COVID-19 Transmission Research Articles

Using Social Network Analysis during COVID-19 contact tracing in Libya, 2020

Abstract Background Social network analysis (SNA) may provide valuable insights into individual-level transmission dynamics, allowing for targeted contact tracing interventions. We used SNA to understand the variability in SARS-CoV-2 transmission at the case level during the first wave in Libya. Methods We analyzed the spread of COVID-19 in Libya from March to August 2020 using contact tracing data from 287 confirmed cases. We created a network to visualize the transmission patterns, representing each case as a node and connections as links. We focused on direct contacts identified through contact tracing and calculated network measures such as out-degree centrality, in-degree centrality, and betweenness centrality. Gephi software was used for data visualization and understanding the network structure. Results Of the 287 COVID-19 cases, 264 (92%) had nodes with zero outdegrees, indicating that they did not contribute to infection transmission. The remaining 23 (8%) had non-zero out-degree measures and transmitter infection to other cases, 10 (4%) of whom were super-spreaders (outdegree≥5) causing 70% of the transmission. Six (2%) had no epidemiological contact, indicated by zero in-degree centrality measures; 63% had zero betweenness centrality measures, suggesting they did not act as intermediaries connecting separate transmission chains within the network. The network contained nine clusters of connected nodes, with the largest having 91 (24%) cases; the mean path length between cases was 1.846, and the network diameter was 4. Conclusions The study revealed distinct clusters of connected cases, along with the presence of super-spreaders. This network structure underscores the critical role of early identification and isolation of individuals with high transmission potential to effectively contain outbreaks. This revised conclusion directly references the findings of nine clusters and super-spreaders, demonstrating how these results support the importance of early intervention. Key messages • Social network analysis identifies clusters and super-spreaders in COVID-19 transmission, emphasizing the need for targeted interventions. • Connected clusters and super-spreaders highlight the importance of contact tracing and early isolation for effective control of COVID-19 outbreaks.

Reflective model assessment of adherence to COVID-19 non-pharmaceutical interventions

Abstract Background Non-pharmaceutical interventions (NPIs) decrease COVID-19 transmission. We identified behavioral factors that affected NPI adherence according to Normalization Process Theory (NPT) in medical students, travel seekers, and people with substance use disorder using reflective model measurement, which previously had not been conducted. Public health officials should consider factors that influence adherence to create preventive strategies for COVID-19. Methods In a cross-sectional design, medical students and people with substance use disorders in treatment comprised a probabilistic sample, and travelers and COVID-19 suspected persons comprised a convenience sample. Participants were from Split-Dalmatia County (n = 656) in the Mediterranean and consented to self-administered surveys in 02/2021. Sociodemographic data, locus of control (LoC), and moral behavior were collected. Partial least squares structural equation modeling (PLS-SEM) was used to measure reflective assessment of NPI adherence according to NPT. Results PLS-SEM reflective model assessment provided two-group specific factors in inverse relationships which determined NPI adherence with excellent goodness of fit (χ2=1.292, df = 1; p = 0.297). Significant negative factors covariance estimate (-0.716) revealed an increase in adherence to the first factor, NPIs and internal LoC [0.640], as the second factor, age (≤25) and highest education level (1.362), decreased. LoC was the mechanism by which sex (MLsex=-0.017, SE = 0.007, p < 0.016) and being a student or person with substance use disorder (MLgroup=-0.008, SE = 0.003, p = 0.015) showed indirect effect on NPI adherence. Conclusions Students and people with substance use disorders more adhered to NPIs. Higher-educated younger adults with a tendency toward external LoC facilitated the normalization. Public health interventions could encourage these subgroups to act as promoters to increase NPI adherence in similar settings. Key messages • COVID-19 non-pharmaceutical interventions and psychological factors of students and people with substance abuse disorder influenced adherence. Targeted interventions could promote adherence. • Public health officials should consider that preventive strategies may be more effective in highly educated adults younger than 25 years of age.

A conformable fractional finite difference method for modified mathematical modeling of SAR-CoV-2 (COVID-19) disease.

