Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. Our simulations show that long delays between the adoption of control measures and observed declines in cases, hospitalizations, and deaths occur in many scenarios. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. We suggest limited conditions under which the formation of household “bubbles” can be safe. These findings can improve future predictions of the timescale and efficacy of interventions needed to control second waves of COVID-19 as well as other similar outbreaks, and highlight the need for better quantification and control of household transmission.     

Disparities in COVID-19 mortality by county racial composition and the role of spring social distancing measures

Available COVID-19 data shows higher shares of cases and deaths occur among Black Americans, but reporting of data by race is poor. This paper investigates disparities in county-level mortality rates across counties with higher and lower than national average Black population shares using nonlinear regression decomposition and estimates potential differential impact of social distancing measures. I find counties with Black population shares above the national share have mortality rates 2 to 3 times higher than in other counties. Observable differences in living conditions, health, and work characteristics reduce the disparity to approximately 1.25 to 1.65 overall, and explain 100% of the disparity at 21 days after the first case. Though higher rates of comorbidities in counties with higher Black population shares are an important predictor, living situation factors like single parenthood and population density are just as important. Higher rates of co-residence with grandchildren explain 11% of the 21 day disparity but do not appear important by 42 days, suggesting families may have been better able to protect vulnerable family members later in the epidemic. To analyze differential effects of social distancing measures use two approaches. First, I exploit the timing of interventions relative to the first case among counties that began their epidemic at the same time. Second, I use event study analysis to analyze within-county changes in mortality. Findings for social distancing measures are not always consistent across approaches. Overall, I find no evidence that school closures were less effective in counties with larger Black population shares, and some estimates suggest closures may have disproportionately helped more diverse counties and counties with high rates of grandparent and grandchild co-residence. Conversely, stay at home orders are less clearly associated with mortality in any counties, reaching peak unemployment did not reduce mortality in any models, and some estimates indicate reaching peak unemployment before the first case was associated with higher mortality rates, especially in more diverse counties.     

COVID-19 and “natural” experiments arising from physical distancing: a hypothetical case study from chronobiology

ABSTRACT

With countless “natural” experiments triggered by the COVID-19-associated physical distancing, one key question comes from chronobiology: “When confined to homes, how does the reduced exposure to natural daylight arising from the interruption of usual outdoor activities plus lost temporal organization ordinarily provided from workplaces and schools affect the circadian timing system (the internal 24 h clock) and, consequently, health of children and adults of all ages?” Herein, we discuss some ethical and scientific facets of exploring such natural experiments by offering a hypothetical case study of circadian biology.     

Synergistic interventions to control COVID-19: Mass testing and isolation mitigates reliance on distancing

Stay-at-home orders and shutdowns of non-essential businesses are powerful, but socially costly, tools to control the pandemic spread of SARS-CoV-2. Mass testing strategies, which rely on widely administered frequent and rapid diagnostics to identify and isolate infected individuals, could be a potentially less disruptive management strategy, particularly where vaccine access is limited. In this paper, we assess the extent to which mass testing and isolation strategies can reduce reliance on socially costly non-pharmaceutical interventions, such as distancing and shutdowns. We develop a multi-compartmental model of SARS-CoV-2 transmission incorporating both preventative non-pharmaceutical interventions (NPIs) and testing and isolation to evaluate their combined effect on public health outcomes. Our model is designed to be a policy-guiding tool that captures important realities of the testing system, including constraints on test administration and non-random testing allocation. We show how strategic changes in the characteristics of the testing system, including test administration, test delays, and test sensitivity, can reduce reliance on preventative NPIs without compromising public health outcomes in the future. The lowest NPI levels are possible only when many tests are administered and test delays are short, given limited immunity in the population. Reducing reliance on NPIs is highly dependent on the ability of a testing program to identify and isolate unreported, asymptomatic infections. Changes in NPIs, including the intensity of lockdowns and stay at home orders, should be coordinated with increases in testing to ensure epidemic control; otherwise small additional lifting of these NPIs can lead to dramatic increases in infections, hospitalizations and deaths. Importantly, our results can be used to guide ramp-up of testing capacity in outbreak settings, allow for the flexible design of combined interventions based on social context, and inform future cost-benefit analyses to identify efficient pandemic management strategies.     

Effectiveness of social distancing interventions in containing COVID-19 incidence: International evidence using Kalman filter

The epidemiological literature has widely documented the importance of social distancing interventions in containing the spread of the COVID-19 pandemic. However, the epidemiological measure of virus reproduction, R₀, provides a myopic view of containment, especially when the absolute number of cases is still high. The paper investigates cross-country variations concerning the impact of social distancing interventions on COVID-19 incidence by employing a statistical measure of containment, which models the daily number of cases as a structural time-series, state-space vector. Countries that adopt strict lockdown policies and provide economic support in the form of income augmentations and debt relief improve the response towards the pandemic. Countries like China and South Korea have been most influential in containing the spread of infections. European nations of France, Italy, Spain and the UK are witnessing a second wave of the virus, indicating that re-opening the European economy perhaps has instigated an exponential spread.     

