PANDEM-Source, a tool to collect or generate surveillance indicators for pandemic management: a use case with COVID-19 data.

Introduction: PANDEM-Source (PS) is a tool to collect and integrate openly available public health-related data from heterogeneous data sources to support the surveillance of infectious diseases for pandemic management. The tool may also be used for pandemic preparedness by generating surveillance data for training purposes. It was developed as part of the EU-funded Horizon 2020 PANDEM-2 project during the COVID-19 pandemic as a result of close collaboration in a consortium of 19 partners, including six European public health agencies, one hospital, and three first responder organizations. This manuscript describes PS's features and design to disseminate its characteristics and capabilities to strengthen pandemic preparedness and response. Methods: A requirement-gathering process with EU pandemic managers in the consortium was performed to identify and prioritize a list of variables and indicators useful for surveillance and pandemic management. Using the COVID-19 pandemic as a use case, we developed PS with the purpose of feeding all necessary data to be displayed in the PANDEM-2 dashboard. Results: PS routinely monitors, collects, and standardizes data from open or restricted heterogeneous data sources (users can upload their own data). It supports indicators and health resources related data from traditional data sources reported by national and international agencies, and indicators from non-traditional data sources such as those captured in social and mass media, participatory surveillance, and seroprevalence studies. The tool can also calculate indicators and be used to produce data for training purposes by generating synthetic data from a minimal set of indicators to simulate pandemic scenarios. PS is currently set up for COVID-19 surveillance at the European level but can be adapted to other diseases or threats and regions. Conclusion: With the lessons learnt during the COVID-19 pandemic, it is important to keep building capacity to monitor potential threats and develop tools that can facilitate training in all the necessary aspects to manage future pandemics. PS is open source and its design provides flexibility to collect heterogeneous data from open data sources or to upload end users's own data and customize surveillance indicators. PS is easily adaptable to future threats or different training scenarios. All these features make PS a unique and valuable tool for pandemic management.

Insights from Avian Influenza: A Review of Its Multifaceted Nature and Future Pandemic Preparedness.

Avian influenza viruses (AIVs) have posed a significant pandemic threat since their discovery. This review mainly focuses on the epidemiology, virology, pathogenesis, and treatments of avian influenza viruses. We delve into the global spread, past pandemics, clinical symptoms, severity, and immune response related to AIVs. The review also discusses various control measures, including antiviral drugs, vaccines, and potential future directions in influenza treatment and prevention. Lastly, by summarizing the insights from previous pandemic control, this review aims to direct effective strategies for managing future influenza pandemics.

A Survey of the Use of Modeling, Simulation, Visualization, and Mapping in Public Health Emergency Operations Centers during the COVID-19 Pandemic.

The COVID-19 pandemic has significantly changed life and work patterns and reshaped the healthcare industry and public health strategies. It posed considerable challenges to public health emergency operations centers (PHEOCs). In this period, digital technologies such as modeling, simulation, visualization, and mapping (MSVM) emerged as vital tools in these centers. Despite their perceived importance, the potential and adaptation of digital tools in PHEOCs remain underexplored. This study investigated the application of MSVM in the PHEOCs during the pandemic in Canada using a questionnaire survey. The results show that digital tools, particularly visualization and mapping, are frequently used in PHEOCs. However, critical gaps, including data management issues, technical and capacity issues, and limitations in the policy-making sphere, still hinder the effective use of these tools. Key areas identified in this study for future investigation include collaboration, interoperability, and various supports for information sharing and capacity building.

Pandemic Management: Health Data and Public Health Surveillance.

The inefficiency of the healthcare system in addressing pandemics is highlighted after COVID-19 which is mostly rooted in data availability and accuracy. As it is believed we might witness more pandemics in future, our research's main objective is to propose an integrated health system to support healthcare preparedness for future infectious outbreaks and pandemics. The system could support managers and authorities in healthcare and disaster management, and policymakers through data collection, sharing, and analysis.

