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We investigate the emergence, mutation profile, and dissemination of SARS-CoV-2 lineage B.1.214.2, first identified in Belgium in January 2021. This variant, featuring a 3-amino acid insertion in the spike protein similar to the Omicron variant, was speculated to enhance transmissibility or immune evasion. Initially detected in international travelers, it substantially transmitted in Central Africa, Belgium, Switzerland, and France, peaking in April 2021. Our travel-aware phylogeographic analysis, incorporating travel history, estimated the origin to the Republic of the Congo, with primary European entry through France and Belgium, and multiple smaller introductions during the epidemic. We correlate its spread with human travel patterns and air passenger data. Further, upon reviewing national reports of SARS-CoV-2 outbreaks in Belgian nursing homes, we found this strain caused moderately severe outcomes (8.7% case fatality ratio). A distinct nasopharyngeal immune response was observed in elderly patients, characterized by 80% unique signatures, higher B- and T-cell activation, increased type I IFN signaling, and reduced NK, Th17, and complement system activation, compared to similar outbreaks. This unique immune response may explain the variant's epidemiological behavior and underscores the need for nasal vaccine strategies against emerging variants., (© 2024. The Author(s).)

Research on pandemics in institutional settings often assumes that all human interactions within a jail pose similar viral transmission risks. We developed an agent-based model (ABM) called Simulation Applications for Forecasting Effective Responses in Corrections (SAFER-C™) to simulate nine scenarios of possible interactions and virus transmission among incarcerated individuals and jail staff and tested this assumption. We found that resumption of high-contact activities has a greater impact on the number of infections, while out-of-cell group sizes and initial vaccination rates had lower impact. This work emphasizes the importance of understanding and modeling human interactions in confinement facilities, as well as understanding, responding to, and limiting the mechanism of viral transmission in jails. Insights from ABMs provide correctional administrators with realistic options for managing responses.

Objective: This study sought to better understand the types of locations that serve as hubs for the transmission of COVID-19., Methods: Contact tracers interviewed individuals who tested positive for SARS-CoV-2 between November 2020 and March 2021, as well as the people with whom those individuals had contact. We conducted a 2-mode social network analysis of people by the types of places they visited, focusing on the forms of centrality exhibited by place types., Results: The most exposed locations were grocery stores, commercial stores, restaurants, commercial services, and schools. These types of locations also have the highest "betweenness," meaning that they tend to serve as hubs between other kinds of locations since people would usually visit more than 1 location in a day or when infected. The highest pairs of locations were grocery store/retail store, restaurant/retail store, and restaurant/grocery store. Schools are not at the top but are 3 times in the top 7 pairs of locations and connected to the 3 types of locations in those top pairs., Conclusions: As the pandemic progressed, location hotspots shifted between businesses, schools, and homes. In this social network analysis, certain types of locations appeared to be potential hubs of transmission.

Climate change is acknowledged to directly affect not only the environment, economy, and society but also the transmission dynamics of infectious diseases, thereby impacting public health. The recent experiences with the spread of SARS-CoV-2 and Monkeypox have highlighted the complex and interconnected nature of infectious diseases, which are strongly linked to various determinants of health. Considering these challenges, adopting a new vision such as the trans-disciplinary approach appears to be imperative. This paper proposes a new theory about viruses' spread, based on a biological model, accounting for the optimisation of energy and material resources for organisms' survival and reproduction in the environment. The approach applies Kleiber's law scaling theory, originally developed in biology, to model community dynamics in cities. A simple equation can be used to model pathogen spread without accounting for each species' physiology by leveraging the superlinear scaling of variables with population size. This general theory offers several advantages, including the ability to explain the rapid and surprising spread of both SARS-CoV-2 and Monkeypox. The proposed model shows similarities in the spreading processes of both viruses, based on the resulting scaling factors, and opens new avenues for research. By fostering cooperation and integrating knowledge from different disciplines to effectively tackle the multifaceted dimensions of disease outbreaks, we can work towards preventing future health emergencies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Aim: To determine the effects of obesity in childhood on SARS-CoV-2 infection., Methods: A population-based, cross-sectional study combining the Israeli Growth Survey and COVID-19 data for children with at least one SARS-CoV-2 test from 16 February 2020 to 20 December 2021. Overweight and obesity status were based on body mass index and the Center for Disease Control criteria. Multivariate logistics regression was performed to validate reliability for weight categories at the age of approximately 6 years compared with weights at approximately 12 years., Results: A total of 444 868 records for children with an overall positivity rate of 22% were studied. The mean age was 9.5 years. The odds ratios of children with obesity or overweight after controlling for sex at 6 years to test positive were 1.07-1.12 and 1.06-1.08 (depending on the model), respectively, compared to those with healthy range body mass index., Conclusion: Excess weight appears to increase the risk of SARS-CoV-2 infection. This finding should be considered for public health planning. For example, children with overweight and obesity should be prioritised for vaccination. Excess weight in childhood can be harmful at a young age and not only for long-term health., (© 2023 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.)