The significance of advanced COVID-19 diagnostic testing in pandemic control measures

During the 2 years since the start of the novel coronavirus disease 2019 (COVID-19) pandemic, the scientific world made an enormous effort to fight against this disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has high transmissibility. Advancements in vaccine and treatment strategies have reduced both the hospitalization and mortality rates. However, the virus has shown its ability to evolve and evade from our COVID-19 combating armamentaria by the most common evolution mechanism—mutation. Diagnostic testing has been the first line of defense following the identification of the causative agent. Ever since, the scientific community has developed nuclei acid-based, antigen-based, and antibody-based diagnostic tests, and these testing methodologies are still playing a central role in slowing down viral transmission. These testing methods have different sensitivity and specificity and could be optimally used in areas facing different challenges owing to different level and conditions of COVID-19 outbreak. In this review, we discuss these testing methodologies as well as the considerations on how to apply these diagnostic tests optimally in the community to cope with the ever-changing pandemic conditions.     

Bayesian time-varying autoregressive models of COVID-19 epidemics

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.     

COVID-19 prevention and control strategies: learning from the Macau model

Background: Macau is a densely populated international tourist city. Compared to most tensely populated countries/territories, the prevalence and mortality of COVID-19 in Macau are lower. The experiences in Macau could be helpful for other areas to combat the COVID-19 pandemic. This article introduced the endeavours and achievements of Macau in combatting the COVID-19 pandemic.Method: Both qualitative and quantitative analysis methods were used to explore the work, measures, and achievements of Macau in dealing with the COVID-19 pandemic.Results: The results revealed that Macau has provided undifferentiated mask purchase reservation services, COVID-19 vaccination services to all residents and non-residents in Macau along with delivering multilingual services, in Chinese, English and Portuguese, to different groups of the population. To facilitate the travels of people, business and trades between Macau and mainland China, the Macau government launched the Macau Health Code System, which uses the health status declaration, residence history declaration, contact history declaration of the declarant to match various relevant backend databases within the health authority and provide a risk-related colour code operations. The Macau Health Code System connects to the Chinese mainland''s own propriety health code system seamlessly, whilst effectively protecting the privacy of the residents. Macau has also developed the COVID-19 Vaccination Appointment system, the Nucleic Acid Test Appointment system, the Port and Entry/Exit Quarantine system, the medical and other supporting systems.Conclusion: The efforts in Macau have achieved remarkable results in COVID-19 prevention and control, effectively safeguarding the lives and health of the people and manifesting the core principle of “serving the public”. The measures used are sustainable and can serve as an important reference for other countries/regions.     

Bayesian survival analysis using a MARS model

A Bayesian multivariate adaptive regression spline fitting approach is used to model univariate and multivariate survival data with censoring. The possible models contain the proportional hazards model as a subclass and automatically detect departures from this. A reversible jump Markov chain Monte Carlo algorithm is described to obtain the estimate of the hazard function as well as the survival curve.     

On realized serial and generation intervals given control measures: The COVID-19 pandemic case

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.     

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.     

The impact of COVID-19 lockdown on professional soccer players’ body composition and physical fitness

During the COVID-19 lockdown, professional soccer players ceased their regular team training sessions and were provided with exercise programs to follow independently. This investigation assessed the impact of a 7-week COVID-19 lockdown and home-based individual physical training on professional soccer players’ body composition and physical fitness. The study consisted of nineteen division 1 elite soccer players (age 27.68 ± 5.99 years, height 178.47 ± 5.44 cm) and compared the anthropometric and physical fitness parameters obtained post-transition period to those obtained post-COVID-19 lockdown. The statistical analysis indicated that body fat percentage was significantly higher after the lockdown period [t(18) = -5.59, p < 0.01, d = 0.56]. Furthermore, VO_2max [t(17) = -11.54, p < 0.01, d = 0.57] and running time [t(17) = 3.94, p < 0.01, d = 0.76] values were significantly higher after the COVID-19 lockdown than those obtained after the transition period. In addition, significantly higher level of performance was demonstrated on squat jump [t(18) = -4.10, p < 0.01, d = 0.30], countermovement jump [t(18) = -7.43, p < 0.01, d = 1.11] and sit and reach tests [t(19) = -5.33, p < 0.01, d = 0.32]. Concurrently, lower body strength was indicated to be significantly greater (p < 0.01) following the COVID-19 lockdown. The training protocol provided during the confinement, due to the COVID-19 outbreak, was effective in keeping physical fitness at a significantly higher level compared to the transition period. Coaches and trainers are encouraged to examine the effectiveness of this protocol, as it may help them develop effective periodization programs during the transition period. This protocol may aid in the development of effective periodization programs that require minimal equipment and can be followed in similar situations.     