Factors associated with wearing a facemask in shops in England following removal of a legal requirement to do so during the COVID-19 pandemic.

OBJECTIVES: We aimed to identify psychological factors associated with the use of facemasks in shops in England following removal of legal requirements to do so, and to compare associations with and without legal restrictions. DESIGN: Repeated cross-sectional online surveys (n ≈ 2000 adults) between August 2020 and April 2022 (68,716 responses from 45,682 participants) using quota sampling. METHODS: The outcome measure was whether those who had visited a shop for essentials in the previous seven days reported always having worn a facemask versus sometimes or not at all. Psychological predictor variables included worry, perceived risk and severity of COVID-19 and the perceived effectiveness of facemasks. Socio-demographic variables and measures of clinical vulnerability were also measured. For the period following removal of legal restrictions, multivariable regression was used to assess associations between the primary outcome variable and predictors adjusting for socio-demographic and clinical vulnerability measures. The analysis was repeated including interactions between psychological predictors and presence versus absence of legal restrictions. RESULTS: Worry about COVID-19, beliefs about risks and severity of COVID-19 and effectiveness of facemasks were substantially and independently associated with the use of facemasks. Removal of legal obligations to wear facemasks was associated with a 25% decrease in wearing facemasks and stronger associations between psychological predictors and wearing facemasks. CONCLUSIONS: Legal obligations increase rates of wearing a facemask. Psychological factors associated with wearing a facemask could be targets for interventions aiming to alter rates of wearing a facemask. These interventions may be more effective when there are no legal obligations to wear a face covering in place.

"We literally worked in parking lots, cars, garages, and separately set up party tents": qualitative study on the experiences of GPs in the frame of the SARS-CoV-2 pandemic in Austria.

BACKGROUND: Primary care is internationally recognised as one of the cornerstones of health care. During the COVID-19 pandemic, primary care physicians were assigned a variety of tasks and thus made a significant contribution to a country's pandemic response. They were expected to perform a variety of tasks, such as diagnosing and treating people with COVID-19, maintaining health care for all other patients, as well as several public health tasks, such as diagnostic testing and vaccination, protecting patients and staff from infection, and serving as community trusted persons. In Austria, there are no structured levels of care, no definition of the role of the general practitioner during a pandemic is given, and no specific support structures are present. The aim of this study was to assess the views and experiences of primary care physicians regarding supportive and hindering factors for pandemic preparedness in Austria. METHODS: Qualitative study using semi-structured interviews. A total of 30 general practitioners were interviewed, with particular attention to an equitable distribution in small, medium and large primary care facilities. Qualitative content analysis was performed. RESULTS: Interviewees described a wide range of infection control, organisational and communication measures that they had implemented. They made changes to practise equipment, found makeshift solutions when supplies were scarce, and established communication and information pathways when official communication lines were inadequate. CONCLUSION: General practitioners took on essential tasks and showed a high level of understanding of their role in the pandemic response. This was achieved mainly at an informal level and with high personal commitment. Their functioning in the absence of structural regulations and support shows that they had a clear intrinsic understanding of their responsibilities. To ensure reliability and sustainability and to reduce their burden, it will be necessary to clarify the role and tasks of a general practitioner and to provide the necessary support. This concerns both infrastructural support and communication and information strategies. As part of the reform to strengthen primary care, primary care needs to be seen, valued and involved in decision-making processes.

Resident Impact of the Single Site Order Restricting Staff Mobility across Long-Term Care Homes in British Columbia, Canada.