Background: While many studies evaluated the reliability of digital mobility metrics as a proxy of SARS-CoV-2 transmission potential, none examined the relationship between dining-out behavior and the superspreading potential of COVID-19., Objective: We employed the mobility proxy of dining out in eateries to examine this association in Hong Kong with COVID-19 outbreaks highly characterized by superspreading events., Methods: We retrieved the illness onset date and contact-tracing history of all laboratory-confirmed cases of COVID-19 from February 16, 2020, to April 30, 2021. We estimated the time-varying reproduction number (R t ) and dispersion parameter (k), a measure of superspreading potential, and related them to the mobility proxy of dining out in eateries. We compared the relative contribution to the superspreading potential with other common proxies derived by Google LLC and Apple Inc., Results: A total of 6391 clusters involving 8375 cases were used in the estimation. A high correlation between dining-out mobility and superspreading potential was observed. Compared to other mobility proxies derived by Google and Apple, the mobility of dining-out behavior explained the highest variability of k (ΔR-sq=9.7%, 95% credible interval: 5.7% to 13.2%) and R t (ΔR-sq=15.7%, 95% credible interval: 13.6% to 17.7%)., Conclusions: We demonstrated that there was a strong link between dining-out behaviors and the superspreading potential of COVID-19. The methodological innovation suggests a further development using digital mobility proxies of dining-out patterns to generate early warnings of superspreading events., (©Ka Chun Chong, Kehang Li, Zihao Guo, Katherine Min Jia, Eman Yee Man Leung, Shi Zhao, Chi Tim Hung, Carrie Ho Kwan Yam, Tsz Yu Chow, Dong Dong, Huwen Wang, Yuchen Wei, Eng Kiong Yeoh. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 07.03.2023.)

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Multiple introductions of SARS-COV-2 Omicron variant BA.1 and BA.1.1. lineages to Finland were detected in early December 2021. Within 3 weeks, Omicron overtook Delta as the most common variant in the capital region. Sequence analysis demonstrated the emergence and spread through community transmission of a large cluster of BA.1.1 virus.

Recently, the sudden outbreak of the COVID-19 virus caused a major health crisis by affecting masses around the world. The virus, which is known to be highly contagious, has forced the research community and governments to fight the disease and take prompt actions by applying various strategies to keep the numbers under control. These strategies range from imposing strict social distancing measures, isolating infected cases, and enforcing either a partial or a full lockdown, to mathematical modeling and contact-tracing applications. In this work, we survey the current contact-tracing apps and organize them based on underlying technologies such as Bluetooth, Wi-Fi, GPS, geofencing, and Quick Response (QR) codes. We compare the main features of 22 existing applications and highlight each of the pros and cons associated with these different technologies., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

