Non-pharmaceutical Interventions Research Articles

Generalized contact matrices allow integrating socioeconomic variables into epidemic models.

Variables related to socioeconomic status (SES), including income, ethnicity, and education, shape contact structures and affect the spread of infectious diseases. However, these factors are often overlooked in epidemic models, which typically stratify social contacts by age and interaction contexts. Here, we introduce and study generalized contact matrices that stratify contacts across multiple dimensions. We demonstrate a lower-bound theorem proving that disregarding additional dimensions, besides age and context, might lead to an underestimation of the basic reproductive number. By using SES variables in both synthetic and empirical data, we illustrate how generalized contact matrices enhance epidemic models, capturing variations in behaviors such as heterogeneous levels of adherence to nonpharmaceutical interventions among demographic groups. Moreover, we highlight the importance of integrating SES traits into epidemic models, as neglecting them might lead to substantial misrepresentation of epidemic outcomes and dynamics. Our research contributes to the efforts aiming at incorporating socioeconomic and other dimensions into epidemic modeling.

Respiratory pathogen analysis in pediatric inpatients unraveled the infection pattern of Mycoplasma pneumoniae post the COVID-19 pandemic.

To counteract the COVID-19 pandemic, nonpharmaceutical interventions (NPIs) were implemented globally, exerting a profound influence on a wide spectrum of infectious diseases, encompassing respiratory tract infections (RTIs). Subsequent to the easing of NPIs, China experienced a significant outbreak of Mycoplasma pneumoniae (MP). Over a decade from 2015 to 2024, our study scrutinized 12 common infectious diseases among pediatric children. Etiologically diagnostic data and clinical outcome metrics of children with RTIs, tested for 13 pathogens, were analyzed to evaluate changes during and after the pandemic compared to pre-pandemic periods, with a notable emphasis on age profile and coinfection patterns of MP. Among 57,471 hospitalized children, 23,178 were diagnosed with infectious diseases. Under NPIs, most respiratory infections declined compared to pre-pandemic levels, rebounding by 69.64% in 2023. While the infection rate of common respiratory pathogens decreased, cases of respiratory syncytial virus increased during the period of extensive NPI implementation. In 2023, pediatric intensive care unit durations for these pathogens increased, suggesting greater severity of illness compared to 2019. MP exhibited the highest infection rate (31.38% average), with a notable outbreak post-pandemic due to severity increase in <3 year olds and rise among older children. NPIs reduced MP coinfections and mitigated their severity, while exerting a significant influence on bacterial coinfections with MP over the span of 5 years, in contrast to their impact on viral pathogens. NPIs effectively curb transmission of respiratory infections by most pathogens, resulting in increased average age of MP infections and altered patterns of coinfection post-pandemic.

The optimal spatially-dependent control measures to effectively and economically eliminate emerging infectious diseases.

Non-pharmaceutical interventions (NPIs) are effective in mitigating infections during the early stages of an infectious disease outbreak. However, these measures incur significant economic and livelihood costs. To address this, we developed an optimal control framework aimed at identifying strategies that minimize such costs while ensuring full control of a cross-regional outbreak of emerging infectious diseases. Our approach uses a spatial SEIR model with interventions for the epidemic process, and incorporates population flow in a gravity model dependent on gross domestic product (GDP) and geographical distance. We applied this framework to identify an optimal control strategy for the COVID-19 outbreak caused by the Delta variant in Xi'an City, Shaanxi, China, between December 2021 and January 2022. The model was parameterized by fitting it to daily case data from each district of Xi'an City. Our findings indicate that an increase in the basic reproduction number, the latent period or the infectious period leads to a prolonged outbreak and a larger final size. This indicates that diseases with greater transmissibility are more challenging and costly to control, and so it is important for governments to quickly identify cases and implement control strategies. Indeed, the optimal control strategy we identified suggests that more costly control measures should be implemented as soon as they are deemed necessary. Our results demonstrate that optimal control regimes exhibit spatial, economic, and population heterogeneity. More populated and economically developed regions require a robust regular surveillance mechanism to ensure timely detection and control of imported infections. Regions with higher GDP tend to experience larger-scale epidemics and, consequently, require higher control costs. Notably, our proposed optimal strategy significantly reduced costs compared to the actual expenditures for the Xi'an outbreak.

