Estimate of the Basic Reproduction Number for COVID-19: A Systematic Review and Meta-analysis

A quantum mechanics-based framework for infectious disease modeling

Traditional infectious disease models often use fixed compartments to represent different states of individuals. However, these models can be limited in accurately reflecting real-world conditions of individuals. In this study, we integrate quantum mechanics into infectious disease modeling, developing a quantum mechanics-based model that effectively addresses the limitations of traditional compartmental models and introduces a novel approach to understanding disease dynamics. Firstly, we examined the individual infection process and the model's evolutionary dynamics, deriving both the disease-free equilibrium point and the model's basic reproduction number. Secondly, the proposed model is simulated on a quantum circuit. The simulation results are utilized to analyze the model's parameter sensitivity and verify its rationality. The results indicate that the model's predictions align with the general patterns of viral transmission and are capable of replicating the structural attributes of compartmental models. Finally, we apply the model to simulate the spread of COVID-19. The observed similarity between the simulated results and actual infection trends demonstrates the model's effectiveness in accurately capturing viral transmission dynamics. Comparative experiments show that the proposed model significantly improves accuracy over traditional models. By leveraging quantum mechanics, our method offers a fresh perspective in infectious disease modeling, broadening the application of quantum mechanics methodologies in understanding information propagation within the macroscopic world.

The feasibility of using machine learning to predict COVID-19 cases

BACKGROUND

Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, emerged as a global health crisis in 2019, resulting in widespread morbidity and mortality. A persistent challenge during the pandemic has been the accuracy of reported epidemic data, particularly in underdeveloped regions with limited access to COVID-19 test kits and healthcare infrastructure. In the post-COVID era, this issue remains crucial. This study introduces a novel approach by leveraging machine learning to predict cases and uncover critical discrepancies, focusing on African regions where reported daily cases per million often deviate significantly from machine learning-predicted cases. These findings strongly suggest widespread underreporting of cases. By identifying these gaps, our research provides valuable insights for future pandemic preparedness, improving epidemic forecasting accuracy, data reliability, and response strategies to mitigate the impact of emerging global health crises.

OBJECTIVE

This study aims to assess the reliability of reported COVID-19 incidence data globally, particularly in underdeveloped regions, and to identify discrepancies between reported and predicted cases using machine learning methodologies.

METHODS

Data collected from March 2020 to September 2022 included demographic, healthcare, economic, and testing-related parameters. Several machine learning models-neural networks, decision trees, random forests, cross-validation, support vector machines, and logistic regression-were employed to predict COVID-19 incidence rates. Model performance was evaluated using testing accuracy metrics.

RESULTS

Testing accuracy rates for the models were as follows: neural networks (65.50 %), decision trees (63.76 %), random forests (63.33 %), cross-validation (55.92 %), support vector machines (63.62 %), and logistic regression (64.70 %). Comparative analysis using neural networks revealed significant discrepancies between reported and predicted COVID-19 cases, particularly in numerous African countries. These results suggest a considerable volume of underreported cases in regions with limited testing capabilities.

CONCLUSION

This study highlights the critical need for improved data accuracy and reporting mechanisms, especially in resource-constrained regions. International organizations and policymakers must implement strategies to enhance testing capacity and data reliability to better understand and manage the global impact of the pandemic. Our work emphasizes the potential of machine learning to identify gaps in epidemic reporting, facilitating evidence-based interventions.

Uptake, Acceptability, and Results of SARS-CoV-2 Antigen Rapid Diagnostic Testing in Community Settings in Cameroon

Mass gathering event restrictions were part of mitigation measures during the COVID-19 pandemic that were lifted as prevalence decreased and after vaccination rollout. We explored SARS-CoV-2 antigen rapid diagnostic test acceptability and positivity in community settings in Cameroon. In August-October 2022, community workers sensitized and referred individuals for COVID-19 testing to nearby testing points in Douala and Yaoundé. Participants consented to SARS-CoV-2 antigen rapid diagnostic testing, a survey, or both components. We describe the positivity rate, COVID-19-related history, and Likert-scale testing perceptions. Factors associated with testing acceptance were analyzed using logistic regression. Overall, 20.5% (2,449/11,945) of sensitized individuals visited testing points, and 1,864 (76.1%) were enrolled; 50.6% accepted the survey and testing (46.0% accepted survey only). Seven (0.7%) of 1,006 individuals tested positive. Most (71.8%; 1,292/1,800) considered community testing more accessible than hospital-based testing. Individuals accepting versus refusing testing differed in perceived COVID-19 risk (67%, 49%; P <0.001), belief in accurate test results (79%, 47%; P <0.001), and ability to test easily (96%, 55%; P <0.001). Males (adjusted odds ratio [aOR]: 1.26 [1.04-1.53]) and those over 50 years (aOR: 1.9 [1.4-2.7]), with symptoms (aOR: 1.80 [1.30-2.50]), and at least partial vaccination (aOR: 0.76 [0.58-0.99]) were significantly associated with test acceptance. Refusal reasons included lack of perceived need for testing (33.8%) and testing discomfort (26.3%). Although community-based testing was generally perceived as important, actual testing uptake was low. In future pandemics, community testing should be optimized by addressing misinformation, offering alternative testing modalities for greater comfort, creating demand, and tailoring approaches to maximize testing uptake.

Comparative bibliometric study of mental health research trends during COVID-19, Mpox, dengue, and Ebola outbreaks infectious diseases

As the prevalence of mental health issues continues to rise, the impact of widespread diseases on mental health has garnered increasing attention. This study employs bibliometric analysis to evaluate the state of research on mental health concerns associated with four infectious diseases: COVID-19, mpox, dengue fever, and Ebola. Utilizing Citespace, we conducted an in-depth analysis encompassing publication trends, author networks, institutional affiliations, and international collaborations, alongside themes in references and keywords. Our findings reveal that each of these diseases has significantly affected mental health over the last two decades. Notably, the volume of mental health literature related to COVID-19 far surpasses that of the other diseases, with 34 833 documents compared to 36 for mpox, 62 for dengue, and 279 for Ebola. The United States emerges as the most influential country in this field. International cooperation during infectious diseases was not strong, and the contribution of low-middle income countries was lower than that of high income countries. Our research underscores the growing societal relevance of mental health, influenced by factors including social distancing and mortality due to these diseases. Looking ahead, there is a crucial need for enhanced international cooperation and a focused attention on the mental health of vulnerable populations during pandemics.

Evaluating a novel reproduction number estimation method: a comparative analysis

This paper presents practical methodologies for determining effective reproduction numbers, R(t), providing valuable insights for researchers and public health officials. It proposes multiple simplified approaches for estimating R(t) of infectious diseases and compares their effectiveness. These approaches include methods based on exponential, fixed (delta), normal, and gamma distributions for the generation time. The exponential and fixed generation time methods offer convenience as they rely solely on the mean generation time and the number of new infections. However, they are sensitive to the variance of the generation time distribution: the exponential method may underestimate R(t) when the variance is small, while the fixed generation time method may overestimate R(t) when the variance is large. The normal distribution method also risks underestimation, depending on the growth rate. In contrast, the gamma distribution method demonstrates greater robustness and accuracy across a variety of scenarios. A key contribution of this work is the consolidated presentation of these estimation methods, along with the novel derivation of an accurate R(t) formula based on the gamma distribution. This research offers practical guidance for selecting the most appropriate R(t) estimation method, emphasizing the importance of accounting for the specific characteristics of the infectious disease's generation time distribution.

