Urban Determinants of COVID-19 Spread: a Comparative Study across Three Cities in New York State.

The ongoing pandemic is laying bare dramatic differences in the spread of COVID-19 across seemingly similar urban environments. Identifying the urban determinants that underlie these differences is an open research question, which can contribute to more epidemiologically resilient cities, optimized testing and detection strategies, and effective immunization efforts. Here, we perform a computational analysis of COVID-19 spread in three cities of similar size in New York State (Colonie, New Rochelle, and Utica) aiming to isolate urban determinants of infections and deaths. We develop detailed digital representations of the cities and simulate COVID-19 spread using a complex agent-based model, taking into account differences in spatial layout, mobility, demographics, and occupational structure of the population. By critically comparing pandemic outcomes across the three cities under equivalent initial conditions, we provide compelling evidence in favor of the central role of hospitals. Specifically, with highly efficacious testing and detection, the number and capacity of hospitals, as well as the extent of vaccination of hospital employees are key determinants of COVID-19 spread. The modulating role of these determinants is reduced at lower efficacy of testing and detection, so that the pandemic outcome becomes equivalent across the three cities.

Detecting switching leadership in collective motion.

Detecting causal relationships in complex systems from the time series of the individual units is a pressing area of research that has attracted the interest of a broad community. As an open area of study, this entails the development of methodologies to unravel causal relationships that evolve over time, such as switching of leader-follower roles in animal groups. Here, we augment the information theoretic measure of transfer entropy to establish a fitness function suitable for optimal partitioning of time series data to robustly detect leadership switches in collective behavior. The fitness function computes the information outflow from any agent in the group and rewards large sample sizes while normalizing with respect to available information. Our results indicate that for information-rich interactions, leadership switches within a group can be detected over relatively short time durations, with more than 90% accuracy. On a real soccer dataset, instances of leadership counted using the proposed approach are interestingly correlated with ball possession.

Extracting Interactions between Flying Bat Pairs Using Model-Free Methods.

Social animals exhibit collective behavior whereby they negotiate to reach an agreement, such as the coordination of group motion. Bats are unique among most social animals, since they use active sensory echolocation by emitting ultrasonic waves and sensing echoes to navigate. Bats' use of active sensing may result in acoustic interference from peers, driving different behavior when they fly together rather than alone. The present study explores quantitative methods that can be used to understand whether bats flying in pairs move independently of each other or interact. The study used field data from bats in flight and is based on the assumption that interactions between two bats are evidenced in their flight patterns. To quantify pairwise interaction, we defined the strength of coupling using model-free methods from dynamical systems and information theory. We used a control condition to eliminate similarities in flight path due to environmental geometry. Our research question is whether these data-driven methods identify directed coupling between bats from their flight paths and, if so, whether the results are consistent between methods. Results demonstrate evidence of information exchange between flying bat pairs, and, in particular, we find significant evidence of rear-to-front coupling in bats' turning behavior when they fly in the absence of obstacles.

Classification of collective behavior: a comparison of tracking and machine learning methods to study the effect of ambient light on fish shoaling.

Traditional approaches for the analysis of collective behavior entail digitizing the position of each individual, followed by evaluation of pertinent group observables, such as cohesion and polarization. Machine learning may enable considerable advancements in this area by affording the classification of these observables directly from images. While such methods have been successfully implemented in the classification of individual behavior, their potential in the study collective behavior is largely untested. In this paper, we compare three methods for the analysis of collective behavior: simple tracking (ST) without resolving occlusions, machine learning with real data (MLR), and machine learning with synthetic data (MLS). These methods are evaluated on videos recorded from an experiment studying the effect of ambient light on the shoaling tendency of Giant danios. In particular, we compute average nearest-neighbor distance (ANND) and polarization using the three methods and compare the values with manually-verified ground-truth data. To further assess possible dependence on sampling rate for computing ANND, the comparison is also performed at a low frame rate. Results show that while ST is the most accurate at higher frame rate for both ANND and polarization, at low frame rate for ANND there is no significant difference in accuracy between the three methods. In terms of computational speed, MLR and MLS take significantly less time to process an image, with MLS better addressing constraints related to generation of training data. Finally, all methods are able to successfully detect a significant difference in ANND as the ambient light intensity is varied irrespective of the direction of intensity change.

