Network interventions for managing the COVID-19 pandemic and sustaining economy

A dynamic approach to support outbreak management using reinforcement learning and semi-connected SEIQR models

BACKGROUND

Containment measures slowed the spread of COVID-19 but led to a global economic crisis. We establish a reinforcement learning (RL) algorithm that balances disease control and economic activities.

METHODS

To train the RL agent, we design an RL environment with 4 semi-connected regions to represent the COVID-19 epidemic in Tokyo, Osaka, Okinawa, and Hokkaido, Japan. Every region is governed by a Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) model and has a transport hub to connect with other regions. The allocation of the synthetic population and inter-regional traveling is determined by population-weighted density. The agent learns the best policy from interacting with the RL environment, which involves obtaining daily observations, performing actions on individual movement and screening, and receiving feedback from the reward function. After training, we implement the agent into RL environments describing the actual epidemic waves of the four regions to observe the agent's performance.

RESULTS

For all epidemic waves covered by our study, the trained agent reduces the peak number of infectious cases and shortens the epidemics (from 165 to 35 cases and 148 to 131 days for the 5th wave). The agent is generally strict on screening but easy on movement, except for Okinawa, where the agent is easy on both actions. Action timing analyses indicate that restriction on movement is elevated when the number of exposed or infectious cases remains high or infectious cases increase rapidly, and stringency on screening is eased when the number of exposed or infectious cases drops quickly or to a regional low. For Okinawa, action on screening is tightened when the number of exposed or infectious cases increases rapidly.

CONCLUSIONS

Our experiments exhibit the potential of the RL in assisting policy-making and how the semi-connected SEIQR models establish an interactive environment for imitating cross-regional human flows.

COVID-19 transmission in a resource dependent community with heterogeneous populations: An agent-based modeling approach

Outbreaks of COVID-19 in crowded work locations led to mass infection events during the pandemic that stressed health capacity in rural communities. This led to disparate responses - either isolating and restricting workers to facilities and potentially amplifying spread between them, more intense community wide restrictions, or an acceptance of higher disease spread. An extreme case is the salmon fishery in Bristol Bay, Alaska, where fishermen, factory workers, and residents all interact during the summer fishing season. During the pandemic, policy measures were debated, including community mask mandates, restricting workers to their boats and factories, and even closing the valuable seasonal fishery. We develop an agent-based SIR model (ABM) to examine COVID-19 transmission in a resource-dependent community populated by distinct subgroups. The model includes a virus spreading within and between three heterogenous populations who interact with other members of their type in their home location, and with different types of agents when out in the community. We simulate various non-pharmaceutical interventions and vaccination rates across these groups. Results demonstrate the efficacy of non-pharmaceutical interventions and vaccinations, as well as tradeoffs between duration and intensity and tradeoffs between groups impacted by the outbreak. This ABM demonstrates the impact of public policy mechanisms on health outcomes in resource-dependent communities with distinct populations.

The unintended consequences of inconsistent closure policies and mobility restrictions during epidemics

Background

Controlling the spread of infectious diseases-even when safe, transmission-blocking vaccines are available-may require the effective use of non-pharmaceutical interventions (NPIs), e.g., mask wearing, testing, limits on group sizes, venue closure. During the SARS-CoV-2 pandemic, many countries implemented NPIs inconsistently in space and time. This inconsistency was especially pronounced for policies in the United States of America (US) related to venue closure.

Methods

Here, we investigate the impact of inconsistent policies associated with venue closure using mathematical modeling and high-resolution human mobility, Google search, and county-level SARS-CoV-2 incidence data from the USA. Specifically, we look at high-resolution location data and perform a US-county-level analysis of nearly 8 million SARS-CoV-2 cases and 150 million location visits, including 120 million church visitors across 184,677 churches, 14 million grocery visitors across 7662 grocery stores, and 13.5 million gym visitors across 5483 gyms.

