The "supercompensation" effect of children's lockdown during COVID-19: based on the analysis of changes in physical activity, sleep, and psychology.

OBJECTIVE: To investigate the "supercompensation" effect of preschoolers during the coronavirus disease 2019 lockdown by comparing the changes in physical activity (PA), psychological, and sleep indicators before and after the lockdown. METHODS: A total of 127 children (aged 3-6 years) were recruited. Before and after the lockdown, the children's PA levels were measured using the ActiGraph GT3X+, and their psychological and sleep indicators were measured using the Strengths and Difficulties Questionnaire (SDQ) and Child Sleep Habit Questionnaire (CSHQ), respectively. RESULTS: Regarding PA, the children's total physical activity, low-intensity physical activity, and medium-intensity physical activity (MVPA) were higher after the lockdown than before the lockdown, with significant differences in MVPA (p < 0.05). Regarding psychology, the children's SDQ and multidimensional scores were better after the lockdown than before the lockdown, with a significant difference in SDQ scores (p < 0.05). Regarding sleep, the children's CSHQ scores were better after the lockdown than before the lockdown, with a highly significant difference in CSHQ scores (p < 0.01). CONCLUSION: After lockdown, children's PA, psychological, and sleep effects were "supercompensated." In particular, the PA of preschoolers before, during, and after the lockdown may show a "baseline-inhibition-supercompensation" process.

Age at death during the Covid-19 lockdown in French metropolitan regions: a non parametric quantile regression approach.

BACKGROUND: Lockdowns have been implemented to limit the number of hospitalisations and deaths during the first wave of 2019 coronavirus disease. These measures may have affected differently death characteristics, such age and sex. France was one of the hardest hit countries in Europe with a decreasing east-west gradient in excess mortality. This study aimed at describing the evolution of age at death quantiles during the lockdown in spring 2020 (17 March-11 May 2020) in the French metropolitan regions focusing on 3 representatives of the epidemic variations in the country: Bretagne, Ile-de-France (IDF) and Bourgogne-Franche-Comté (BFC). METHODS: Data were extracted from the French public mortality database from 1 January 2011 to 31 August 2020. The age distribution of mortality observed during the lockdown period (based on each decile, plus quantiles 1, 5, 95 and 99) was compared with the expected one using Bayesian non-parametric quantile regression. RESULTS: During the lockdown, 5457, 5917 and 22 346 deaths were reported in Bretagne, BFC and IDF, respectively. An excess mortality from + 3% in Bretagne to + 102% in IDF was observed during lockdown compared to the 3 previous years. Lockdown led to an important increase in the first quantiles of age at death, irrespective of the region, while the increase was more gradual for older age groups. It corresponded to fewer young people, mainly males, dying during the lockdown, with an increase in the age at death in the first quantile of about 7 years across regions. In females, a less significant shift in the first quantiles and a greater heterogeneity between regions were shown. A greater shift was observed in eastern region and IDF, which may also represent excess mortality among the elderly. CONCLUSIONS: This study focused on the innovative outcome of the age distribution at death. It shows the first quantiles of age at death increased differentially according to sex during the lockdown period, overall shift seems to depend on prior epidemic intensity before lockdown and complements studies on excess mortality during lockdowns.

Utilizing disease transmission and response capacities to optimize covid-19 control in Malaysia.

OBJECTIVES: Public Health Social Measures (PHSM) such as movement restriction movement needed to be adjusted accordingly during the COVID-19 pandemic to ensure low disease transmission alongside adequate health system capacities based on the COVID-19 situational matrix proposed by the World Health Organization (WHO). This paper aims to develop a mechanism to determine the COVID-19 situational matrix to adjust movement restriction intensity for the control of COVID-19 in Malaysia. METHODS: Several epidemiological indicators were selected based on the WHO PHSM interim guidance report and validated individually and in several combinations to estimate the community transmission level (CT) and health system response capacity (RC) variables. Correlation analysis between CT and RC with COVID-19 cases was performed to determine the most appropriate CT and RC variables. Subsequently, the CT and RC variables were combined to form a composite COVID-19 situational matrix (SL). The SL matrix was validated using correlation analysis with COVID-19 case trends. Subsequently, an automated web-based system that generated daily CT, RC, and SL was developed. RESULTS: CT and RC variables were estimated using case incidence and hospitalization rate; Hospital bed capacity and COVID-19 ICU occupancy respectively. The estimated CT and RC were strongly correlated [ρ = 0.806 (95% CI 0.752, 0.848); and ρ = 0.814 (95% CI 0.778, 0.839), p < 0.001] with the COVID-19 cases. The estimated SL was strongly correlated with COVID-19 cases (ρ = 0.845, p < 0.001) and responded well to the various COVID-19 case trends during the pandemic. SL changes occurred earlier during the increase of cases but slower during the decrease, indicating a conservative response. The automated web-based system developed produced daily real-time CT, RC, and SL for the COVID-19 pandemic. CONCLUSIONS: The indicators selected and combinations formed were able to generate validated daily CT and RC levels for Malaysia. Subsequently, the CT and RC levels were able to provide accurate and sensitive information for the estimation of SL which provided valuable evidence on the progression of the pandemic and movement restriction adjustment for the control of Malaysia.