Sociality modulates the effects of ethanol in zebra fish.

BACKGROUND: The complex social behavior exhibited by zebra fish is often leveraged in preclinical studies to investigate whether and how psychoactive compounds modulate inter individual interactions. Due to theoretical and methodological constraints, previous studies on the effects of ethanol (EtOH) on social behavior focused on homogeneous groups in which all individuals were treated, thereby limiting the possibility of isolating all the intervening variables. METHODS: To identify how a social group affects the individual response to EtOH, we quantified the behavior of a single treated individual (acute 0.00, 0.25, 0.50, and 1.00% concentration/volume) swimming together with a group of untreated subjects or alone. A novel in-house-developed automated tracking system was utilized to extract the trajectories of each subject and analyze individual and social behavior. Specifically, we characterized the locomotion of each individual, the cohesion and degree of alignment of the group of untreated subjects, and the interaction between treated and untreated subjects. RESULTS: Individual response to high EtOH concentrations varied depending on the presence or absence of conspecifics. Specifically, EtOH-exposed subjects swam faster when group-tested than in isolation. Remarkably, the presence of the exposed individual substantially influenced the behavior of the untreated subjects. Thus, untreated subjects swam faster when the treated individual was exposed to intermediate EtOH concentrations, without varying their cohesion and degree of alignment. No change in the distance between treated and untreated subjects was found; however, the likelihood that the swimming direction of the treated individual anticipated the response of the group was influenced by EtOH concentration. CONCLUSIONS: Our results demonstrate the feasibility of exposing a single individual to EtOH and test it together with untreated subjects. This approach has the potential to unravel the social determinants of individual response to alcohol, by enabling us to dissociate EtOH exposure from sociality.

Analysis and classification of collective behavior using generative modeling and nonlinear manifold learning.

In this paper, we build a framework for the analysis and classification of collective behavior using methods from generative modeling and nonlinear manifold learning. We represent an animal group with a set of finite-sized particles and vary known features of the group structure and motion via a class of generative models to position each particle on a two-dimensional plane. Particle positions are then mapped onto training images that are processed to emphasize the features of interest and match attainable far-field videos of real animal groups. The training images serve as templates of recognizable patterns of collective behavior and are compactly represented in a low-dimensional space called embedding manifold. Two mappings from the manifold are derived: the manifold-to-image mapping serves to reconstruct new and unseen images of the group and the manifold-to-feature mapping allows frame-by-frame classification of raw video. We validate the combined framework on datasets of growing level of complexity. Specifically, we classify artificial images from the generative model, interacting self-propelled particle model, and raw overhead videos of schooling fish obtained from the literature.

The dance of male Anopheles gambiae in wild mating swarms.

An important element of mating in the malaria vector Anopheles gambiae Giles in nature is the crepuscular mating aggregation (swarm) composed almost entirely of males, where most coupling and insemination is generally believed to occur. In this study, we mathematically characterize the oscillatory movement of male An. gambiae in terms of an established individual-based mechanistic model that parameterizes the attraction of a mosquito toward the center of the swarm using the natural frequency of oscillation and the resistance to its motion, characterized by the damping ratio. Using three-dimensional trajectory data of ten wild mosquito swarms filmed in Mali, Africa, we show two new results for low and moderate wind conditions, and indicate how these results may vary in high wind. First, we show that in low and moderate wind the vertical component of the mosquito motion has a lower frequency of oscillation and higher damping ratio than horizontal motion. In high wind, the vertical and horizontal motions are similar to one another and the natural frequencies are higher than in low and moderate wind. Second, we show that the predicted average disagreement in the direction of motion of swarming mosquitoes moving randomly is greater than the average disagreement we observed between each mosquito and its three closest neighbors, with the smallest level of disagreement occurring for the nearest neighbor in seven out of 10 swarms. The alignment of the direction of motion between nearest neighbors is the highest in high wind. This result provides evidence for flight-path coordination between swarming male mosquitoes.

