response.

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.

Assessing gender responsiveness of COVID-19 response plans for populations in conflict-affected humanitarian emergencies

Background

The COVID-19 pandemic has necessitated rapid development of preparedness and response plans to quell transmission and prevent illness across the world. Increasingly, there is an appreciation of the need to consider equity issues in the development and implementation of these plans, not least with respect to gender, given the demonstrated differences in the impacts both of the disease and of control measures on men, women, and non-binary individuals. Humanitarian crises, and particularly those resulting from conflict or violence, exacerbate pre-existing gender inequality and discrimination. To this end, there is a particularly urgent need to assess the extent to which COVID-19 response plans, as developed for conflict-affected states and forcibly displaced populations, are gender responsive.

Methods

Using a multi-step selection process, we identified and analyzed 30 plans from states affected by conflict and those hosting forcibly displaced refugees and utilized an adapted version of the World Health Organization’s Gender Responsive Assessment Scale (WHO-GRAS) to determine whether existing COVID-19 response plans were gender-negative, gender-blind, gender-sensitive, or gender-transformative.

Results

We find that although few plans were gender-blind and none were gender-negative, no plans were gender-transformative. Most gender-sensitive plans only discuss issues specifically related to women (such as gender-based violence and reproductive health) rather than mainstream gender considerations throughout all sectors of policy planning.

Conclusions

Despite overwhelming evidence about the importance of intentionally embedding gender considerations into the COVID-19 planning and response, none of the plans reviewed in this study were classified as ‘gender transformative.’ We use these results to make specific recommendations for how infectious disease control efforts, for COVID-19 and beyond, can better integrate gender considerations in humanitarian settings, and particularly those affected by violence or conflict.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13031-022-00435-3.

Spatial variability of COVID-19 and its risk factors in Nigeria: A spatial regression method

The novel and unprecedented Coronavirus disease (COVID-19) pandemic has negatively impacted most nations of the world within a short period. While its disproportionate social and spatial variability has been established, the reality in Nigeria is yet to be studied. In this paper, advanced spatial statistical techniques were engaged to study the burden of COVID-19 and its risk factors within the first quarter (March-May) of its incidence in Nigeria. The spatial autocorrelation (Moran's I) test reveals a significant but marginal cluster of COVID-19 occurrence in Nigeria (I = 0.11, p < 0.05). A model comparison between ordinary least square (OLS) and spatial error model (SER) was explored having checked for multicollinearity in the dataset. The OLS model explained about 64% (adjusted R² = 0.64) of variation in COVID-19 cases, however with significantly clustered residuals. The SER model performed better with randomly distributed residuals. The significant predictors were population density, international airport, and literacy ratio. Furthermore, this study addressed the spatial planning implications of the ongoing disease outbreak while it advocates transdisciplinary approach to urban planning practices in Nigeria.

Data-driven scenario-based model projections and management of the May 2021 COVID-19 resurgence in India

The resurgence of the May 2021 COVID-19 wave in India not only pointed to the explosive speed with which SARS-CoV-2 can spread in vulnerable populations if unchecked, but also to the gross misreading of the status of the pandemic when decisions to reopen the economy were made in March 2021. In this combined modelling and scenario-based analysis, we isolated the population and policy-related factors underlying the May 2021 viral resurgence by projecting the growth and magnitude of the health impact and demand for hospital care that would have arisen if the spread was not impeded, and by evaluating the intervention options best able to curb the observed rapidly developing contagion. We show that only by immediately re-introducing a moderately high level of social mitigation over a medium-term period alongside a swift ramping up of vaccinations could the country be able to contain and ultimately end the pandemic safely. We also show that delaying the delivery of the 2nd dose of the Astra Zeneca vaccine, as proposed by the Government of India, would have had only slightly more deleterious impacts, supporting the government's decision to vaccinate a greater fraction of the population with at least a single dose as rapidly as possible. Our projections of the scale of the virus resurgence based on the observed May 2021 growth in cases and impacts of intervention scenarios to control the wave, along with the diverse range of variable control actions taken by state authorities, also exemplify the importance of shifting from the use of science and knowledge in an ad hoc reactive fashion to a more effective proactive strategy for assessing and managing the risk of fast-changing hazards, like a pandemic. We show that epidemic models parameterized with data can be used in combination with plausible intervention scenarios to enable such policy-making.

