The unequal effects of the health-economy trade-off during the COVID-19 pandemic

Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during "Disease X" outbreaks

During the COVID-19 pandemic, healthcare systems worldwide faced severe strain. This study, utilizing wastewater virus surveillance, identified that periodic spontaneous avoidance behaviours significantly impacted infectious disease transmission during rapid and intense outbreaks. To incorporate these behaviours into disease transmission analysis, we introduced the Su-SEIQR model and validated it using COVID-19 wastewater data from Beijing and Hong Kong. The results demonstrated that the Su-SEIQR model accurately reflected trends in susceptible populations and confirmed cases during the COVID-19 pandemic, highlighting the role of spontaneous collective avoidance behaviours in generating periodic fluctuations. These fluctuations helped reduce infection peaks, thereby alleviating pressure on healthcare systems. However, the effect of these spontaneous behaviours on mitigating healthcare overload was limited. Consequently, we incorporated healthcare capacity constraints into the model, adjusting parameters to further guide population behaviours during the pandemic, aiming to keep the outbreak within manageable limits and reduce strain on healthcare resources. This study provides robust support for the development of environmental and public health policies during pandemics by constructing an innovative transmission model, which effectively prevents healthcare overload. Additionally, this approach can be applied to managing future outbreaks of unknown viruses or "Disease X".

An agent-based model of COVID- 19 in the food industry for assessing public health and economic impacts of infection control strategies

The COVID- 19 pandemic exposed challenges of balancing public health and economic goals of infection control in essential industries like food production. To enhance decision-making during future outbreaks, we developed a customizable agent-based model (FInd CoV Control) that predicts and counterfactually compares COVID- 19 transmission in a food production operation under various interventions. The model tracks the number of infections as well as economic outcomes (e.g., number of unavailable workers, direct expenses, production losses). The results revealed strong tradeoffs between public health and economic impacts of interventions. Temperature screening and virus testing protect public health but have substantial economic downsides. Vaccination, while inexpensive, is too slow as a reactive strategy. Intensive physical distancing and biosafety interventions prove cost-effective. The variability and bimodality in predicted impacts of counterfactual interventions, explained by the chance effects and early stochastic infection die-off, caution against relying on single-operation real-world data for decision-making. These findings underscore the need for a proactive infrastructure capable of rapidly developing integrated infection-economic mechanistic models for the essential industries to guide infection control, policy-making, and socially acceptable decisions.

Socio-environmental modeling shows physics-like confidence with water modeling surpassing it in numerical claims

Several modern scientific fields rely on computationally intensive mathematical models to study uncertain, complex socio-environmental phenomena such as the spread of a virus, climate change, or the water cycle. However, the degree of epistemic commitment of these fields is unclear. By using machine learning to extract the knowledge claims of around 755,000 abstracts from 14 scientific fields spanning the human and physical sciences, we show that epidemic, integrated assessment, and water modeling display a degree of linguistic assertiveness akin to physics. Water modeling surpasses even the most accurate physical sciences in substantiating knowledge claims with numbers, which are largely produced without accompanying uncertainty and sensitivity analysis. By exploring the balance between doubt and certainty in academic writing, our study reflects on whether the strong conviction and quantification of fields modeling socio-environmental processes, especially water modeling, are epistemically justified.

Using human mobility data to quantify experienced urban inequalities

The lived experience of urban life is shaped by personal mobility through dynamic relationships and resources, marked not only by access and opportunity, but also inequality and segregation. The recent availability of fine-grained mobility data and context attributes ranging from venue type to demographic mixture offer researchers a deeper understanding of experienced inequalities at scale, and pose many new questions. Here we review emerging uses of urban mobility behaviour data, and propose an analytic framework to represent mobility patterns as a temporal bipartite network between people and places. As this network reconfigures over time, analysts can track experienced inequality along three critical dimensions: social mixing with others from specific demographic backgrounds, access to different types of facilities, and spontaneous adaptation to unexpected events, such as epidemics, conflicts or disasters. This framework traces the dynamic, lived experiences of urban inequality and complements prior work on static inequalities experience at home and work.