Exploring a COVID-19 Endemic Scenario: High-Resolution Agent-Based Modeling of Multiple Variants.

Our efforts as a society to combat the ongoing COVID-19 pandemic are continuously challenged by the emergence of new variants. These variants can be more infectious than existing strains and many of them are also more resistant to available vaccines. The appearance of these new variants cause new surges of infections, exacerbated by infrastructural difficulties, such as shortages of medical personnel or test kits. In this work, a high-resolution computational framework for modeling the simultaneous spread of two COVID-19 variants: a widely spread base variant and a new one, is established. The computational framework consists of a detailed database of a representative U.S. town and a high-resolution agent-based model that uses the Omicron variant as the base variant and offers flexibility in the incorporation of new variants. The results suggest that the spread of new variants can be contained with highly efficacious tests and mild loss of vaccine protection. However, the aggressiveness of the ongoing Omicron variant and the current waning vaccine immunity point to an endemic phase of COVID-19, in which multiple variants will coexist and residents continue to suffer from infections.

Predicting the Effects of Waning Vaccine Immunity Against COVID-19 through High-Resolution Agent-Based Modeling.

The potential waning of the vaccination immunity to COVID-19 could pose threats to public health, as it is tenable that the timing of such waning would synchronize with the near-complete restoration of normalcy. Should also testing be relaxed, a resurgent COVID-19 wave in winter 2021/2022 might be witnessed. In response to this risk, an additional vaccine dose, the booster shot, is being administered worldwide. A projected study with an outlook of 6 months explores the interplay between the rate at which boosters are distributed and the extent to which testing practices are implemented, using a highly granular agent-based model tuned on a medium-sized US town. Theoretical projections indicate that the administration of boosters at the rate at which the vaccine is currently administered could yield a severe resurgence of the pandemic. Projections suggest that the peak levels of mid-spring 2021 in the vaccination rate may prevent such a scenario to occur, although exact agreement between observations and projections should not be expected due to the continuously evolving nature of the pandemic. This study highlights the importance of testing, especially to detect asymptomatic individuals in the near future, as the release of the booster reaches full speed.

Activity-driven network modeling and control of the spread of two concurrent epidemic strains.

The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain-phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible-exposed-infectious-removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time.

COVID-19 Modeling: High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town (Adv. Theory Simul. 3/2021).

Since 2020, COVID-19 has wreaked havoc across the planet, taking the lives of more than one million people. The uncertainty and novelty of the current conditions call for the development of theory and simulation tools that can support effective policy-making. In article number 2000277, Agnieszka Truszkowska, Maurizio Porfiri, and co-workers report a high-resolution, agent-based modeling platform to simulate the spreading of COVID-19 in the city of New Rochelle, NY-one of the first outbreaks registered in the United States. Image by Anna Sawulska, Agnieszka Truszkowska, Beata Truszkowska, and Maurizio Porfiri.

Designing the Safe Reopening of US Towns Through High-Resolution Agent-Based Modeling.

As COVID-19 vaccine is being rolled out in the US, public health authorities are gradually reopening the economy. To date, there is no consensus on a common approach among local authorities. Here, a high-resolution agent-based model is proposed to examine the interplay between the increased immunity afforded by the vaccine roll-out and the transmission risks associated with reopening efforts. The model faithfully reproduces the demographics, spatial layout, and mobility patterns of the town of New Rochelle, NY - representative of the urban fabric of the US. Model predictions warrant caution in the reopening under the current rate at which people are being vaccinated, whereby increasing access to social gatherings in leisure locations and households at a 1% daily rate can lead to a 28% increase in the fatality rate within the next three months. The vaccine roll-out plays a crucial role on the safety of reopening: doubling the current vaccination rate is predicted to be sufficient for safe, rapid reopening.

High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town.

Amid the ongoing COVID-19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of "what-if" scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent-based modeling platform is proposed to simulate the spreading of COVID-19 in small towns and cities, with a single-individual resolution. The platform is validated on real data from New Rochelle, NY-one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches-in hospitals or drive-through facilities-and vaccination strategies that could prioritize vulnerable groups. Decision-making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features.