In this research, the ongoing COVID-19 disease by considering the vaccination strategies into mathematical models is discussed. A modified and comprehensive mathematical model that captures the complex relationships between various population compartments, including susceptible (Sα), exposed (Eα), infected (Uα), quarantined (Qα), vaccinated (Vα), and recovered (Rα) individuals. Using conformable derivatives, a system of equations that precisely captures the complex interconnections inside the COVID-19 transmission. The basic reproduction number (R0), which is an essential indicator of disease transmission, is the subject of investigation calculating using the next-generation matrix approach. We also compute the R0 sensitivity indices, which offer important information about the relative influence of various factors on the overall dynamics. Local stability and global stability of R0 have been proved at a disease-free equilibrium point. By designing the finite difference approach of the conformable fractional derivative using the Taylor series. The present methodology provides us highly accurate convergence of the obtained solution. Present research fills research addresses the understanding gap between conceptual frameworks and real-world implementations, demonstrating the vaccination therapy's significant possibilities in the struggle against the COVID-19 pandemic.

Knowledge, Attitude, and Practice of Office Employees in Qom, Iran Regarding Dental Treatment During the COVID-19 Pandemic Based on the Health Belief Model

Background and Objectives: COVID-19 can spread from human to human primarily through respiratory secretions. In this regard, dental procedures can pose a high risk of COVID-19 transmission. This study aims to investigate the knowledge, attitude and practice (KAP) of office employees in Qom, Iran regarding dental treatment during COVID-19 pandemic based on health belief model (HBM). Methods: This cross-sectional study was conducted on 510 office employees in Qom Province. Data collection tools included a demographic form and a KAP questionnaire based on the HBM constructs. Results: The mean age of participants was 36.7±9.1 and 252(52%) were female. The practice variable had a statistically significant relationship with perceived susceptibility (P<0.001), perceived barriers (P<0.001), history of vaccination (P<0.001), and history of chronic diseases (P<0.001). Age had a significant relationship with perceived severity (P<0.001) and perceived benefits (P<0.001). Gender had a significant relationship only with perceived severity (P<0.001). Marital status had a significant relationship with knowledge (P<0.001), perceived severity (P<0.001) and perceived barriers (P=0.03). Educational level had a significant relationship with knowledge (P=0.01), perceived severity (P<0.001), perceived benefits (P<0.001), perceived barriers (P<0.001). History of vaccination had a significant relationship with knowledge (P<0.001), perceived susceptibility (P<0.001), perceived severity (P=0.02), and perceived benefits (P=0.02). History of chronic disease had a significant relationship with perceived susceptibility (P<0.001), perceived severity (P<0.001), perceived benefits (P<0.001), and perceived barriers (P<0.001). Conclusion: knowledge, perceived susceptibility, perceived severity, perceived benefits and perceived barriers of office employees in Qom regarding dental treatment during COVID-19 pandemic are at moderate level. Their perceived susceptibility and severity have a positive relationship with visiting dental clinics.

Chance-constrained stochastic optimal control of epidemic models: A fourth moment method-based reformulation

This work proposes a methodology for the reformulation of chance-constrained stochastic optimal control problems that ensures reliable uncertainty management of epidemic outbreaks. Specifically, the chance constraints are reformulated in terms of the first four moments of the stochastic state variables through the so-called fourth moment method for reliability. Moreover, a spectral technique is employed to obtain surrogate models of the stochastic state variables, which enables the efficient computation of the required statistics. The practical implementation of the proposed approach is demonstrated via the optimal control of two different stochastic mathematical models of the COVID-19 transmission. The numerical experiments confirm that, unlike those reformulations based on the Chebyshev–Cantelli’s inequality, the proposed method does not exhibit the undesired outcomes that are typically observed when a higher precision is required for the risk level associated to the given chance constraints.

