Exploring the impact of mobility restrictions on the COVID-19 spreading through an agent-based approach

Forecasting SARS-CoV-2 epidemic dynamic in Poland with the pDyn agent-based model

We employ pDyn (derived from "pandemics dynamics"), an agent-based epidemiological model, to forecast the fourth wave of the SARS-CoV-2 epidemic, primarily driven by the Delta variant, in Polish society. The model captures spatiotemporal dynamics of the epidemic spread, predicting disease-related states based on pathogen properties and behavioral factors. We assess pDyn's validity, encompassing pathogen variant succession, immunization level, and the proportion of vaccinated among confirmed cases. We evaluate its predictive capacity for pandemic dynamics, including wave peak timing, magnitude, and duration for confirmed cases, hospitalizations, ICU admissions, and deaths, nationally and regionally in Poland. Validation involves comparing pDyn's estimates with real-world data (excluding data used for calibration) to evaluate whether pDyn accurately reproduced the epidemic dynamics up to the simulation time. To assess the accuracy of pDyn's predictions, we compared simulation results with real-world data acquired after the simulation date. The findings affirm pDyn's accuracy in forecasting and enhancing our understanding of epidemic mechanisms.

Factors Influencing Occupational Stress of State Security Forces During the COVID-19 Pandemic: A Scoping Review

Objective

The aim of this review was to assess the factors influencing the occupational stress of state security forces during the COVID-19 pandemic.

Methods

We conducted a scoping review using the Pubmed, Scopus, and Web of Science databases, adhering to the PRISMA statement standards and the guidelines for narrative syntheses.

Results

We included a total of 26 studies. The prevalence of stress varied from 22% to 87.2%. Factors that may have influenced the stress levels of police officers during the pandemic include not having basic personal protective equipment, having little or no rest periods between tasks, long working hours, fear of contagion to themselves or others, pressure to maintain law and order, emotion regulation and preparedness, sex, marital status, work experience, age, presence of chronic underlying illnesses, family-work conflict, lack of psychological support, and others. The long working hours, the fear of infecting themselves or others, the pressure to maintain law and order, sex, and age are the six main factors evaluated for more studies.

Conclusion

Organisational, situational, and personal factors may have influenced the stress levels of police officers during the pandemic, and measures need to be taken to minimise their impact.

Exploring the Role of Genetic and Environmental Features in Colorectal Cancer Development: An Agent-Based Approach

The complexity of issues in cancer research has led to the introduction of powerful computational tools to help experimental in vivo and in vitro methods. These tools, which typically focus on studying cell behavior and dynamic cell populations, range from systems of differential equations that are solved numerically to lattice models and agent-based simulations. In particular, agent-based models (ABMs) are increasingly used due to their ability to incorporate multi-scale features, ranging from the individual to the population level. This approach allows for the combination of statistically aggregated assumptions with individual heterogeneity. In this work, we present an ABM that simulates tumor progression in a colonic crypt, to provide an experimental in silico environment for testing results achieved in traditional laboratory research and developing alternative scenarios of tumor development. The model also allows some speculations about causal relationships in biologically inspired systems.

How did the COVID-19 pandemic affect antibiotic consumption within humanitarian emergencies? Results from five humanitarian contexts

Introduction

Both high- and low-income countries reported increased antibiotic consumption among COVID-19 patients during the first months of the pandemic. To date, however, no studies have examined changes in antibiotic consumption during the COVID-19 pandemic within humanitarian emergency contexts.

Method

Data was collected by Médecins Sans Frontières (MSF) for the years 2018-2021 across the following humanitarian settings: Afghanistan (Lashkar Gah), Bangladesh (Kutupalong), the Democratic Republic of Congo (Mweso and Baraka), and South Sudan (Bentiu). Inpatient and outpatient antibiotic consumption was calculated as Daily Defined Dose (DDD) per 1000 inhabitants per day, as per the World Health Organisation's (WHO) Collaborating Centre for Drug Statistics Methodology. Interrupted time series (ITS) analysis, using an autoregressive integrated moving average (ARIMA) model was used to analyse retrospective monthly antibiotic consumption. The impact of COVID-19 pandemic was evaluated as total antibiotic consumption and according to WHO Access, Watch, Reserve (AWaRe) group classifications within each humanitarian setting.

Results

The COVID-19 pandemic had no statistically significant impact on total antibiotic consumption in South Sudan (Bentiu) and Bangladesh (Kutupalong). Similarly, the pandemic had no impact on total antibiotic consumption in DR Congo (Baraka), despite an initial 0.27% (estimate=.274, p-value=0.006) increase in March 2020 driven by Access group antibiotics. Meanwhile, total antibiotic consumption in DR Congo (Mweso) and Afghanistan (Lashkar Gah) declined by 0.74% (estimate = -.744, p = 0.003) and 0.26% (estimate = -.26, p < 0.001), respectively with the COVID-19 pandemic.