Drawing power of virtual crowds.

In 1969, social psychologist Milgram and his colleagues conducted an experiment on a busy city street where passers-by witnessed a set of actors spontaneously looking up towards a building. The experiment showed that the crowd's propensity to mimic the actor's gaze increased with the number of actors that looked up. This form of behavioural contagion is found in many social organisms and is central to how information travels through large groups. With the advancement of virtual reality and its continued application towards understanding human response to crowd behaviour, it remains to be verified if behavioural contagion occurs in walkable virtual environments, and how it compares with results from real-world experiments. In this study, we adapt Milgram's experiment for virtual environments and use it to reproduce behavioural contagion. Specifically, we construct a replica of an indoor location and combine two established pedestrian motion models to create an interactive crowd of 60 virtual characters that walk through the indoor location. The stimulus group comprised a subset of the characters who look up at a random time as the participants explore the virtual environment. Our results show that the probability of looking up by a participant is dependent on the size of the stimulus group saturating to near certainty when three or more characters look up. The role of stimulus size was also evident when participant actions were compared with survey responses which showed that more participants selected to not look up even though they saw characters redirect their gaze upwards when the size of the stimulus group was small. Participants also spent more time looking up and exhibited frequent head turns with a larger stimulus group. Results from this study provide evidence that behavioural contagion can be triggered in the virtual environment, and can be used to build and test complex hypotheses for understanding human behaviour in a variety of crowd scenarios.

In-silico experiments of zebrafish behaviour: modeling swimming in three dimensions.

Zebrafish is fast becoming a species of choice in biomedical research for the investigation of functional and dysfunctional processes coupled with their genetic and pharmacological modulation. As with mammals, experimentation with zebrafish constitutes a complicated ethical issue that calls for the exploration of alternative testing methods to reduce the number of subjects, refine experimental designs, and replace live animals. Inspired by the demonstrated advantages of computational studies in other life science domains, we establish an authentic data-driven modelling framework to simulate zebrafish swimming in three dimensions. The model encapsulates burst-and-coast swimming style, speed modulation, and wall interaction, laying the foundations for in-silico experiments of zebrafish behaviour. Through computational studies, we demonstrate the ability of the model to replicate common ethological observables such as speed and spatial preference, and anticipate experimental observations on the correlation between tank dimensions on zebrafish behaviour. Reaching to other experimental paradigms, our framework is expected to contribute to a reduction in animal use and suffering.

Three-dimensional scoring of zebrafish behavior unveils biological phenomena hidden by two-dimensional analyses.

The study of zebrafish behavior represents a cornerstone upon which basic researchers promise to advance knowledge in life sciences. Although zebrafish swim in a three-dimensional (3D) space, their behavior in the lab is almost exclusively scored in two dimensions, whereby zebrafish are recorded using a single camera providing 2D videos. Whether this dimensional reduction preserves the reliability of data has not been addressed. Here we show that, compared to a 3D observation, 2D data are flawed by over-reporting and under-reporting of locomotory differences. Specifically, we first reconstructed 3D trajectories through the integration of synchronous information derived from two cameras, and then compared them with the original 2D views in classical experimental paradigms assessing shoaling tendency, fear, anxiety, and general locomotion. Our results suggest that traditional behavioral scoring of individual zebrafish performed in 2D may undermine data integrity, thereby requiring a general reconsideration of scoring zebrafish behavior to incorporate a 3D approach. We then demonstrate that, compared to 2D, a 3D approach requires a reduced number of subjects to achieve the same degree of validity. We anticipate these findings to largely benefit animal welfare by reducing the number of experimental subjects, without affecting statistical power.

Model-free information-theoretic approach to infer leadership in pairs of zebrafish.