Diagnostics for Public Health - Infectious Disease Surveillance and Control.

AbstractAccurate diagnostics are critical in public health to ensure successful disease tracking, prevention, and control. Many of the same characteristics are desirable for diagnostic procedures in both medicine and public health: for example, low cost, high speed, low invasiveness, ease of use and interpretation, day-to-day consistency, and high accuracy. This review lays out five principles that are salient when the goal of diagnosis is to improve the overall health of a population rather than that of a particular patient, and it applies them in two important use cases: pandemic infectious disease and antimicrobial resistance.

Temporal shifts in 24 notifiable infectious diseases in China before and during the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic, along with the implementation of public health and social measures (PHSMs), have markedly reshaped infectious disease transmission dynamics. We analysed the impact of PHSMs on 24 notifiable infectious diseases (NIDs) in the Chinese mainland, using time series models to forecast transmission trends without PHSMs or pandemic. Our findings revealed distinct seasonal patterns in NID incidence, with respiratory diseases showing the greatest response to PHSMs, while bloodborne and sexually transmitted diseases responded more moderately. 8 NIDs were identified as susceptible to PHSMs, including hand, foot, and mouth disease, dengue fever, rubella, scarlet fever, pertussis, mumps, malaria, and Japanese encephalitis. The termination of PHSMs did not cause NIDs resurgence immediately, except for pertussis, which experienced its highest peak in December 2023 since January 2008. Our findings highlight the varied impact of PHSMs on different NIDs and the importance of sustainable, long-term strategies, like vaccine development.

Impact of COVID-19 pandemic responses on tuberculosis incidence: insights from Shantou, China.

BACKGROUND: Various measures taken against the COVID-19 pandemic are not only effective in reducing the spread of the disease, but also lead to some unexpected results. This article regarded these measures as an intervention and explored their impact on the incidence of tuberculosis in Shantou, China. METHODS: The incidence rate and the surveillance data of tuberculosis from January 1st, 2018 to December 31st, 2021 were provided by the Shantou Tuberculosis Prevention and Control Institute. Data were divided into pre-pandemic period (January 1st, 2018 - December 31st, 2019) and pandemic periods (January 1st, 2020 - December 31st, 2021). The Interrupted Time Series (ITS) was used to analyze the trend of tuberculosis incidence prior to and during the COVID-19 epidemic. RESULTS: The results showed that the incidence of tuberculosis cases in Shantou decreased significantly (p < 0.05) during the pandemic as compared to that prior to the pandemic. Among them, the 45-64 age group and the 65 + age group have statistically significant declines. When patients were stratified by occupation, the unemployed and those working in agriculture reduced the most. CONCLUSIONS: In response to the pandemic, measures like lockdowns and quarantines seem to have reduced tuberculosis incidence. However, this does not imply a true decrease. Underlying causes for the reduced true incidence need further scrutiny. Findings offer a preliminary exploration of interventions designed for one disease but functioning as unexpected results for another.

Infectious Disease Preparedness for Homeless Populations: Recommendations from a Community-Academic Partnership.

BACKGROUND: People experiencing homelessness are at increased risk of infectious disease transmission due to congregate living conditions, barriers to healthcare, and excess burden of underlying chronic disease. OBJECTIVES: We are a multisectoral community-academic partnership working to address the intersecting crises of homelessness and health disparities in Tippecanoe County, Indiana. We offer key recommendations for infectious disease preparedness and risk mitigation for homeless populations based on our ongoing community-based participatory research and lessons learned through COVID-19 response and Monkeypox preparations. LESSONS LEARNED: Infectious disease preparedness and response in homeless populations requires strong local partnerships; ongoing training and support for staff and volunteers of homeless shelters and service agencies; tailored outreach, education, and communication with people experiencing homelessness; and standardized processes for creating, disseminating, enforcing, and evaluating public health policies in homeless shelters. Consistency and open communication are key to a successful community-academic partnership. CONCLUSIONS: Community-academic partnerships are critical to effective infectious disease preparedness in homeless populations. The lessons learned from community-based participatory research with homeless communities and multisectoral partners on the frontline can improve future outbreak and pandemic response for people experiencing homelessness and other vulnerable communities in the United States.