Time-varying reproduction number estimation: fusing compartmental models with generalized additive models

The reproduction number, the mean number of secondary cases infected by each primary case, gives an indication of the effort required to control the disease. Beyond the well-known basic reproduction number, there are two natural extensions, namely the control and effective reproduction numbers. As behaviour, population immunity and viral characteristics can change with time, these reproduction numbers can vary over time. Real-world data can be complex, so in this work we consider a generalized additive model to smooth surveillance data through the explicit incorporation of day-of-the-week effects, to provide a simple measure of the time-varying growth rate associated with the data. Converting the resulting spline into an estimator for both the control and effective reproduction numbers requires assumptions on a model structure, which we here assume to be a compartmental model. The reproduction numbers calculated are based on both simulated and real-world data, and are compared with estimates from an already existing tool. The derived method for estimating the time-varying reproduction number is effective, efficient and comparable with other methods. It provides a useful alternative approach, which can be included as part of a toolbox of models, that is particularly apt at smoothing out day-of-the-week effects in surveillance.

Data-driven mathematical modeling approaches for COVID-19: A survey

In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).

A tree-based explainable AI model for early detection of Covid-19 using physiological data

With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 cases have declined, there are still cases and deaths around the world. Therefore, early detection of COVID-19 before the onset of symptoms has become crucial in reducing its extensive impact. Fortunately, wearable devices such as smartwatches have proven to be valuable sources of physiological data, including Heart Rate (HR) and sleep quality, enabling the detection of inflammatory diseases. In this study, we utilize an already-existing dataset that includes individual step counts and heart rate data to predict the probability of COVID-19 infection before the onset of symptoms. We train three main model architectures: the Gradient Boosting classifier (GB), CatBoost trees, and TabNet classifier to analyze the physiological data and compare their respective performances. We also add an interpretability layer to our best-performing model, which clarifies prediction results and allows a detailed assessment of effectiveness. Moreover, we created a private dataset by gathering physiological data from Fitbit devices to guarantee reliability and avoid bias.The identical set of models was then applied to this private dataset using the same pre-trained models, and the results were documented. Using the CatBoost tree-based method, our best-performing model outperformed previous studies with an accuracy rate of 85% on the publicly available dataset. Furthermore, this identical pre-trained CatBoost model produced an accuracy of 81% when applied to the private dataset. You will find the source code in the link: https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data.git .

Estimating the COVID-19 prevalence from wastewater

Wastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2. Therefore, in 2022, also in Rhineland-Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own. Therefore, the state of Rhineland-Palatinate has commissioned the University Medical Center at Johannes Gutenberg University Mainz to conduct a representative cohort study called SentiSurv, in which an increasing number of up to 12,000 participants have been using sensitive antigen self-tests once or twice a week to test themselves for SARS-CoV-2 and report their status. This puts the state of Rhineland-Palatinate in the fortunate position of having time series of both, the viral load in wastewater and the prevalence of SARS-CoV-2 in the population. Our main contribution is a calibration study based on the data from 2023-01-08 until 2023-10-01 where we identified a scaling factor ( 0.208 ± 0.031 ) and a delay ( 5.07 ± 2.30 days) between the virus load in wastewater, normalized by the pepper mild mottle virus (PMMoV), and the prevalence recorded in the SentiSurv study. The relation is established by fitting an epidemiological model to both time series. We show how that can be used to estimate the prevalence when the cohort data is no longer available and how to use it as a forecasting instrument several weeks ahead of time. We show that the calibration and forecasting quality and the resulting factors depend strongly on how wastewater samples are normalized.

SARS-CoV-2 incidence monitoring and statistical estimation of the basic and time-varying reproduction number at the early onset of the pandemic in 45 sub-Saharan African countries

The world battled to defeat a novel coronavirus 2019 (SARS-CoV-2 or COVID-19), a respiratory illness that is transmitted from person to person through contacts with droplets from infected persons. Despite efforts to disseminate preventable messages and adoption of mitigation strategies by governments and the World Health Organization (WHO), transmission spread globally. An accurate assessment of the transmissibility of the coronavirus remained a public health priority for many countries across the world to fight this pandemic, especially at the early onset. In this paper, we estimated the transmission potential of COVID-19 across 45 countries in sub-Saharan Africa using three approaches, namely, [Formula: see text] based on (i) an exponential growth model (ii) maximum likelihood (ML) estimation and (iii) a time-varying basic reproduction number at the early onset of the pandemic. Using data from March 14, 2020, to May 10, 2020, sub-Saharan African countries were still grappling with COVID-19 at that point in the pandemic. The region's basic reproduction number ([Formula: see text]) was 1.89 (95% CI: 1.767 to 2.026) using the growth model and 1.513 (95% CI: 1.491 to 1.535) with the maximum likelihood method, indicating that, on average, infected individuals transmitted the virus to less than two secondary persons. Several countries, including Sudan ([Formula: see text]: 2.03), Ghana ([Formula: see text]: 1.87), and Somalia ([Formula: see text]: 1.85), exhibited high transmission rates. These findings highlighted the need for continued vigilance and the implementation of effective control measures to combat the pandemic in the region. It is anticipated that the findings in this study would not only function as a historical record of reproduction numbers during the COVID-19 pandemic in African countries, but can serve as a blueprint for addressing future pandemics of a similar nature.

Looking under the lamp-post: quantifying the performance of contact tracing in the United States during the SARS-CoV-2 pandemic

Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2's propensity for asymptomatic transmission, raise the question "how reliable was contact tracing for COVID-19 in the United States"? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62-1.68%) of transmission events with PCR testing and 1.00% (95% uncertainty interval 0.98-1.02%) with rapid antigen testing. When considering a more robust contact tracing scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6-62.8%). We did not assume presence of asymptomatic transmission or superspreading, making our estimates upper bounds on the actual percentages traced. These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens.

Optimal resource allocation model for COVID-19: a systematic review and meta-analysis

BACKGROUND

A lack of health resources is a common problem after the outbreak of infectious diseases, and resource optimization is an important means to solve the lack of prevention and control capacity caused by resource constraints. This study systematically evaluated the similarities and differences in the application of coronavirus disease (COVID-19) resource allocation models and analyzed the effects of different optimal resource allocations on epidemic control.

METHODS

A systematic literature search was conducted of CNKI, WanFang, VIP, CBD, PubMed, Web of Science, Scopus and Embase for articles published from January 1, 2019, through November 23, 2023. Two reviewers independently evaluated the quality of the included studies, extracted and cross-checked the data. Moreover, publication bias and sensitivity analysis were evaluated.

RESULTS

A total of 22 articles were included for systematic review; in the application of optimal allocation models, 59.09% of the studies used propagation dynamics models to simulate the allocation of various resources, and some scholars also used mathematical optimization functions (36.36%) and machine learning algorithms (31.82%) to solve the problem of resource allocation; the results of the systematic review show that differential equation modeling was more considered when testing resources optimization, the optimization function or machine learning algorithm were mostly used to optimize the bed resources; the meta-analysis results showed that the epidemic trend was obviously effectively controlled through the optimal allocation of resources, and the average control efficiency was 0.38(95%CI 0.25-0.51); Subgroup analysis revealed that the average control efficiency from high to low was health specialists 0.48(95%CI 0.37-0.59), vaccines 0.47(95%CI 0.11-0.82), testing 0.38(95%CI 0.19-0.57), personal protective equipment (PPE) 0.38(95%CI 0.06-0.70), beds 0.34(95%CI 0.14-0.53), medicines and equipment for treatment 0.32(95%CI 0.12-0.51); Funnel plots and Egger's test showed no publication bias, and sensitivity analysis suggested robust results.

