Vaccination plays a crucial role in safeguarding individuals with inflammatory bowel disease (IBD) from potential epidemics. In light of the resurgence of COVID-19 in China, unvaccinated IBD patients are vulnerable to infection and potentially serious complications. The aim of this study is to assess the vaccination uptake and willingness among IBD patients, as well as to explore the factors influencing their decision to decline vaccination. An online questionnaire was distributed and analyzed. Bivariate analyses and logistic regression models were used to identify relevant factors. Two hundred and three patients from 243 non-vaccinated respondents were included in the analysis. A total of 167 (82.3%) respondents continued to decline vaccination, with individuals holding stable employment and higher family income displaying significantly lower intent (p < .05). The primary factors contributing to their hesitancy were misinformation and apprehension regarding potential side effects. Obtaining vaccine information from online sources, particularly text-based content, and apprehensions surrounding the adverse effects of COVID-19 vaccination were also found to significantly diminish willingness to receive the vaccine (p < .01). The present study revealed that unreliable information about vaccines is a key factor of hesitancy among non-vaccinated IBD patients. Making efforts to spread true information about the COVID-19 vaccine is of great importance.

The COVID-19 pandemic in New York City (NYC) was characterized by marked disparities in disease burdens across neighborhoods. Accurate neighborhood-level forecasts are critical for planning more equitable resource allocation to reduce health inequalities; however, such spatially high-resolution forecasts remain scarce in operational use. In this study, we analyze aggregated foot traffic data derived from mobile devices to measure the connectivity among 42 NYC neighborhoods driven by various human activities such as dining, shopping, and entertainment. Using real-world time-varying contact patterns in different place categories, we develop a parsimonious behavior-driven epidemic model that incorporates population mixing, indoor crowdedness, dwell time, and seasonality of virus transmissibility. We fit this model to neighborhood-level COVID-19 case data in NYC and further couple this model with a data assimilation algorithm to generate short-term forecasts of neighborhood-level COVID-19 cases in 2020. We find differential contact patterns and connectivity between neighborhoods driven by different human activities. The behavior-driven model supports accurate modeling of neighborhood-level SARS-CoV-2 transmission throughout 2020. In the best-fitting model, we estimate that the force of infection (FOI) in indoor settings increases sublinearly with crowdedness and dwell time. Retrospective forecasting demonstrates that this behavior-driven model generates improved short-term forecasts in NYC neighborhoods compared to several baseline models. Our findings indicate that aggregated foot-traffic data for routine human activities can support neighborhood-level COVID-19 forecasts in NYC. This behavior-driven model may be adapted for use with other respiratory pathogens sharing similar transmission routes.

Predicting human displacements is crucial for addressing various societal challenges, including urban design, traffic congestion, epidemic management, and migration dynamics. While predictive models like deep learning and Markov models offer insights into individual mobility, they often struggle with out-of-routine behaviors. Our study introduces an approach that dynamically integrates individual and collective mobility behaviors, leveraging collective intelligence to enhance prediction accuracy. Evaluating the model on millions of privacy-preserving trajectories across five US cities, we demonstrate its superior performance in predicting out-of-routine mobility, surpassing even advanced deep learning methods. The spatial analysis highlights the model's effectiveness near urban areas with a high density of points of interest, where collective behaviors strongly influence mobility. During disruptive events like the COVID-19 pandemic, our model retains predictive capabilities, unlike individual-based models. By bridging the gap between individual and collective behaviors, our approach offers transparent and accurate predictions, which are crucial for addressing contemporary mobility challenges.

The emergence of highly transmissible SARS-CoV-2 variants has posed significant challenges to public health efforts worldwide. During the summer of 2021, the Delta variant (B.1.617.2) rapidly displaced the Alpha variant (B.1.1.7) in Catalonia, Spain, leading to a resurgence in infections despite ongoing vaccination campaigns. Understanding the epidemiological drivers of this outbreak is critical for refining future mitigation strategies.

We employed a Bayesian age-stratified epidemiological model, incorporating vaccination status and variant-specific transmission dynamics, to analyze the outbreak in Catalonia. The model was calibrated using daily reported cases, hospitalizations, sequencing data, and vaccination coverage across age groups. We inferred contact patterns dynamically to assess their role in the epidemic resurgence and estimated the transmission advantage of the Delta variant over Alpha.

