Lasting Declines in Couples' Social Network Interactions in the First Years of COVID.

Since the onset of COVID-19, a rise in loneliness has raised concerns about the social impact of lockdowns and distancing mandates. Yet, to date, the effects of the pandemic on social networks have been studied only indirectly. To evaluate how the pandemic affected social networks, the current analyses analyzed five waves of detailed social network interviews conducted before and during the first 18 months of the pandemic in a sample especially vulnerable to contracting the virus: mostly non-White couples (243 husbands and 250 wives) recruited from lower income neighborhoods. Pre-COVID interviews asked spouses to name 24 individuals with whom they interact regularly. Post-COVID interviews indicated a nearly 50% decline in face-to-face interactions and a nearly 40% decline in virtual interactions, with little recovery over the first 18 months of the pandemic. Compared with less affluent couples, those with higher incomes maintained more of their network relationships, especially when virtual interactions were taken into account.

Seroprevalence of SARS-CoV-2 antibodies among Forcibly Displaced Myanmar Nationals in Cox's Bazar, Bangladesh 2020: a population-based cross-sectional study.

OBJECTIVES: The study aimed to determine the seroprevalence, the fraction of asymptomatic infections, and risk factors of SARS-CoV-2 infections among the Forcibly Displaced Myanmar Nationals (FDMNs). DESIGN: It was a population-based two-stage cross-sectional study at the level of households. SETTING: The study was conducted in December 2020 among household members of the FDMN population living in the 34 camps of Ukhia and Teknaf Upazila of Cox's Bazar district in Bangladesh. PARTICIPANTS: Among 860 697 FDMNs residing in 187 517 households, 3446 were recruited for the study. One individual aged 1 year or older was randomly selected from each targeted household. PRIMARY AND SECONDARY OUTCOME MEASURES: Blood samples from respondents were tested for total antibodies for SARS-CoV-2 using Wantai ELISA kits, and later positive samples were validated by Kantaro kits. RESULTS: More than half (55.3%) of the respondents were females, aged 23 median (IQR 14-35) years and more than half (58.4%) had no formal education. Overall, 2090 of 3446 study participants tested positive for SARS-CoV-2 antibody. The weighted and test adjusted seroprevalence (95% CI) was 48.3% (45.3% to 51.4%), which did not differ by the sexes. Children (aged 1-17 years) had a significantly lower seroprevalence 38.6% (95% CI 33.8% to 43.4%) compared with adults (58.1%, 95% CI 55.2% to 61.1%). Almost half (45.7%, 95% CI 41.9% to 49.5%) of seropositive individuals reported no relevant symptoms since March 2020. Antibody seroprevalence was higher in those with any comorbidity (57.8%, 95% CI 50.4% to 64.5%) than those without (47.2%, 95% CI 43.9% to 50.4%). Multivariate logistic regression analysis of all subjects identified increasing age and education as risk factors for seropositivity. In children (≤17 years), only age was significantly associated with the infection. CONCLUSIONS: In December 2020, about half of the FDMNs had antibodies against SARS-CoV-2, including those who reported no history of symptoms. Periodic serosurveys are necessary to recommend appropriate public health measures to limit transmission.

Pathogen evolution during vaccination campaigns.

Following the initiation of the unprecedented global vaccination campaign against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), attention has now turned to the potential impact of this large-scale intervention on the evolution of the virus. In this Essay, we summarize what is currently known about pathogen evolution in the context of immune priming (including vaccination) from research on other pathogen species, with an eye towards the future evolution of SARS-CoV-2.

Citizen science unoccupied aerial vehicles: A technique for advancing coastal data acquisition for management and research

Coasts are highly dynamic systems. Understanding how they respond to individual storms events and to future climate change is difficult as local boundary conditions determines their evolutionary trajectory. A lack of field data at this local scale therefore limits the ability of managers and researchers to apply existing modelling frameworks to their region of interest to ensure preservation of the natural environment. Data acquisition through low-cost Unoccupied Aerial Vehicles (UAVs) has become a viable means for obtaining high-resolution surveys (cm-scale) on the coast for whole sediment compartments (km-scale). A continued limitation however is the intensive labour costs involved in data acquisition. Here we show the power of Citizen Science in providing high quality, cost-effective data collection, when provided with adequate training and resources along a high-energy, temperate coast in Victoria, Australia. This was conducted through the Victorian Coastal Monitoring Program (VCMP), formed in 2018 as a collaboration between Australian universities and the Victorian State Government. As of 2022, this program covered 28 sites, with over 450 individual surveys taken at 6–8 week intervals. The VCMP has guided and driven significant management actions on the coast from realignment of coastal walking paths for public safety to measuring sand renourishment success. In this paper we (i) present the Citizen Science UAV program methodology, as an example that can be replicated in other jurisdictions, and (ii) illustrate, through a case-study of a sandy beach and rocky cliff, the benefits and precision achievable using our Citizen Science approach. We outline how outputs can be made widely available and applied to coastal management, with the aid of data portals and decision support systems. This data accessibility has been central to our community engagement, enabling citizen scientists to conduct their own bespoke analysis for co-creation of management solutions for their local area. It was also found to be key for facilitating continued community engagement during one of the world's longest lockdowns of the COVID-19 pandemic, impacting the program for almost two years.

