This study presents an integrated approach to understanding epidemic dynamics through a stochastic spatio-temporal simulation model on a multiplex network, blending physical and informational layers. The physical layer maps the geographic movement of individuals, while the information layer tracks the spread of knowledge and health behavior via social interactions. We explore the interplay between physical mobility, information flow, and epidemic outcomes by simulating disease spread within this dual-structured network. Our model employs stochastic elements to mirror human behavior, mobility, and information dissemination uncertainties. Through simulations, we assess the impact of network structure, mobility patterns, and information spread speed on epidemic dynamics. The findings highlight the crucial role of effective communication in curbing disease transmission, even in highly mobile societies. Additionally, our agent-based simulation allows for real-time scenario analysis through a user interface, offering insights into leveraging physical and informational networks for epidemic control. This research sheds light on designing strategic interventions in complex social systems to manage disease outbreaks.

The recent COVID-19 pandemic underscores the significance of early-stage non-pharmacological intervention strategies. The widespread use of masks and the systematic implementation of contact tracing strategies provide a potentially equally effective and socially less impactful alternative to more conventional approaches, such as large-scale mobility restrictions. However, manual contact tracing faces strong limitations in accessing the network of contacts, and the scalability of currently implemented protocols for smartphone-based digital contact tracing becomes impractical during the rapid expansion phases of the outbreaks, due to the surge in exposure notifications and associated tests. A substantial improvement in digital contact tracing can be obtained through the integration of probabilistic techniques for risk assessment that can more effectively guide the allocation of new diagnostic tests. In this study, we first quantitatively analyze the diagnostic and social costs associated with these containment measures based on contact tracing, employing three state-of-the-art models of SARS-CoV-2 spreading. Our results suggest that probabilistic techniques allow for more effective mitigation at a lower cost. Secondly, our findings reveal a remarkable efficacy of probabilistic contact-tracing techniques in capturing backward propagations and super-spreading events, relevant features of the diffusion of many pathogens, including SARS-CoV-2.

Identifying the infection status of each individual during infectious diseases informs public health management. However, performing frequent individual-level tests may not be feasible. Instead, sparse and sometimes group-level tests are performed. Determining the infection status of individuals using sparse group-level tests remains an open problem. We have tackled this problem by extending graph-coupled hidden Markov models with individuals infection statuses as the hidden states and the group test results as the observations. We fitted the model to simulation datasets using the Gibbs sampling method. The model performed about 0.55 AUC for low testing frequencies and increased to 0.80 AUC in the case where the groups were tested every day. The model was separately tested on a daily basis case to predict the statuses over time and after 15 days of the beginning of the spread, which resulted in 0.98 AUC at day 16 and remained above 0.80 AUC until day 128. Therefore, although dealing with sparse tests remains unsolved, the results open the possibility of using initial group screenings during pandemics to accurately estimate individuals infection statuses.

A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases. Firstly, the effectiveness of this approach is demonstrated on synthetic data as generated from the numerical solution of the susceptible-asymptomatic-infected-recovered-dead (SAIRD) model. Then, the method is applied to COVID-19 data reported for Germany and shows that it can accurately identify and predict virus spread trends. The results indicate that an incomplete physics-informed model can approach more complicated dynamics efficiently. Thus, the present work demonstrates the high potential of using machine learning methods, e.g., PINNs, to study and predict epidemic dynamics in combination with compartmental models.

The COVID-19 pandemic has emphasized the need for a robust understanding of epidemic models. Current models of epidemics are classified as either mechanistic or non-mechanistic: mechanistic models make explicit assumptions on the dynamics of disease, whereas non-mechanistic models make assumptions on the form of observed time series. Here, we introduce a simple mixture-based model which bridges the two approaches while retaining benefits of both. The model represents time series of cases and fatalities as a mixture of Gaussian curves, providing a flexible function class to learn from data compared to traditional mechanistic models. Although the model is non-mechanistic, we show that it arises as the natural outcome of a stochastic process based on a networked SIR framework. This allows learned parameters to take on a more meaningful interpretation compared to similar non-mechanistic models, and we validate the interpretations using auxiliary mobility data collected during the COVID-19 pandemic. We provide a simple learning algorithm to identify model parameters and establish theoretical results which show the model can be efficiently learned from data. Empirically, we find the model to have low prediction error. The model is available live at covidpredictions.mit.edu. Ultimately, this allows us to systematically understand the impacts of interventions on COVID-19, which is critical in developing data-driven solutions to controlling epidemics.