The Single Site Order (SSO)-a policy restricting staff from working at multiple long-term care (LTC) homes-was mandated by the Public Health Agency of Canada to control the spread of COVID-19 in LTC homes, where nearly 70% of COVID-19-related deaths in Canada occurred. This mixed methods study assesses the impact of the SSO on LTC residents in British Columbia. Interviews were conducted (residents (n = 6), family members (n = 9), staff (n = 18), and leadership (n = 10) from long-term care homes (n = 4)) and analyzed using thematic analysis. Administrative data were collected between April 2019 and March 2020 and between April 2020 and March 2021 and analyzed using descriptive statistics and data visualization. Qualitative and quantitative data were triangulated and demonstrated that staffing challenges became worse during the implementation of the SSO, resulting in the mental and physical health deterioration of LTC residents. Qualitative data demonstrated decreased time for personalized and proactive care, increased communication challenges, and increased loneliness and isolation. Quantitative data showed a decline in activities of daily living, increased antipsychotic medication use, pressure ulcers, behavioural symptoms, and an increase in falls. Addressing staff workload and staffing shortages during SSO-related policy implementation is essential to avoid resident health deterioration.

Research on the collaborative evolution process of information in public health emergencies based on complex adaptive system theory and social network analysis: a case study of the COVID-19 pandemic.

Introduction: This review aimed to elucidate the significance of information collaboration in the prevention and control of public health emergencies, and its evolutionary pathway guided by the theory of complex adaptive systems. Methods: The study employed time-slicing techniques and social network analysis to translate the dynamic evolution of information collaboration into a stage-based static representation. Data were collected from January to April 2020, focusing on the COVID-19 pandemic. Python was used to amass data from diverse sources including government portals, public commentary, social organizations, market updates, and healthcare institutions. Post data collection, the structures, collaboration objectives, and participating entities within each time slice were explored using social network analysis. Results: The findings suggest that the law of evolution for information collaboration in public health emergencies primarily starts with small-scale collaboration, grows to full-scale in the middle phase, and then reverts to small-scale in the final phase. The network's complexity increases initially and then gradually decreases, mirroring changes in collaboration tasks, objectives, and strategies. Discussion: The dynamic pattern of information collaboration highlighted in this study offers valuable insights for enhancing emergency management capabilities. Recognizing the evolving nature of information collaboration can significantly improve information processing efficiency during public health crises.

Proportionality and Mexico's pandemic management during the COVID-19 crisis.

Mexico's pandemic management and the absence of measures have been harshly criticized as being disproportionate. This paper examines whether the proportionality principle was properly applied to Mexico's COVID-19 response and outlines three reasons against such an endeavor, namely (i) the content of "proportionate measures" remained insufficiently well defined, (ii) there were yet fundamental rights conflicts to resolve, and (iii) the situation was moreover characterized by epistemic uncertainty.

Large-scale sporting events during the COVID-19 pandemic: insights from the FIFA World Cup 2022 in Qatar with an analysis of patterns of COVID-19 metrics.

The 2022 FIFA World Cup (FIFA-WC) held in Qatar presented unique challenges, given the potential for rapid transmission of coronavirus disease 2019 (COVID-19) among over 1.4 million international fans attending the event. This study aimed to investigate the impact of the FIFA-WC 2022 on COVID-19 cases, deaths, and reproduction rate (R0) in Qatar. Additionally, it sought to understand the implications of hosting large-scale events during a pandemic without COVID-19 restrictive measures, providing critical insights for future decision-making. Data from "Our World in Data" were analysed for three distinct periods: one week before the FIFA-WC (week-preWC), the four weeks of the event (week-1WC to week-4WC), and one week after (week-postWC). The results revealed a significant increase in COVID-19 cases during week-3WC and week-4WC (compared to week-preWC) in Qatar, followed by a subsequent decrease during the week-postWC. Notably, Qatar experienced a more pronounced surge in positive cases than the global trend. Regarding COVID-19-related deaths, Qatar's peak occurred during week-2WC, while globally deaths peaked from week-3WC to week-postWC. Nevertheless, Qatar's death toll remained relatively low compared to the global trend throughout the event. The findings highlight that the FIFA-WC 2022 in Qatar demonstrated the feasibility of organizing large-scale sporting events during a pandemic with appropriate measures in place. They emphasize the importance of high vaccination coverage, continuous monitoring, and effective collaboration between event organizers, healthcare authorities, and governments. As such, the event serves as a valuable model for future gatherings, underlining the significance of evidence-based decision-making and comprehensive public health preparedness.