Assessment of willingness to pay for a quality-adjusted life year in the post COVID-19 pandemic era in Japan

OBJECTIVES

Data regarding willingness to pay (WTP) for one quality-adjusted life year (QALY) may need to be updated given rapidly changing modern lifestyles and dynamic shifts of population's values related to healthcare and economic factors as influenced by the COVID-19 pandemic.

STUDY DESIGN

A cross-sectional study using online-based questionnaire surveys.

METHODS

We conducted an online survey in March 2024 of 2,000 Japanese adults aged 20-69 years to evaluate their individual WTP for one QALY gained. We constructed a case scenario in which participants were asked to answer a series of yes/no questions to evaluate their willingness to pay for a new treatment to prolong a patient's life with a certain health status for one year. The scenario was stratified by the treatment cost per case, the annual number of patients, and the health status of patients. A probit model was implemented to estimate the WTP for one QALY gained and included the total cost, the total QALYs gained, recruitment method, participant's age, sex, household income, and educational background as explanatory variables.

RESULTS

WTP per one QALY gained was estimated to be 16.98 [95%CI 14.43-19.91] million Japanese yen. A positive QALY gain (p < 0.001), male sex (p < 0.001), and higher household income (p < 0.001) were positively correlated with having a higher WTP. A higher total cost (p < 0.001), increased age (p = 0.002) and living alone (p < 0.001) were negatively correlated with WTP.

CONCLUSION

Our study showed that the updated WTP threshold for a QALY gained was much larger than previously reported values. This suggests that WTP data should be reviewed and updated regularly.

Socioeconomic disparities in mobility behavior during the COVID-19 pandemic in developing countries

Mobile phone data have played a key role in quantifying human mobility during the COVID-19 pandemic. Existing studies on mobility patterns have primarily focused on regional aggregates in high-income countries, obfuscating the accentuated impact of the pandemic on the most vulnerable populations. Leveraging geolocation data from mobile-phone users and population census for 6 middle-income countries across 3 continents between March and December 2020, we uncovered common disparities in the behavioral response to the pandemic across socioeconomic groups. Users living in low-wealth neighborhoods were less likely to respond by self-isolating, relocating to rural areas, or refraining from commuting to work. The gap in the behavioral responses between socioeconomic groups persisted during the entire observation period. Among users living in low-wealth neighborhoods, those who commute to work in high-wealth neighborhoods pre-pandemic were particularly at risk of experiencing economic stress, facing both the reduction in economic activity in the high-wealth neighborhood and being more likely to be affected by public transport closures due to their longer commute distances. While confinement policies were predominantly country-wide, these results suggest that, when data to identify vulnerable individuals are not readily available, GPS-based analytics could help design targeted place-based policies to aid the most vulnerable.

Supplementary Information

The online version contains supplementary material available at 10.1140/epjds/s13688-025-00532-2.

Isolation and structure elucidation of Dm-CVNH, a new cyanovirin-N homolog with activity against SARS-CoV-2 and HIV-1

An anti-HIV screen of natural product extracts resulted in the discovery of a new antiviral protein through bioassay-guided fractionation of an aqueous extract of the ascidian Didemnum molle. The protein was sequenced through a combination of tandem mass spectroscopy and N-terminal Edman degradation of peptide fragments after a series of endoproteinase digestions. The primary amino acid sequence and disulfide bonding pattern of the 102-amino acid protein were closely related to the antiviral protein cyanovirin-N (CV-N). This new CV-N homolog was named Dm-CVNH. Alphafold2 prediction resulted in a tertiary structure, highly similar to CV-N, comprised of two symmetrically related domains that contained five β-strands and two α-helical turns each. Dm-CVNH showed specificity for high mannose and oligomannose structures, bound to HIV-1 gp-120 and potently inactivated HIV in neutralization assays (EC50 of 0.95 nM). Dm-CVNH inhibited infection in a SARS-CoV-2 live virus assays and was shown to bind to the S1 domain of SARS-CoV-2 Spike glycoprotein. Dm-CVNH behaved in a manner similar to CV-N, binding with a 2:1 stoichiometry to Spike (both to WH-1 and Omicron variants) and preferring the Omicron variant (Kd 42 nM) to original WH-1 (Kd = 89 nM) Spike. This sensitivity to emergent strains was mirrored in viral neutralization assays where Dm-CVNH potently inhibited the infection of Omicron strains XBB.1.16 and JN.1 (IC50 = 11-18 nM).