Reconstructing missing information in epidemic spreading on contact networks can be essential in prevention and containment strategies. For instance, identifying and warning infective but asymptomatic individuals (e.g., manual contact tracing) helped contain outbreaks in the COVID-19 pandemic. The number of possible epidemic cascades typically grows exponentially with the number of individuals involved. The challenge posed by inference problems in the epidemics processes originates from the difficulty of identifying the almost negligible subset of those compatible with the evidence (for instance, medical tests). Here we present a new generative neural networks framework that can sample the most probable infection cascades compatible with observations. Moreover, the framework can infer the parameters governing the spreading of infections. The proposed method obtains better or comparable results with existing methods on the patient zero problem, risk assessment, and inference of infectious parameters in synthetic and real case scenarios like spreading infections in workplaces and hospitals.

The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; (2) an RLbased methodology for optimizing fine-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts. This article is part of the special track on AI and COVID-19.

With the emergence of the COVID-19 pandemic, there have been global calls for the implementation of “social distancing” to control transmission. Throughout the world, some have resisted this requirement with the unfounded argument that it is unnecessary or ineffective. Social distancing, however, is a natural consequence of disease across animals, both human and nonhuman. Stockmaier et al. reviewed responses to disease across animal taxa and reveal how these responses naturally limit disease transmission. Understanding such natural responses and their impacts on pathogenic transmission provides epidemiological insight into our own responses to pandemic challenges. Science , this issue p. [eabc8881][1] ### BACKGROUND Contagious pathogens can trigger diverse changes in host social behaviors, rewiring their social networks and profoundly influencing the extent and pace of pathogen spread. Although “social distancing” is now an all too familiar strategy to manage COVID-19, nonhuman animals also exhibit a suite of pathogen-induced changes in social interactions, either as precautionary measures by healthy hosts or as physiological consequences of infection in sick individuals. These diverse changes in the social behaviors of both healthy and infected hosts in response to pathogens are widespread across taxa, but we still have much to learn about their underlying mechanisms and epidemiological and evolutionary consequences. Studies of social distancing behaviors in nonhuman animals have the potential to provide important and unique insights into ecological and evolutionary processes relevant to human public health, including pathogen transmission dynamics and virulence evolution. ### ADVANCES We synthesize the literature on pathogen-induced changes in sociality in nonhuman animals and in humans. These include active and passive changes in pathogen-exposed and -unexposed group members occurring both before and after individuals develop an active infection. Behavioral changes that reduce social interactions—and thus pathogen spread—include changes driven by infectious hosts, such as sickness behaviors and active self-isolation, as well as changes driven by healthy hosts, including active avoidance or exclusion of infectious individuals and proactive social distancing in the face of pathogenic threats. Although species have evolved behavioral social distancing because it reduces infection risk, these behaviors also incur costs by limiting access to the many benefits of group living, such as protection against predators and cooperative food finding. Thus, many species appear to have evolved the ability to alter the expression of these behaviors in ways that maximize benefits and minimize costs. The most susceptible individuals of some species show the strongest avoidance of sick conspecifics, and social distancing behaviors are sometimes foregone in interactions with close relatives. Pathogen-induced changes in sociality also apply important selection pressures on pathogens. Because social distancing reduces transmission and thus fitness, pathogens may evolve lower levels of virulence, presymptomatic transmission, or the ability to disguise cues that enable hosts to recognize their presence. Finally, pathogen infection can also increase social interactions when healthy individuals lend aid to pathogen-contaminated or sick conspecifics. Helping sick individuals is a major part of human and eusocial insect societies but is less commonly observed in other, nonhuman animals. Whether pathogens can evolve to elicit helping behavior in hosts, thus augmenting their own transmission, remains unknown. ### OUTLOOK The structure and dynamics of social contact networks fundamentally determine the fate of disease outbreaks, that is, how fast and far they spread and who will be infected. In the race to combat the COVID-19 pandemic, numerous studies have begun to address the public health utility of unprecedented social distancing efforts. Nonhuman animal systems, particularly those with social structures similar to those of humans, present unique opportunities to inform relevant public health questions such as the effectiveness, variability, and required duration of social distancing measures. Further, the experimental tractability of nonhuman animal systems allows study of the coevolutionary dynamics generated by social distancing behaviors, which themselves have public health implications. Selection for or against social distancing behaviors has the potential to create a conflict of interest and could incentivize selfish behaviors that are not in the best interest of everyone. ![Figure][2] Social distancing in humans and nonhuman animals. ( A ) Pathogen-exposed forager ants self-isolate and their nestmates increase social distance to each other (image: Timothée Brütsch). ( B ) People social distance during COVID-19 (image: Forest Simon). ( C ) Sick vampire bats reduce grooming non-close kin (image: Gerald Carter). ( D and E ) Under certain conditions, Trinidadian guppies avoid parasitized individuals (D), (image: Sean Earnshaw, University of St. Andrews) and house finches avoid sick conspecifics (E) (image: Jeremy Stanley). Spread of contagious pathogens critically depends on the number and types of contacts between infectious and susceptible hosts. Changes in social behavior by susceptible, exposed, or sick individuals thus have far-reaching downstream consequences for infectious disease spread. Although “social distancing” is now an all too familiar strategy for managing COVID-19, nonhuman animals also exhibit pathogen-induced changes in social interactions. Here, we synthesize the effects of infectious pathogens on social interactions in animals (including humans), review what is known about underlying mechanisms, and consider implications for evolution and epidemiology. [1]: /lookup/doi/10.1126/science.abc8881 [2]: pending:yes