Healthcare Internet of Things system implementations for COVID-19 prevention

BackgroundIn response to the widespread transmission of COVID-19 in Australia, healthcare facilities implemented stringent infection control measures, and mandatory and manual screening procedures were introduced to ensure the safety of patients and healthcare staff. However, these necessary measures resulted in imbalances within the healthcare system, a shortage of front-line workers and impacts on patient experience and wait times. The prioritization of infection control measures shifted resources away from routine care, causing delays in accessing necessary healthcare services.Methods and FindingsTo address these challenges, we developed and implemented an Internet of Things (IoT) Smart Screening eGate solution in partnership with a large metropolitan children’s hospital in Australia. This solution integrated a contactless health self-service web app, thermal camera, and physical barrier to automate the COVID-19 health screening and data recording process. During the 3-month pilot period, we deployed the eGate at multiple entrances to the hospital, and monitored the number of users of the system in different periods. We also used a framework of formative evaluation to classify user design challenges within limited resources and improved the design of the eGate to enhance its effectiveness. Our findings show that the IoT eGate solution improved the efficiency of the screening process and reduced the workload and exposure risks of front-line staff and anyone who required access to the hospital. By automating the screening process, we reduced the need for manual screening and minimized contact between individuals, thus reducing the risk of potentially infected.ConclusionIn conclusion, our pilot study demonstrated the potential of IoT technologies in improving the efficiency and safety of medical facilities during pandemics and provided a series of recommendations for the translation of IoT technologies for medical facilities, including the importance of co-design and collaboration with stakeholders, user-centered design, and ongoing monitoring and evaluation.

An investigation of the impact of ‘Living with COVID’ on workplace COVID-19 transmission risk, response and resilience - lessons learnt and future challenges

BackgroundPrevious research has highlighted links between occupation and risk of COVID-19 transmission and suggests that occupational risk is influenced by covariates including socio-economic status, and deprivation. This study examined the perspectives of local authority teams of how changes in policy and advice, as set out in the UK government report ‘Living with COVID,’ affected COVID-19 transmission risk, response and resilience in workplace settings in Greater Manchester, an area that was disproportionately affected by the pandemic.MethodsThe project, which took place between August and November 2022, undertook a mixed methods approach to incorporate wide-ranging reflections of changes following the publication of ‘Living with COVID’. Quantitative data was collected from local employers (n = 149) and employees (n = 397) using online surveys, and qualitative interview data was collected from Greater Manchester local authority teams (n = 19).ResultsThe research highlighted the inequitable impact of the pandemic on those already experiencing health inequalities, including people on more precarious employment contracts or those who were unable to work from home during the pandemic.The study found that the facilitators that helped local authorities to support employers to manage transmission included clear, detailed and timely national and local guidance, good communication, partnership working, funding, and timely access to data. Barriers to supporting employers included contradictory or confusing national guidance, structural inequalities, lack of funding, and delayed access to data. Interview participants reported that they were now utilising lessons learnt during the pandemic, along with the that partnerships developed, in order to tackle wider health issues and to prepare for future pandemics or health crises.ConclusionThe findings of the study, which concur with previously published research conducted as part of the PROTECT project, highlight the importance of active reflection on the lessons learned during the course of the pandemic. The study draws on PH and EH teams’ perspectives of managing COVID-19 transmission, in an area that was disproportionately affected by the pandemic, in order to add to our understanding of the best ways to ensure preparedness for future pandemics or health crises at a national level.

Effects of non-pharmaceutical interventions on COVID-19 transmission: rapid review of evidence from Italy, the United States, the United Kingdom, and China

Effects of non-pharmaceutical interventions on COVID-19 transmission: rapid review of evidence from Italy, the United States, the United Kingdom, and China

Anaesthesia healthcare workers' interactions with COVID-19-positive and -suspect patients: A multi-site observational study from Queensland, Australia.