COVID-19: Identifying countries with indicators of success in responding to the outbreak.

Background: In designing responses to the COVID-19 pandemic, it is critical to understand what has already worked well. We aimed to identify countries with emerging success stories from whom policymakers might draw important lessons.  Methods: We developed a process to first include countries with large enough populations that results were unlikely to be due to chance, that had sufficient cases for response mechanisms to be tested, and that shared the necessary publicly available data. Within these countries, we looked at indicators suggesting success in terms of detecting disease, containing the outbreak, and treating those who were unwell. To support comparability, we measured indicators per capita (per million) and across time. We then used the indicators to identify three countries with emerging success stories to include some diversity in global region, population demographics and form of government. Results: We identified 66 countries that met our inclusion criteria on 18 th May 2020. Several of these countries had indicators of success against the set indicators at different times in the outbreak. Vietnam had high levels of testing and successful containment with no deaths reported. South Korea had high levels of testing early in the outbreak, supporting containment. Germany had high levels of sustained testing and slower increases in cases and deaths than seen in other comparable settings. Conclusions: At the time of our assessment, Vietnam and South Korea were able to contain the outbreak of COVID-19 and avoid the exponential growth in cases seen elsewhere. Germany had more cases and deaths, but was nevertheless able to contain and mitigate the outbreak. Despite the many limitations to the data currently available, looking at comparative data can help identify countries from whom we can draw lessons, so that countries can inform and adapt their strategies for success in response to COVID-19.

Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward.

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world's most vulnerable populations at risk. Epidemiological modelling is vital to guiding evidence-informed or data-driven decision making. In forced displacement contexts, and in particular refugee and internally displaced people (IDP) settlements, it meets several challenges including data availability and quality, the applicability of existing models to those contexts, the accurate modelling of cultural differences or specificities of those operational settings, the communication of results and uncertainties, as well as the alignment of strategic goals between diverse partners in complex situations. In this paper, we systematically review the limited epidemiological modelling work applied to refugee and IDP settlements so far, and discuss challenges and identify lessons learnt from the process. With the likelihood of disease outbreaks expected to increase in the future as more people are displaced due to conflict and climate change, we call for the development of more approaches and models specifically designed to include the unique features and populations of refugee and IDP settlements. To strengthen collaboration between the modelling and the humanitarian public health communities, we propose a roadmap to encourage the development of systems and frameworks to share needs, build tools and coordinate responses in an efficient and scalable manner, both for this pandemic and for future outbreaks.

Dynamic modeling of hospitalized COVID-19 patients reveals disease state-dependent risk factors.

OBJECTIVE: The study sought to investigate the disease state-dependent risk profiles of patient demographics and medical comorbidities associated with adverse outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. MATERIALS AND METHODS: A covariate-dependent, continuous-time hidden Markov model with 4 states (moderate, severe, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1362 patients from ProMedica Health System admitted between March 20, 2020 and December 29, 2020 with a positive nasopharyngeal PCR test for SARS-CoV-2. Demographic characteristics, comorbidities, vital signs, and laboratory test results were retrospectively evaluated to infer a patient's clinical progression. RESULTS: The association between patient-level covariates and risk of progression was found to be disease state dependent. Specifically, while being male, being Black or having a medical comorbidity were all associated with an increased risk of progressing from the moderate disease state to the severe disease state, these same factors were associated with a decreased risk of progressing from the severe disease state to the deceased state. DISCUSSION: Recent studies have not included analyses of the temporal progression of COVID-19, making the current study a unique modeling-based approach to understand the dynamics of COVID-19 in hospitalized patients. CONCLUSION: Dynamic risk stratification models have the potential to improve clinical outcomes not only in COVID-19, but also in a myriad of other acute and chronic diseases that, to date, have largely been assessed only by static modeling techniques.

A snapshot of the practicality and barriers to COVID-19 interventions: Public health and healthcare workers' perceptions in high and low- and middle-income countries.