New Tools and Nuanced Interventions to Accelerate Achievement of the 2030 Roadmap for Neglected Tropical Diseases.

The World Health Organization roadmap for neglected tropical diseases (NTDs) sets out ambitious targets for disease control and elimination by 2030, including 90% fewer people requiring interventions against NTDs and the elimination of at least 1 NTD in 100 countries. Mathematical models are an important tool for understanding NTD dynamics, optimizing interventions, assessing the efficacy of new tools, and estimating the economic costs associated with control programs. As NTD control shifts to increased country ownership and programs progress toward disease elimination, tailored models that better incorporate local context and can help to address questions that are important for decision-making at the national level are gaining importance. In this introduction to the supplement, New Tools and Nuanced Interventions to Accelerate Achievement of the 2030 Roadmap for Neglected Tropical Diseases, we discuss current challenges in generating more locally relevant models and summarize how the articles in this supplement present novel ways in which NTD modeling can help to accelerate achievement and sustainability of the 2030 targets.

A social acceptability scale: Validation in the context of government measures to curb the COVID-19 pandemic in Senegal.

INTRODUCTION: In March 2020, the government of Senegal introduced a curfew, a ban on travel between regions, the closure of markets, and a ban on attending places of worship to contain the spread of the COVID-19 pandemic. As part of research into the response to COVID-19, we developed a scale to measure the social acceptability of these measures. METHODS: We used Sekhon's theoretical framework of acceptability (TFA) to formulate the content of the scale items. We assessed the homogeneity of the scale using Cronbach's Alpha and average interitem correlations. We measured the dimensional properties of the scale using rating scale models. We tested the sensitivity of the scale to sociodemographic characteristics using mixed linear regressions and rating scale models. RESULTS: The final scale consisted of seven items corresponding to the constructs of acceptability. Analysis performed on data from 813 individuals showed that the scale has satisfactory statistical properties (Cronbach's α > 0.8, Loevinger's coefficient>0.3, intraclass correlation>0.4). CONCLUSION: This scale was one of the first to test the TFA. The small number of items was advantageous for use under challenging data collection contexts. Measuring the acceptability of public health interventions with this tool can help in their design and implementation.

The dangers of having only one pandemic exit strategy.

BACKGROUND: When it comes to pandemic response, preparation can be the key. Between 2020 and 2024, the fast-paced development of COVID-19-often compounded by pubic policies' failures to reflect the latest reality and the public's divergent reactions to the pandemic and the policies-means that society should prepare for exit strategies that can reflect the reality of the pandemic and the interests of the people. Yet oftentimes societies only have one exit strategy with limited scope. This paper investigates the dangers of having only one pandemic exit strategy for pandemics like COVID-19. METHODS: Analyses were based on a review of the literature on COVID-19 exit strategies and our own research. The PubMed literature search focused on two concepts-"COVID-19″ and "exit strategy"-and was limited to peer-reviewed papers published between 2020 and 2024 in English. RESULTS: A total of 31 articles were included in the final review. Analyses showed that existing studies on COVID-19 exit strategies often focused on using the modelling method to gauge one exit strategy. Exit strategies were often discussed in the context of implementing, easing, or lifting specific pharmaceutical or non-pharmaceutical interventions. Staged and country-wide coordinated exit strategies were also discussed in the literature, both of which were often deemed as comparatively rigorous options compared to single or stand-alone exit strategies. Drawing on the overall development of COVID-19 and our own research, we presented and discussed the importance of having multiple exit strategies that are considerate of all possible pandemic trajectories, diverse interests of the public, and the communication challenges officials might face in introducing or implementing pandemic policies. CONCLUSION: This paper underscored the importance of having multiple exit strategies for societies to prepare for pandemics. The insights of this study can help inform health policies so that they can more comprehensively and compassionately protect the needs and wants of the "public" in public health, particularly in grave times like COVID-19.

Modeling COVID-19 spread using multi-agent simulation with small-world network approach.