CONCLUSION

When the data are insufficient and the simulation time is short, the researchers mostly use the constructor for research; When the data are relatively sufficient and the simulation time is long, researchers choose differential equations or machine learning algorithms for research. In addition, our study showed that control efficiency is an important indicator to evaluate the effectiveness of epidemic prevention and control. Through the optimization of medical staff and vaccine allocation, greater prevention and control effects can be achieved.

Building a Simple Model to Assess the Impact of Case Investigation and Contact Tracing for Sexually Transmitted Diseases: Lessons From COVID-19

Introduction

During the COVID-19 pandemic, the U.S. Centers for Disease Control and Prevention developed a simple spreadsheet-based tool to help state and local public health officials assess the performance and impact of COVID-19 case investigation and contact tracing in their jurisdiction. The applicability and feasibility of building such a tool for sexually transmitted diseases were assessed.

Methods

The key epidemiologic differences between sexually transmitted diseases and respiratory diseases (e.g., mixing patterns, incubation period, duration of infection, and the availability of treatment) were identified, and their implications for modeling case investigation and contact tracing impact with a simple spreadsheet tool were remarked on. Existing features of the COVID-19 tool that are applicable for evaluating the impact of case investigation and contact tracing for sexually transmitted diseases were also identified.

Results

Our findings offer recommendations for the future development of a spreadsheet-based modeling tool for evaluating the impact of sexually transmitted disease case investigation and contact tracing efforts. Generally, we advocate for simplifying sexually transmitted disease-specific complexities and performing sensitivity analyses to assess uncertainty. The authors also acknowledge that more complex modeling approaches might be required but note that it is possible that a sexually transmitted disease case investigation and contact tracing tool could incorporate features from more complex models while maintaining a user-friendly interface.

Conclusions

A sexually transmitted disease case investigation and contact tracing tool could benefit from the incorporation of key features of the COVID-19 model, namely its user-friendly interface. The inherent differences between sexually transmitted diseases and respiratory viruses should not be seen as a limitation to the development of such tool.

A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States

To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March-December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time.

A Brighton Collaboration standardized template with key considerations for a benefit/risk assessment for the Novavax COVID-19 Vaccine (NVX-CoV2373), a recombinant spike protein vaccine with Matrix-M adjuvant to prevent disease caused by SARS-CoV-2 viruses

Novavax, a global vaccine company, began evaluating NVX-CoV2373 in human studies in May 2020 and the pivotal placebo-controlled phase 3 studies started in November 2020; five clinical studies provided adult and adolescent clinical data for over 31,000 participants who were administered NVX-CoV2373. This extensive data has demonstrated a well-tolerated response to NVX-CoV2373 and high vaccine efficacy against mild, moderate, or severe COVID-19 using a two-dose series (Dunkle et al., 2022) [1], (Heath et al., 2021) [2], (Keech et al., 2020) [3], (Mallory et al., 2022) [4]. The most common adverse events seen after administration with NVX-CoV2373 were injection site tenderness, injection site pain, fatigue, myalgia, headache, malaise, arthralgia, nausea, or vomiting. In addition, immunogenicity against variants of interest (VOI) and variants of concern (VOC) was established with high titers of ACE2 receptor-inhibiting and neutralizing antibodies in these studies (EMA, 2022) [5], (FDA, 2023) [6]. Further studies on correlates of protection determined that titers of anti-Spike IgG and neutralizing antibodies correlated with efficacy against symptomatic COVID-19 established in clinical trials (p < 0.001 for recombinant protein vaccine and p = 0.005 for mRNA vaccines for IgG levels) (Fong et al., 2022) [7]. Administration of a booster dose of the recombinant protein vaccine approximately 6 months following the primary two-dose series resulted in substantial increases in humoral antibodies against both the prototype strain and all evaluated variants, similar to or higher than the antibody levels observed in phase 3 studies that were associated with high vaccine efficacy (Dunkle et al., 2022) [1], (Mallory et al., 2022) [4]. These findings, together with the well tolerated safety profile, support use of the recombinant protein vaccine as primary series and booster regimens.

SARS-CoV-2: An Update on the Biological Interplay with the Human Host

Coronavirus Disease 2019 (COVID-19) is an infectious respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease, first identified in the Chinese city of Wuhan in November 2019, has since spread worldwide, is the latest human pandemic and has officially infected over 800 million people and has caused nearly seven million deaths to date. Although SARS-CoV-2 belongs to the large family of coronaviruses, it has some unique biological characteristics in its interplay with the human host. Therefore, this narrative review aims to provide an up-to-date overview of the structure of the virus, incubation and shedding in the human host, infectivity and biological evolution over time, as well as the main mechanisms for invading human host cells and replicating within. We also proffer that ongoing epidemiological surveillance of newly emerged variants must always be accompanied by biological studies aimed at deciphering new advantageous traits that may contribute to increasing virulence and pathogenicity, such that the most appropriate strategies for establishing a (relatively) safe coexistence with the human host can be implemented.

Measuring the efficacy of a vaccine during an epidemic

The urgency to develop vaccines during the COVID-19 pandemic has resulted in the acceleration of clinical trials. Specifically, a broad spectrum of efficacy levels has been reported for various vaccines based on phase III cohort studies. Our study demonstrates that conducting large cohort phase III clinical trials during the peak of an epidemic leads to a significant underestimation of vaccine efficacy, even in the absence of confounding factors. Furthermore, we find that this underestimation increases with the proportion of infectious individuals in the population during the experiment and the severity of the epidemic, as measured by its basic reproduction number.

Direct indicators of social distancing effectiveness in COVID-19 outbreak stages: a correlational analysis of case contacts and population mobility in Korea

OBJECTIVES

The effectiveness of social distancing during the coronavirus disease 2019 (COVID-19) pandemic has been evaluated using the magnitude of changes in population mobility. This study aimed to investigate a direct indicator-namely, the number of close contacts per patient with confirmed COVID-19.

METHODS

From week 7, 2020 to week 43, 2021, population movement changes were calculated from the data of two Korean telecommunication companies and Google in accordance with social distancing stringency levels. Data on confirmed cases and their close contacts among residents of Gyeonggi Province, Korea were combined at each stage. Pearson correlation analysis was conducted to compare the movement data with the change in the number of contacts for each confirmed case calculated by stratification according to age group. The reference value of the population movement data was set using the value before mid-February 2020, considering each data's characteristics.

RESULTS

In the age group of 18 or younger, the number of close contacts per confirmed case decreased or increased when the stringency level was strengthened or relaxed, respectively. In adults, the correlation was relatively low, with no correlation between the change in the number of close contacts per confirmed case and the change in population movement after the commencement of vaccination for adults.

CONCLUSIONS

The effectiveness of governmental social distancing policies against COVID-19 can be evaluated using the number of close contacts per confirmed case as a direct indicator, especially for each age group. Such an analysis can facilitate policy changes for specific groups.