Collective behavior affords several advantages to fish in avoiding predators, foraging, mating, and swimming. Although fish schools have been traditionally considered egalitarian superorganisms, a number of empirical observations suggest the emergence of leadership in gregarious groups. Detecting and classifying leader-follower relationships is central to elucidate the behavioral and physiological causes of leadership and understand its consequences. Here, we demonstrate an information-theoretic approach to infer leadership from positional data of fish swimming. In this framework, we measure social interactions between fish pairs through the mathematical construct of transfer entropy, which quantifies the predictive power of a time series to anticipate another, possibly coupled, time series. We focus on the zebrafish model organism, which is rapidly emerging as a species of choice in preclinical research for its genetic similarity to humans and reduced neurobiological complexity with respect to mammals. To overcome experimental confounds and generate test data sets on which we can thoroughly assess our approach, we adapt and calibrate a data-driven stochastic model of zebrafish motion for the simulation of a coupled dynamical system of zebrafish pairs. In this synthetic data set, the extent and direction of the coupling between the fish are systematically varied across a wide parameter range to demonstrate the accuracy and reliability of transfer entropy in inferring leadership. Our approach is expected to aid in the analysis of collective behavior, providing a data-driven perspective to understand social interactions.

Zebrafish response to 3D printed shoals of conspecifics: the effect of body size.

Recent progress in three-dimensional (3D) printing technology has enabled rapid prototyping of complex models at a limited cost. Virtually every research laboratory has access to a 3D printer, which can assist in the design and implementation of hypothesis-driven studies on animal behavior. In this study, we explore the possibility of using 3D printing technology to understand the role of body size in the social behavior of the zebrafish model organism. In a dichotomous preference test, we study the behavioral response of zebrafish to shoals of 3D printed replicas of varying size. We systematically vary the size of each replica without altering the coloration, aspect ratio, and stripe patterns, which are all selected to closely mimic zebrafish morphophysiology. The replicas are actuated through a robotic manipulator, mimicking the natural motion of live subjects. Zebrafish preference is assessed by scoring the time spent in the vicinity of the shoal of replicas, and the information theoretic construct of transfer entropy is used to further elucidate the influence of the replicas on zebrafish motion. Our results demonstrate that zebrafish adjust their behavior in response to variations in the size of the replicas. Subjects exhibit an avoidance reaction for larger replicas, and they are attracted toward and influenced by smaller replicas. The approach presented in this study, integrating 3D printing technology, robotics, and information theory, is expected to significantly aid preclinical research on zebrafish behavior.

Effect of acute ethanol administration on zebrafish tail-beat motion.

Zebrafish is becoming a species of choice in neurobiological and behavioral studies of alcohol-related disorders. In these efforts, the activity of adult zebrafish is typically quantified using indirect activity measures that are either scored manually or identified automatically from the fish trajectory. The analysis of such activity measures has produced important insight into the effect of acute ethanol exposure on individual and social behavior of this vertebrate species. Here, we leverage a recently developed tracking algorithm that reconstructs fish body shape to investigate the effect of acute ethanol administration on zebrafish tail-beat motion in terms of amplitude and frequency. Our results demonstrate a significant effect of ethanol on the tail-beat amplitude as well as the tail-beat frequency, both of which were found to robustly decrease for high ethanol concentrations. Such a direct measurement of zebrafish motor functions is in agreement with evidence based on indirect activity measures, offering a complementary perspective in behavioral screening.

A jump persistent turning walker to model zebrafish locomotion.

Zebrafish are gaining momentum as a laboratory animal species for the investigation of several functional and dysfunctional biological processes. Mathematical models of zebrafish behaviour are expected to considerably aid in the design of hypothesis-driven studies by enabling preliminary in silico tests that can be used to infer possible experimental outcomes without the use of zebrafish. This study is motivated by observations of sudden, drastic changes in zebrafish locomotion in the form of large deviations in turn rate. We demonstrate that such deviations can be captured through a stochastic mean reverting jump diffusion model, a process that is commonly used in financial engineering to describe large changes in the price of an asset. The jump process-based model is validated on trajectory data of adult subjects swimming in a shallow circular tank obtained from an overhead camera. Through statistical comparison of the empirical distribution of the turn rate against theoretical predictions, we demonstrate the feasibility of describing zebrafish as a jump persistent turning walker. The critical role of the jump term is assessed through comparison with a simplified mean reversion diffusion model, which does not allow for describing the heavy-tailed distributions observed in the fish turn rate.