BACKGROUND: The rapid global spread of COVID-19 has seriously impacted people's daily lives and the social economy while also posing a threat to their lives. The analysis of infectious disease transmission is of significant importance for the rational allocation of epidemic prevention and control resources, the management of public health emergencies, and the improvement of future public health systems. METHODS: We propose a spatiotemporal COVID-19 transmission model with a neighborhood as an agent unit and an urban spatial network with long and short edge connections. The spreading model includes a network of defined agent attributes, transformation rules, and social relations and a small world network representing agents' social relations. Parameters for each stage are fitted by the Runge-Kutta method combined with the SEIR model. Using the NetLogo development platform, accurate dynamic simulations of the spatial and temporal evolution of the early epidemic were achieved. RESULTS: Experimental results demonstrate that the fitted curves from the four stages agree with actual data, with only a 12.27% difference between the average number of infected agents and the actual number of infected agents after simulating 1 hundred times. Additionally, the model simulates and compares different "city closure" scenarios. The results showed that implementing a 'lockdown' 10 days earlier would lead to the peak number of infections occurring 7 days earlier than in the normal scenario, with a reduction of 40.35% in the total number of infections. DISCUSSION: Our methodology emphasizes the crucial role of timely epidemic interventions in curbing the spread of infectious diseases, notably in the predictive assessment and evaluation of lockdown strategies. Furthermore, this approach adeptly forecasts the influence of varying intervention timings on peak infection rates and total case numbers, accurately reflecting real-world virus transmission patterns. This highlights the importance of proactive measures in diminishing epidemic impacts. It furnishes a robust framework, empowering policymakers to refine epidemic response strategies based on a synthesis of predictive modeling and empirical data.

Is COVID-19 as Lethal as the Spanish Flu? The Australian Experience in 1919 and 2020 and the Role of Nonpharmaceutical Interventions (NPIs).

We conducted a comparative historical study to interrogate Professor Peter Doherty's warning to Australians in April 2020 that 'COVID-19 is just as lethal as the Spanish flu'. We identified the epicentres of both pandemics, namely, metropolitan Sydney in 1919 and metropolitan Melbourne in 2020 and compared the lethality of the Spanish Flu and COVID-19 in these two cities. Lethality was measured by the number and rate of hospital admissions, death rates, age-specific death rates and age-standardised mortality rates (ASMRs). Using these measures, we demonstrated the strikingly different waves of infection, their severity at various points in time and the cumulative impact of the viruses by the end of our study period, i.e., 30 September in 1919 and 2020. Hospital admissions and deaths from the Spanish Flu in 1919 were more than 30 times higher than those for COVID-19 in 2020. The ASMR per 100,000 population for the Spanish Flu was 383 compared to 7 for COVID-19: The former was about 55 times higher than the latter. These results suggest that the Spanish Flu was more lethal than COVID-19. Professor Doherty's warning was perhaps taken seriously and that partly explains the findings of this study. Containing infection in 1919 and 2020 threw the burden on nonpharmaceutical interventions (NPIs) such as 'protective sequestration' (quarantine), contact tracing, lockdowns and masks. It is likely that the persistent and detailed contact tracing scheme provides the best possible explanation for why NPIs in 2020 were more effective than in 1919 and therefore contributed to the lower lethality of the COVID-19 pandemic in its first year.

The impact of public transportation on the transmission of COVID-19 in Rwanda.

Introduction: The onset of the COVID-19 pandemic has placed a significant burden on healthcare systems worldwide, particularly in sub-Saharan regions where healthcare resources are limited. The transmission of SARS-CoV-2 is facilitated by the movement of people from place to place. Therefore, implementing measures that restrict movement of people and contacts is crucial in controlling the spread of the disease. Following the identification of the first COVID-19 case in Rwanda, the government implemented stringent measures, including a complete nationwide lockdown, border closures, curfews, reduced capacity in public transportation and businesses, and mandatory testing. This study aims to assess epidemiological trends in COVID-19 cases in relation to changes in population mobility within the public transportation system. Methods: A descriptive analysis using publicly available data on COVID-19 epidemiological indicators (cases, deaths, vaccinations, and stringency index) and mobility data was conducted. Results: The results reveal a strong correlation between mobility in public transportation and other activities, underscoring Rwanda's reliance on its public transportation system. The study also identifies a pattern where increases in transit station mobility preceded spikes in COVID-19 cases, suggesting that the subsequent rise in public transportation usage may contribute to higher infection rates. Discussion: Therefore, this study emphasizes the importance of ongoing vigilance and regulatory measures regarding public transportation during infectious disease outbreaks.