A Stochastic Mobility-Driven Spatially Explicit SEIQRD covid-19 Model with VOCs, Seasonality, and Vaccines

In this work, we extend our previously developed compartmental SEIQRD model for sars-cov-2 in Belgium. We introduce sars-cov-2 variants of concern, vaccines, and seasonality in our model, as their addition has proven necessary for modelling sars-cov-2 transmission dynamics during the 2020-2021 covid-19 pandemic in Belgium. The model is geographically stratified into eleven spatial patches (provinces), and a telecommunication dataset provided by Belgium's biggest operator is used to incorporate interprovincial mobility. We calibrate the model using the daily number of hospitalisations in each province and serological data. We find the model adequately describes these data, but the addition of interprovincial mobility was not necessary to obtain an accurate description of the 2020-2021 sars-cov-2 pandemic in Belgium. We further demonstrate how our model can be used to help policymakers decide on the optimal timing of the release of social restrictions.We find that adding spatial heterogeneity by geographically stratifying the model results in more uncertain model projections as compared to an equivalent nation-level model, which has both communicative advantages and disadvantages. We finally discuss the impact of imposing local mobility or social contact restrictions to contain an epidemic in a given province and find that lowering social contact is a more effective strategy than lowering mobility.

Trajectories of COVID-19: A longitudinal analysis of many nations and subnational regions

The COVID-19 pandemic is the first to be rapidly and sequentially measured by nation-wide PCR community testing for the presence of the viral RNA at a global scale. We take advantage of the novel "natural experiment" where diverse nations and major subnational regions implemented various policies including social distancing and vaccination at different times with different levels of stringency and adherence. Initially, case numbers expand exponentially with doubling times of ~1-2 weeks. In the nations where interventions were not implemented or perhaps lees effectual, case numbers increased exponentially but then stabilized around 102-to-103 new infections (per km2 built-up area per day). Dynamics under effective interventions were perturbed and infections decayed to low levels. They rebounded concomitantly with the lifting of social distancing policies or pharmaceutical efficacy decline, converging on a stable equilibrium setpoint. Here we deploy a mathematical model which captures this V-shape behavior, incorporating a direct measure of intervention efficacy. Importantly, it allows the derivation of a maximal estimate for the basic reproductive number Ro (mean 1.6-1.8). We were able to test this approach by comparing the approximated "herd immunity" to the vaccination coverage observed that corresponded to rapid declines in community infections during 2021. The estimates reported here agree with the observed phenomena. Moreover, the decay (0.4-0.5) and rebound rates (0.2-0.3) were similar throughout the pandemic and among all the nations and regions studied. Finally, a longitudinal analysis comparing multiple national and regional results provides insights on the underlying epidemiology of SARS-CoV-2 and intervention efficacy, as well as evidence for the existence of an endemic steady state of COVID-19.

Looking under the lamp-post: quantifying the performance of contact tracing in the United States during the SARS-CoV-2 pandemic

Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests (with a high false negative rate) due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2's propensity for asymptomatic transmission, raise the question "how reliable was contact tracing for COVID-19 in the United States"? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62%-1.68%) of transmission events with PCR testing and 0.88% (95% uncertainty interval 0.86%-0.89%) with rapid antigen testing. When considering an optimal scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6%-62.8%). These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens.

Reducing the basic reproduction number of COVID-19: a model simulation focused on QALYs, hospitalisation, productivity costs and optimal (soft) lockdown

Even if public health interventions are successful at reducing the spread of COVID-19, there is no guarantee that they will bring net benefits to the society because of the dynamic nature of the pandemic, e.g., the risk of a second outbreak if those interventions are stopped too early, and the costs of a continued lockdown. In this analysis, a discrete-time dynamic model is used to simulate the effect of reducing the effective reproduction number, driven by lockdowns ordered in March 2020 in four European countries (UK, France, Italy and Spain), on QALYs and hospitalisation costs. These benefits are valued in monetary terms (€30,000 per QALY assumed) and compared to productivity costs due to reduced economic activity during the lockdown. An analysis of the optimal duration of lockdown is performed where a net benefit is maximised. The switch to a soft lockdown is analysed and compared to a continued lockdown or no intervention. Results vary for two assumptions about hospital capacity of the health system: (a) under unlimited capacity, average benefit ranges from 8.21 to 14.21% of annual GDP, for UK and Spain, respectively; (b) under limited capacity, average benefits are higher than 30.32% of annual GDP in all countries. The simulation results imply that the benefits of lockdown are not substantial unless continued until vaccination of high-risk groups is complete. It is illustrated that lockdown may not bring net benefits under some scenarios and a soft lockdown will be a more efficient alternative from mid-June 2020 only if the basic reproduction number is maintained low (not necessarily below 1) and productivity costs are sufficiently reduced.

Household transmission dynamics of COVID-19 among residents of Delhi, India: a prospective case-ascertained study

Objective

The aim of this study was to observe the secondary infection rate and transmission dynamics of COVID-19 among household contacts, and their associations with various factors across four dimensions of interaction.

Methods

This was a case-ascertained study among unvaccinated household contacts of a laboratory-confirmed COVID-19 case in New Delhi between December 2020 and July 2021. For this study, 99 index cases and their 316 household contacts were interviewed and sampled (blood and oro-nasal swab) on days 1, 7, 14, and 28.

Results

The secondary infection rate among unvaccinated household contacts was 44.6% (95% confidence interval (CI) 39.1-50.1). The predictors of secondary infection among individual contact levels were: being female (odds ratio (OR) 2.13), increasing age (OR 1.01), symptoms at baseline (OR 3.39), and symptoms during follow-up (OR 3.18). Among index cases, age of the primary case (OR 1.03) and symptoms during follow-up (OR 6.29) were significantly associated with secondary infection. Among household-level and contact patterns, having more rooms (OR 4.44) and taking care of the index case (OR 2.02) were significantly associated with secondary infection.

Conclusion

A high secondary infection rate highlights the need to adopt strict measures and advocate COVID-19-appropriate behaviors. A targeted approach for higher-risk household contacts would efficiently limit infections among susceptible contacts.

Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA

In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.

Macroeconomic consequences of the COVID-19 pandemic

We estimate the economic impacts of COVID-19 in the U.S. using a disaster economic consequence analysis framework implemented by a dynamic computable general equilibrium (CGE) model. This facilitates identification of relative influences of several causal factors as "shocks" to the model, including mandatory business closures, disease spread trajectories, behavioral responses, resilience, pent-up demand, and government stimulus packages. The analysis is grounded in primary data on avoidance behavior and healthcare parameters. The decomposition of the influence of various causal factors will help policymakers offset the negative influences and reinforce the positive ones during the remainder of this pandemic and future ones.

Forty-seven year trend of measles in Iran: An interrupted time series analysis

Background and Aim

Measles is an acute viral infectious disease usually characterized by erythematous maculopapular rash and sometimes pneumonia, diarrhea, and Central Nervous System disturbance. The current study aimed to describe the trend of measles in Iran before and after the 1978 revolution and COVID-19 pandemic.

Methods

In the current quasi-experimental study, we used annual data on confirmed cases of measles in Iran, from 1974 to 2021. Data were extracted from the World Health Organization website. An interrupted time series model was used to assess the effect of different events on the incidence of measles.

Results

The trend of new cases increase every year until 1980 according to the preintervention slope of 2040 (95% confidence interval [CI] = -1965-2045; p < 0.31). After Iran's revolution, the occurrence of new cases significantly decreased (-845 [95% CI = -1262 to -432; p = 0.001]). After the COVID-19 pandemic, the trend of new cases significantly increased (41 [95% CI = 12-70; p = 0.006]).

Conclusion

It seems that social or health-related events are among the effective factors on the incidence of measles. But with maintaining vaccination coverage in the community and vaccination of immigrants, this fluctuation in the disease trend can be decreased.

COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion

Background

The Shanghai COVID-19 epidemic is an important example of a local outbreak and of the implementation of normalized prevention and disease control strategies. The precise impact of public health interventions on epidemic prevention and control is unknown.