Occupationally acquired COVID-19 is a hazard for healthcare workers (HCWs). In four hospitals of the Metro North Hospitals and Health Service in Queensland, Australia, we invited HCWs to report the nature of any anaesthesia interactions with COVID-19-positive and COVID-19-suspect patients. This was to assist workforce planning in future pandemics. Data collection consisted of three phases; 1) participating HCWs recorded details of episodes of care (EOCs) with COVID-19-positive or COVID-19-suspect patients; 2) these HCWs were invited to complete a follow-up survey about their own health status regarding COVID-19 infections; 3) electronic health records were used to assess patient demographics, infectious status and outcomes. Between 21 March 2020 and 17 May 2022, 63 anaesthesia HCWs reported 90 EOCs with 67 unique patients. The median (interquartile range (IQR)) age of the HCWs was 40.5 years (34-46); 55% (34/62) were male, 62% (39/63) were consultants and 21% (13/63) were registrars. The median (IQR) age of patients was 39 (28-63) years. Personal protective equipment (PPE) was deemed to be appropriate by the HCWs in (86/88) 98% of the EOCs. Twenty-four HCWs (38%) responded to a follow-up survey. Of 12 HCWs who subsequently tested positive to COVID-19, only four had cared for a COVID-19-positive patient in the two weeks prior to their diagnosis. Most responding HCWs perceived they contracted COVID-19 in the community, experiencing mild illness. We found relatively low COVID-19 case numbers, high PPE use and low transmission of COVID-19 within the hospital setting.

Social epidemiology of urban COVID-19 inequalities in Latin America and Canada

BackgroundThe COVID-19 pandemic has spread through pre-existing fault lines in societies, deepening structural barriers faced by precarious workers, low-income populations, and racialized communities in lower income sub-city units. Many studies have quantified the magnitude of inequalities in COVID-19 distribution within cities, but few have taken an international comparative approach to draw inferences on the ways urban epidemics are shaped by social determinants of health.MethodsGuided by critical epidemiology, this study quantifies sub-city unit-level COVID-19 inequalities across eight of the largest metropolitan areas of Latin America and Canada. Leveraging new open-data sources, we use concentration indices to quantify income- and vulnerability-related inequalities in incidence, test positivity, and deaths over the first 125 weeks of the pandemic between January 2020 and May 2022.ResultsOur findings demonstrate that incidence, deaths, and test positivity are all less concentrated in low-income sub-city units than would be expected, with incidence ranging concentration in lower income neighbourhoods in Toronto (CI = -0.07) to concentration in higher income neighbourhoods in Mexico City (CI = 0.33). Drawing on relevant studies and evaluations of data reliability, we conclude that the best available public surveillance data for the largest cities in Latin America are likely not reliable measures of the true COVID-19 disease burden. We also identify recurring trends in the evolution of inequalities across most cities, concluding that higher income sub-city units were frequent early epicentres of COVID-19 transmission across the Latin America and Canada.ConclusionsJust as critical epidemiology points to individuals biologically embodying the material and social conditions in which we live, it may be just as useful to think of cities reifying their material and social inequities in the form of sub-city unit-level infectious disease inequities. By shifting away from a typical vulnerability-based social determinants of health frame, policymakers could act to redress and reduce externalities stemming from sub-city unit-level income inequality through redistributive and equity-promoting policies to shift the centre of gravity of urban health inequalities before the next infectious disease epidemic occurs.

Animal Models in COVID-19 Research: Insights and Outcomes

Animal models have been critical in understanding the pathogenesis, transmission, and therapeutic interventions for COVID-19, caused by SARS-CoV-2. The primary challenges include species-specific differences in viral receptor expression, particularly the ACE2 receptor, and immune system responses, which can affect how closely these models mimic human COVID-19 pathology. Additionally, ethical concerns around the use of animals, especially non-human primates, and logistical issues such as high costs, housing requirements, and limited availability of certain models, further complicate research efforts. Transgenic mice expressing human ACE2 (hACE2) are widely used because of their ease of handling and cost-effectiveness. Other animal models include non-human primates, hamsters, cats, and ferrets, which were used to study viral transmission and host-pathogen interactions. The rapid dynamics of the SARSCoV-2 evolution is one of the challenges and the complementary use of multiple models provides a more comprehensive understanding of the disease. Nonetheless, these models have been essential for rapid advancements in COVID19 vaccine and therapeutic development, offering insights that continue to guide public health strategies. The review discusses the various animal models used in SARS-CoV-2 research, the outcomes, and the challenges and questions to be addressed Keywords: Animal models, SARS-CoV-2, COVID-19

Decision making techniques in mass gathering medicine during the COVID-19 pandemia: a scoping review