Conclusion

Further studies are required to investigate what may have contributed to these results.

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

Introduction

The onset of the COVID-19 pandemic has placed a significant burden on healthcare systems worldwide, particularly in sub-Saharan regions where healthcare resources are limited. The transmission of SARS-CoV-2 is facilitated by the movement of people from place to place. Therefore, implementing measures that restrict movement of people and contacts is crucial in controlling the spread of the disease. Following the identification of the first COVID-19 case in Rwanda, the government implemented stringent measures, including a complete nationwide lockdown, border closures, curfews, reduced capacity in public transportation and businesses, and mandatory testing. This study aims to assess epidemiological trends in COVID-19 cases in relation to changes in population mobility within the public transportation system.

Methods

A descriptive analysis using publicly available data on COVID-19 epidemiological indicators (cases, deaths, vaccinations, and stringency index) and mobility data was conducted.

Results

The results reveal a strong correlation between mobility in public transportation and other activities, underscoring Rwanda's reliance on its public transportation system. The study also identifies a pattern where increases in transit station mobility preceded spikes in COVID-19 cases, suggesting that the subsequent rise in public transportation usage may contribute to higher infection rates.

Discussion

Therefore, this study emphasizes the importance of ongoing vigilance and regulatory measures regarding public transportation during infectious disease outbreaks.

Uncertain SEIAR system dynamics modeling for improved community health management of respiratory virus diseases: A COVID-19 case study

The study investigates the significance of employing advanced systemic models in community health management, with a focus on COVID-19 as a respiratory virus. Through the development of a system dynamics model integrating an uncertain SEIAR model, our research addresses the critical issue of parameter uncertainty using Ensemble Kalman Filter (EnKF) and Metropolis-Hastings (MH) algorithms. We present a case study using real COVID-19 outbreaks in Iran, offering insights into effective outbreak control scenarios and considering the global impact of respiratory viruses. The research yields distinctive results, showcasing variable mortality rates (40,500 to 436,500) across scenarios in Iran. Model accuracy is rigorously evaluated using the Normalized Root-Mean-Square Deviation (NRMSD) for new cases, deaths, and recoveries (0.2 %, 1.2 %, and 0.6 % respectively). The outcomes not only contribute to the existing body of knowledge but also offer practical implications for healthcare policies, economic considerations, and sensitivity assessments related to respiratory diseases. This study stands out from others in its approach to modeling and addressing uncertainty within a system dynamics framework. The integration of EnKF and MH algorithms provides a nuanced understanding of parameter uncertainty, adding a layer of sophistication to the analysis. The application of the model to real-world COVID-19 outbreaks in Iran further enhances the study's relevance and applicability. In conclusion, the research introduces an uncertain SEIAR system dynamics model with unique contributions to policymaking, economic considerations, and sensitivity assessments for respiratory diseases. The outcomes and insights derived from the study not only advance our understanding of disease dynamics but also provide actionable information for effective public health management.

Connected in health: Place-to-place commuting networks and COVID-19 spillovers

Biweekly county COVID-19 data were linked with Longitudinal Employer-Household Dynamics data to analyze population risk exposures enabled by pre-pandemic, country-wide commuter networks. Results from fixed-effects, spatial, and computational statistical approaches showed that commuting network exposure to COVID-19 predicted an area's COVID-19 cases and deaths, indicating spillovers. Commuting spillovers between counties were independent from geographic contiguity, pandemic-time mobility, or social media ties. Results suggest that commuting connections form enduring social linkages with effects on health that can withstand mobility disruptions. Findings contribute to a growing relational view of health and place, with implications for neighborhood effects research and place-based policies.

A systematic review of the impacts of the coronavirus crisis on urban transport: Key lessons learned and prospects for future cities

The COVID-19 pandemic continues to have a significant impact on the transport sector worldwide. Lockdown and physical distancing requirements continue to be enforced in many cities leading to severe travel restrictions and travel demand reduction to limit the spread of the disease. This article provides bibliometric evidence-based insights into how the pandemic has exposed the vulnerability of established public transport systems and shared mobility solutions. It shows how some transport interventions can accelerate the shift to sustainable urban mobility practices such as micro-mobility and active transport. To accomplish this, the article examines recent studies (244 publications) from the Scopus database using a rigorous systematic literature review approach covering the period from January 2020 to February 2021. Importantly, the mapping of bibliographic coupling and co-citation analysis showed four heterogeneous clusters representing research efforts into "environment", "travel behavior and mode choice", "public transport", and "interventions". Inductive reasoning is used to analyze the disruptions that cities have encountered worldwide, the rapid interventions that were put in place, the aftershocks and the short and long-term impacts. Finally, the paper summarizes the lessons learned and opportunities ahead, and the challenges that must be overcome. The article also outlines pathways to build on the momentum of sustainable practices as part of a holistic approach for enabling resilient transport solutions for the new urban world.