An interpretable time series machine learning method for varying forecast and nowcast lengths in wastewater-based epidemiology.

Wastewater-based epidemiology has emerged as a viable tool for monitoring disease prevalence in a population. This paper details a time series machine learning (TSML) method for predicting COVID-19 cases from wastewater and environmental variables. The TSML method utilizes a number of techniques to create an interpretable, hypothesis-driven framework for machine learning that can handle different nowcast and forecast lengths. Some of the techniques employed include:•Feature engineering to construct interpretable features, like site-specific lead times, hypothesized to be potential predictors of COVID-19 cases.•Feature selection to identify features with the best predictive performance for the tasks of nowcasting and forecasting.•Prequential evaluation to prevent data leakage while evaluating the performance of the machine learning algorithm.

Assessing healthcare capacity crisis preparedness: development of an evaluation tool by a Canadian health authority.

Introduction: The COVID-19 pandemic presented health systems across the globe with unparalleled socio-political, ethical, scientific, and economic challenges. Despite the necessity for a unified, innovative, and effective response, many jurisdictions were unprepared to such a profound health crisis. This study aims to outline the creation of an evaluative tool designed to measure and evaluate the Vitalité Health Network's (New Brunswick, Canada) ability to manage health crises. Methods: The methodology of this work was carried out in four stages: (1) construction of an evaluative framework; (2) validation of the framework; (3) construction of the evaluative tool for the Health Authority; and (4) evaluation of the capacity to manage a health crisis. Results: The resulting evaluative tool incorporated 8 dimensions, 74 strategies, and 109 observable elements. The dimensions included: (1) clinical care management; (2) infection prevention and control; (3) governance and leadership; (4) human and logistic resources; (5) communication and technologies; (6) health research; (7) ethics and values; and (8) training. A Canadian Health Authority implemented the tool to support its future preparedness. Conclusion: This study introduces a methodological strategy adopted by a Canadian health authority to evaluate its capacity in managing health crises. Notably, this study marks the first instance where a Canadian health authority has created a tool for emergency healthcare management, informed by literature in the field and their direct experience from handling the SARS-CoV-2 pandemic.

A time series based machine learning strategy for wastewater-based forecasting and nowcasting of COVID-19 dynamics.

Monitoring COVID-19 infection cases has been a singular focus of many policy makers and communities. However, direct monitoring through testing has become more onerous for a number of reasons, such as costs, delays, and personal choices. Wastewater-based epidemiology (WBE) has emerged as a viable tool for monitoring disease prevalence and dynamics to supplement direct monitoring. The objective of this study is to intelligently incorporate WBE information to nowcast and forecast new weekly COVID-19 cases and to assess the efficacy of such WBE information for these tasks in an interpretable manner. The methodology consists of a time-series based machine learning (TSML) strategy that can extract deeper knowledge and insights from temporal structured WBE data in the presence of other relevant temporal variables, such as minimum ambient temperature and water temperature, to boost the capability for predicting new weekly COVID-19 case numbers. The results confirm that feature engineering and machine learning can be utilized to enhance the performance and interpretability of WBE for COVID-19 monitoring, along with identifying the different recommended features to be applied for short-term and long-term nowcasting and short-term and long-term forecasting. The conclusion of this research is that the proposed time-series ML methodology performs as well, and sometimes better, than simple predictions that assume available and accurate COVID-19 case numbers from extensive monitoring and testing. Overall, this paper provides an insight into the prospects of machine learning based WBE to the researchers and decision-makers as well as public health practitioners for predicting and preparing the next wave of COVID-19 or the next pandemic.

Analysing role of airborne particulate matter in abetting SARS-CoV-2 outbreak for scheming regional pandemic regulatory modalities.