BackgroundThe COVID-19 pandemic has profoundly affected mass gatherings (MGs) worldwide, necessitating the implementation of advanced decision support techniques. These techniques, including mathematical models and risk assessment tools, have played a critical role in ensuring the safe conduct of events by mitigating the spread of SARS-CoV-2.AimThis mini-review aims to explore and synthesize the decision support methodologies employed in managing MGs during the COVID-19 pandemic.MethodsA scoping review was conducted following the PRISMA guidelines covering the period from 2020 to 2024. Studies were categorized by event type (e.g., academic, religious, political, sports) and decision-making tools applied. The review identified a range of decision support techniques, with risk assessment and simulation tools being the most commonly employed across various event types.ResultsA total of 199 studies were initially identified, with 10 selected finally for inclusion based on relevance to decision support techniques. Case studies included the successful risk mitigation strategies during the 2020 Hajj, the 2021 Tokyo Olympics, and the 2022 FIFA World Cup in Qatar. Techniques such as fuzzy logic, Bayesian analysis, and multi-criteria decision-making were also highlighted, particularly in complex scenarios. These tools significantly contributed to reducing COVID-19 transmission risks at large-scale events.ConclusionThe review underscores the importance of decision support systems in the safe management of MGs during the pandemic. Further research should focus on the integration of emerging technologies and the long-term impacts of decision support tools on public health management.

Isolation to stabilization: A Housing First approach to address homelessness in Kingston, Ontario.

Homelessness is a significant and growing public health concern across Canada. In Kingston, Ontario, the number of people experiencing chronic homelessness has more than doubled from 136 people in 2020 to 296 in 2023. An emergency shelter-in-place hotel program was established in April 2020 to provide non-congregate shelter to people experiencing homelessness and vulnerable to SARS-CoV-2 infections. Beyond preventing COVID transmission, the unintentional consequence was that a population that experienced chronic homelessness reduced drug consumption and became stable. In 2022, with increased funding from the Ministry of Health and the City of Kingston, a new Housing First program was implemented to transition individuals from homelessness to long-term stable housing. Between November 2022 and June 2023, a total of 34 clients initiated the program. Of these clients, 10 completed the program and were successfully housed, 10 remained active participants, and 14 were discharged before completion. Strengths and challenges were identified. Diverse services provided to meet the population's needs and strong collaborations with various community partners were facilitating factors. Inadequate external resources, a lack of evening and prosocial activities, and outside peers (not part of the program) who influenced recovery plans were identified as challenges. This program illustrates that simultaneously integrating housing, community building, mental health, and addiction services is possible and provides an innovative way to stabilize this vulnerable population of people experiencing homelessness. Results from this program and the knowledge generated through implementation are being used to further scale up the program.

Impacts of indoor masks wearing on air contamination during 10-minute speaking in patients with SARS-CoV-2 omicron variant infection.

In 10-minute speaking, N95 respirators significantly decreased SARS-CoV-2 emissions compared with no-mask wearing. However, SARS-CoV-2 was detected in the air even when wearing N95 and surgical masks in patients with high viral loads. Therefore, universal masking of infected and uninfected persons is important for preventing COVID-19 transmission via the air.

Impact of pharmaceutical and non-pharmaceutical interventions on COVID-19 in Tunisia