COVID-19 Modeling: A Review

The SARS-CoV-2 viruses and their triggered COVID-19 pandemic have fundamentally reshaped the world in almost every aspect, their evolution and influences remain. While over a million of literature have been produced on these unprecedented, overwhelming global disaster, one critical question is open: How has COVID-19 been quantified globally? This further inspires many other questions: What COVID-19 problems have been modeled? How have modeling methods in areas such as epidemiology, artificial intelligence (AI), data science, machine learning, deep learning, mathematics and social science played their roles in characterizing COVID-19? Where are the gaps and issues of these COVID-19 modeling studies? What are the lessons for quantifying future disasters? Answering these questions involves the analysis of a very broad spectrum of literature across different disciplines and domains. Distinguishing from specific efforts, this review takes the first attempt to generate a systematic, structured and contrastive landscape and taxonomy of global COVID-19 modeling. First, the surveyed problems span over a full range of COVID-19, including epidemic transmission processes, case identification and tracing, infection diagnosis and medical treatments, non-pharmaceutical interventions and their influence, drug and vaccine development, psychological, economic and social influence and impact, and misinformation, and so on. Second, the reviewed modeling methods traverse all relevant disciplines, from statistic modeling to epidemic modeling, medical analysis, biomedical analysis, AI, deep and machine learning, analytics, and simulation. Critical analyses further identify significant issues and gaps, for example, simple techniques and similar problems have been overwhelmingly addressed everywhere, while intrinsic and foundational issues and deep insights have been overlooked. The review discloses significant opportunities for more deeply, effectively and uniquely quantifying COVID-19-like global disasters from their intrinsic working mechanisms, interactions and dynamics.

Rights-Based Legal Considerations for TB Isolation Practices in Community Settings in the Post-Pandemic Era.

State and local governments are free to enact infectious disease control laws to protect the public health but must conform those laws to right-based limitations imposed by the United States Constitution. TB control laws that empower public health agencies and their representatives to institute restrictions on movement and activities for persons with TB have remained largely unchanged for decades and warrant review to ensure consistency with modern legal principles. During the COVID-19 pandemic, there was increased attention to the tension between non-pharmaceutical public health interventions (masking, isolation, lock-downs) intended to reduce transmission and improve community health and their potential negative consequences on learning, mental health, individual finances and the economy at large. Increasing evidence suggests that much of TB transmission is likely to have occurred prior to diagnosis and treatment initiation, and there is limited scientific evidence for the efficacy of community-based isolation in reducing TB incidence or TB mortality; by contrast, there is evidence that treatment rapidly reduces infectiousness, and that prolonged isolation may have deleterious health and financial effects for persons with TB. The post-pandemic era presents an opportunity to reassess public health authorities' legal obligations in designing voluntary and involuntary isolation policies for persons with tuberculosis. In so doing, state and local governments can recalibrate the balance between respect for individual constitutional rights and achieving public health TB goals.

Coevolution of non-pharmaceutical interventions and infectious disease spreading in age-structured populations

In the face of emerging major infectious diseases, non-pharmaceutical interventions (NPIs) such as mask-wearing are the primary means of disease control in the early stages due to the lack of effective pharmaceutical interventions. Yet the current understanding of the dynamic feedback loop between NPIs and infectious disease spreading remains limited. In this study, we proposed an asymmetrically coupled dynamic model of the coevolution of mask-wearing behaviour and infectious disease spreading in an age-structured population to describe the bidirectional relationship between NPIs such as mask-wearing and infectious disease spreading. A theoretical analysis framework was developed by extending the microscopic Markov chain method. To reflect the spreading process more realistically, we conducted numerical simulations from a mesoscopic perspective through an age-contact data-driven approach. Critical transition, multi-wave epidemics, multi-transitions, and mixed-phase transitions resulting from the coevolution of mask and disease are observed. The results highlight the critical role of NPIs in controlling disease spreading. The mask-wearing can effectively reduce the daily peak infection density and prevent large-scale outbreaks within a short period. However, it will also prolong the duration of the epidemic and cause the disease to exist in the population for a long time. There is a trade-off between the two. For certain diseases with specific spreading capabilities, pharmaceutical interventions such as vaccination, which can directly reduce the disease’s infectiousness, may result in more people being infected if NPIs are not implemented as early as possible. In addition, results from Anhui indicate that people aged 42 years are at high risk of being infected because of their large population base, which suggests that priority should be given to the prevention and control of this group.