This paper studies the interplay between social distancing and the spread of the COVID-19 disease-a global pandemic that has affected most of the world's population. Our goals are to (1) to observe the correlation between the strictness of social distancing policies and the spread of disease and (2) to determine the optimal adoption level of social distancing policies. The earliest instances of the virus were found in China, and the virus has reached the United States with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, the United Kingdom, Spain, India, Italy, and France. Although it is impossible to stop it, it is possible to slow down its spread to reduce its impact on the society and economy. Governments around the world have deployed various policies to reduce the virus spread in response to the pandemic. To assess the effectiveness of these policies, the system's dynamics of the society needs to be analyzed, which is generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with continuous feedback loops. Because of the challenges with the other methods, we chose agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise in assisting decision-makers in managing the crisis by applying policies such as social distancing, disease testing, contact tracing, home isolation, emergency hospitalization, and travel prevention to reduce infection rates. Based on modeling studies conducted in Imperial College, increasing levels of interventions could slow the spread of disease and infection. We ran the model with six different percentages of social distancing while keeping the other parameters constant. The results show that social distancing affects the spread of COVID-19 significantly, in turn decreasing the spread of disease and infection rates when implemented at higher levels. We also validated these results by using the behavior space tool with ten experiments with varying social distancing levels. We conclude that applying and increasing social distancing policy levels leads to a significant reduction in infection spread and the number of deaths. Both experiments show that infection rates are reduced drastically when social distancing intervention is implemented between 80% to 100%.

We estimate the effects of shelter-in-place (SIP) orders during the first wave of the COVID-19 pandemic. We do not find detectable effects of these policies on disease spread or deaths. We find small but measurable effects on mobility that dissipate over time. And we find small, delayed effects on unemployment. We conduct additional analyses that separately assess the effects of expanding versus withdrawing SIP orders and test whether there are spillover effects in other states. Our results are consistent with prior studies showing that SIP orders have accounted for a relatively small share of the mobility trends and economic disruptions associated with the pandemic. We reanalyze two prior studies purporting to show that SIP orders caused large reductions in disease prevalence, and show that those results are not reliable. Our results do not imply that social distancing behavior by individuals, as distinct from SIP policy, is ineffective., Competing Interests: The authors declare no competing interest.

Since March 2020, many countries around the world have been experiencing a large outbreak of a novel coronavirus (2019-nCoV). Because there is a higher rate of contact between humans in cities with higher population weighted densities, Covid-19 spreads faster in these areas. In this study, we examined the relationship between population weighted density and the spread of Covid-19. Using data from Turkey, we calculated the elasticity of Covid-19 spread with respect to population weighted density to be 0.67 after controlling for other factors. In addition to the density, the proportion of people over 65, the per capita GDP, and the number of total health care workers in each city positively contributed to the case numbers, while education level and temperature had a negative effect. We suggested a policy measure on how to transfer health care workers from different areas to the areas with a possibility of wide spread., Competing Interests: Declaration of Competing Interest The authors report no declarations of interest., (Copyright © 2020. Published by Elsevier B.V.)

Controlling the regional re-emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) after its initial spread in ever-changing personal contact networks and disease landscapes is a challenging task. In a landscape context, contact opportunities within and between populations are changing rapidly as lockdown measures are relaxed and a number of social activities re-activated. Using an individual-based metapopulation model, we explored the efficacy of different control strategies across an urban-rural gradient in Wales, UK. Our model shows that isolation of symptomatic cases or regional lockdowns in response to local outbreaks have limited efficacy unless the overall transmission rate is kept persistently low. Additional isolation of non-symptomatic infected individuals, who may be detected by effective test-and-trace strategies, is pivotal to reducing the overall epidemic size over a wider range of transmission scenarios. We define an 'urban-rural gradient in epidemic size' as a correlation between regional epidemic size and connectivity within the region, with more highly connected urban populations experiencing relatively larger outbreaks. For interventions focused on regional lockdowns, the strength of such gradients in epidemic size increased with higher travel frequencies, indicating a reduced efficacy of the control measure in the urban regions under these conditions. When both non-symptomatic and symptomatic individuals are isolated or regional lockdown strategies are enforced, we further found the strongest urban-rural epidemic gradients at high transmission rates. This effect was reversed for strategies targeted at symptomatic individuals only. Our results emphasize the importance of test-and-trace strategies and maintaining low transmission rates for efficiently controlling SARS-CoV-2 spread, both at landscape scale and in urban areas.