SARS-CoV-2/COVID-19 and physical distancing: risk for circadian rhythm dysregulation,advice to alleviate it,and natural experiment research opportunities

ABSTRACT

SARS-CoV-2/COVID-19 leads to numerous unplanned or natural experiments with health and disease. Physical (social) distancing – a counter-measure with no alternative, but with no precedence in scope and scale either – is a key intervention and trigger of natural experiments. From a practical perspective, concerned disciplines should increase awareness of, provide recommendations to meet, and develop research for, health challenges arising from physical distancing at home. From the field of chronobiology, prolonged home stays may place undue strain on the body’s circadian timing system but straightforward and often underestimated advice for coping can be provided (herein we provide such advice). Of course, advice or recommendations from other concerned disciplines that identify challenges associated with current COVID-19 mitigation strategies are also needed. From a research perspective, different disciplines should rise to the occasion and explore unsuspected natural experiment angles toward novel insights to promote health and prevent disease.     

Contribution to COVID-19 spread modelling: a physical phenomenological dissipative formalism

Biomechanics and Modeling in Mechanobiology - In this study, we propose an evolution law of COVID-19 transmission. An infinite ordered lattice represents population. Epidemic evolution is...     

Quantifying the risk from ovine BSE and the impact of control strategies

Although no naturally infected sheep with bovine spongiform encephalopathy (BSE) has ever been discovered, it remains possible that BSE once infected the UK sheep population, has been transmitted between sheep, and is still present today. We constructed a mathematical model to assess the current maximum theoretical exposure to consumers from BSE-infected ovine material and to estimate the risk reduction that could be achieved by abattoir-based control options if BSE-infected sheep were ever found in the national flock. We predict that, if present, the exposure to consumers from a single BSE-infected sheep would be high: one sheep, close to the end of its incubation period, is likely to contribute 10-1000 times more infectious material than a fully infectious cow. Furthermore, 30% of this exposure comes from infectivity residing in lymphatic and peripheral tissue that cannot be completely removed from a carcass.We are 95% confident that throughout Great Britain, no more than four sheep flocks currently harbour an ongoing BSE epidemic. However, since the exposure from a single infected sheep is high, the annual human exposure from four 'typical' BSE-infected flocks could be considerable. Small reductions in exposure could be achieved by strategies based on tissue testing, a 12-month age restriction or expanded definitions of high-risk tissues. A six-month age restriction is likely to be more effective and genotype-based strategies the most effective.     

Insights on the implications of COVID-19 mitigation measures for mental health

Given the unprecedented level and duration of mitigation policies during the 2020 COVID-19 pandemic, it is not surprising that the public and the media have raised important questions about the potential for negative mental health consequences of the measures. To answer them, natural variability in policy implementation across US states and over time was analyzed to determine if mitigation policies correlated with Google searches for terms associated with symptoms of depression and anxiety. Findings indicated that restaurant/bar limits and stay-at-home orders correlated with immediate increases in searches for isolation and worry but the effects tapered off two to four weeks after their respective peaks. Moreover, the policies correlated with a reduction in searches for antidepressants and suicide, thus revealing no evidence of increases in severe symptomatology. The policy implications of these findings are discussed.     

Gene selection using a two-level hierarchical Bayesian model

SUMMARY: The fundamental problem of gene selection via cDNA data is to identify which genes are differentially expressed across different kinds of tissue samples (e.g. normal and cancer). cDNA data contain large number of variables (genes) and usually the sample size is relatively small so the selection process can be unstable. Therefore, models which incorporate sparsity in terms of variables (genes) are desirable for this kind of problem. This paper proposes a two-level hierarchical Bayesian model for variable selection which assumes a prior that favors sparseness. We adopt a Markov chain Monte Carlo (MCMC) based computation technique to simulate the parameters from the posteriors. The method is applied to leukemia data from a previous study and a published dataset on breast cancer. SUPPLEMENTARY INFORMATION: http://stat.tamu.edu/people/faculty/bmallick.html.     

Impact of Isolation measures on pregnancy outcome during the COVID-19 pandemic

This study aims to explore the impact of isolation measures implemented during the COVID-19 pandemic on childbirth outcomes in pregnant women. The design was a retrospective cohort study. The pregnant women during the outbreak lockdown and isolation from February 1 to April 30, 2020, were defined as the exposed population, and the pregnant women in the same time frame in 2019 as the non-exposed population. All data for the study were obtained from the National Health Care Data Platform of Shandong University. Generalized linear regression models were used to analyze the differences in pregnancy outcomes between the two study groups. A total of 34,698 pregnant women from Shandong Province, China in the data platform met the criteria and were included in the study. The proportions were 11.53% and 8.93% for macrosomia in the exposed and the non-exposed groups and were 3.47% and 4.37% for low birth weight infants, respectively, which were significantly different. They were 22.55% and 25.94% attributed to average exposed effect for macrosomia and low birth weight infants. Meanwhile, the mean weight and standard deviation of full-term infants in the exposure group were 3414.80 ± 507.43 g, which were significantly higher than in the non-exposed group (3347.22 ± 502.57 g, P < 0.001). The effect of exposure was significant in the third trimester. In conclusion, the isolation during the COVID-19 pandemic increases the birth weight of infants and the probability of macrosomia, regardless of which trimester in isolation a pregnant woman was, while the third trimester is the sensitive window of exposure. Our findings provide a basis for health care and policy development during pregnancy in COVID-19, due to COVID-19 still showing a pandemic trend around the world in 2022.