Resurgence of influenza and enterovirus infections in Taiwan post-COVID-19: A nationwide surveillance study

BackgroundThe global impact of COVID-19 has prompted profound shifts in public health policies. The epidemiology of respiratory infectious disease may change in the post-covid era. This study investigates the repercussions of these policies on respiratory infectious diseases, specifically the resurgence of severe influenza and enterovirus infections in the post-COVID-19 era. MethodsExamining the period from January 2020 to December 2023, our nationwide study analyzes data from the Taiwan Centers for Disease Control and Our World in Data. Two distinct phases, containing (Week 1, 2020, to Week 43, 2022) and coexisting (Week 44, 2022, to Week 50, 2023), are scrutinized, emphasizing policy changes and their potential impact on epidemiology. ResultsEpidemiological trends reveal a decline in COVID-19 and all-cause pneumonia during the co-existing period but a notable rise in severe influenza and enterovirus infections. Interrupted time series analysis confirms the surge in severe influenza and enterovirus cases post-restriction ease. ConclusionThe post-COVID-19 era introduces challenges with the resurgence of traditional respiratory diseases, necessitating continuous surveillance, timely non-pharmaceutical interventions, and vaccination as crucial strategies. Vigilance and targeted measures by policymakers and healthcare providers are imperative to navigate the evolving landscape of respiratory infectious diseases in the aftermath of the pandemic.

Post-pandemic consumption of outpatient antibiotics in Switzerland up to pre-pandemic levels, 2018–2023: An interrupted time series analysis

BackgroundThe COVID-19 pandemic has been a challenge for health-care systems and antibiotic stewards as uncertainty regarding treatment and bacterial coinfections raised concern. MethodsThis retrospective observational study examined the association of the pandemic on outpatient antibiotic sales and prescriptions in Switzerland using interrupted time series (ITS) analyses. Data from IQVIA™ and the Sentinella Network were used to analyze antibiotic consumption and prescription patterns over a 72-month period from January 2018 to December 2023, divided into pre-pandemic, pandemic, and post-pandemic periods. ResultsAntibiotic consumption decreased during the pandemic and returned to pre-pandemic levels in the post-pandemic period. A decrease in level was most pronounced in the French-speaking region (−2.82 defined daily doses per 1,000 inhabitants per day (DID) per month, 95 %CI [−4.34, −1.30], p < 0.001) and the Italian-speaking region (−2.80 DID per month, 95 %CI [−4.78, −0.82], p < 0.01), followed by the German-speaking region (−1.72 DID per month, 95 %CI [−2.71, −0.74], p < 0.01). Similarly, in the ITS, the relative change of model estimates in antibiotic prescriptions by GPs and pediatricians for upper respiratory tract infections, was of −36.0 % and −50.3 % resp. in the pandemic period and +10.1 % and −2.6 % in the post-pandemic period compared with the pre-pandemic period. ConclusionsA decrease of antibiotic prescriptions was observed in GPs and pediatricians during the COVID-19 pandemic, followed by a return to pre-pandemic levels. The patterns in antibiotic prescriptions aligned with the epidemiology of respiratory infections and demonstrated a pronounced association with the implementation and subsequent removal of non-pharmaceutical interventions.

Nonpharmaceutical Interventions and Neurodevelopmental Outcomes in School-Age Preterm Children and Adolescents: A Systematic Review

Abstract Objective: To systematically review nonpharmaceutical interventions aiming to enhance neurodevelopment in preterm children and adolescents (aged 4–18 years). Method: A systematic review of the literature was conducted for all studies published up to May 1, 2022, across Medline, Web of Science, and PsycINFO databases. Studies were evaluated for inclusion by 2 independent reviewers using predetermined inclusion criteria. The Risk of Bias In Non-randomized Studies of Interventions and the Cochrane risk-of-bias tool for randomized trials (RoB 2) tools were used to assess bias in the selected studies. Results: Of the 1778 articles identified, 23 were included. Quality assessment revealed moderate bias in 52.2%, low bias in 21.7%, and serious bias in 26.1%. The selected studies comprised 60.9% randomized controlled trials and 21.7% pre- versus postdesigns. Interventions included Cogmed Working Memory Training® (43.5%), BrainGame Brian (13%), physiotherapy (13%), and others (30.4%). Qualitative analysis showed the limited impact of interventions on neurodevelopmental outcomes in preterm children aged 4–18 years. Conclusion: Despite recent efforts to use more rigorous methodologies, current research on school-age interventions for preterm neurodevelopment exhibits methodological limitations. There is a pressing need for well-designed, large-scale clinical trials to evaluate the efficacy of nonpharmaceutical interventions in this vulnerable population.