The overall purpose of this paper is to define a new metric on the spreadability of a disease. Herein, we define a variant of the well-known graph-theoretic burning number (BN) metric that we coin the contagion number (CN). We aver that the CN is a better metric to model disease spread than the BN as the CN concentrates on first time infections. This is important because the Centers for Disease Control and Prevention report that COVID-19 reinfections are rare. This paper delineates a novel methodology to solve for the CN of any tree, in polynomial time, which addresses how fast a disease could spread (i.e., a worst-cast analysis). We then employ Monte Carlo simulation to determine the average contagion number (ACN) (i.e., a mostlikely analysis) of how fast a disease would spread. The latter is analyzed on scale-free graphs, which are specifically designed to model human social networks (sociograms). We test our method on some randomly generated scale-free graphs and our findings indicate the CN to be a robust, tractable (the BN is NP-hard even for a tree), and effective disease spread metric for decision makers. The contributions herein advance disease spread understanding and reveal the importance of the underlying network structure. Understanding disease spreadability informs public policy and the associated managerial allocation decisions. [ABSTRACT FROM AUTHOR]

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model. [ABSTRACT FROM AUTHOR]

The new Acute Respiratory Syndrome, COVID-19, has affected the health and the economy worldwide. Therefore, scientists have been looking for ways to understand this disease. In this context, the main objective of this study was the spatialization of COVID-19, thinking in distinguishing areas with high transmissibility yet, verifying if these areas were associated with the elderly population occurrence. The work was delineated, supposing that spatialization could support the decision-making to combat the outbreak and that the same method could be used for spatialization and prevent other diseases. The study area was a municipality near Sao Paulo Metropolis, one of Brazil's main disease epicenters. Using official data and an empirical Bayesian model, we spatialized people infected by region, including older people, obtaining reasonable adjustment. The results showed a weak correlation between regions infected and older adults. Thus, we define a robust model that can support the definition of actions aiming to control the COVID-19 spread. [ABSTRACT FROM AUTHOR]

Lacking a federal policy to control the spread of COVID-19, state governors ordered lockdowns and mask mandates, at different times, generating a massive natural experiment. The authors exploit this natural experiment to address four issues: (1) Were lockdowns effective in reducing infections? (2) What were the costs to consumers? (3) Did lockdowns increase (signaling effect) or reduce (substitution effect) consumers' mask adoption? (4) Did governors' decisions depend on medical science or nonmedical drivers? Analyses via difference-in-differences and generalized synthetic control methods indicate that lockdowns causally reduced infections. Although lockdowns reduced infections by 480 per million consumers per day (equivalent to a reduction of 56%), they reduced customer satisfaction by 2.2%, consumer spending by 7.5%, and gross domestic product by 5.4% and significantly increased unemployment by 2% per average state by the end of the observation period. A counterfactual analysis shows that a nationwide lockdown on March 15, 2020, would have reduced total cases by 60%, whereas the absence of any state lockdowns would have resulted in five times more cases by April 30. The average cost of reducing the number of cases by one new infection was about $28,000 in lower gross domestic product. [ABSTRACT FROM AUTHOR]

The informal economy absorbs a large share (more than 80%) of the labour force in sub-Saharan Africa (SSA). We examine whether a high level of informality tends to exacerbate the spread of the COVID-19. Using publicly available data for a cross-section of 46 SSA, we find evidence that confirms this hypothesis. [ABSTRACT FROM AUTHOR]

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Background: Serial testing for SARS-CoV-2 is recommended to reduce spread of the virus; however, little is known about adherence to recommended testing schedules and reporting practices to health departments. Objective: The Self-Testing for Our Protection from COVID-19 (STOP COVID-19) study aims to examine adherence to a risk-based COVID-19 testing strategy using rapid antigen tests and reporting of test results to health departments. Methods: STOP COVID-19 is a 12-week digital study, facilitated using a smartphone app for testing assistance and reporting. We are recruiting 20,000 participants throughout the United States. Participants are stratified into high- and low-risk groups based on history of COVID-19 infection and vaccination status. High-risk participants are instructed to perform twice-weekly testing for COVID-19 using rapid antigen tests, while low-risk participants test only in the case of symptoms or exposure to COVID-19. All participants complete COVID-19 surveillance surveys, and rapid antigen results are recorded within the smartphone app. Primary outcomes include participant adherence to a risk-based serial testing protocol and percentage of rapid tests reported to health departments. Results: As of February 2022, 3496 participants have enrolled, including 1083 high-risk participants. Out of 13,730 tests completed, participants have reported 13,480 (98.18%, 95% CI 97.9%-98.4%) results to state public health departments with full personal identifying information or anonymously. Among 622 high-risk participants who finished the study period, 35.9% showed high adherence to the study testing protocol. Participants with high adherence reported a higher percentage of test results to the state health department with full identifying information than those in the moderate- or low-adherence groups (high: 71.7%, 95% CI 70.3%-73.1%; moderate: 68.3%, 95% CI 66.0%-70.5%; low: 63.1%, 59.5%-66.6%). Conclusions: Preliminary results from the STOP COVID-19 study provide important insights into rapid antigen test reporting and usage, and can thus inform the use of rapid testing interventions for COVID-19 surveillance. [ABSTRACT FROM AUTHOR]