BACKGROUND: In response to the COVID-19 pandemic, governments have implemented a range of non-pharmaceutical interventions (NPIs) and pharmaceutical interventions (PIs) to reduce transmission and minimise morbidity and mortality, whilst maintaining social and economic activities. The perceptions of public health workers (PHWs) and healthcare workers (HCWs) are essential to inform future COVID-19 strategies as they are viewed as trusted sources and are at the forefront of COVID-19 response. The objectives of this study were to 1) describe the practicality of implementing NPIs and PIs and 2) identify potential barriers to implementation, as perceived by HCWs and PHWs. METHODS: We conducted a cross-sectional study of PHWs and HCWs perceptions of the implementation, practicality of, and barriers to implementation of NPIs and PIs using an online survey (28/9/2020-1/11/2020) available in English, French and Portuguese. We used descriptive statistics and thematic analysis to analyse quantitative and qualitative responses. RESULTS: In total, 226 respondents (67 HCWs and 159 PHWs) from 52 countries completed the survey and 222 were included in the final analysis. Participants from low and middle-income countries (LMICs) accounted for 63% of HCWs and 67% of PHWs, with the remaining from high-income (HICs). There was little difference between the perceptions of PHWs and HCWs in HICs and LMICs, with the majority regarding a number of common NPIs as difficult to implement. However, PHWs in HICs perceived restrictions on schools and educational institutions to be more difficult to implement, with a lack of childcare support identified as the main barrier. Additionally, most contact tracing methods were perceived to be more difficult to implement in HICs than LMICs, with a range of barriers reported. A lack of public support was the most commonly reported barrier to NPIs overall across both country income and professional groups. Similarly, public fear of vaccine safety and lack of vaccine supply were the main reported barriers to implementing a COVID-19 vaccine. However, PHWs and HCWs in LMICs perceived a lack of financial support and the vaccine being manufactured in another country as additional barriers. CONCLUSION: This snapshot provides insight into the difficulty of implementing interventions as perceived by PHWs and HCWs. There is no one-size-fits-all solution to implementing interventions, and barriers in different contexts do vary. Barriers to implementing a vaccine programme expressed here by HCWs and PHCWs have subsequently come to the fore internationally.

Operational response simulation tool for epidemics within refugee and IDP settlements: A scenario-based case study of the Cox's Bazar settlement.

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world's most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements under various non-pharmaceutical intervention strategies. The model, based on the June open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. The development and testing of this approach focuses on the Cox's Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings. Our findings suggest the encouraging self-isolation at home of mild to severe symptomatic patients, as opposed to the isolation of all positive cases in purpose-built isolation and treatment centers, does not increase the risk of secondary infection meaning the centers can be used to provide hospital support to the most intense cases of COVID-19. Secondly we find that mask wearing in all indoor communal areas can be effective at dampening viral spread, even with low mask efficacy and compliance rates. Finally, we model the effects of reopening learning centers in the settlement under various mitigation strategies. For example, a combination of mask wearing in the classroom, halving attendance regularity to enable physical distancing, and better ventilation can almost completely mitigate the increased risk of infection which keeping the learning centers open may cause. These modeling efforts are being incorporated into decision making processes to inform future planning, and further exercises should be carried out in similar geographies to help protect those most vulnerable.

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients.

The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

'Learn from the lessons and don't forget them': identifying transferable lessons for COVID-19 from meningitis A, yellow fever and Ebola virus disease vaccination campaigns.

INTRODUCTION: COVID-19 vaccines are now being distributed to low- and middle-income countries (LMICs), with global urgency surrounding national vaccination plans. LMICs have significant experience implementing vaccination campaigns to respond to epidemic threats but are often hindered by chronic health system challenges. We sought to identify transferable lessons for COVID-19 vaccination from the rollout of three vaccines that targeted adult groups in Africa and South America: MenAfriVac (meningitis A); 17D (yellow fever) and rVSV-ZEBOV (Ebola virus disease). METHODS: We conducted a rapid literature review and 24 semi-structured interviews with technical experts who had direct implementation experience with the selected vaccines in Africa and South America. We identified barriers, enablers, and key lessons from the literature and from participants' experiences. Interview data were analysed thematically according to seven implementation domains. RESULTS: Participants highlighted multiple components of vaccination campaigns that are instrumental for achieving high coverage. Community engagement is an essential and effective tool, requiring dedicated time, funding and workforce. Involving local health workers is a key enabler, as is collaborating with community leaders to map social groups and tailor vaccination strategies to their needs. Vaccination team recruitment and training strategies need to be enhanced to support vaccination campaigns. Although recognised as challenging, integrating vaccination campaigns with other routine health services can be highly beneficial if well planned and coordinated across health programmes and with communities. CONCLUSION: As supplies of COVID-19 vaccines become available to LMICs, countries need to prepare to efficiently roll out the vaccine, encourage uptake among eligible groups and respond to potential community concerns. Lessons from the implementation of these three vaccines that targeted adults in LMICs can be used to inform best practice for COVID-19 and other epidemic vaccination campaigns.

