The Overlooked Vector: Rethinking the Human Factor in Disease Transmission

Editor's evaluation: Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data

Decision letter: Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data

The role of human behaviour in the dynamics of infectious diseases cannot be underestimated. A clear understanding of how human behaviour influences the spread of infectious diseases is critical in establishing and designing control measures. To study the role that human behaviour plays in Ebola disease dynamics, in this paper, we design an Ebola virus disease model with disease transmission dynamics based on a new exponential nonlinear incidence function. This new incidence function that captures the reduction in disease transmission due to human behaviour innovatively considers the efficacy and the speed of behaviour change. The model's steady states are determined and suitable Lyapunov functions are built. The proofs of the global stability of equilibrium points are presented. To demonstrate the utility of the model, we fit the model to Ebola virus disease data from Liberia and Sierra Leone. The results which are comparable to existing findings from the outbreak of 2014 − 2016 show a better fit when the efficacy and the speed of behaviour change are higher. A rapid and efficacious behaviour change as a control measure to rapidly control an Ebola virus disease epidemic is advocated. Consequently, this model has implications for the management and control of future Ebola virus disease outbreaks.

The health and economic impacts of infectious diseases such as COVID-19 affect all levels of a community from the individual to the governing bodies. However, the spread of an infectious disease is intricately linked to the behaviour of the people within a community since crowd behaviour affects individual human behaviour, while human behaviour affects infection spread, and infection spread affects human behaviour. Capturing these feedback loops of behaviour and infection is a well-known challenge in infectious disease modelling. Here, we investigate the interface of behavioural science theory and infectious disease modelling to explore behaviour and disease (BaD) transmission models. Specifically, we incorporate a visible protective behaviour into the susceptible-infectious-recovered-susceptible (SIRS) transmission model using the socio-psychological Health Belief Model to motivate behavioural uptake and abandonment. We characterize the mathematical thresholds for BaD emergence in the BaD SIRS model and the feasible steady states. We also explore, under different infectious disease scenarios, the effects of a fully protective behaviour on long-term disease prevalence in a community, and describe how BaD modelling can investigate non-pharmaceutical interventions that target-specific components of the Health Belief Model. This transdisciplinary BaD modelling approach may reduce the health and economic impacts of future epidemics.

Modeling to inform economy‐wide pandemic policy: Bringing epidemiologists and economists together

Nosocomial infections cause considerable morbidity and mortality. Healthcare workers (HCWs) may serve as vectors of many infectious diseases, many of which are not often primarily considered as healthcare-associated. The probability of pathogen transmission to patients depends on several factors, such as the characteristics of a pathogen, HCW and patient. Pathogens with high transmission potential from HCWs to patients include norovirus, respiratory infections, measles and influenza. In contrast, human immunodeficiency virus (HIV) and viral hepatitis are unlikely to be transferred. The prevention of HCW-associated transmission of pathogens include systematic vaccinations towards preventable diseases, continuous education, hand hygiene surveillance, active feedback and adequate staff resources.

Complex dynamics of epidemic models on adaptive networks

To comprehend the dynamics of infectious disease transmission, it is imperative to incorporate human protective behavior into models of disease spreading. While models exist for both infectious disease and behavior dynamics independently, the integration of these aspects has yet to yield a cohesive body of literature. Such an integration is crucial for gaining insights into phenomena like the rise of infodemics, the polarization of opinions regarding vaccines, and the dissemination of conspiracy theories during a pandemic. We make a threefold contribution. First, we introduce a framework todescribemodels coupling infectious disease and behavior dynamics, delineating four distinct update functions. Reviewing existing literature, we highlight a substantial diversity in the implementation of each update function. This variation, coupled with a dearth of model comparisons, renders the literature hardly informative for researchers seeking to develop models tailored to specific populations, infectious diseases, and forms of protection. Second, we advocate an approach tocomparingmodels' assumptions about human behavior, the model aspect characterized by the strongest disagreement. Rather than representing the psychological complexity of decision-making, we show that 'influence-response functions' allow one to identify which model differences generate different disease dynamics and which do not, guiding both model development and empirical research testing model assumptions. Third, we propose recommendations for future modeling endeavors and empirical research aimed atselectingmodels of coupled infectious disease and behavior dynamics. We underscore the importance of incorporating empirical approaches from the social sciences to propel the literature forward.

Movement dynamics: reduced dengue cases during the COVID-19 pandemic

Epidemic models are used to understand the dynamics of disease transmission and explore the possible measures for preventing the spread of infection in the population. Disease transmission is intrinsically random and severely affected by environmental factors. We investigated a stochastic population model of the susceptible-infected-susceptible (SIS) type, in which infection spreads via both vertical and horizontal transmission routes. To incorporate stochasticity to the system, white multiplicative noise was taken into account in the horizontal disease transmission term. We proved that noise intensity, disease transmission, and recovery rates are potential routes for eradicating the disease. Furthermore, the parasite population reduces its fitness for some fixed noise if the relative fecundity of infected hosts and the disease transmission are low. However, if either of these is increased, it observes enhanced fitness. A simulation study illustrated the system's analytically dynamic properties and provided different insights. A case study for the imperfect vertical and horizontal infection transmission is also presented, supporting some of our observed theoretical results.

Linking the disease transmission to information dissemination dynamics: An insight from a multi-scale model study

Within military settings, soldiers are arranged to eat together in a self-service manner for every meal. The process of food selection and consumption often leads to close contact amongst individuals, heightening the risk of respiratory infectious disease transmission. To comprehend the transmission dynamics during communal dining, we have conducted an in-depth epidemiological investigation. The dining process was divided into two phases: lining up for food and dining at designated seats. Soldiers were randomly split into two queues and entered the food selection area from the same side. The movements of the soldiers dynamically altered both the queues and the contact duration and distance between susceptible individuals and infection sources. We utilized a random computer model using MATLAB software, with the individual as the unit of study, for simulating the food selection process. This model quantitatively analyzed the dynamic process of disease transmission within the queues due to the dispersion of small pathogen-carrying particles. Our findings indicate that close interactions between individuals during picking up food can result in the persistent transmission of airborne infectious diseases. Implementing measures such as discontinuing buffet-style meals, implementing staggered dining schedules, and mandating mask-wearing during food collection could help control disease transmission during an epidemic. This study demonstrates that the individual-based model can simulate the dynamic process of disease transmission through complex behavioral activities and is more suitable for conducting research on the dynamics of infectious diseases in small-scale populations. Since this is a simulation conducted in a virtual scenario, the results of the model still need to be verified through field investigations. Nevertheless, once robust outbreak investigation studies have yielded reliable model parameters, these parameters can be adapted to this and other similar situations to demonstrate the potential for transmission.

International Attention for Zoonotic Infections

The COVID-19 pandemic is a global indoor air crisis that should lead to change: A message commemorating 30 years of Indoor Air.

Historical records of childhood disease incidence reveal complex dynamics. For measles, a simple model has indicated that epidemic patterns represent attractors of a nonlinear dynamic system and that transitions between different attractors are driven by slow changes in birth rates and vaccination levels. The same analysis can explain the main features of chickenpox dynamics, but fails for rubella and whooping cough. We show that an additional (perturbative) analysis of the model, together with knowledge of the population size in question, can account for all the observed incidence patterns by predicting how stochastically sustained transient dynamics should be manifested in these systems.