Non-pharmaceutical interventions to reduce COVID-19 transmission in the UK: a rapid mapping review and interactive evidence gap map.

BACKGROUND: Non-pharmaceutical interventions (NPIs) were crucial in the response to the COVID-19 pandemic, although uncertainties about their effectiveness remain. This work aimed to better understand the evidence generated during the pandemic on the effectiveness of NPIs implemented in the UK. METHODS: We conducted a rapid mapping review (search date: 1 March 2023) to identify primary studies reporting on the effectiveness of NPIs to reduce COVID-19 transmission. Included studies were displayed in an interactive evidence gap map. RESULTS: After removal of duplicates, 11 752 records were screened. Of these, 151 were included, including 100 modelling studies but only 2 randomized controlled trials and 10 longitudinal observational studies.Most studies reported on NPIs to identify and isolate those who are or may become infectious, and on NPIs to reduce the number of contacts. There was an evidence gap for hand and respiratory hygiene, ventilation and cleaning. CONCLUSIONS: Our findings show that despite the large number of studies published, there is still a lack of robust evaluations of the NPIs implemented in the UK. There is a need to build evaluation into the design and implementation of public health interventions and policies from the start of any future pandemic or other public health emergency.

Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context.

We discuss how to assess the reliability of partial, anonymized mobility data and compare two different methods to identify spatial communities based on movements: Greedy Modularity Clustering (GMC) and the novel Critical Variable Selection (CVS). These capture different aspects of mobility: direct population fluxes (GMC) and the probability for individuals to move between two nodes (CVS). As a test case, we consider movements of Italians before and during the SARS-Cov2 pandemic, using Facebook users' data and publicly available information from the Italian National Institute of Statistics (Istat) to construct daily mobility networks at the interprovincial level. Using the Perron-Frobenius (PF) theorem, we show how the mean stochastic network has a stationary population density state comparable with data from Istat, and how this ceases to be the case if even a moderate amount of pruning is applied to the network. We then identify the first two national lockdowns through temporal clustering of the mobility networks, define two representative graphs for the lockdown and non-lockdown conditions and perform optimal spatial community identification on both graphs using the GMC and CVS approaches. Despite the fundamental differences in the methods, the variation of information (VI) between them assesses that they return similar partitions of the Italian provincial networks in both situations. The information provided can be used to inform policy, for example, to define an optimal scale for lockdown measures. Our approach is general and can be applied to other countries or geographical scales.

Online spaces and the control of communicable diseases: implications for nursing practice.

The digital revolution has significantly altered healthcare, including communicable disease control, with online spaces emerging as vital tools in preventing, identifying and controlling the spread of diseases. However, healthcare professionals, including nurses, need to find a balance between harnessing the benefits of mass communication and mitigating the potentially harmful effects of online misinformation. This article explores the benefits and challenges of using online spaces such as social media platforms in the control of communicable diseases and discusses the potential use of telehealth in reducing the risk of healthcare-associated infection and antimicrobial resistance. The author also describes a framework that nurses can use to explore potential roles and practice in the context of communicable disease control in online spaces.

Public Acceptance of Measures to Control Infectious Diseases Under Different Scenarios of Severity and Transmissibility.

OBJECTIVES: Public health measures to control future epidemic threats of contagious disease, such as new variants of COVID-19, may be usefully informed by evidence about how acceptable they are likely to be, and the circumstances that condition this acceptance. This study considers how the acceptability of nonpharmaceutical interventions (NPIs) might depend on scenarios about the severity and transmissibility of the disease. METHODS: A telephone survey was conducted among a representative cross-sectional sample of the Spanish adult population. Each respondent was randomly assigned to 1 of 4 possible hypothetical scenarios about the severity and transmissibility of the disease. Participants' responses about the acceptability of 11 NPI under this scenario were analyzed using multivariate regression and latent class cluster analysis. RESULTS: A high risk of severe disease increases the acceptability of mask wearing, social distancing outdoors, lockdown, and isolation of infected cases, close contacts, and the vulnerable. A scenario in which the disease is highly transmissible would increase the acceptability of NPI that restrict movement and isolation. Most respondents would broadly accept most NPI in situations when either the severity or transmissibility was high. CONCLUSIONS: This study showed that people are more willing to accept NPIs such as mask wearing, social distancing outdoors, lockdown, and isolation in severe disease scenarios. A highly transmissible disease scenario increases the acceptability of NPIs that isolate. A majority would broadly accept NPIs to counter public health emergencies, whereas 3% to 9% of the population would always be strongly against.