Results

Analyzing the interaction between venue closure and changing mobility using a mathematical model shows that, across a broad range of model parameters, inconsistent or partial closure can be worse in terms of disease transmission as compared to scenarios with no closures at all. Importantly, changes in mobility patterns due to epidemic control measures can lead to increase in the future number of cases. In the most severe cases, individuals traveling to neighboring jurisdictions with different closure policies can result in an outbreak that would otherwise have been contained. To motivate our mathematical models, we turn to mobility data and find that while stay-at-home orders and closures decreased contacts in most areas of the USA, some specific activities and venues saw an increase in attendance and an increase in the distance visitors traveled to attend. We support this finding using search query data, which clearly shows a shift in information seeking behavior concurrent with the changing mobility patterns.

Conclusions

While coarse-grained observations are not sufficient to validate our models, taken together, they highlight the potential unintended consequences of inconsistent epidemic control policies related to venue closure and stress the importance of balancing the societal needs of a population with the risk of an outbreak growing into a large epidemic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44263-023-00028-z.

Social Mixing and Network Characteristics of COVID-19 Patients Before and After Widespread Interventions: A Population-based Study

SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9 to 62.8 ; the average shortest path length decreased from 1.53 to 1.14 ; the average betweenness reduced from 0.65 to 0.11 ; the average cluster size dropped from 4.05 to 2.72 ; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks’ dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.

Model based on COVID-19 evidence to predict and improve pandemic control

Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus.

Big data technology in infectious diseases modeling, simulation, and prediction after the COVID-19 outbreak

After the outbreak of COVID-19, the interaction of infectious disease systems and social systems has challenged traditional infectious disease modeling methods. Starting from the research purpose and data, researchers improved the structure and data of the compartment model or used agents and artificial intelligence based models to solve epidemiological problems. In terms of modeling methods, the researchers use compartment subdivision, dynamic parameters, agent-based model methods, and artificial intelligence related methods. In terms of factors studied, the researchers studied 6 categories: human mobility, nonpharmaceutical interventions (NPIs), ages, medical resources, human response, and vaccine. The researchers completed the study of factors through modeling methods to quantitatively analyze the impact of social systems and put forward their suggestions for the future transmission status of infectious diseases and prevention and control strategies. This review started with a research structure of research purpose, factor, data, model, and conclusion. Focusing on the post-COVID-19 infectious disease prediction simulation research, this study summarized various improvement methods and analyzes matching improvements for various specific research purposes.

Impact of policy response on health protection and economic recovery in OECD and BRIICS countries during the early stages of the COVID-19 pandemic

OBJECTIVES

During the early stages of the COVID-19 pandemic, the full reopening of the economy typically accelerated viral transmission. This study aims to determine whether policy response could contribute to the dual objective of both reducing the spread of the epidemic and revitalising economic activities.

STUDY DESIGN

This is a longitudinal study of Organization for Economic Cooperation and Development (OECD) and Brazil, Russia, India, Indonesia, China, and South Africa (BRIICS) from the first quarter (Q1) of 2020 to the same period of 2021.

METHODS

From a health-economic perspective, this study established a framework to illustrate the following outcomes: suppression-prosperity, outbreak-stagnancy, outbreak-prosperity and suppression-stagnancy scenarios. Multinomial logistic models were used to analyse the associations between policy response with both the pandemic and the economy. The study further examined two subtypes of policy response, stringency/health measures and economic support measures, separately. The probabilities of the different scenarios were estimated.

RESULTS

Economic prosperity and epidemic suppression were significantly associated with policy response. The effects of policy response on health-economic scenarios took the form of inverse U-shapes with the increase in intensity. 'Leptokurtic', 'bimodal' and 'long-tailed' curves demonstrated the estimated possibilities of suppression-prosperity, outbreak-prosperity and suppression-stagnancy scenarios, respectively. In addition, stringency/health policies followed the inverted U-shaped pattern, whereas economic support policies showed a linear pattern.

CONCLUSIONS

It was possible to achieve the dual objective of economic growth and epidemic control simultaneously, and the effects of policy response were shaped like an inverse U. These findings provide a new perspective for balancing the economy with public health during the early stages of the pandemic.

Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction

Background

U.S. school closures due to the coronavirus disease 2019 (COVID-19) pandemic led to extended periods of remote learning and social and economic impact on families. Uncertainty about virus dynamics made it difficult for school districts to develop mitigation plans that all stakeholders consider to be safe.

Methods

We developed an agent-based model of infection dynamics and preventive mitigation designed as a conceptual tool to give school districts basic insights into their options, and to provide optimal flexibility and computational ease as COVID-19 science rapidly evolved early in the pandemic. Elements included distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. Model elements were designed to be updated as the pandemic and scientific knowledge evolve. An online interface enables school districts and their implementation partners to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions.

Results

The model shows infection dynamics that school districts should consider. For example, under default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education.

Conclusions

Our model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model was designed in a period of considerable uncertainty and rapidly evolving science. It had practical use early in the pandemic to surface dynamics for school districts and to enable manipulation of parameters as well as rapid update in response to changes in epidemiological conditions and scientific information about COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.

A general urban spreading pattern of COVID-19 and its underlying mechanism

Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding the spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies explained the differences in contagion rates due to the urban socio-political measures, while fine-grained geographic urban spreading pattern still remains an open issue. Here, we fill this gap by leveraging the trajectory data of 197,808 smartphone users (including 17,808 anonymous confirmed cases) in nine cities in China. We find a general spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid human mobility is time-invariant. Moreover, we reveal that long average traveling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases in the fine-grained geographic model. With such insight, we adopt the Kendall model to simulate the urban spreading of COVID-19 which can well fit the real spreading process. Our results unveil the underlying mechanism behind the spatial-temporal urban evolution of COVID-19, and can be used to evaluate the performance of mobility restriction policies implemented by many governments and to estimate the evolving spreading situation of COVID-19.

Multilevel network interventions: Goals, actions, and outcomes

COVID-19 has resulted in dramatic and widespread social network interventions across the globe, with public health measures such as distancing and isolation key epidemiological responses to minimize transmission. Because these measures affect social interactions between people, the networked structure of daily lives is changed. Such largescale changes to social structures, present simultaneously across many different societies and touching many different people, give renewed significance to the conceptualization of social network interventions. As social network researchers, we need a framework for understanding and describing network interventions consistent with the COVID-19 experience, one that builds on past work but able to cast interventions across a broad societal framework. In this theoretical paper, we extend the conceptualization of social network interventions in these directions. We follow Valente (2012) with a tripartite categorization of interventions but add a multilevel dimension to capture hierarchical aspects that are a key feature of any society and implicit in any network. This multilevel dimension distinguishes goals, actions, and outcomes at different levels, from individuals to the whole of the society. We illustrate this extended taxonomy with a range of COVID-19 public health measures of different types and at multiple levels, and then show how past network intervention research in other domains can also be framed in this way. We discuss what counts as an effective network, an effective intervention, plausible causality, and careful selection and evaluation, as central to a full theory of network interventions.

Target recovery of the economic system based on the target reinforcement path method

An effective and stable operation of an economic system leads to a prosperous society and sustainable world development. Unfortunately, the system faces inevitable perturbations of extreme events and is frequently damaged. To maintain the system's stability, recovering its damaged functionality is essential and is complementary to strengthening its resilience and forecasting extreme events. This paper proposes a target recovery method based on network and economic equilibrium theories to defend the economic system against perturbations characterized as localized attacks. This novel method stimulates a set of economic sectors that mutually reinforce damaged economic sectors and is intuitively named the target reinforcement path (TRP) method. Developing a nonlinear dynamic model that simulates the economic system's operation after being perturbed by a localized attack and recovering based on a target recovery method, we compute the relaxation time for this process to quantify the method's efficiency. Furthermore, we adopt a rank aggregation method to comprehensively measure the method's efficiency by studying the target recovery of three country-level economic systems (China, India, and Japan) for 73 different regional attack scenarios. Through a comparative analysis of the TRP method and three other classic methods, the TRP method is shown to be more effective and less costly. Applicatively, the proposed method exhibits the potential to recover other vital complex systems with spontaneous recovery ability, such as immune, neurological, and ecological systems.