Assessment of pandemic (COVID-19) preparedness in a teaching hospital in northern India using available (CDC-Atlanta) checklist

Background:

Hospitals are at the forefront of dealing infectious public health emergencies. Recently, COVID-19 has been declared as pandemic by the World Health Organization. Dealing with COVID-19 pandemic requires high intensity of administrative activity.

Objective:

We conducted this study to assess and compare, objectively, hospital preparedness with available Centre of Disease Control and Prevention (CDC) standards.

Methods:

CDC has issued checklist for the assessment of hospital preparedness for COVID-19 pandemic, globally. This list contains 10 elements with sub-sections. We objectified the same and scored the hospital preparations accordingly. Various financial efforts made by the hospital to procure COVID19-specified items was also recorded.

Results:

As per the CDC checklist, the hospital scored 197 points (72.06%) out of 270 points with highest points in element two and eight. Element two is for the development for written COVID-19 plan. Element eight consists of addressing the occupational health of healthcare workers. Lowest scoring was in the element seven represented visitor access and movement within facility. During the study period, the hospital procured items of approximately 55 lakhs. In the study period, doctors, nursing staff, housekeeping staff, and security staff were channelized for doing COVID-19 duties.

Conclusions:

We obtained a score above 70% (good) which is quite encouraging, and we concluded that pandemic preparations in hospitals are necessary and it can be assessed objectively against prevailing standards. It is important in poor countries like India where spending on healthcare is minimal compared to other countries. Additionally, this assessment can be used to guide us further changes in policies and identifying the gaps in pandemic preparedness in hospitals which require special attention.

Main Factors Affecting the Readiness and Responsiveness of Healthcare Systems during Epidemic Crises: A Scoping Review on Cases of SARS, MERS, and COVID-19

Background:

Given the significance of the preparedness and responsiveness of healthcare systems in relation to epidemics, this study aimed to determine their influencing factors during epidemic crises with a view to utilizing the findings in the battle against the coronavirus disease 2019 (COVID-19) outbreak.

Methods:

This scoping study was conducted in 2020 via the Arksey and O’Malley approach. A systematic search was conducted on five online databases from January 2000 to June 15, 2020. Initially, 1926 English articles were retrieved based on their abstracts. After the screening process, 60 articles were considered for the final analysis. Data were charted by applying Microsoft Office Excel 2013 and were synthesized via thematic analysis.

Results:

Five main factors have affected the responsiveness and preparedness of countries during the epidemics of severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19: community-related interventions, managerial interventions, socioeconomic factors, the readiness of hospitals and health centers, and environmental factors. These themes are associated with 38 related sub-themes. The thematic framework shows that interactions between these five determinantes can affect the preparedness and responsiveness of healthcare systems during pandemics/epidemics.

Conclusion:

According to the results, healthcare systems need to pay attention to their internal capacities, managerial interventions, and health centers to overcome the current pandemic. They should also consider such external factors as socioeconomic and environmental determinants that can affect their potential preparedness against pandemic/epidemic crises. Community-related interventions such as improvement of the community health literacy, teamwork, and social responsibility can enhance the readiness of healthcare systems against the COVID-19 outbreak.