The mutating SARS-CoV-2 necessitates gauging the role of airborne particulate matter in the COVID-19 outbreak for designing area-specific regulation modalities based on the environmental state-of-affair. To scheme the protocols, the hotspots of air pollutants such as PM2.5, PM10, NH3, NO, NO2, SO2, and and environmental factors including relative humidity (RH), and temperature, along with COVID-19 cases and mortality from January 2020 till December 2020 from 29 different ground monitoring stations spanning Delhi, are mapped. Spearman correlation coefficients show a positive relationship between SARS-COV-2 with particulate matter (PM2.5 with r > 0.36 and PM10 with r > 0.31 and p-value <0·001). Besides, SARS-COV-2 transmission showed a substantial correlation with NH3 (r = 0.41), NO2 (r = 0.36), and NO (r = 0.35) with a p-value <0.001, which is highly indicative of their role in SARS-CoV-2 transmission. These outcomes are associated with the source of PM and its constituent trace elements to understand their overtone with COVID-19. This strongly validates temporal and spatial variation in COVID-19 dependence on air pollutants as well as on environmental factors. Besides, the bottlenecks of missing latent data, monotonous dependence of variables, and the role air pollutants with secondary environmental variables are discussed. The analysis set the foundation for strategizing regional-based modalities considering environmental variables (i.e., pollutant concentration, relative humidity, temperature) as well as urban and transportation planning for efficient control and handling of future public health emergencies.

Impact of COVID-19 on cancer care pathways in a comprehensive cancer center in northern Italy.

The COVID-19 pandemic burdened health care systems worldwide. Health services were reorganized with the dual purpose of ensuring the most adequate continuity of care and, simultaneously, the safety of patients and health professionals. The provision of care to patients within cancer care pathways (cCPs) was not touched by such reorganization. We investigated whether the quality of care provided by a local comprehensive cancer center has been maintained using cCP indicators. A retrospective single-cancer center study was conducted on eleven cCPs from 2019 to 2021 by comparing three timeliness indicators, five care indicators and three outcome indicators yearly calculated on incident cases. Comparisons of indicators between 2019 and 2020, and 2019 and 2021, were performed to assess the performance of cCP function during the pandemic. Indicators displayed heterogeneous significant changes attributed to all cCPs over the study period, affecting eight (72%), seven (63%) and ten (91%) out of eleven cCPs in the comparison between 2019 and 2020, 2020 and 2021, and 2019 and 2021, respectively. The most relevant changes were attributed to a negative increase in time-to-treatment surgery-related indicators and to a positive increase in the number of cases discussed by cCP team members. No variations were found attributed to outcome indicators. Significant changes did not account for clinical relevance once discussed by cCP managers and team members. Our experience demonstrated that the CP model constitutes an appropriate tool for providing high levels of quality care, even in the most critical health situations.

Assessment of COVID-19 Positive Rates Amongst COVID-19 Close Contacts Through the Health Risk Warning System.

From 6 Jan 22 to 17 Feb 22, a total of 729,367 close contacts classified into four distinct groups (i.e., household, social, school and nursing/ welfare home contacts) were managed by the Health Risk Warning system. High COVID-19-positive rates were demonstrated amongst household contacts, i.e., 10.9% (37,220/342,302) were detected via antigen rapid test kits and 56.5% (4,952/8,767) were detected via polymerase chain reaction testing. Household contacts represent the highest risk of being infected by virtue of the sustained close-proximity interactions in the household setting. Social, school and nursing/ welfare home contacts continue to remain at-risk groups for close monitoring. At a population level, household and symptomatic close contacts should be the groups of focus in the early phases of the pandemic, including future potential waves involving COVID-19 variants of concern.

Evaluation of COVID-19 pandemic management in Türkiye.

Three years since the first cases were identified and 2 years since an effective vaccine was developed, COVID-19 continues to claim lives and impact people's health and wellbeing, both socially and economically. While the world has been waiting for its leaders to come together to form a collective response to end the pandemic, we still have not seen a multisectoral response, nor any whole-of-society approach. Like many other countries around the globe, Türkiye was caught unprepared by the pandemic. This was exacerbated by the unsuccessful management of the pandemic by the authorities. The reasoning and/or scientific explanations for enforcing or lifting public health measures have never shared with the public. Throughout the pandemic, no epidemiological details have been released on cases and deaths, other than the numbers of these two measures. Civil society organizations, professional associations, and the public in general have been kept out from policy formulation and decision making. As a result, community engagement has never been properly put into practice. In this paper, we analyzed Türkiye's pandemic management response through the continuum of the response cycle to emergencies: prevention, preparedness, readiness, response, and recovery.