SARS-CoV-2 suppression and early closure of bars and restaurants: a longitudinal natural experiment

Despite severe economic damage, full-service restaurants and bars have been closed in hopes of suppressing the spread of SARS-CoV-2 worldwide. This paper explores whether the early closure of restaurants and bars in February 2021 reduced symptoms of SARS-CoV-2 in Japan. Using a large-scale nationally representative longitudinal survey, we found that the early closure of restaurants and bars decreased the utilization rate among young persons (OR 0.688; CI95 0.515-0.918) and those who visited these places before the pandemic (OR 0.754; CI95 0.594-0.957). However, symptoms of SARS-CoV-2 did not decrease in these active and high-risk subpopulations. Among the more inactive and low-risk subpopulations, such as elderly persons, no discernible impacts are observed in both the utilization of restaurants and bars and the symptoms of SARS-CoV-2. These results suggest that the early closure of restaurants and bars without any other concurrent measures does not contribute to the suppression of SARS-CoV-2.

Transmission dynamics of SARS-CoV-2 in a strictly-Orthodox Jewish community in the UK

Some social settings such as households and workplaces, have been identified as high risk for SARS-CoV-2 transmission. Identifying and quantifying the importance of these settings is critical for designing interventions. A tightly-knit religious community in the UK experienced a very large COVID-19 epidemic in 2020, reaching 64.3% seroprevalence within 10 months, and we surveyed this community both for serological status and individual-level attendance at particular settings. Using these data, and a network model of people and places represented as a stochastic graph rewriting system, we estimated the relative contribution of transmission in households, schools and religious institutions to the epidemic, and the relative risk of infection in each of these settings. All congregate settings were important for transmission, with some such as primary schools and places of worship having a higher share of transmission than others. We found that the model needed a higher general-community transmission rate for women (3.3-fold), and lower susceptibility to infection in children to recreate the observed serological data. The precise share of transmission in each place was related to assumptions about the internal structure of those places. Identification of key settings of transmission can allow public health interventions to be targeted at these locations.

Properties of the Omicron Variant of SARS-CoV-2 Affect Public Health Measure Effectiveness in the COVID-19 Epidemic

Nonpharmaceutical and pharmaceutical public health interventions are important to mitigate the coronavirus disease 2019 (COVID-19) epidemic. However, it is still unclear how the effectiveness of these interventions changes with the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) novel variants. This simulation study utilized data from Japan and investigated how the characteristic properties of the Omicron variant, which emerged in late 2021, influence the effectiveness of public health interventions, including vaccination, the reduction of interpersonal contact, and the early isolation of infectious people. Although the short generation time of the Omicron variant increases the effectiveness of vaccination and the reduction of interpersonal contact, it decreases the effectiveness of early isolation. The latter feature may make the containment of case clusters difficult. The increase of infected children during the Omicron-dominant epidemic diminishes the effects of previously adult-targeted interventions. These findings underscore the importance of monitoring viral evolution and consequent changes in epidemiological characteristics. An assessment and adaptation of public health measures against COVID-19 are required as SARS-CoV-2 novel variants continue to emerge.

Prioritizing high-contact occupations raises effectiveness of vaccination campaigns

A twenty-year-old idea from network science is that vaccination campaigns would be more effective if high-contact individuals were preferentially targeted. Implementation is impeded by the ethical and practical problem of differentiating vaccine access based on a personal characteristic that is hard-to-measure and private. Here, we propose the use of occupational category as a proxy for connectedness in a contact network. Using survey data on occupation-specific contact frequencies, we calibrate a model of disease propagation in populations undergoing varying vaccination campaigns. We find that vaccination campaigns that prioritize high-contact occupational groups achieve similar infection levels with half the number of vaccines, while also reducing and delaying peaks. The paper thus identifies a concrete, operational strategy for dramatically improving vaccination efficiency in ongoing pandemics.