Understanding small Chinese cities as COVID-19 hotspots with an urban epidemic hazard index

Multiple small- to middle-scale cities, mostly located in northern China, became epidemic hotspots during the second wave of the spread of COVID-19 in early 2021. Despite qualitative discussions of potential social-economic causes, it remains unclear how this unordinary pattern could be substantiated with quantitative explanations. Through the development of an urban epidemic hazard index (EpiRank) for Chinese prefectural districts, we came up with a mathematical explanation for this phenomenon. The index is constructed via epidemic simulations on a multi-layer transportation network interconnecting local SEIR transmission dynamics, which characterizes intra- and inter-city population flow with a granular mathematical description. Essentially, we argue that these highlighted small towns possess greater epidemic hazards due to the combined effect of large local population and small inter-city transportation. The ratio of total population to population outflow could serve as an alternative city-specific indicator of such hazards, but its effectiveness is not as good as EpiRank, where contributions from other cities in determining a specific city's epidemic hazard are captured via the network approach. Population alone and city GDP are not valid signals for this indication. The proposed index is applicable to different epidemic settings and can be useful for the risk assessment and response planning of urban epidemic hazards in China. The model framework is modularized and the analysis can be extended to other nations.

Guidance for Health Care Leaders During the Recovery Stage of the COVID-19 Pandemic: A Consensus Statement

IMPORTANCE

The COVID-19 pandemic is the greatest global test of health leadership of our generation. There is an urgent need to provide guidance for leaders at all levels during the unprecedented preresolution recovery stage.

OBJECTIVE

To create an evidence- and expertise-informed framework of leadership imperatives to serve as a resource to guide health and public health leaders during the postemergency stage of the pandemic.

EVIDENCE REVIEW

A literature search in PubMed, MEDLINE, and Embase revealed 10 910 articles published between 2000 and 2021 that included the terms leadership and variations of emergency, crisis, disaster, pandemic, COVID-19, or public health. Using the Standards for Quality Improvement Reporting Excellence reporting guideline for consensus statement development, this assessment adopted a 6-round modified Delphi approach involving 32 expert coauthors from 17 countries who participated in creating and validating a framework outlining essential leadership imperatives.

FINDINGS

The 10 imperatives in the framework are: (1) acknowledge staff and celebrate successes; (2) provide support for staff well-being; (3) develop a clear understanding of the current local and global context, along with informed projections; (4) prepare for future emergencies (personnel, resources, protocols, contingency plans, coalitions, and training); (5) reassess priorities explicitly and regularly and provide purpose, meaning, and direction; (6) maximize team, organizational, and system performance and discuss enhancements; (7) manage the backlog of paused services and consider improvements while avoiding burnout and moral distress; (8) sustain learning, innovations, and collaborations, and imagine future possibilities; (9) provide regular communication and engender trust; and (10) in consultation with public health and fellow leaders, provide safety information and recommendations to government, other organizations, staff, and the community to improve equitable and integrated care and emergency preparedness systemwide.

CONCLUSIONS AND RELEVANCE

Leaders who most effectively implement these imperatives are ideally positioned to address urgent needs and inequalities in health systems and to cocreate with their organizations a future that best serves stakeholders and communities.

Addressing the double burden of the COVID-19 and noncommunicable disease pandemics: a new global governance challenge

Purpose

As the coronavirus disease 2019 (COVID-19) continues to spread across countries, it is becoming increasingly clear that the presence of pre-existing noncommunicable diseases (NCDs) dramatically increases the risk of aggravation in persons who contract the virus. The neglect in managing NCDs during emergencies may result in fatal consequences for individuals living with comorbidities. This paper aims to highlight the need for a paradigm shift in the governance of public health emergencies to simultaneously address NCD and noncommunicable disease (CD) pandemics while taking into account the needs of high-risk populations, underlying etiological factors, and the social, economic, and environmental determinants that are relevant for both CDs and NCDs.

Design/methodology/approach

The paper reviews the available global frameworks for pandemic preparedness to highlight the governance challenges of addressing the dual agenda of NCDs and CDs during a public health emergency. It proposes key strategies to strengthen multilevel governance in support of countries to better prepare for public health emergencies through the engagement of a wide range of stakeholders across sectors.

Findings

Addressing both CD and NCD pandemics during public health emergencies requires (1) a new framework that unites the narratives and overcomes service and system fragmentations; (2) a multisectoral and multistakeholder governance mechanism empowered and resourced to include stakeholders across sectors and (3) a prioritized research agenda to understand the political economy of pandemics, the role played by different political systems and actors and implementation challenges, and to identify combined strategies to address the converging agendas of CDs and NCDs.