Methods

We collected information on COVID-19 patients reported in Shanghai, China, from January 30 to May 31, 2022. These newly added cases were classified as local confirmed cases, local asymptomatic infections, imported confirmed cases and imported asymptomatic infections. We used polynomial fitting correlation analysis and illustrated the time lag plot in the correlation analysis of local and imported cases. Analyzing the conversion of asymptomatic infections to confirmed cases, we proposed a new measure of the conversion rate (C _r ). In the evolution of epidemic transmission and the analysis of intervention effects, we calculated the effective reproduction number (R _t ). Additionally, we used simulated predictions of public health interventions in transmission, correlation, and conversion analyses.

Results

(1) The overall level of R _t in the first three stages was higher than the epidemic threshold. After the implementation of public health intervention measures in the third stage, R _t decreased rapidly, and the overall R _t level in the last three stages was lower than the epidemic threshold. The longer the public health interventions were delayed, the more cases that were expected and the later the epidemic was expected to end. (2) In the correlation analysis, the outbreak in Shanghai was characterized by double peaks. (3) In the conversion analysis, when the incubation period was short (3 or 7 days), the conversion rate fluctuated smoothly and did not reflect the effect of the intervention. When the incubation period was extended (10 and 14 days), the conversion rate fluctuated in each period, being higher in the first five stages and lower in the sixth stage.

Conclusion

Effective public health interventions helped slow the spread of COVID-19 in Shanghai, shorten the outbreak duration, and protect the healthcare system from stress. Our research can serve as a positive guideline for addressing infectious disease prevention and control in China and other countries and regions.

Bounding pandemic spread by heat spread

The beginning of a pandemic is a crucial stage for policymakers. Proper management at this stage can reduce overall health and economical damage. However, knowledge about the pandemic is insufficient. Thus, the use of complex and sophisticated models is challenging. In this study, we propose analytical and stochastic heat spread-based boundaries for the pandemic spread as indicated by the Susceptible-Infected-Recovered (SIR) model. We study the spread of a pandemic on an interaction (social) graph as a diffusion and compared it with the stochastic SIR model. The proposed boundaries are not requiring accurate biological knowledge such as the SIR model does.

Waco COVID Survey: A Community-Based SARS-CoV-2 Serological Surveillance Study in Central Texas

In early-2020, the epidemiology of the SARS-CoV-2 virus was still in discovery and initial reports about the role of asymptomatic individuals were developing. The Waco COVID Survey was implemented in mid-2020 with targeted serological surveillance to assess relationships among risk factors and asymptomatic transmission in McLennan County, Texas, USA. Because large-scale random sampling of the population was not feasible, a targeted and repeated sampling of specific clustered groups of asymptomatic individuals was employed. This included four waves (initial intake [n = 495], two follow-ups separated by a month [n = 348; n = 287], and a final follow-up one year later [n = 313]) of sampling participants in different risk categories: (a) healthcare workers (e.g., physicians, nurses, etc.) and first responders, (b) essential service employees (e.g., convenience and grocery stores, restaurants focused on delivery and carry-out), (c) employees whose businesses began reopening on May 1 (e.g., dine-in restaurants, churches, etc.) including church attendees, and (d) individuals that practiced intensive isolation. The survey collected information on demographics, compliance with public health recommendations, satisfaction with government responses, health history, attitudes regarding the SARS-CoV-2 virus and COVID-19 disease, health behaviors, personality, stress, and general affect. Results illustrate pandemic fatigue over time, the influence of political leniency on opinions and behaviors, the importance of face coverings in preventing infection, and the positive impact of vaccination in the community. This project remains one of the largest longitudinal SARS-CoV-2 antibody seroprevalence surveys in the US, and details for successful implementation and community involvement are discussed.

Fast bilateral weighted least square for the detail enhancement of COVID-19 chest X-rays

Background

X-ray is an effective measure in the diagnosis of coronavirus disease 2019. However, it suffers from low visibility and poor details. A plausible solution is to decompose the captured images and enhance the details. The bilateral weighted least square model can be an effective tool for this task. However, it is highly computationally expensive.

Method

In this article, we propose an efficient algorithm for the bilateral weighted least square model. We approximate the bilateral weight with the bilateral grid and then incorporate it into the optimization model. This significantly reduces the number of variables in the linear system. Therefore, the model can be efficiently solved. We employ the proposed algorithm to decompose the input X-rays into base and detail layers. The detail layers are then boosted and added back to the input to derive the detail-enhanced results.

Results

The subjective results indicate that our method achieves higher contrast than the best-performing method (442.30 > 410.09 , 426.40 > 403.34 , 564.51 > 531.38 ). Furthermore, our method is highly efficient. It takes 0.92  s to process a 720P color image on an Intel i7-6700 CPU. The objective results derive from the chi-square test indicate that subjects hold more positive attitudes toward our detail-enhanced images than the original X-ray images (3.53 > 2.72 , 3.42 > 2.61 , 3.5 > 2.56 ).

Conclusion

We have conducted extensive experiments to evaluate the proposed image detail enhancement method. It can be concluded that (1) our method could significantly improve the visibility of the X-ray images. (2) our method is fast and effective, thus facilitating real applications.

Risk Factors for In-Hospital Mortality among Patients with Coronavirus-19 in Isfahan City, Iran

Background

The aim of the study is to explore the risk factors of mortality for hospitalized patients in three designated hospitals in Isfahan province.

Materials and Methods

This retrospective cohort study was conducted on all positive coronavirus disease (COVID)-19 patients admitted to Khorshid, Isabn Maryam, and Amin hospitals in Isfahan province. The demographic, clinical, laboratory, and outcome data of patients who were died or discharged from February 24, 2020, to April 18, 2020, were extracted from patient's medical records.

Results

Overall 1044 COVID-19 patients were included in this analysis. Based on the findings of this study, older age (≥65 years) (adjusted hazard ratio [aHR]: 2.06; 95% confidence interval [CI]: 1.13-3.76), chronic obstructive pulmonary disease (COPD) history (aHR: 2.52; 95% CI: 1.09-5.83), white blood cell (WBC) counts more than 10 × 10^3/L (aHR: 3.05; 95% CI: 1.42-6.55), Hb level <13 gr/L (aHR: 2.82; 95% CI: 1.34-5.93), bilateral pulmonary infiltrates (aHR: 2.02; 95% CI: 1.12-3.64) at admission, development of acute respiratory distress syndrome (ARDS) (aHR: 1.87; 95% CI: 1.01-3.47), and intensive care unit (ICU) admission (aHR: 2.09; 95% CI: 1.04-4.18) during hospitalization were risk factors for in-hospital mortality in patients with COVID-19.

Conclusions

Multiple factors were found related to the severity and death among COVID-19 patients. We were found that older age (≥65 years) with COPD history, high level of WBC, low level of Hb (<13 g/L), bilateral pulmonary infiltrates at admission, development of ARDS, and ICU admission during hospitalization were identified as risk factors of death among COVID-19 patients. More related studies are needed in the future.