Understanding the uneven spread of COVID-19 in the context of the global interconnected economy

The worldwide spread of the COVID-19 pandemic is a complex and multivariate process differentiated across countries, and geographical distance is acceptable as a critical determinant of the uneven spreading. Although social connectivity is a defining condition for virus transmission, the network paradigm in the study of the COVID-19 spatio-temporal spread has not been used accordingly. Toward contributing to this demand, this paper uses network analysis to develop a multidimensional methodological framework for understanding the uneven (cross-country) spread of COVID-19 in the context of the globally interconnected economy. The globally interconnected system of tourism mobility is modeled as a complex network and studied within the context of a three-dimensional (3D) conceptual model composed of network connectivity, economic openness, and spatial impedance variables. The analysis reveals two main stages in the temporal spread of COVID-19, defined by the cutting-point of the 44th day from Wuhan. The first describes the outbreak in Asia and North America, the second stage in Europe, South America, and Africa, while the outbreak in Oceania intermediates. The analysis also illustrates that the average node degree exponentially decays as a function of COVID-19 emergence time. This finding implies that the highly connected nodes, in the Global Tourism Network (GTN), are disproportionally earlier infected by the pandemic than the other nodes. Moreover, countries with the same network centrality as China are early infected on average by COVID-19. The paper also finds that network interconnectedness, economic openness, and transport integration are critical determinants in the early global spread of the pandemic, and it reveals that the spatio-temporal patterns of the worldwide spreading of COVID-19 are more a matter of network interconnectivity than of spatial proximity.

Vaccination and Quarantine Effect on COVID-19 Transmission Dynamics Incorporating Chinese-Spring-Festival Travel Rush: Modeling and Simulations

The COVID-19 pandemic has adversely affected the entire world. The effective implementation of vaccination strategy is critical to prevent the resurgence of the pandemic, especially during large-scale population migration. We establish a multiple patch coupled model based on the transportation network among the 31 provinces in China, under the combined strategies of vaccination and quarantine during large-scale population migration. Based on the model, we derive a critical quarantine rate to control the pandemic transmission and a vaccination rate to achieve herd immunity. Furthermore, we evaluate the influence of passenger flow on the effective reproduction number during the Chinese-Spring-Festival travel rush. Meanwhile, the spread of the COVID-19 pandemic is investigated for different control strategies, viz. global control and local control. The impact of vaccine-related parameters, such as the number, the effectiveness and the immunity period of vaccine, are explored. It is believed that the articulated models as well as the presented simulation results could be beneficial to design of feasible strategies for preventing COVID-19 transmission during the Chinese-Spring-Festival travel rush or the other future events involving large-scale population migration.

Analysis of efficacy of intervention strategies for COVID-19 transmission: A case study of Hong Kong

By the end of February 2021, COVID-19 had spread to over 230 countries, with more than 100 million confirmed cases and 2.5 million deaths. To control infection spread with the least disruption to economic and societal activities, it is crucial to implement the various interventions effectively. In this study, we developed an agent-based SEIR model, using real demographic and geographic data from Hong Kong, to analyse the efficiency of various intervention strategies in preventing infection by the SARS-CoV-2 virus. Close contact route including short-range airborne is considered as the main transmission routes for COVID-19 spread. Contact tracing is not that useful if all other interventions have been fully deployed. The number of infected individuals could be halved if people reduced their close contact rate by 25%. For reducing transmission, students should be prioritized for vaccination rather than retired older people and preschool aged children. Home isolation, and taking the nucleic acid test (NAT) as soon as possible after symptom onset, are much more effective interventions than wearing masks in public places. Temperature screening in public places only disrupted the infection spread by a small amount when other interventions have been fully implemented. Our results may be useful for other highly populated cities, when choosing their intervention strategies to prevent outbreaks of COVID-19 and similar diseases.

Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations

Governments around the globe use non-pharmaceutical interventions (NPIs) to curb the spread of coronavirus disease 2019 (COVID-19) cases. Making decisions under uncertainty, they all face the same temporal paradox: estimating the impact of NPIs before they have been implemented. Due to the limited variance of empirical cases, researchers could so far not disentangle effects of individual NPIs or their impact on different demographic groups. In this paper, we utilize large-scale agent-based simulations in combination with Susceptible-Exposed-Infectious-Recovered (SEIR) models to investigate the spread of COVID-19 for some of the most affected federal states in Germany. In contrast to other studies, we sample agents from a representative survey. Including more realistic demographic attributes that influence agents' behavior yields accurate predictions of COVID-19 transmissions and allows us to investigate counterfactual what-if scenarios. Results show that quarantining infected people and exploiting industry-specific home office capacities are the most effective NPIs. Disentangling education-related NPIs reveals that each considered institution (kindergarten, school, university) has rather small effects on its own, yet, that combined openings would result in large increases in COVID-19 cases. Representative survey-characteristics of agents also allow us to estimate NPIs' effects on different age groups. For instance, re-opening schools would cause comparatively few infections among the risk-group of people older than 60 years.

A simple model for control of COVID-19 infections on an urban campus

A customized susceptible, exposed, infected, and recovered compartmental model is presented for describing the control of asymptomatic spread of COVID-19 infections on a residential, urban college campus embedded in a large urban community by using public health protocols, founded on surveillance testing, contact tracing, isolation, and quarantine. Analysis in the limit of low infection rates-a necessary condition for successful operation of the campus-yields expressions for controlling the infection and understanding the dynamics of infection spread. The number of expected cases on campus is proportional to the exogenous infection rate in the community and is decreased by more frequent testing and effective contact tracing. Simple expressions are presented for the dynamics of superspreader events and the impact of partial vaccination. The model results compare well with residential data from Boston University's undergraduate population for fall 2020.

Associations between mobility patterns and COVID-19 deaths during the pandemic: A network structure and rank propagation modelling approach

BACKGROUND

From February 2020, both urban and rural Ireland witnessed the rapid proliferation of the COVID-19 disease throughout its counties. During this period, the national COVID-19 responses included stay-at-home directives issued by the state, subject to varying levels of enforcement.

METHODS

In this paper, we present a new method to assess and rank the causes of Ireland COVID-19 deaths as it relates to mobility activities within each county provided by Google while taking into consideration the epidemiological confirmed positive cases reported per county. We used a network structure and rank propagation modelling approach using Personalised PageRank to reveal the importance of each mobility category linked to cases and deaths. Then a novel feature-selection method using relative prominent factors finds important features related to each county's death. Finally, we clustered the counties based on features selected with the network results using a customised network clustering algorithm for the research problem.

FINDINGS

Our analysis reveals that the most important mobility trend categories that exhibit the strongest association to COVID-19 cases and deaths include retail and recreation and workplaces. This is the first time a network structure and rank propagation modelling approach has been used to link COVID-19 data to mobility patterns. The infection determinants landscape illustrated by the network results aligns soundly with county socio-economic and demographic features. The novel feature selection and clustering method presented clusters useful to policymakers, managers of the health sector, politicians and even sociologists. Finally, each county has a different impact on the national total.

Mitigating COVID-19 outbreaks in workplaces and schools by hybrid telecommuting

The COVID-19 epidemic has forced most countries to impose contact-limiting restrictions at workplaces, universities, schools, and more broadly in our societies. Yet, the effectiveness of these unprecedented interventions in containing the virus spread remain largely unquantified. Here, we develop a simulation study to analyze COVID-19 outbreaks on three real-life contact networks stemming from a workplace, a primary school and a high school in France. Our study provides a fine-grained analysis of the impact of contact-limiting strategies at workplaces, schools and high schools, including: (1) Rotating strategies, in which workers are evenly split into two shifts that alternate on a daily or weekly basis; and (2) On-Off strategies, where the whole group alternates periods of normal work interactions with complete telecommuting. We model epidemics spread in these different setups using a stochastic discrete-time agent-based transmission model that includes the coronavirus most salient features: super-spreaders, infectious asymptomatic individuals, and pre-symptomatic infectious periods. Our study yields clear results: the ranking of the strategies, based on their ability to mitigate epidemic propagation in the network from a first index case, is the same for all network topologies (workplace, primary school and high school). Namely, from best to worst: Rotating week-by-week, Rotating day-by-day, On-Off week-by-week, and On-Off day-by-day. Moreover, our results show that below a certain threshold for the original local reproduction number [Formula: see text] within the network (< 1.52 for primary schools, < 1.30 for the workplace, < 1.38 for the high school, and < 1.55 for the random graph), all four strategies efficiently control outbreak by decreasing effective local reproduction number to [Formula: see text] < 1. These results can provide guidance for public health decisions related to telecommuting.