Research limitations/implications

The article is based on the review of available published evidence.

Practical implications

The uptake of the strategies proposed will better prepare countries to respond to NCD and CD pandemics during public health emergencies.

Originality/value

The article is the first of its kind addressing the governance challenges of the dual pandemic of NCDs and CDs in emergencies.

Using a Process Approach to Pandemic Planning: A Case Study

The purpose of this article was to demonstrate the difference between a pandemic plan’s textual prescription and its effective processing using graphical notation. Before creating a case study of the Business Process Model and Notation (BPMN) of the Czech Republic’s pandemic plan, we conducted a systematic review of the process approach in pandemic planning and a document analysis of relevant public documents. The authors emphasized the opacity of hundreds of pages of text records in an explanatory case study and demonstrated the effectiveness of the process approach in reengineering and improving the response to such a critical situation. A potential extension to the automation and involvement of SMART technologies or process optimization through process mining techniques is presented as a future research topic.

Assessing the potential use of blockchain technology to improve the sharing of public health data in a western Canadian province

This exploratory, qualitative study set out to identify the encountered and perceived barriers to public health (PH) data sharing in a Canadian province with a view to assessing blockchain technology as a potential solution. A topic guide was developed, based on previous research in the area. This was then utilised for ten in-depth, semi-structured interviews with PH professionals between 27 May and 18 June 2019. Each stage of research was congruent with the philosophical underpinning of Gadamerian hermeneutic phenomenology. The major themes that emerged from the data collected were related to the information systems in use, data quality and ownership, as well as client identity management. The recurring core theme throughout all interviews was related to ineffective leadership and management, contributing to each major theme. Overwhelmingly the results show that the majority of barriers faced in this province are human-related. It is concluded that while blockchain technology shows promise for enhancing data sharing in healthcare, it is still many years away from being implemented in this Canadian province. As the results of this study indicate, there are human related barriers that could be addressed in the meantime, which are outside the scope of a technical solution. Future work should explore the perspectives of other stakeholders, such as the provincial government to fully understand the potential for using blockchain to share PH data in this province.

A scientometric analysis on coronaviruses research (1900-2020): Time for a continuous, cooperative and global approach

Infectious diseases remain a complex, recurring, and challenging public health hazard. Coronaviruses have led to multidimensional consequences on health, mobility, and socio-economic conditions. Despite the significance and magnitude of impact from epidemics to the pandemic, literature is sparse on comprehensive coronaviruses related research performance over time. This study aimed at a scientometric evaluation of coronaviruses related literature including COVID-19. Data related to Coronavirus research was extracted from the Web of Science (WoS). All types of publications (28,846) were included and retrieved. To measure the quantity and quality of the publications, "R-Bibliometrix" package was used for detailed analysis exploring a wide range of indicators. Generally, an increasing trend was observed over time led by the USA and China followed by the United Kingdom, Europe, and few other developed countries. The last two decades contributed around 39.5% of documents while only 06 months of 2020 additionally contributed around 46.5% of total documents. Earlier shorter spikes of increased post epidemic publications followed by decreased productivity were detected in the last 2 decades and showed a lack of continuity-'a research epidemic following a disease epidemic'. Articles (53.4%) were the most common publication type. Journal of Virology, British Medical Journal (BMJ), and Virology were leading sources while BMJ, and Lancet showed increased contributions recently. Overall, similar trends of top authors were observed in terms of productivity, impact, collaborations, funding sources, and affiliations with few exceptions mainly from affected regions. Top 20 countries contributed >89% of documents suggesting a lack of global efforts. Networking was found to be mainly among developed nations with limited contributions from resource-limited countries perhaps requiring more cooperation. Recent post-COVID publications rise is highest, unprecedented, and rapidly growing. Authors strongly recommend recent COVID-19 pandemic as a call for continuous, more cooperative, and collective global research.