BackgroundIn COVID-19 management, a variety of pharmaceutical interventions (PI) and non- pharmaceutical interventions (NPI) were adopted to limit the spread of the disease and its associated deaths. We aimed to evaluate the impact of PI and NPI on risks of COVID-19 transmission and deaths.MethodWe collected aggregate data from March 2nd, 2020, to December 1, 2022 from the Tunisian Ministry of Health's website and OurWorldInData.org site. NPI Periods (NPIP: March 2020 to March 2021) and PI Periods (PIP) were distributed to NPIP1, 2, 3 and 4 and to PIP1, 2, 3 and 4, respectively. We calculated the Relative Risks (RR) and 95% Confidence Intervals (CI) by comparing the subsequent period with previous one.ResultsThe risk of SARS-CoV-2 transmission increased progressively from the zero cases period (NPIP2) to the mitigate strategy period (NPIP3) (RR = 14.0; 95% CI: 12.4–15.8) and to the stop-and-go epidemic control period (NPIP4) (RR = 23.1 (95% CI: 22.4–23.9). It was stabilized in the targeted vaccination period (PIP1) (RR = 1.08, 95% CI: 1.07–1.08) and reduced during the mass vaccination period (PIP2) (RR: 0.50, 95% CI: 0.50–0.51). SARS-CoV-2 transmission, increased during PIP3 concomitant with the Omicron wave (RR = 2.65, 95% CI: 2.64–2.67). It remained at a low level in PIP4 (RR = 0.18; 95% CI: 0.18–0.18).Compared to NPIP2, NPIP3 and NPIP4 were associated with a higher risk of COVID-19 mortality (RR = 3.337; 95% CI: 1.797–6.195) and (RR = 72.63 (95% CI: 54.01–97.68), respectively. Since the start of the immunization program, the risk of COVID-19 death has consistently decreased. In comparison to each previous period, the risk transitioned in PIP1 to RR = 0.91; 95% CI: 0.88–0.93, then to RR = 0.85; 95% CI: 0.83–0.88 in PIP2, to RR = 0.47; 95% CI: 0.45–0.50 in PIP3, and to RR = 0.19; 95% CI: 0.18–0.24 during PIP4.ConclusionIn terms of lowering the risk of transmission and mortality, the NP strategy at the beginning of the epidemic outperformed the IP strategy during the outbreak.

Quantifying the impact of prevalence-dependent adaptive behavior on COVID-19 transmission: A modeling case study in Maryland

The COVID-19 pandemic highlighted the need for robust epidemic forecasts, projecting health burden over short- and medium-term time horizons. Many COVID-19 forecasting models incorporate information on infection transmission, disease progression, and the effects of interventions, but few combine information on how individuals change their behavior based on altruism, fear, risk perception, or personal economic circumstances. Moreover, early models of COVID-19 produced under- and over-estimates, failing to consider the complexity of human responses to disease threat and prevention measures. In this study, we modeled adaptive behavior during the first year of the COVID-19 pandemic in Maryland, USA. The adapted compartmental model incorporates time-varying transmissibility informed on data of environmental factors (e.g., absolute humidity) and behavioral factors (aggregate mobility and perceived risk). We show that humidity and mobility alone did little to explain transmissibility after the first 100 days. Including adaptive behavior in the form of perceived risk as a function of hospitalizations more effectively explained inferred transmissibility and improved out-of-sample fit, demonstrating the model’s potential in real-time forecasting. These results demonstrate the importance of incorporating endogenous behavior in models, particularly during a pandemic, to produce more accurate projections, which could lead to more impactful and efficient decision making and resource allocation.

Influenza-Like Illness in Lesotho From July 2020 to July 2021: Population-Based Participatory Surveillance Results.

Participatory surveillance involves at-risk populations reporting their symptoms using technology. In Lesotho, a landlocked country of 2 million people in Southern Africa, laboratory and case-based COVID-19 surveillance systems were complemented by a participatory surveillance system called "LeCellPHIA" (Lesotho Cell Phone Population-Based HIV Impact Assessment Survey). This report describes the person, place, and time characteristics of influenza-like illness (ILI) in Lesotho from July 15, 2020, to July 15, 2021, and reports the risk ratio of ILI by key demographic variables. LeCellPHIA employed interviewers to call participants weekly to inquire about ILI. The average weekly incidence rate for the year-long period was created using a Quasi-Poisson model, which accounted for overdispersion. To identify factors associated with an increased risk of ILI, we conducted a weekly data analysis by fitting a multilevel Poisson regression model, which accounted for 3 levels of clustering. The overall response rate for the year of data collection was 75%, which resulted in 122,985 weekly reports from 1776 participants. ILI trends from LeCellPHIA mirrored COVID-19 testing data trends, with an epidemic peak in mid to late January 2021. Overall, any ILI symptoms (eg, fever, dry cough, and shortness of breath) were reported at an average weekly rate of 879 per 100,000 (95% CI 782-988) persons at risk. Compared to persons in the youngest age group (15-19 years), all older age groups had an elevated risk of ILI, with the highest risk of ILI in the oldest age group (≥60 years; risk ratio 2.6, 95% CI 1.7-3.8). Weekly data were shared in near real time with the National COVID-19 Secretariat and other stakeholders to monitor ILI trends, identify and respond to increases in reports of ILI, and inform policies and practices designed to reduce COVID-19 transmission in Lesotho. LeCellPHIA, an innovative and cost-effective system, could be replicated in countries where cell phone ownership is high but internet use is not yet high enough for a web- or app-based surveilance system.