Assessing the Association Between Social Gatherings and COVID-19 Risk Using Birthdays

Importance

Many policies designed to stop the spread of COVID-19 address formal gatherings, such as workplaces and dining locations. Informal social gatherings are a potentially important mode of SARS-CoV-2 transmission, but studying their role in transmission is challenged by data and methodological limitations; birthdays offer an opportunity to empirically quantify the potential role of small social gatherings in COVID-19 spread.

Objective

To assess the association between social gatherings and SARS-CoV-2 transmission by studying whether COVID-19 rates increase after birthdays in a household.

Design, Setting, and Participants

This cross-sectional study used nationwide data from January 1 to November 8, 2020, from 2.9 million US households with private insurance to compare COVID-19 infections between households with and without a birthday in the preceding 2 weeks, stratified according to county-level COVID-19 prevalence in that week and adjusting for household size and both week- and county-specific differences. The study also compared how birthday-associated infection rates differed by type of birthday (eg, child vs adult birthday, or a milestone birthday such as a 50th birthday), county-level precipitation on the Saturday of each week (which could move gatherings indoors), political leanings in the county, and state shelter-in-place policies.

Main Outcomes and Measures

Household-level COVID-19 infection.

Results

Among the 2.9 million households in the study, in the top decile of counties in COVID-19 prevalence, households with a birthday in the 2 weeks prior had 8.6 more diagnoses per 10 000 individuals (95% CI, 6.6-10.7 per 10 000 individuals) compared with households without a birthday in the 2 weeks prior, a relative increase of 31% above the county-level prevalence of 27.8 cases per 10 000 individuals, vs 0.9 more diagnoses per 10 000 individuals (95% CI, 0.6-1.3 per 10 000 individuals) in the fifth decile (P < .001 for interaction). Households in the tenth decile of COVID-19 prevalence had an increase in COVID-19 diagnoses of 15.8 per 10 000 persons (95% CI, 11.7-19.9 per 10 000 persons) after a child birthday, compared with an increase of 5.8 per 10 000 persons (95% CI, 3.7-7.9 per 10 000 persons) among households with an adult birthday (P < .001 in a test of interactions). No differences were found by milestone birthdays, county political leaning, precipitation, or shelter-in-place policies.

Conclusions and Relevance

This cross-sectional study suggests that birthdays, which likely correspond with social gatherings and celebrations, were associated with increased rates of diagnosed COVID-19 infection within households in counties with high COVID-19 prevalence.

[Evaluation of interventions during the COVID-19 pandemic: development of a model based on subpopulations with different contact rates]

Si bien se han realizado múltiples intentos de modelar matemáticamente la pandemia de la enfermedad por coronavirus 2019 (COVID-19), causada por SARS-CoV-2, pocos modelos han sido pensados como herramientas interactivas accesibles para usuarios de distintos ámbitos. El objetivo de este trabajo fue desarrollar un modelo que tuviera en cuenta la heterogeneidad de las tasas de contacto de la población e implementarlo en una aplicación accesible, que permitiera estimar el impacto de posibles intervenciones a partir de información disponible. Se desarrolló una versión ampliada del modelo susceptible-expuesto-infectado-resistente (SEIR), denominada SEIR-HL, que asume una población dividida en dos subpoblaciones, con tasas de contacto diferentes. Asimismo, se desarrolló una fórmula para calcular el número básico de reproducción (R₀) para una población dividida en n subpoblaciones, discriminando las tasas de contacto de cada subpoblación según el tipo o contexto de contacto. Se compararon las predicciones del SEIR-HL con las del SEIR y se demostró que la heterogeneidad en las tasas de contacto puede afectar drásticamente la dinámica de las simulaciones, aun partiendo de las mismas condiciones iniciales y los mismos parámetros. Se empleó el SEIR-HL para mostrar el efecto sobre la evolución de la pandemia del desplazamiento de individuos desde posiciones de alto contacto hacia posiciones de bajo contacto. Finalmente, a modo de ejemplo, se aplicó el SEIR-HL al análisis de la pandemia de COVID-19 en Argentina; también se desarrolló un ejemplo de uso de la fórmula del R₀. Tanto el SEIR-HL como una calculadora del R₀ fueron implementados informáticamente y puestos a disposición de la comunidad.