Epidemiologic Parameters for COVID-19: A Systematic Review and Meta-Analysis

Background: The World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) outbreak to be a public health emergency and international concern and recognized it as a pandemic. This study aimed to estimate the epidemiologic parameters of the COVID-19 pandemic for clinical and epidemiological help. Methods: In this systematic review and meta-analysis study, 4 electronic databases, including Web of Science, PubMed, Scopus, and Google Scholar were searched for the literature published from early December 2019 up to 23 March 2020. After screening, we selected 76 articles based on epidemiological parameters, including basic reproduction number, serial interval, incubation period, doubling time, growth rate, case-fatality rate, and the onset of symptom to hospitalization as eligibility criteria. For the estimation of overall pooled epidemiologic parameters, fixed and random effect models with 95% CI were used based on the value of between-study heterogeneity (I2). Results: A total of 76 observational studies were included in the analysis. The pooled estimate for R0 was 2.99 (95% CI, 2.71-3.27) for COVID-19. The overall R0 was 3.23, 1.19, 3.6, and 2.35 for China, Singapore, Iran, and Japan, respectively. The overall serial interval, doubling time, and incubation period were 4.45 (95% CI, 4.03-4.87), 4.14 (95% CI, 2.67-5.62), and 4.24 (95% CI, 3.03-5.44) days for COVID-19. In addition, the overall estimation for the growth rate and the case fatality rate for COVID-19 was 0.38% and 3.29%, respectively. Conclusion: The epidemiological characteristics of COVID-19 as an emerging disease may be revealed by computing the pooled estimate of the epidemiological parameters, opening the door for health policymakers to consider additional control measures.

No magic bullet: Limiting in-school transmission in the face of variable SARS-CoV-2 viral loads

In the face of a long-running pandemic, understanding the drivers of ongoing SARS-CoV-2 transmission is crucial for the rational management of COVID-19 disease burden. Keeping schools open has emerged as a vital societal imperative during the pandemic, but in-school transmission of SARS-CoV-2 can contribute to further prolonging the pandemic. In this context, the role of schools in driving SARS-CoV-2 transmission acquires critical importance. Here we model in-school transmission from first principles to investigate the effectiveness of layered mitigation strategies on limiting in-school spread. We examined the effect of masks and air quality (ventilation, filtration and ionizers) on steady-state viral load in classrooms, as well as on the number of particles inhaled by an uninfected person. The effectiveness of these measures in limiting viral transmission was assessed for variants with different levels of mean viral load (ancestral, Delta, Omicron). Our results suggest that a layered mitigation strategy can be used effectively to limit in-school transmission, with certain limitations. First, poorly designed strategies (insufficient ventilation, no masks, staying open under high levels of community transmission) will permit in-school spread even if some level of mitigation is present. Second, for viral variants that are sufficiently contagious, it may be difficult to construct any set of interventions capable of blocking transmission once an infected individual is present, underscoring the importance of other measures. Our findings provide practical recommendations; in particular, the use of a layered mitigation strategy that is designed to limit transmission, with other measures such as frequent surveillance testing and smaller class sizes (such as by offering remote schooling options to those who prefer it) as needed.

Why COVID-19 modelling of progression and prevention fails to translate to the real-world

Mathematical models were used widely to inform policy during the COVID pandemic. However, there is a poor understanding of their limitations and how they influence decision-making. We used systematic review search methods to find early modelling studies that determined the reproduction number and analysed its use and application to interventions and policy in the UK. Up to March 2020, we found 42 reproduction number estimates (39 based on Chinese data: R₀ range 2.1-6.47). Several biases affect the quality of modelling studies that are infrequently discussed, and many factors contribute to significant differences in the results of individual studies that go beyond chance. The sources of effect estimates incorporated into mathematical models are unclear. There is often a lack of a relationship between transmission estimates and the timing of imposed restrictions, which is further affected by the lag in reporting. Modelling studies lack basic evidence-based methods that aid their quality assessment, reporting and critical appraisal. If used judiciously, models may be helpful, especially if they openly present the uncertainties and use sensitivity analyses extensively, which need to consider and explicitly discuss the limitations of the evidence. However, until the methodological and ethical issues are resolved, predictive models should be used cautiously.

Pre-clinical validation of a turbine-based ventilator for invasive ventilation-The ACUTE-19 ventilator

BACKGROUND

The Severe Acute Respiratory Syndrome (SARS)-Coronavirus 2 (CoV-2) pandemic pressure on healthcare systems can exhaust ventilator resources, especially where resources are restricted. Our objective was a rapid preclinical evaluation of a newly developed turbine-based ventilator, named the ACUTE-19, for invasive ventilation.

METHODS

Validation consisted of (a) testing tidal volume (V_T) delivery in 11 simulated models, with various resistances and compliances; (b) comparison with a commercial ventilator (VIVO-50) adapting the United Kingdom Medicines and Healthcare products Regulatory Agency-recommendations for rapidly manufactured ventilators; and (c) in vivo testing in a sheep before and after inducing acute respiratory distress syndrome (ARDS) by saline lavage.

RESULTS

Differences in V_T in the simulated models were marginally different (largest difference 33ml [95%-confidence interval (CI) 31-36]; P<.001ml). Plateau pressure (P_plat) was not different (-0.3cmH₂O [95%-CI -0.9 to 0.3]; P=.409), and positive end-expiratory pressure (PEEP) was marginally different (0.3 cmH₂O [95%-CI 0.2 to 0.3]; P<.001) between the ACUTE-19 and the commercial ventilator. Bland-Altman analyses showed good agreement (mean bias, -0.29, [limits of agreement, 0.82 to -1.42], and mean bias 0.56 [limits of agreement, 1.94 to -0.81], at a P_plat of 15 and 30cmH₂O, respectively). The ACUTE-19 achieved optimal oxygenation and ventilation before and after ARDS induction.

CONCLUSIONS

The ACUTE-19 performed accurately in simulated and animal models yielding a comparable performance with a VIVO-50 commercial device. The acute 19 can provide the basis for the development of a future affordable commercial ventilator.

Overview of the Role of Spatial Factors in Indoor SARS-CoV-2 Transmission: A Space-Based Framework for Assessing the Multi-Route Infection Risk

The COVID-19 pandemic has lasted from 2019 to 2022, severely disrupting human health and daily life. The combined effects of spatial, environmental, and behavioral factors on indoor COVID-19 spread and their interactions are usually ignored. Especially, there is a lack of discussion on the role of spatial factors in reducing the risk of virus transmission in complex and diverse indoor environments. This paper endeavours to summarize the spatial factors and their effects involved in indoor virus transmission. The process of release, transport, and intake of SARS-CoV-2 was reviewed, and six transmission routes according to spatial distance and exposure way were classified. The triangular relationship between spatial, environmental and occupant behavioral parameters during virus transmission was discussed. The detailed effects of spatial parameters on droplet-based, surface-based and air-based transmission processes and virus viability were summarized. We found that spatial layout, public-facility design and openings have a significant indirect impact on the indoor virus distribution and transmission by affecting occupant behavior, indoor airflow field and virus stability. We proposed a space-based indoor multi-route infection risk assessment framework, in which the 3D building model containing detailed spatial information, occupant behavior model, virus-spread model and infection-risk calculation model are linked together. It is also applicable to other, similar, respiratory infectious diseases such as SARS, influenza, etc. This study contributes to developing building-level, infection-risk assessment models, which could help building practitioners make better decisions to improve the building's epidemic-resistance performance.

Epidemiological investigation of the COVID-19 outbreak in Vellore district in South India using Geographic Information Surveillance (GIS)

OBJECTIVES

Geographical Information Surveillance (GIS) is an advanced digital technology tool that maps location-based data and helps in epidemiological modeling. We applied GIS to analyze patterns of spread and hotspots of COVID-19 cases in the Vellore district in South India.