COVID-19 is currently considered one of the greatest threats to health, safety, and the economy worldwide. An intense movement of international passengers may be critical for either the contingency or the spread of disease in a pandemic situation. Various research on international and national epidemics and their evolution have already been addressed in the medical literature. However, there are few studies to measure the influence of air transport on the proliferation of COVID-19 in Brazil yet. Thus, this article aims to verify if the movement of international air passengers influenced the COVID-19 spreading in Brazil. Therefore, air traffic and its concentration were analyzed, trying to identify its main entrances and volume of passengers. Afer that, statistical regression methods were used to estimate how passengers coming from countries contaminated by COVID-19 influenced the spread of the disease in Brazil. As a main result, it was found that foreign passengers disembarked in Brazil are directly related to the cases of COVID-19 in the country, making air transport a gateway to the virus in Brazil.

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed epidemic modeling at the center of attention of public policymaking. Predicting the severity and speed of transmission of COVID-19 is crucial to resource management and developing strategies to deal with this epidemic. Based on the available data from current and previous outbreaks, many efforts have been made to develop epidemiological models, including statistical models, computer simulations, mathematical representations of the virus and its impacts, and many more. Despite their usefulness, modeling and forecasting the spread of COVID-19 remains a challenge. In this article, we give an overview of the unique features and issues of COVID-19 data and how they impact epidemic modeling and projection. In addition, we illustrate how various models could be connected to each other. Moreover, we provide new data science perspectives on the challenges of COVID-19 forecasting, from data collection, curation, and validation to the limitations of models, as well as the uncertainty of the forecast. Finally, we discuss some data science practices that are crucial to more robust and accurate epidemic forecasting. [ABSTRACT FROM AUTHOR]

Changes in human activities caused by the COVID-19 pandemic can have multiple effects on biodiversity but there is limited knowledge of how this can impact invasive alien species (IAS). Societal measures against the spread of COVID-19 can have both short-term and long-term consequences on IAS. In the short-term, reduced human disturbance on natural habitats can increase the activity of IAS and accelerate their spread. Furthermore, management agencies have reduced control activities, sometimes allowing IAS to thrive, and ongoing monitoring programs have been interrupted, hindering rapid identification and management of biological invasions. Long-term impacts could include global modifications to wildlife trade and increased releases of captive-bred species because of the fear of zoonotic diseases and also greater public awareness of the risks of pathogens being spread among animal populations. Long-term collection and sharing of data are crucial to modulate IAS management during and after the lockdowns. [ABSTRACT FROM AUTHOR]

Epidemiological models have been used extensively to predict disease spread in large populations. Among these models, Susceptible Infectious Exposed Recovered (SEIR) is considered to be a suitable model for COVID-19 spread predictions. However, SEIR in its classical form is unable to quantify the impact of lockdowns. In this work, we introduce a variable in the SEIR system of equations to study the impact of various degrees of social distancing on the spread of the disease. As a case study, we apply our modified SEIR model on the initial spread data available (till April 9, 2020) for the Kingdom of Saudi Arabia (KSA). Our analysis shows that with no lockdown around 2.1 million people might get infected during the peak of spread around 2 months from the date the lockdown was first enforced in KSA (March 25th). On the other hand, with the Kingdom's current strategy of partial lockdowns, the predicted number of infections can be lowered to 0.4 million by September 2020. We further demonstrate that with a stricter level of lockdowns, the COVID-19 curve can be effectively flattened in KSA.