Global Public Health Database Support to Population-Based Management of Pandemics and Global Public Health Crises, Part I: The Concept

This two-part article examines the global public health (GPH) information system deficits emerging in the coronavirus disease 2019 (COVID-19) pandemic. It surveys past, missed opportunities for public health (PH) information system and operational improvements, examines current megatrend changes to information management, and describes a new multi-disciplinary model for population-based management (PBM) supported by a GPH Database applicable to pandemics and GPH crises.

Assessing the interplay between travel patterns and SARS-CoV-2 outbreak in realistic urban setting

BACKGROUND

The dense social contact networks and high mobility in congested urban areas facilitate the rapid transmission of infectious diseases. Typical mechanistic epidemiological models are either based on uniform mixing with ad-hoc contact processes or need real-time or archived population mobility data to simulate the social networks. However, the rapid and global transmission of the novel coronavirus (SARS-CoV-2) has led to unprecedented lockdowns at global and regional scales, leaving the archived datasets to limited use.

FINDINGS

While it is often hypothesized that population density is a significant driver in disease propagation, the disparate disease trajectories and infection rates exhibited by the different cities with comparable densities require a high-resolution description of the disease and its drivers. In this study, we explore the impact of creation of containment zones on travel patterns within the city. Further, we use a dynamical network-based infectious disease model to understand the key drivers of disease spread at sub-kilometer scales demonstrated in the city of Ahmedabad, India, which has been classified as a SARS-CoV-2 hotspot. We find that in addition to the contact network and population density, road connectivity patterns and ease of transit are strongly correlated with the rate of transmission of the disease. Given the limited access to real-time traffic data during lockdowns, we generate road connectivity networks using open-source imageries and travel patterns from open-source surveys and government reports. Within the proposed framework, we then analyze the relative merits of social distancing, enforced lockdowns, and enhanced testing and quarantining mitigating the disease spread.

SCOPE

Our results suggest that the declaration of micro-containment zones within the city with high road network density combined with enhanced testing can help in containing the outbreaks until clinical interventions become available.

Forecasting COVID-19 cases based on a parameter-varying stochastic SIR model

We address the prediction of the number of new cases and deaths for the coronavirus disease 2019 (COVID-19) over a future horizon from historical data (forecasting). We use a model-based approach based on a stochastic Susceptible-Infections-Removed (SIR) model with time-varying parameters, which captures the evolution of the disease dynamics in response to changes in social behavior, non-pharmaceutical interventions, and testing rates. We show that, in the presence of asymptomatic cases, such model includes internal parameters and states that cannot be uniquely identified solely on the basis of measurements of new cases and deaths, but this does not preclude the construction of reliable forecasts for future values of these measurements. Such forecasts and associated confidence intervals can be computed using an iterative algorithm based on nonlinear optimization solvers, without the need for Monte Carlo sampling. Our results have been validated on an extensive COVID-19 dataset covering the period from March through December 2020 on 144 regions around the globe.

Graph modelling for tracking the COVID-19 pandemic spread

The modelling is widely used in determining the best strategies for the mitigation of the impact of infectious diseases. Currently, the modelling of a complex system such as the spread of COVID-19 infection is among the topical issues. The aim of this article is graph-based modelling of the COVID-19 infection spread. The article investigates the studies related to the modelling of COVID-19 pandemic and analyses the factors affecting the spread of the disease and its main characteristics. We propose a conceptual model of COVID-19 epidemic by considering the social distance, the duration of contact with an infected person and their location-based demographic characteristics. Based on the hypothetical scenario of the spread of the virus, a graph model of the process are developed starting from the first confirmed infection case to human-to-human transmission of the virus and visualized by considering the epidemiological characteristics of COVID-19. The application of graph for the pandemic modelling allows for considering multiple factors affecting the epidemiological process and conducting numerical experiments. The advantage of this approach is justified with the fact that it enables the reverse analysis the spread as a result of the dynamic record of detected cases of the infection in the model. This approach allows for to determining undetected cases of infection based on the social distance and duration of contact and eliminating the uncertainty significantly. Note that social, economic, demographic factors, the population density, mental values and etc. affect the increase in number of cases of infection and hence, the research was not able to consider all factors. In future research will analyze multiple factors impacting the number of infections and their use in the models will be considered.