A minority of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmit most infections. How does this happen? Sun et al. reconstructed transmission in Hunan, China, up to April 2020. Such detailed data can be used to separate out the relative contribution of transmission control measures aimed at isolating individuals relative to population-level distancing measures. The authors found that most of the secondary transmissions could be traced back to a minority of infected individuals, and well over half of transmission occurred in the presymptomatic phase. Furthermore, the duration of exposure to an infected person combined with closeness and number of household contacts constituted the greatest risks for transmission, particularly when lockdown conditions prevailed. These findings could help in the design of infection control policies that have the potential to minimize both virus transmission and economic strain. Science , this issue p. [eabe2424][1] ### INTRODUCTION The role of transmission heterogeneities in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) dynamics remains unclear, particularly those heterogeneities driven by demography, behavior, and interventions. To understand individual heterogeneities and their effect on disease control, we analyze detailed contact-tracing data from Hunan, a province in China adjacent to Hubei and one of the first regions to experience a SARS-CoV-2 outbreak in January to March 2020. The Hunan outbreak was swiftly brought under control by March 2020 through a combination of nonpharmaceutical interventions including population-level mobility restriction (i.e., lockdown), traveler screening, case isolation, contact tracing, and quarantine. In parallel, highly detailed epidemiological information on SARS-CoV-2–infected individuals and their close contacts was collected by the Hunan Provincial Center for Disease Control and Prevention. ### RATIONALE Contact-tracing data provide information to reconstruct transmission chains and understand outbreak dynamics. These data can in turn generate valuable intelligence on key epidemiological parameters and risk factors for transmission, which paves the way for more-targeted and cost-effective interventions. ### RESULTS On the basis of epidemiological information and exposure diaries on 1178 SARS-CoV-2–infected individuals and their 15,648 close contacts, we developed a series of statistical and computational models to stochastically reconstruct transmission chains, identify risk factors for transmission, and infer the infectiousness profile over the course of a typical infection. We observe overdispersion in the distribution of secondary infections, with 80% of secondary cases traced back to 15% of infections, which indicates substantial transmission heterogeneities. We find that SARS-CoV-2 transmission risk scales positively with the duration of exposure and the closeness of social interactions, with the highest per-contact risk estimated in the household. Lockdown interventions increase transmission risk in families and households, whereas the timely isolation of infected individuals reduces risk across all types of contacts. There is a gradient of increasing susceptibility with age but no significant difference in infectivity by age or clinical severity. Early isolation of SARS-CoV-2–infected individuals drastically alters transmission kinetics, leading to shorter generation and serial intervals and a higher fraction of presymptomatic transmission. After adjusting for the censoring effects of isolation, we find that the infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom onset, with 53% of transmission occurring in the presymptomatic phase in an uncontrolled setting. We then use these results to evaluate the effectiveness of individual-based strategies (case isolation and contact quarantine) both