Confinement policies: controlling contagion without compromising mental health

ABSTRACT A growing literature shows that confinement policies used by governments to slow COVID-19 transmission have negative impacts on mental health, but the differential effects of individual policies on mental health remain poorly understood. We used data from the COVID-19 questionnaire of the Survey of Health, Ageing and Retirement in Europe (SHARE), which focuses on populations aged 50 and older, and the Oxford COVID-19 Government Response Tracker for 28 countries to estimate the effects of eight different confinement policies on three outcomes of mental health: insomnia, anxiety and depression. We applied robust machine learning methods to estimate the effects of interest. Our results indicate that closure of schools and public transportation, restrictions on domestic and international travel, and gathering restrictions did not worsen the mental health of older populations in Europe. In contrast, stay at home policies and workplace closures aggravated the three health outcomes analysed. Based on these findings, we close with a discussion of which policies should be implemented, intensified, or relaxed to control the spread of the virus without compromising the mental health of older populations.

Correlation of Triethylamine N-oxide (TMAO), LPS, and TNF-Alpha Levels With Clinical Features of the Disease in Patients With and Without Septic Shock Infected With COVID-19 Virus

BackgroundInflammation is a response of the immune system to protect the body against various diseases or injuries. Serum trimethylamine N-oxide (TMAO) levels may vary depending on age, gender, habits, comorbidities, and microbiota. AimsIn this study, we investigated whether TMAO levels have diagnostic significance and their potential as a marker in the early diagnosis of the disease. Another aim of the research was to identify changes in TMAO levels as a reflection of the deterioration in the microflora, and IL-6, IL-10, IL-1β, TNF-alpha, and LPS levels in patient groups. Then, we recognized relationships between these parameters in patients infected with COVID-19 without septic shock and with COVID-19 who were without transmission of COVID-19 in septic shock. Study DesignA total of 160 patients were investigated, including 40 patients infected with COVID-19 without septic contact, 40 patients with COVID-19 positive septic shock, 40 patients with COVID-19 negative septic shock, and 40 healthy individuals as the control group. ResultsTNF-α and IL-1β levels were significantly lower (P < 0.001) and IL-6 and IL-10 levels were significantly higher (P < 0.001) in patient groups than in control groups. IL-1β showed a significant decrease, especially in the groups infected with COVID-19. Although IL-6, increased even more in the groups infected with COVID-19. ConclusionsLPS level was remarkably high in the sepsis group infected with COVID-19 compared to the other groups. TMAO level was significantly higher (P < 0.001) in the sepsis group. Therefore, TMAO is a potential biomarker in sepsis and septic shock.

Analyzing the Dynamics of COVID-19 Transmission in Select Regions of the Philippines: A Modeling Approach to Assess the Impact of Various Tiers of Community Quarantines

The COVID-19 pandemic has significantly impacted communities worldwide, and effective management strategies are critical to reduce transmission rates and minimize the impact of the disease. In this study, we modeled and analyzed the COVID-19 transmission dynamics and derived relevant epidemiological values for three regions of the Philippines, namely, the National Capital Region (NCR), Davao City, and Baguio City, under different community quarantine implementations. The unique features and differences of these regions-of-interest were accounted for in simulating the disease spread and in estimating key epidemiological parameters fitted to the reported COVID-19 cases. Results support the robustness of the model formulated and provides insights into the effect of the government's implemented intervention protocols. With a forecasting feature, this modeling framework is beneficial for science-based decision support, policy making, and assessment for recent and future pandemics wherever regions-of-interest.