Analysing the impact of the Australian health sector emergency response plan for Novel Coronavirus (COVID-19): A proposed approach

The COVID-19 pandemic has forced Australia, and our global counterparts, on a steep learning curve and rapid adaptation journey. Understanding the real impact of pandemic response interventions will be crucial to our collective success in this ongoing endeavour. This article will propose and outline an integrated, sequential, mixed methods approach to analysing the impact of Australia’s emergency response to the COVID-19 pandemic across its states and territories. We begin with a brief description of the Australian Health Sector Emergency Response Plan for Novel Coronavirus (COVID-19). In the second section, the discussion will move on to defining the nature of impact analysis and evaluation and when to use them. The third section outlines considerations relevant to selecting an impact analysis approach as it pertains to infectious disease pandemic response and virus transmission, morbidity and mortality outcomes. The fourth section presents and discusses a conceptual framework for an integrated impact analysis approach, clearly delineating the link between proposed approach, impact analysis questions and the outlined pandemic response considerations.

Potential Scenarios and Hazards in the Work of the Future: A Systematic Review of the Peer-Reviewed and Gray Literatures

It would be useful for researchers, practitioners, and decision-makers to anticipate the hazards that workers will face in the future. The focus of this study is a systematic review of published information to identify and characterize scenarios and hazards in the future of work. Eleven bibliographic databases were systematically searched for papers and reports published from 1999 to 2019 that described future of work scenarios or identified future work-related hazards. To compile a comprehensive collection of views of the future, supplemental and ad hoc searches were also performed. After screening all search records against a set of predetermined criteria, the review yielded 36 references (17 peer-reviewed, 4 gray, and 15 supplemental) containing scenarios. In these, the future of work was described along multiple conceptual axes (e.g. labor market changes, societal values, and manual versus cognitive work). Technology was identified as the primary driver of the future of work in most scenarios, and there were divergent views in the literature as to whether technology will create more or fewer jobs than it displaces. Workforce demographics, globalization, climate change, economic conditions, and urbanization were also mentioned as influential factors. Other important themes included human enhancement, social isolation, loneliness, worker monitoring, advanced manufacturing, hazardous exposures, sustainability, biotechnology, and synthetic biology. Pandemics have not been widely considered in the future of work literature, but the recent COVID-19 pandemic illustrates that was short-sighted. Pandemics may accelerate future of work trends and merit critical consideration in scenario development. Many scenarios described 'new' or 'exacerbated' psychosocial hazards of work, whereas comparatively fewer discussed physical, chemical, or biological hazards. Various preventive recommendations were identified. In particular, reducing stress associated with precarious work and its requirements of continual skill preparation and training was acknowledged as critical for protecting and promoting the health and well-being of the future workforce. In conclusion, the future of work will be comprised of diverse complex scenarios and a mosaic of old and new hazards. These findings may serve as the basis for considering how to shape the future of work.

Group Membership and Social and Personal Identities as Psychosocial Coping Resources to Psychological Consequences of the COVID-19 Confinement

The confinement imposed by measures to deal with the COVID-19 pandemic may in the short and medium term have psychological and psychosocial consequences affecting the well-being and mental health of individuals. This study aims to explore the role played by group membership and social and personal identities as coping resources to face the experience of the COVID-19 confinement and radical disruption of social, work, family and personal life in a sample of 421 people who have experienced a month of strict confinement in the Region of Madrid. Our results show that identity-resources (membership continuity/new group memberships, and personal identity strength) are positively related to process-resources (social support and perceived personal control), and that both are related to better perceived mental health, lower levels of anxiety and depression, and higher well-being (life satisfaction and resilience) during confinement. These results, in addition to providing relevant information about the psychological consequences of this experience, constitute a solid basis for the design of psychosocial interventions based on group memberships and social identity as coping resources.