What the reproductive number R0 can and cannot tell us about COVID-19 dynamics.

The reproductive number R (or R0, the initial reproductive number in an immune-naïve population) has long been successfully used to predict the likelihood of pathogen invasion, to gauge the potential severity of an epidemic, and to set policy around interventions. However, often ignored complexities have generated confusion around use of the metric. This is particularly apparent with the emergent pandemic virus SARS-CoV-2, the causative agent of COVID-19. We address some misconceptions about the predictive ability of the reproductive number, focusing on how it changes over time, varies over space, and relates to epidemic size by referencing the mathematical definition of R and examples from the current pandemic. We hope that a better appreciation of the uses, nuances, and limitations of R and R0 facilitates a better understanding of epidemic spread, epidemic severity, and the effects of interventions in the context of SARS-CoV-2.

Monitor for COVID-19 vaccine resistance evolution during clinical trials.

Although less common than the evolution of antimicrobial drug resistance, vaccine resistance can and has evolved. How likely is it that COVID-19 vaccines currently in development will be undermined by viral evolution? We argue that this can be determined by repurposing samples that are already being collected as part of clinical trials. Such information would be useful for prioritizing investment among candidate vaccines and maximizing the potential long-term impact of COVID-19 vaccines.

Impact of Comorbidities on SARS-CoV-2 Viral Entry-Related Genes.

Viral entry mechanisms for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are an important aspect of virulence. Proposed mechanisms involve host cell membrane-bound angiotensin-converting enzyme 2 (ACE2), type II transmembrane serine proteases (TTSPs), such as transmembrane serine protease isoform 2 (TMPRSS2), lysosomal endopeptidase Cathepsin L (CTSL), subtilisin-like proprotein peptidase furin (FURIN), and even potentially membrane bound heparan sulfate proteoglycans. The distribution and expression of many of these genes across cell types representing multiple organ systems in healthy individuals has recently been demonstrated. However, comorbidities such as diabetes and cardiovascular disease are highly prevalent in patients with Coronavirus Disease 2019 (COVID-19) and are associated with worse outcomes. Whether these conditions contribute directly to SARS-CoV-2 virulence remains unclear. Here, we show that the expression levels of ACE2, TMPRSS2 and other viral entry-related genes, as well as potential downstream effector genes such as bradykinin receptors, are modulated in the target organs of select disease states. In tissues, such as the heart, which normally express ACE2 but minimal TMPRSS2, we found that TMPRSS2 as well as other TTSPs are elevated in individuals with comorbidities compared to healthy individuals. Additionally, we found the increased expression of viral entry-related genes in the settings of hypertension, cancer, or smoking across target organ systems. Our results demonstrate that common comorbidities may contribute directly to SARS-CoV-2 virulence and we suggest new therapeutic targets to improve outcomes in vulnerable patient populations.

Pain Management Best Practices from Multispecialty Organizations During the COVID-19 Pandemic and Public Health Crises.

BACKGROUND: It is nearly impossible to overestimate the burden of chronic pain, which is associated with enormous personal and socioeconomic costs. Chronic pain is the leading cause of disability in the world, is associated with multiple psychiatric comorbidities, and has been causally linked to the opioid crisis. Access to pain treatment has been called a fundamental human right by numerous organizations. The current COVID-19 pandemic has strained medical resources, creating a dilemma for physicians charged with the responsibility to limit spread of the contagion and to treat the patients they are entrusted to care for. METHODS: To address these issues, an expert panel was convened that included pain management experts from the military, Veterans Health Administration, and academia. Endorsement from stakeholder societies was sought upon completion of the document within a one-week period. RESULTS: In these guidelines, we provide a framework for pain practitioners and institutions to balance the often-conflicting goals of risk mitigation for health care providers, risk mitigation for patients, conservation of resources, and access to pain management services. Specific issues discussed include general and intervention-specific risk mitigation, patient flow issues and staffing plans, telemedicine options, triaging recommendations, strategies to reduce psychological sequelae in health care providers, and resource utilization. CONCLUSIONS: The COVID-19 public health crisis has strained health care systems, creating a conundrum for patients, pain medicine practitioners, hospital leaders, and regulatory officials. Although this document provides a framework for pain management services, systems-wide and individual decisions must take into account clinical considerations, regional health conditions, government and hospital directives, resource availability, and the welfare of health care providers.