METHODS

Laboratory-confirmed COVID-19 cases from the Vellore district and neighboring taluks from March 2020 to June 2021 were geocoded and spatial maps were generated. Time trends exploring urban-rural burden with an age-sex distribution of cases and other variables were correlated with outcomes.

RESULTS

A total of 45,401 cases of COVID-19 were detected, with 20,730 cases during the first wave and 24,671 cases during the second wave. The overall incidence rates of COVID-19 were 462.8 and 588.6 per 100,000 population during the first and second waves, respectively. The spread pattern revealed epicenters in densely populated urban areas with radial spread sparing rural areas in the first wave. The case fatality rate was 1.89% and 1.6% during the first and second waves, which increased with advancing age.

CONCLUSIONS

Modern surveillance systems like GIS can accurately predict the trends and spread patterns during future pandemics. In addition, real-time mapping can help design risk mitigation strategies, thereby preventing the spread to rural areas.

Update on COVID-19 and Effectiveness of a Vaccination Campaign in a Global Context

The COVID-19 pandemic caused by SARS-CoV-2 remains a significant issue for global health, the economy, and society. When SARS-CoV-2 began to spread, the most recent serious infectious disease of this century around the world, with its high morbidity and mortality rates, it is understandable why such infections have generally been spread in the past, mainly from international travel movements. This perspective review aimed to provide an update for clinicians on the recent developments related to the microbiological perspectives in pandemics, diagnostics, prevention (such as the spread of a virus), vaccination campaigns, treatment options, and health consequences for COVID-19 based on the current literature. In this way, the authors attempt to raise awareness on the transversal nature of these challenges by identifying the main risk/vulnerability factors that the scientific community must face including our current knowledge on the virus capacity of the mechanism of entry into the cells, the current classifications of viral variants, the knowledge of the mathematical model on the spread of viruses (the possible routes of transmission), and the effectiveness of vaccination campaigns in a global context of pandemic, particularly from COVID-19, with a look at new or future vaccines.

Burden of Hospital-acquired SARS-CoV-2 Infections in Germany

Background

Avoiding in-hospital transmissions has been crucial in the COVID-19 pandemic. Little is known on the extent to which hospital-acquired SARS-CoV-2 variants have caused infections in Germany.

Aim

To analyse the occurrence and the outcomes of HAI with regard to different SARS-CoV-2 variants.

Methods

Patients with SARS-CoV-2 infections hospitalized between March 1^st, 2020 and May 17^th, 2022 in 79 hospitals of the Helios Group were included. Information on patients' characteristics and outcomes were retrieved from claims data. In accordance with the Robert Koch Institute, infections were classified as hospital-acquired when tested positive >6 days after admission and if no information hinted at a different source.

Findings

In all, 62,875 SARS-CoV-2 patients were analysed, of whom 10.6% had HAI. HAIs represented 14.7% of SARS-CoV-2 inpatients during the Wildtype period, 3.5% during Alpha (odds ratio: 0.21; 95% confidence interval: 0.19–0.24), 8.8% during Delta (2.70; 2.35–3.09) and 10.1% during Omicron (1.10; 1.03–1.19). When age and comorbidities were accounted for, HAI had lower odds for death than community-acquired infections (0.802; 0.740–0.866). Compared to the Wildtype period, HAIs during Omicron were associated with lower odds for ICU (0.78; 0.69–0.88), ventilation (0.47; 0.39–0.56), and death (0.33; 0.28–0.40).

Conclusion

Hospital-acquired SARS-CoV-2 infections occurred throughout the pandemic, affecting highly vulnerable patients. Although transmissibility increased with newer variants, the proportion of HAIs decreased, indicating improved infection prevention and/or the effect of immunization. Furthermore, the Omicron period was associated with improved outcomes. However, the burden of hospital-acquired SARS-CoV-2 infections remains high.

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2

The effective reproductive number R_e is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of R_e, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated R_e dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated R_e. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent R_e estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of R_e estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2

The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.

Different forms of superspreading lead to different outcomes: Heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2

Superspreading events play an important role in the spread of several pathogens, such as SARS-CoV-2. While the basic reproduction number of the original Wuhan SARS-CoV-2 is estimated to be about 3 for Belgium, there is substantial inter-individual variation in the number of secondary cases each infected individual causes-with most infectious individuals generating no or only a few secondary cases, while about 20% of infectious individuals is responsible for 80% of new infections. Multiple factors contribute to the occurrence of superspreading events: heterogeneity in infectiousness, individual variations in susceptibility, differences in contact behavior, and the environment in which transmission takes place. While superspreading has been included in several infectious disease transmission models, research into the effects of different forms of superspreading on the spread of pathogens remains limited. To disentangle the effects of infectiousness-related heterogeneity on the one hand and contact-related heterogeneity on the other, we implemented both forms of superspreading in an individual-based model describing the transmission and spread of SARS-CoV-2 in a synthetic Belgian population. We considered its impact on viral spread as well as on epidemic resurgence after a period of social distancing. We found that the effects of superspreading driven by heterogeneity in infectiousness are different from the effects of superspreading driven by heterogeneity in contact behavior. On the one hand, a higher level of infectiousness-related heterogeneity results in a lower risk of an outbreak persisting following the introduction of one infected individual into the population. Outbreaks that did persist led to fewer total cases and were slower, with a lower peak which occurred at a later point in time, and a lower herd immunity threshold. Finally, the risk of resurgence of an outbreak following a period of lockdown decreased. On the other hand, when contact-related heterogeneity was high, this also led to fewer cases in total during persistent outbreaks, but caused outbreaks to be more explosive in regard to other aspects (such as higher peaks which occurred earlier, and a higher herd immunity threshold). Finally, the risk of resurgence of an outbreak following a period of lockdown increased. We found that these effects were conserved when testing combinations of infectiousness-related and contact-related heterogeneity.

COVID-19 vaccination intention and hesitancy: Mistrust on COVID-19 vaccine benefit a major driver for vaccine hesitancy among healthcare workers; a cross-sectional study in North India

Background

The advent of an effective novel COVID-19 vaccine could extinguish the current devastating pandemic but the vaccine hesitancy is a hurdle for the public health system, so this study estimated the COVID-19 vaccination intention and hesitancy among the healthcare workers, the priority target group for the COVID-19 vaccination in India.

Methods

A web-based cross-sectional survey was conducted among the healthcare workers in Chandigarh, a union territory in North India, using a Snowball sampling technique. A total of 403 healthcare workers participated in the study between 2^nd and 25^th January 2021. The primary data collected were the intention to get vaccinated against the available COVID-19 vaccine and the concerns regarding the new vaccines. The attitude towards novel COVID-19 vaccine was assessed using developed Vaccine attitude examination scale. These questionnaire, which were delivered via WhatsApp, was filled by the participants over Google forms.

Results

Among the 403 respondents surveyed, the majority (54.6%) reported they were definitely intended to get vaccinated against COVID-19, however, 7% expressed a resistance for inoculation with COVID-19 vaccination. The perceived susceptibility (aOR = 0.511, CI 0.265-0.987) and severity of COVID-19 infection (aOR = 0.551 CI 0.196-0.704) and not being concerned about the efficacy of new COVID-19 vaccines (aOR = 0.702 CI 1.109-26.55) were found to have the highest significant odds of intention to take the COVID-19 vaccine. The majority (62%) were concerned about the safety of the vaccine, in terms of side-effects, quality control, and doubted efficacy of the vaccine. The mistrust of the benefits of the vaccine is a significant predictor for vaccine hesitancy among the healthcare workers (aOR = 5.205 CI 3.106-8.723).