Exploring pathogen population density as a metric for understanding post-COVID infectious disease surges.

After the easing of COVID-19 restrictions, peaks of common infectious diseases surpassed pre-pandemic levels, raising questions about causes and ways to monitor these changes. A proposed measure, the Pathogen Population Density (PPD) score, could help track these shifts. PPD refers to the concentration of infectious agents within a population at a given time and location, serving as a potential indicator of infection levels in susceptible individuals at the population level. It is likely that PPD remains relatively stable within a specific community, as an equilibrium forms between infections and susceptibility. During the pandemic, nonpharmaceutical interventions (NPIs) led to a reduction in infectious diseases, possibly lowering population immunity and decreasing the PPD score. Once NPIs were lifted, the PPD score likely increased sharply due to a larger pool of susceptible individuals, causing more primary infections and stronger recurrent infections, faster transmission, and more severe pathogenic outcomes at the individual level. Monitoring the PPD score over time could help predict when infection peaks will occur. PPD is influenced by factors such as public health strategies, vaccination programs, and the behavior of high-risk individuals. As a quantitative measure, PPD has the potential to serve as a valuable predictive and monitoring tool, helping public health officials anticipate and track changes in infectious disease dynamics. It could be an effective tool for managing future outbreaks or pandemics and serve as a communication tool between scientists and the public to understand the emergence of new disease peaks.

Evolution of COVID-19 dynamics in Guangdong Province, China: an endemic-epidemic modeling study

BackgroundFrom January 2020 to June 2022, strict interventions against COVID-19 were implemented in Guangdong Province, China. However, the evolution of COVID-19 dynamics remained unclear in this period.ObjectivesThis study aims to investigate the evolution of within- and between-city COVID-19 dynamics in Guangdong, specifically during the implementation of rigorous prevention and control measures. The intent is to glean valuable lessons that can be applied to refine and optimize targeted interventions for future crises.MethodsData of COVID-19 cases and synchronous interventions from January 2020 to June 2022 in Guangdong Province were collected. The epidemiological characteristics were described, and the effective reproduction number (Rt) was estimated using a sequential Bayesian method. Endemic-epidemic multivariate time-series model was employed to quantitatively analyze the spatiotemporal component values and variations, to identify the evolution of within- and between-city COVID-19 dynamics.ResultsThe incidence of COVID-19 in Guangdong Province was 12.6/100,000 population (15,989 cases) from January 2020 to June 2022. The Rt predominantly remained below 1 and increased to a peak of 1.39 in Stage 5. As for the evolution of variations during the study period, there were more spatiotemporal components in stage 1 and 5. All components were fewer from Stage 2 to Stage 4. Results from the endemic-epidemic multivariate time-series model revealed a strong follow-up impact from previous infections in Dongguan, Guangzhou and Zhanjiang, with autoregressive components of 0.48, 0.45 and 0.36, respectively. Local risk was relatively high in Yunfu, Shanwei and Shenzhen, with endemic components of 1.17, 1.04 and 0.71, respectively. The impact of the epidemic on the neighboring regions was significant in Zhanjiang, Shenzhen and Zhuhai, with epidemic components of 2.14, 1.92, and 1.89, respectively.ConclusionThe findings indicate the presence of spatiotemporal variation of COVID-19 in Guangdong Province, even with the implementation of strict interventions. It’s significant to prevent transmissions within cities with dense population. Preventing spatial transmissions between cities is necessary when the epidemic is severe. To better cope with future crises, interventions including vaccination, medical resource allocation and coordinated non-pharmaceutical interventions were suggested.