Modeling-based design of adaptive control strategy for the effective preparation of 'Disease X'

This study aims at exploring a general and adaptive control strategy to confront the rapid evolution of an emerging infectious disease ('Disease X'), drawing lessons from the management of COVID-19 in China. We employ a dynamic model incorporating age structures and vaccination statuses, which is calibrated using epidemic data. We therefore estimate the cumulative infection rate (CIR) during the first epidemic wave of Omicron variant after China relaxed its zero-COVID policy to be 82.9% (95% CI: 82.3%, 83.5%), with a case fatality rate (CFR) of 0.25% (95% CI: 0.248%, 0.253%). We further show that if the zero-COVID policy had been eased in January 2022, the CIR and CFR would have decreased to 81.64% and 0.205%, respectively, due to a higher level of immunity from vaccination. However, if we ease the zero-COVID policy during the circulation of Delta variant from June 2021, the CIR would decrease to 74.06% while the CFR would significantly increase to 1.065%. Therefore, in the face of a 'Disease X', the adaptive strategies should be guided by multiple factors, the 'zero-COVID-like' policy could be a feasible and effective way for the control of a variant with relative low transmissibility. However, we should ease the strategy as the virus matures into a new variant with much higher transmissibility, particularly when the population is at a high level of immunity.

MetaCity: Data-driven sustainable development of complex cities

Cities are complex systems that develop under complicated interactions among their human and environmental components. Urbanization generates substantial outcomes and opportunities while raising challenges including congestion, air pollution, inequality, etc., calling for efficient and reasonable solutions to sustainable developments. Fortunately, booming technologies generate large-scale data of complex cities, providing a chance to propose data-driven solutions for sustainable urban developments. This paper provides a comprehensive overview of data-driven urban sustainability practice. In this review article, we conceptualize MetaCity, a general framework for optimizing resource usage and allocation problems in complex cities with data-driven approaches. Under this framework, we decompose specific urban sustainable goals, e.g., efficiency and resilience, review practical urban problems under these goals, and explore the probability of using data-driven technologies as potential solutions to the challenge of complexity. On the basis of extensive urban data, we integrate urban problem discovery, operation of urban systems simulation, and complex decision-making problem solving into an entire cohesive framework to achieve sustainable development goals by optimizing resource allocation problems in complex cities.

Global impact of PM2.5 on cardiovascular disease: Causal evidence and health inequities across region from 1990 to 2021

PM2.5 is an important environmental risk factor for cardiovascular disease (CVD) and poses a threat to global health. This study combines bibliometric analysis, Mendelian randomization (MR), and Global Burden of Disease (GBD) data to comprehensively explore the relationship between PM2.5 exposure and CVD. MR analyses provided strong evidence for causality, reinforcing findings from traditional observational studies. The estimated global burden of PM2.5-related CVD indicated, that there exist significant impacts on the elderly, men, and populations in low and medium socio-demographic index (SDI) areas. This study further found that population growth and aging are the main drivers of this burden with large inequities, although medical advances have mitigated some of the effects. Overall, the opportunity to reduce the burden of CVD remains significant, particularly in medium SDI countries. Projections to 2045 suggested that the absolute burden will increase, while age-standardized rates will decline due to improvements in air quality and health care. These findings emphasized the urgent need for targeted interventions to mitigate the deleterious effects of PM2.5 on global cardiovascular health and to address health inequalities between regions.

Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges

Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for epidemiological modeling. While the fusion of AI and traditional mechanistic approaches is rapidly advancing, efforts remain fragmented. This scoping review provides a comprehensive overview of emerging integrated models applied across the spectrum of infectious diseases. Through systematic search strategies, we identified 245 eligible studies from 15,460 records. Our review highlights the practical value of integrated models, including advances in disease forecasting, model parameterization, and calibration. However, key research gaps remain. These include the need for better incorporation of realistic decision-making considerations, expanded exploration of diverse datasets, and further investigation into biological and socio-behavioral mechanisms. Addressing these gaps will unlock the synergistic potential of AI and mechanistic modeling to enhance understanding of disease dynamics and support more effective public health planning and response.

LLMs and generative agent-based models for complex systems research

The advent of Large Language Models (LLMs) offers to transform research across natural and social sciences, offering new paradigms for understanding complex systems. In particular, Generative Agent-Based Models (GABMs), which integrate LLMs to simulate human behavior, have attracted increasing public attention due to their potential to model complex interactions in a wide range of artificial environments. This paper briefly reviews the disruptive role LLMs are playing in fields such as network science, evolutionary game theory, social dynamics, and epidemic modeling. We assess recent advancements, including the use of LLMs for predicting social behavior, enhancing cooperation in game theory, and modeling disease propagation. The findings demonstrate that LLMs can reproduce human-like behaviors, such as fairness, cooperation, and social norm adherence, while also introducing unique advantages such as cost efficiency, scalability, and ethical simplification. However, the results reveal inconsistencies in their behavior tied to prompt sensitivity, hallucinations and even the model characteristics, pointing to challenges in controlling these AI-driven agents. Despite their potential, the effective integration of LLMs into decision-making processes -whether in government, societal, or individual contexts- requires addressing biases, prompt design challenges, and understanding the dynamics of human-machine interactions. Future research must refine these models, standardize methodologies, and explore the emergence of new cooperative behaviors as LLMs increasingly interact with humans and each other, potentially transforming how decisions are made across various systems.

Spatiotemporal variations and the ecological risks of microplastics in the watersheds of China: Implying the impacts of the COVID-19 pandemic

China is not only the first reported place of the COVID-19 pandemic but also is the biggest microplastic emitter in the world. Nevertheless, the impact of the COVID-19 pandemic on microplastic pollution in the watersheds of China remains poorly understood. To address this, the present study conducted a data mining and multivariate statistical analysis based on 8898 microplastic samples from 23 Chinese watershed systems before and during the COVID-19 pandemic. The results showed that the COVID-19 pandemic extensively affected the abundance, colors, shapes, polymer types, and particle sizes of microplastic in Chinese watershed systems. Before and during the COVID-19 pandemic, 77.27 % of the Chinese watershed systems observed increased microplastic abundance. Moreover, the COVID-19 pandemic itself, natural conditions (such as altitude and weather), and anthropogenic factors (such as civil aviation throughput) are highly intertwined, jointly impacting the microplastic in the watersheds of China. From the perspective of ecological risks, the COVID-19 pandemic was more likely to aggravate the microplastic pollution in the middle and down reaches of the Yangtze River Watersheds. Overall, whether before or during the COVID-19 pandemic, the main watershed systems of China still stayed at a high pollution level, which rang the alarm bell that watershed systems of China had been at serious ecological risk accused of microplastic contamination.

Generalized contact matrices allow integrating socioeconomic variables into epidemic models

Variables related to socioeconomic status (SES), including income, ethnicity, and education, shape contact structures and affect the spread of infectious diseases. However, these factors are often overlooked in epidemic models, which typically stratify social contacts by age and interaction contexts. Here, we introduce and study generalized contact matrices that stratify contacts across multiple dimensions. We demonstrate a lower-bound theorem proving that disregarding additional dimensions, besides age and context, might lead to an underestimation of the basic reproductive number. By using SES variables in both synthetic and empirical data, we illustrate how generalized contact matrices enhance epidemic models, capturing variations in behaviors such as heterogeneous levels of adherence to nonpharmaceutical interventions among demographic groups. Moreover, we highlight the importance of integrating SES traits into epidemic models, as neglecting them might lead to substantial misrepresentation of epidemic outcomes and dynamics. Our research contributes to the efforts aiming at incorporating socioeconomic and other dimensions into epidemic modeling.