alone and in combination with population-level contact reductions. We find that a plausible parameter space for SARS-CoV-2 control is restricted to scenarios where interventions are synergistically combined, owing to the particular transmission kinetics of this virus. ### CONCLUSION There is considerable heterogeneity in SARS-CoV-2 transmission owing to individual differences in biology and contacts that is modulated by the effects of interventions. We estimate that about half of secondary transmission events occur in the presymptomatic phase of a primary case in uncontrolled outbreaks. Achieving epidemic control requires that isolation and contact-tracing interventions are layered with population-level approaches, such as mask wearing, increased teleworking, and restrictions on large gatherings. Our study also demonstrates the value of conducting high-quality contact-tracing investigations to advance our understanding of the transmission dynamics of an emerging pathogen. ![Figure][2] Transmission chains, contact patterns, and transmission kinetics of SARS-CoV-2 in Hunan, China, based on case and contact-tracing data from Hunan, China. (Top left) One realization of the reconstructed transmission chains, with a histogram representing overdispersion in the distribution of secondary infections. (Top right) Contact matrices of community, social, extended family, and household contacts reveal distinct age profiles. (Bottom) Earlier isolation of primary infections shortens the generation and serial intervals while increasing the relative contribution of transmission in the presymptomatic phase. A long-standing question in infectious disease dynamics concerns the role of transmission heterogeneities, which are driven by demography, behavior, and interventions. On the basis of detailed patient and contact-tracing data in Hunan, China, we find that 80% of secondary infections traced back to 15% of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primary infections, which indicates substantial transmission heterogeneities. Transmission risk scales positively with the duration of exposure and the closeness of social interactions and is modulated by demographic and clinical factors. The lockdown period increases transmission risk in the family and households, whereas isolation and quarantine reduce risks across all types of contacts. The reconstructed infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom presentation. Modeling indicates that SARS-CoV-2 control requires the synergistic efforts of case isolation, contact quarantine, and population-level interventions because of the specific transmission kinetics of this virus. [1]: /lookup/doi/10.1126/science.abe2424 [2]: pending:yes

The pandemic caused by the SARS-CoV-2 virus has exposed many flaws in the decision-making strategies used to distribute resources to combat global health crises. In this paper, we leverage reinforcement learning and optimization to improve upon the allocation strategies for various resources. In particular, we consider a problem where a central controller must decide where to send testing kits to learn about the uncertain states of the world (active learning); then, use the new information to construct beliefs about the states and decide where to allocate resources. We propose a general model coupled with a tunable lookahead policy for making vaccine allocation decisions without perfect knowledge about the state of the world. The lookahead policy is compared to a population-based myopic policy which is more likely to be similar to the present strategies in practice. Each vaccine allocation policy works in conjunction with a testing kit allocation policy to perform active learning. Our simulation results demonstrate that an optimization-based lookahead decision making strategy will outperform the presented myopic policy.