Towards a comprehensive COVID-19 non-pharmaceutical interventions’ index for the province of Québec

ObjectivesThe primary objective of this project was to develop a comprehensive COVID-19 non-pharmaceutical interventions’ index for the province of Québec (QCnPI-Index). The resulting database systematically categorizes, multiple non-pharmaceutical interventions implemented in the 17 administrative regions (AR) of the province of Québec to mitigate the spread of COVID-19 in the form of an index.Data descriptionData represent interventions and groups of interventions implemented during the COVID-19 period in Québec. They are a compilation of policies, guidelines, and governmental interventions related to COVID-19, considering temporal and geographical dimensions. Data were collected for all 17 AR of Québec using dates as unit of analysis, from March 2020 to April 2022. They were first collected and then coded by an interdisciplinary research team to form the foundation of the QCnPI-Index.ContributionThis quantitative instrument offers the necessary granularity for nuanced spatial and temporal studies within the province of Québec, using AR, for instance, as unit of analysis. With this database, pre-, during-, and post-COVID periods can thus be better analyzed. Additionally, the innovative methodologies employed for data collection, coding, and weighting offer valuable insights that may have broader applications in public health, epidemiology, and other research domains. The QCnPI-Index could be instrumental for public health, epidemiology, and transportation researchers investigating the multifaceted impacts of non-pharmaceutical interventions on various societal domains, such as road safety, alcohol and cannabis consumption, and/or mental health, in the province of Québec.

Impact of COVID-19 Pandemic Interventions on Sudden Unexpected Death in Infancy Incidence in France

To study the impact of non-pharmaceutical interventions (NPIs) implemented during the COVID-19 pandemic on the monthly incidence of sudden unexpected death in infancy (SUDI) cases overall and those with a viral or bacterial identification. We conducted an interrupted time-series analysis using seasonally adjusted Poisson regression models from the French national prospective and multicenter SUDI registry, that included all SUDI cases under age one year who died from 2016 to 2021 in mainland France. Of 998 SUDI cases analyzed, 750 were recorded during the pre-pandemic period (January 2016 through March 2020) and 248 during the NPI period (April 2020 through December 2021). We found a significant seasonal pattern of overall monthly SUDI incidence, with a peak observed periodically from November to February. The monthly SUDI incidence decreased significantly from the pre-pandemic to NPI periods (adjusted incidence rate ratio 0.83 [95% confidence interval 0·72-0·96]). In particular, the monthly incidence of SUDI cases with a viral or bacterial identification decreased, while no significant difference was found for SUDI cases without a viral or bacterial identification. NPIs were associated with a significant change in the incidence of SUDI cases with a viral or bacterial identification. Further investigations are needed to analyze the pathophysiologic role of viruses and bacteria in the SUDI.

Review of current and new drugs for the treatment of metabolic-associated fatty liver disease.

In the past 3 decades, metabolic-associated fatty liver disease (MAFLD) has emerged as a widespread liver condition, with its global prevalence on the rise. It ranks as a leading contributor to hepatocellular carcinoma (HCC) and necessitates liver transplantation. Under the multiple parallel hits model, the pathogenesis of MAFLD stems from various liver stressors, notably nutrient overload and sedentary lifestyles. While medical management for MAFLD is well-established, encompassing non-pharmaceutical and pharmaceutical interventions, determining the most effective pharmaceutical therapy has remained elusive. This review discusses diabetic medications for MAFLD treatment, emphasizing recent studies and emerging drugs while reviewing other nondiabetic agents. Emerging evidence suggests that combination therapies hold promise for resolving MAFLD and metabolic steatohepatitis (MASH) while managing side effects. Ongoing trials play a pivotal role in elucidating the effects of mono, dual, and triple receptor agonists in individuals with MASH. With the rising burden of MAFLD/MASH and its severe consequences, the need for effective treatments is more pressing than ever. This review provides a comprehensive overview of the current landscape of pharmaceutical interventions for MAFLD and MASH, shedding light on the potential of newer drugs especially diabetic medications and the importance of ongoing research in this field.

A Regional-Scale Assessment-Based SARS-CoV-2 Variants Control Modeling with Implications for Infection Risk Characterization.