Beyond six feet: The collective behavior of social distancing

In a severe epidemic such as the COVID-19 pandemic, social distancing can be a vital tool to stop the spread of the disease and save lives. However, social distancing may induce profound negative social or economic impacts as well. How to optimize social distancing is a serious social, political, as well as public health issue yet to be resolved. This work investigates social distancing with a focus on how every individual reacts to an epidemic, what role he/she plays in social distancing, and how every individual's decision contributes to the action of the population and vice versa. Social distancing is thus modeled as a population game, where every individual makes decision on how to participate in a set of social activities, some with higher frequencies while others lower or completely avoided, to minimize his/her social contacts with least possible social or economic costs. An optimal distancing strategy is then obtained when the game reaches an equilibrium. The game is simulated with various realistic restraints including (i) when the population is distributed over a social network, and the decision of each individual is made through the interactions with his/her social neighbors; (ii) when the individuals in different social groups such as children vs. adults or the vaccinated vs. unprotected have different distancing preferences; (iii) when leadership plays a role in decision making, with a certain number of leaders making decisions while the rest of the population just follow. The simulation results show how the distancing game is played out in each of these scenarios, reveal the conflicting yet cooperative nature of social distancing, and shed lights on a self-organizing, bottom-up perspective of distancing practices.

Is SARS-CoV-2 elimination or mitigation best? Regional and disease characteristics determine the recommended strategy

Public health responses to the COVID-19 pandemic varied across the world. Some countries (e.g. mainland China, New Zealand and Taiwan) implemented elimination strategies involving strict travel measures and periods of rigorous non-pharmaceutical interventions (NPIs) in the community, aiming to achieve periods with no disease spread; while others (e.g. many European countries and the USA) implemented mitigation strategies involving less strict NPIs for prolonged periods, aiming to limit community spread. Travel measures and community NPIs have high economic and social costs, and there is a need for guidelines that evaluate the appropriateness of an elimination or mitigation strategy in regional contexts. To guide decisions, we identify key criteria and provide indicators and visualizations to help answer each question. Considerations include determining whether disease elimination is: (1) necessary to ensure healthcare provision; (2) feasible from an epidemiological point of view and (3) cost-effective when considering, in particular, the economic costs of travel measures and treating infections. We discuss our recommendations by considering the regional and economic variability of Canadian provinces and territories, and the epidemiological characteristics of different SARS-CoV-2 variants. While elimination may be a preferable strategy for regions with limited healthcare capacity, low travel volumes, and few ports of entry, mitigation may be more feasible in large urban areas with dense infrastructure, strong economies, and with high connectivity to other regions.

Socio-economic pandemic modelling: case of Spain

A global disaster, such as the recent Covid-19 pandemic, affects every aspect of our lives and there is a need to investigate these highly complex phenomena if one aims to diminish their impact in the health of the population, as well as their socio-economic stability. In this paper we present an attempt to understand the role of the governmental authorities and the response of the rest of the population facing such emergencies. We present a mathematical model that takes into account the epidemiological features of the pandemic and also the actions of people responding to it, focusing only on three aspects of the system, namely, the fear of catching this serious disease, the impact on the economic activities and the compliance of the people to the mitigating measures adopted by the authorities. We apply the model to the specific case of Spain, since there are accurate data available about these three features. We focused on tourism as an example of the economic activity, since this sector of economy is one of the most likely to be affected by the restrictions imposed by the authorities, and because it represents an important part of Spanish economy. The results of numerical calculations agree with the empirical data in such a way that we can acquire a better insight of the different processes at play in such a complex situation, and also in other different circumstances.