Key questions for modelling COVID-19 exit strategies

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Covariates and Spatial Interpolation of HIV Screening in Mozambique: Insight from the 2015 AIDS Indicator Survey

We examined the factors associated with human immunodeficiency virus (HIV) screening and developed a HIV screening prevalence surface map using spatial interpolation techniques to identify the geographical areas with the highest and lowest rates of HIV screening in Mozambique. We analyzed the cross-sectional 2015 Mozambique AIDS Indicator Surveys with an analytic sample of 12,995 participants. Analyses were conducted on SPSS-21, STATA-14, and R freeware 3.5.3. We adjusted for the sample design and population weights. Results indicated that 52.5% of Mozambicans had undergone HIV screening. Mozambicans with these characteristics have a higher probability of undergoing HIV screening: females, those with a primary education or higher, urban dwellers, residents of wealthy households, having at least one lifetime sexual partner, and dwelling in these provinces—Niassa, Tete, Manica, Sofala, Inhambane, Gaza, Maputo Provincia, and Maputo Cidade. The spatial map revealed that the national and regional estimates mask sub-regional level estimates. Generally, zones with the highest HIV screening prevalence are found in southern provinces while the lowest prevalence was found in the northern provinces. The map further revealed intraregional differences in HIV screening estimates. We recommend that HIV screening be expanded, with equitable screening resource allocations that target more nuanced areas within provinces which have a low HIV screening prevalence.

COVID-19 from the land "Down Under" in an upside-down world: an Australian perspective

We frame the world, which then frames all our seeing, thinking and doing [1]. This is true individually, professionally and at the level of all our institutions. It is a perpetual work in progress that occasionally undergoes a single seismic shift. This is the situation that the world is currently experiencing with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), notwithstanding prior warnings [2, 3].

Australia's approach to the current COVID-19 pandemic has resulted in relative containment of the disease. However, it must be remembered that any defence is only as strong as its weakest link. https://bit.ly/2N2hHy1

Resilience in the time of pandemic: The experience of community pharmacists during COVID-19

Background

As a result of the COVID-19 pandemic, there has been a significant disruption of the practice of pharmacy. Importantly, in many parts of the world, this disruption occurred literally overnight, requiring pharmacists to demonstrate significant adaptability and resiliency in order to manage continuing and in some cases expanding needs of patients.

Objectives

The objective of this research was to characterize how community pharmacy in Ontario (Canada) responded to the COVID-19 pandemic of winter/spring 2020, in particular in understanding what factors may influence or predict resiliency of individual practitioners and their workplaces.

Methods

One-on-one interviews mediated through technology (Microsoft Teams) were used, following a semi-structured interview protocol. Verbatim transcripts were produced and analyzed by two independent researchers, using an inductive coding process to identify and characterize themes.

Results

A total of 21 pharmacists participated in this study. Six themes were identified: a) use of and comfort with technology; b) early adoption of corporate and professional guidance; c) workplaces that emphasized task-focus rather than multi-tasking were more resilient; d) scheduling methods and practices in the workplace are important for personal resilience; e) dedicated specialty staff allowed pharmacists to focus on their work; and f) provision of personal protective equipment was essential.

Conclusions

Traditionally, resilience in professional practice has been characterized as a personal, not a workplace, issue. This study suggests that personal resiliency for pharmacists requires substantial workplace support; further work in this area is required to better understand how pharmacists adapt to complex and difficult situations such as pandemics.

Understanding dynamics of pandemics

Along the centuries, novel strain of virus such as influenza produces pandemics which increase illness, death and disruption in the countries. Spanish flu in 1918, Asian flu in 1957, Hong Kong flu in 1968 and swine flu in 2009 were known pandemic which had various characteristics in terms of morbidity and mortality. A current pandemic is caused by novel corona virus originated from China. COVID-19 pandemic is very similar to Spanish, Hong Kong, Asian and swine influenza pandemics in terms of spreading to world by the mobilized people. Burden of pandemic is considered in terms of disease transmissibility and the growth rate of epidemic and duration of pandemic can be calculated by transmissibility characteristic. The case definition, finding out cases and first case cluster, proper treatment, sufficient stockpiles of medicine and population cooperation with the containment strategy should be considered for reduction of burden of pandemic.