The emergence and progression of highly divergent SARS-CoV-2 variants have posed increased risks to global public health, triggering the significant impacts on countermeasures since 2020. However, in addition to vaccination, the effectiveness of non-pharmaceutical interventions, such as social distancing, masking, or hand washing, on different variants of concern (VOC) remains largely unknown. This study provides a mechanistic approach by implementing a control measure model and a risk assessment framework to quantify the impacts of control measure combinations on the transmissions of five VOC (Alpha, Beta, Delta, Gamma, and Omicron), along with a different perspective of risk assessment application. We applied uncontrollable ratios as an indicator by adopting basic reproduction number (R 0) data collected from a regional-scale survey. A risk assessment strategy was established by constructing VOC-specific dose-response profiles to implicate practical uses in risk characterization when exposure data are available. We found that social distancing alone was ineffective without vaccination in almost all countries and VOC when the median R 0 was greater than two. Our results indicated that Omicron could not be contained, even when all control measure combinations were applied, due to its low threshold of infectivity (~3×10-4 plague-forming unit (PFU) mL-1). To facilitate better decision-making in future interventions, we provide a comprehensive evaluation of how combined control measures impact on different countries and various VOC. Our findings indicate the potential application of threshold estimates of infectivity in the context of risk communication and policymaking for controlling future emerging SARS-CoV-2 variant infections.

MUSIC THERAPY ON ANXIETY IN THIRD TRIMESTER PREGNANT WOMEN

Introduction – Anxiety in pregnancy poses a bad risk to the fetus. Pregnancy anxiety puts the fetus at risk for problems like delayed fetal growth. These risks can affect the quality of maternal and infant health. The use of music as therapy can be used to help overcome anxiety, especially in pregnant women. This paper will try to find out the effectiveness of listening to music as therapy against anxiety in third-trimester pregnant women. Methods – Literature obtained from Google Scholar, Elsevier, and Pubmed databases with a period of 2018 to 2023. Results – Findings from kinds of literature on the use of music therapy in managing anxiety during pregnancy are very effective in lowering anxiety levels. Discuss – Music is a non-pharmaceutical intervention that can be used as therapy. The therapeutic influence that music has on the body and mind helps balance out unpleasant feelings. Conclusion – Using music as therapy during the third trimester has beneficial effects in reducing anxiety. Therefore, the use of music therapy is expected to be used as a nonpharmacological method to reduce anxiety in pregnancy. Keywords: music, anxiety, pregnancy

Infection epidemiological effects of school closures during the second COVID-19 pandemic wave-an exemplary analysis within alower Bavarian region

The rapid global spread of the SARS-CoV‑2 virus during the COVID-19 pandemic led to widespread non-pharmaceutical interventions, such as school closures, to curb infections. This study investigates the influence of school closures on the number of new infections to reduce the existing knowledge gap in this area. The effect of school closures starting on 16December 2020, was quantified using COVID-19 infection data from the district and city of Landshut in Bavaria between November 2020 and January 2021. An interrupted time series (ITS) analysis was performed to analyze daily new infections among 6-18 year olds and the entire population in the region before and after the school closures using anegative binomial regression model. The analysis showed that the trend of daily new COVID-19 infections among 6-18 year olds significantly decreased after the school closures (incidence rate ratio (IRR) 0.93; 95% confidence interval (CI): 0.89-0.96; p < 0.001). This was also true for the entire study population (IRR 0.95; 95% CI: 0.93-0.97; p < 0.001). In the context of various concurrent measures to contain the COVID-19 pandemic, the study provides evidence for asignificant association between school closures and reduced COVID-19 infections. By optimizing the existing incomplete evidence base on the role of school closures in pandemic control, this finding can support involved stakeholders and contribute to strengthening future approaches to pandemic prevention.

A six-compartment model for COVID-19 with transmission dynamics and public health strategies.

The global crisis of the COVID-19 pandemic has highlighted the need for mathematical models to inform public health strategies. The present study introduces a novel six-compartment epidemiological model that uniquely incorporates a higher isolation rate for unreported symptomatic cases of COVID-19 compared to reported cases, aiming to enhance prediction accuracy and address the challenge of initial underreporting. Additionally, we employ optimal control theory to assess the cost-effectiveness of interventions and adapt these strategies to specific epidemiological scenarios, such as varying transmission rates and the presence of asymptomatic carriers. By applying this model to COVID-19 data from India (30 January 2020 to 24 November 2020), chosen to capture the initial outbreak and subsequent waves, we calculate a basic reproduction number of 2.147, indicating the high transmissibility of the virus during this period in India. A sensitivity analysis reveals the critical impact of detection rates and isolation measures on disease progression, showing the robustness of our model in estimating the basic reproduction number. Through optimal control simulations, we demonstrate that increasing isolation rates for unreported cases and enhancing detection reduces the spread of COVID-19. Furthermore, our cost-effectiveness analysis establishes that a combined strategy of isolation and treatment is both more effective and economically viable. This research offers novel insights into the efficacy of non-pharmaceutical interventions, providing a tool for strategizing public health interventions and advancing our understanding of infectious disease dynamics.