A qualitative study on the perception of infection prevention and control measures among healthcare workers without patient contact during the SARS-CoV-2 pandemic.

We conducted a qualitative interview-based study to examine the perception of infection prevention and control (IPC) measures implemented during the COVID-19 pandemic among healthcare workers (HCWs) without patient contact in a tertiary academic care center. We compared these findings to those derived from interviews of HCWs with patient contact from the same institution using the same study design. The following main four themes were identified: (1) As for HCWs with patient contact, transparent communication strongly contributes to employees' sense of security. (2) Information on personal protective equipment (PPE) usage needs to be stratified according to different educational backgrounds and professions. (3) Consistency of IPC measures was positively perceived yet a desire for constant reminders to counteract the fatigue effect played a more significant role for HCWs without patient contact. (4) As compared to HCWs with patient contact, HCWs without patient contact preferred uniform digital training resources rather than more face-to-face training. This study shows that the needs of HCWs with and without patient contact differ and need to be considered in pandemic management.

Predicting Public Cooperation Toward Government Actions in the Early Stages of an Influenza Pandemic in the United States: The Role of Authentic Governmental Communication and Relational Quality.

During a public health crisis, government sector is considered the natural leader for overall preparedness and management efforts. Integrating the literature from public relations and public health disciplines, this study proposes a theoretical model to predict individuals' perceptions, communicative action, as well as their behaviors to follow the governments' instructions in the early stages of the COVID-19 outbreak in the United States. Linking relationship management factors and the framework of the situational theory of problem-solving, the findings of this study demonstrate that authentic communication and relational quality can help increase positive perceptual, attitudinal, and behavioral outcomes desired by governments regarding pandemic management. However, our findings suggested that unproductive uses of authentic governmental communication may create adverse effects on publics' perceptions and interpretations and thus pose potential risks, particularly when a public health issue is significantly politicized. Specifically, this study found that, in the case of the COVID-19 pandemic in which the Trump administration was blamed for lack of planning and halting responses in the fight against the virus, conservatives who believe that the federal government is practicing authentic communication during the pandemic would consider the issue less important and irreverent; meanwhile, they would recognize more barriers to adopt preventive actions. Theoretical and practical implications are discussed.

COVID-19 Close Contact Management: An Evolution of Operations Harnessing the Digital Edge.

Singapore, like many other nations globally, had to contend with significant caseloads arising from the Coronavirus disease (COVID-19) pandemic. This paper focuses on using technology as an intervention for pandemic management. With scant scientific evidence on effective medications and vaccinations (i.e., pharmaceutical interventions) initially, disease containment strategies predominated during the early phases. Non-pharmaceutical interventions were critical in slowing disease transmission and preventing public healthcare institutions from being overwhelmed. Such interventions could be broadly divided into case-based interventions (e.g., contact tracing and quarantining of close contacts) and population-based measures (e.g., mask use and social distancing). The paper describes Singapore's experience in the operational implementation of contact-based interventions, and illustrates how harnessing the digital edge enabled fast, accurate, resource-efficient, and flexible execution of ground operations. Singapore applied digital technology and developed an integrated system to facilitate issuance and acknowledgement of quarantine orders, submission of COVID-19 test results, and collection of antigen rapid test kits at the population level. Data was obtained from this proprietary centralised, automated platform. The paper demonstrates how such simple, yet elegant systems could have a direct impact on disease transmission in an outbreak setting and on population health. Moving forward, it is recommended that technology and digital solutions feature prominently in work process designs beyond COVID-19 such as in the management of emerging infectious diseases and non-communicable diseases.