Conclusion

Therefore, strategic communication and vaccine-acceptance programs should be formulated in order to combat the prevailing mistrust on the vaccine safety and efficacy and attain effective coverage to gain herd immunity.

Behavioral responses to risk promote vaccinating high-contact individuals first

How should communities prioritize COVID-19 vaccinations? Prior studies found that prioritizing the elderly and most vulnerable minimizes deaths. However, prior research has ignored how behavioral responses to risk of disease endogenously change transmission rates. We show that incorporating risk-driven behavioral responses enhances fit to data and may change prioritization to vaccinating high-contact individuals. Behavioral responses matter because deaths grow exponentially until communities are compelled to reduce contacts, with deaths stabilizing at levels that oblige higher-contact groups to sufficiently cut their interactions and slow transmissions. More lives may be saved by vaccinating and taking those high-contact groups out of transmission chains earlier because the remaining groups will take more precautions while waiting for their turn for vaccination. These findings are especially important considering the need for further vaccination in many countries, the emergence of new variants, and the expected challenge of distributing new vaccines in the coming months and years. © 2022 The Author. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Testing and isolation to prevent overloaded healthcare facilities and reduce death rates in the SARS-CoV-2 pandemic in Italy

Background

During the first wave of COVID-19, hospital and intensive care unit beds got overwhelmed in Italy leading to an increased death burden. Based on data from Italian regions, we disentangled the impact of various factors contributing to the bottleneck situation of healthcare facilities, not well addressed in classical SEIR-like models. A particular emphasis was set on the undetected fraction (dark figure), on the dynamically changing hospital capacity, and on different testing, contact tracing, quarantine strategies.

Methods

We first estimated the dark figure for different Italian regions. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread, the model was optimized to fit data (infected, hospitalized, ICU, dead) published by the Italian Civil Protection.

Results

We show that testing influenced the infection dynamics by isolation of newly detected cases and subsequent interruption of infection chains. The time-varying reproduction number (R t) in high testing regions decreased to <1 earlier compared to the low testing regions. While an early test and isolate (TI) scenario resulted in up to ~31% peak reduction of hospital occupancy, the late TI scenario resulted in an overwhelmed healthcare system.

Conclusions

An early TI strategy would have decreased the overall hospital usage drastically and, hence, death toll (∼34% reduction in Lombardia) and could have mitigated the lack of healthcare facilities in the course of the pandemic, but it would not have kept the hospitalization amount within the pre-pandemic hospital limit.

Adding a reaction-restoration type transmission rate dynamic-law to the basic SEIR COVID-19 model

The classical SEIR model, being an autonomous system of differential equations, has important limitations when representing a pandemic situation. Particularly, the geometric unimodal shape of the epidemic curve is not what is generally observed. This work introduces the βSEIR model, which adds to the classical SEIR model a differential law to model the variation in the transmission rate. It considers two opposite thrives generally found in a population: first, reaction to disease presence that may be linked to mitigation strategies, which tends to decrease transmission, and second, the urge to return to normal conditions that pulls to restore the initial value of the transmission rate. Our results open a wide spectrum of dynamic variabilities in the curve of new infected, which are justified by reaction and restoration thrives that affect disease transmission over time. Some of these dynamics have been observed in the existing COVID-19 disease data. In particular and to further exemplify the potential of the model proposed in this article, we show its capability of capturing the evolution of the number of new confirmed cases of Chile and Italy for several months after epidemic onset, while incorporating a reaction to disease presence with decreasing adherence to mitigation strategies, as well as a seasonal effect on the restoration of the initial transmissibility conditions.

SARS-CoV-2 Risk Quantification Model and Validation Based on Large-Scale Dutch Test Events

In response to the outbreak of SARS-CoV-2, many governments decided in 2020 to impose lockdowns on societies. Although the package of measures that constitute such lockdowns differs between countries, it is a general rule that contact between people, especially in large groups of people, is avoided or prohibited. The main reasoning behind these measures is to prevent healthcare systems from becoming overloaded. As of 2021 vaccines against SARS-CoV-2 are available, but these do not guarantee 100% risk reduction and it will take a while for the world to reach a sufficient immune status. This raises the question of whether and under which conditions events like theater shows, conferences, professional sports events, concerts, and festivals can be organized. The current paper presents a COVID-19 risk quantification method for (large-scale) events. This method can be applied to events to define an alternative package of measures replacing generic social distancing.

Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts

We consider a retrospective modelling approach for estimating effective reproduction numbers based on death counts during the first year of the COVID-19 pandemic in Germany. The proposed Bayesian hierarchical model incorporates splines to estimate reproduction numbers flexibly over time while adjusting for varying effective infection fatality rates. The approach also provides estimates of dark figures regarding undetected infections. Results for Germany illustrate that our estimates based on death counts are often similar to classical estimates based on confirmed cases; however, considering death counts allows to disentangle effects of adapted testing policies from transmission dynamics. In particular, during the second wave of infections, classical estimates suggest a flattening infection curve following the “lockdown light” in November 2020, while our results indicate that infections continued to rise until the “second lockdown” in December 2020. This observation is associated with more stringent testing criteria introduced concurrently with the “lockdown light”, which is reflected in subsequently increasing dark figures of infections estimated by our model. In light of progressive vaccinations, shifting the focus from modelling confirmed cases to reported deaths with the possibility to incorporate effective infection fatality rates might be of increasing relevance for the future surveillance of the pandemic.

The Basic Reproduction Number and Delayed Action of T Cells for Patients Infected with SARS-CoV-2

COVID-19 has been prevalent for the last two years. The transmission capacity of SARS-CoV-2 differs under the influence of different epidemic prevention policies, making it difficult to measure the infectivity of the virus itself. In order to evaluate the infectivity of SARS-CoV-2 in patients with different diseases, we constructed a viral kinetic model by adding the effects of T cells and antibodies. To analyze and compare the delay time of T cell action in patients with different symptoms, we constructed a delay differential equation model. Through the first model, we found that the basic reproduction number of severe patients is greater than that of mild patients, and accordingly, we constructed classification criteria for severe and mild patients. Through the second model, we found that the delay time of T cell action in severe patients is much longer than that in mild patients, and accordingly, we present suggestions for the prevention, diagnosis, and treatment of different patients.

Quarantine, physical distancing and social isolation measures in individuals potentially exposed to SARS-CoV-2 in community settings and health services: a scoping review

To provide a synthesis of diverse evidence on the impact of the non-therapeutic preventive measures, specifically quarantine, physical distancing and social isolation, on the control of COVID-19. A scoping review conducted in the PubMed, Embase, LILACS, CENTRAL and SCOPUS databases between 2019 and August 28th, 2020. The descriptors used were the following: "quarantine", "physical distancing", "social isolation", "COVID-19" and "SARS-Cov2". Studies that addressed the non-therapeutic preventive measures in people exposed to SARs-CoV-2 in community settings and health services were included. A total of 14,442 records identified through a database search were reduced to 346 studies and, after a standardized selection process, a total of 68 articles were selected for analysis. A total of 35 descriptive, cross-sectional or longitudinal observational studies were identified, as well as 3 reviews, in addition to 30 studies with mathematical modeling. The main intervention assessed was social distancing (56.6%), followed by lockdown (25.0%) and quarantine (18.4%). The main evidence analyzed points to the need for rapid responses to reduce the number of infections, deaths and hospital admissions, especially in intensive care unit beds.The current review revealed consistent reports that the quarantine, physical distancing and social isolation are effective strategies to contain spread of the new coronavirus.