Comparative efficacy of pharmaceutical (Ibuprofen) and non-pharmaceutical (photobiomodulation, and chewing gum) interventions for pain reduction after elastomeric separator placement in orthodontic patients: a randomized clinical trial.

This study compared the efficacy of pharmaceutical (ibuprofen) and non-pharmaceutical (photobiomodulation and chewing gum) interventions for pain reduction after elastomeric separator placement in orthodontic patients. This 3-arm, parallel-group randomized clinical trial was conducted on 90 orthodontic patients. The level of anxiety and pain threshold of patients were measured at baseline using the Pain Catastrophizing Scale (PCS) and an algometer, respectively. The patients were randomly assigned to three groups (n = 30; equal number of males and females). In the laser group, 940nm diode laser (Epic X, Biolase, USA, 12.35J/cm2 energy density and 300 mW power in continuous-wave mode., The cross-sectional area of the laser handpiece tip was 1.7 cm2.) was irradiated to the buccal and lingual surfaces for 35s each, prior to placement of separators. In the gum group, the patients were asked to chew a piece of sugar-free gum immediately after the placement of separators and repeat every 8h for 5min for one week in case of pain. In the ibuprofen group, patients received 400mg ibuprofen (Hakim Pharmaceuticals, Tehran, Iran) after the placement of separators and were asked to take one tablet every 8h for one week in case of pain. The pain score was recorded using the Modified McGill Pain Questionnaire (MPQ). The normality of data distribution was analyzed by the Kolmogorov-Smirnov test. ANOVA was applied to compare age, and the Chi-square and Monte Carlo Chi-square tests were used to compare gender and patient responses to the questions among the groups. Repeated measures ANOVA was used to compare the pain score at different time points and among the three groups. All statistical analyses were conducted using SPSS version 19 (SPSS Inc., Chicago, IL, USA) at 0.05 level of significance. Data analysis in this study had an intention to treat approach. Although the pain score was slightly lower in ibuprofen and gum groups, the difference among the three groups was not statistically significant (P > 0.05). 'Repeated measures ANOVA showed no significant effect of method of pain reduction on pain score (F = 1.520, P = 0.225). Time had a significant effect on pain score (F = 20.310, P < 0.001). The interaction effect of time and pain reduction method on pain score was not significant (F = 0.737, P = 0.651). patients experienced a lower level of pain in the ibuprofen and chewing gum groups, the difference in pain score was not significant among the three groups (P = 0.225). patients experienced a higher level of pain at 12 and 24 hours after the placement of separators in all groups. Considering the comparably equal analgesic efficacy of this modalities, non-pharmaceutical interventions can be used for pain reduction of elastomeric separator. The study protocol was registered in the Iranian Registry of Clinical Trials (IRCT20210927052611N1). Date of registration 2022/03/14.

Impact of COVID-19 Nonpharmaceutical Interventions on Bordetella pertussis, Human Respiratory Syncytial Virus, Influenza Virus, and Seasonal Coronavirus Antibody Levels: A Systematic Review.

During the COVID-19 pandemic, nonpharmaceutical interventions (NPIs) were introduced to reduce the spread of SARS-CoV-2. This also resulted in a reduction of notifications of other acute respiratory infections and an altered seasonality when NPIs were lifted. Without circulation of pathogens, waning of antibodies is expected, which is a first indicator of decreased immunity. Here, by performing a systematic literature review, we investigated whether reduced antibody levels due to waning immunity contributed to the altered seasonality after NPIs were lifted. Thirteen articles met the inclusion criteria and reported antibody levels or seroprevalence of human respiratory syncytial virus, seasonal human coronavirus, Bordetella pertussis, and influenza virus. We show that the COVID-19 pandemic most likely led to waning of pathogen-specific antibodies, with the strongest evidence for human respiratory syncytial virus and seasonal human coronavirus and with a larger decrease in children vs adults. Waning antibodies might have resulted in out-of-season activity for these pathogens.