Network models and the interpretation of prolonged infection plateaus in the COVID19 pandemic

Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of SARS-CoV-2. In the United States, strict social distancing during the first wave of virus spread has resulted in different types of infection dynamics. In some states, such as New York, extensive infection spread was followed by a pronounced decline of infection levels. In other states, such as California, less infection spread occurred before strict social distancing, and a different pattern was observed. Instead of a pronounced infection decline, a long-lasting plateau is evident, characterized by similar daily new infection levels. Here we show that network models, in which individuals and their social contacts are explicitly tracked, can reproduce the plateau if network connections are cut due to social distancing measures. The reason is that in networks characterized by a 2D spatial structure, infection tends to spread quadratically with time, but as edges are randomly removed, the infection spreads along nearly one-dimensional infection “corridors”, resulting in plateau dynamics. Further, we show that plateau dynamics are observed only if interventions start sufficiently early; late intervention leads to a “peak and decay” pattern. Interestingly, the plateau dynamics are predicted to eventually transition into an infection decline phase without any further increase in social distancing measures. Additionally, the models suggest that a second wave becomes significantly less pronounced if social distancing is only relaxed once the dynamics have transitioned to the decline phase. The network models analyzed here allow us to interpret and reconcile different infection dynamics during social distancing observed in various US states.

The mathematics of multiple lockdowns

While vaccination is the optimal response to an epidemic, recent events have obliged us to explore new strategies for containing worldwide epidemics, like lockdown strategies, where the contacts among the population are strongly reduced in order to slow down the propagation of the infection. By analyzing a classical epidemic model, we explore the impact of lockdown strategies on the evolution of an epidemic. We show that repeated lockdowns have a beneficial effect, reducing the final size of the infection, and that they represent a possible support strategy to vaccination policies.

Modeling infection dynamics and mitigation strategies to support K-6 in-person instruction during the COVID-19 pandemic

OBJECTIVE

To support safer in-person K-6 instruction during the coronavirus disease 2019 (COVID- 19) pandemic by providing public health authorities and school districts with a practical model of transmission dynamics and mitigation strategies.

METHODS

We developed an agent-based model of infection dynamics and preventive mitigation strategies such as distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. The model parameters can be updated as the science evolves and are adjustable via an online user interface, enabling users to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions.

RESULTS

Under default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education.

CONCLUSIONS

Our model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model's parameters can be immediately updated in response to changes in epidemiological conditions, science of COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.

When a city must be a tree: rethinking the spatial approach to fighting epidemics based on the notion of ‘intermediate confinement’

Principles of sustainability defend compactness, density and diversity as main characteristics of the optimal development of cities. These factors support public transport efficiency, economic activity, accessibility to equipments and services, proximity and walkability of streets and social exchanges in open public spaces. The Covid-19 pandemic crisis has called into question these factors perceived as booster of infections. However, dense and compact cities can also be the synonym of a more efficient provision of services, along with solidarity networks and creative solutions to fight the sanitary and economic crisis. Based on Alexander's (1965) concepts of 'tree' and 'semi-lattice', this study aims to identify areas in the urban tissue that could be self-sufficient, that is functionally autonomous to manage epidemics from the neighbourhood scale. Encouraging healthier lifestyles during lockdown is fundamental for social resilience. What alternative spatial approach to fighting epidemics could perform better? How could an "intermediate confinement" based on self-sufficiency and the promotion of healthier environments become a major priority for action? The analysis of Madrid (Spain) offers a suitable case study due to its density, diversity and high contagiousness during the Covid-19 crisis, revealing also some issues to apply such 'intermediate confinement' strategy, due to major spatial imbalances.