Our analysis revealed that increased social interactions among younger, less vaccinated populations significantly contributed to the surge in infections. The long weekend of Sant Joan (June 23-24) coincided with a peak in contact rates, driving a rise in the reproduction number, particularly among individuals aged 20-29. We estimated that the Delta variant had a 40-60.

Our findings underscore the critical role of vaccination coverage in mitigating the impact of emerging variants. The combination of increased social interactions and uneven vaccine distribution exacerbated the Delta-driven resurgence. NPIs alone proved insufficient in controlling transmission, highlighting the necessity of targeted vaccination strategies to achieve robust epidemic control. This study provides a framework for assessing future variant-specific threats and informing tailored public health interventions.

The novel coronavirus (COVID-19) pandemic has had a devastating global impact, profoundly affecting daily life, healthcare systems, and public health infrastructure. Despite the availability of treatments and vaccines, hospitalizations and deaths continue. Real-time surveillance of infection trends supports resource allocation and mitigation strategies, but reliable forecasting remains a challenge. While deep learning has advanced time-series forecasting, its effectiveness relies on large datasets, a significant obstacle given the pandemic's evolving nature. Most models use national or state-level data, limiting both dataset size and the granularity of insights. To address this, we propose the Fine-Grained Infection Forecast Network (FIGI-Net), a stacked bidirectional LSTM structure designed to leverage county-level data to produce daily forecasts up to two weeks in advance. FIGI-Net outperforms existing models, accurately predicting sudden changes such as new outbreaks or peaks, a capability many state-of-the-art models lack. This approach could enhance public health responses and outbreak preparedness.

During the COVID-19 pandemic, public sentiment and demands have been prominently reflected on social media platforms like Weibo. Understanding these sentiments and demands is crucial for governments, health officials, and policymakers to make effective responses and adjustments.

The study aims to analyze public sentiment and identify key demands concerning COVID-19 policies and social issues using Weibo data, providing insights to improve China's policies and legal systems in public health emergencies.

The study used Python tools to collect public opinion data from Weibo regarding policy adjustments, social issues, and livelihood concerns. A total of 50,249 valid comments on 100 blog posts were collected from December 2019 to October 2023 in China. The SnowNLP algorithm was employed for sentiment analysis, Latent Dirichlet Allocation was used for topic clustering, and sampling coding was applied to further explore public demands by condensing the comment texts.

The study categorized 100 blog posts into 23 important topics, with average sentiment scores ranging from 0.24 to 0.66. These scores ranging from 0 to 1 reflect sentiment polarity, where lower values indicate more negative public sentiment. The topics of material safety and information security management had the lowest scores, at 0.24 and 0.34, respectively. The analysis further revealed that the 23 topics could be classified into 57 subtopics, and a total of 101 concepts were identified through coding. The study found that public demands fall into five key categories: transportation and travel security, epidemic protection and health security, law building and policy implementation, social services and public demand, and education demand.

The study underscores the complexity of public sentiment during the epidemic, with significant concerns about material safety and information security management. Public demands span basic survival needs to higher-order concerns such as education and legal protections. The findings suggest that policy-making processes must become more responsive, transparent, and equitable, incorporating real-time public feedback and ensuring comprehensive policies and legal systems are in place to address multifaceted public demands effectively.

Spatially explicit simulations of complex systems lead to inherent uncertainties in spatial outcomes. Visualizing the temporal propagation of spatial uncertainties is crucial to communicate the reliability of such models. However, the current Uncertainty Analyses (UAs) either consider spatial uncertainty at the end of model runs, or consider non-spatial uncertainties at different model states. To address this, we propose a Spatio-Temporal UA (ST-UA) approach to generate an uncertainty propagation index and visualize the temporal propagation of different uncertainty measures between two temporal model states. We select the total effects sensitivity measure (a Sobol index) for a sample application within the ST-UA approach. The application is the Tobacco Town ABM, a spatial model simulating smoking behaviours. We showcase the effect of the statistical distributions of wages and smoking rates on the propensity to buy cigarettes, which leads to the propagation of uncertainty in the number of purchased cigarettes by individuals. The findings highlight the usefulness of the ST-UA in (i) communicating the reliability of the spatial outcomes of the model; and (ii) guiding modellers towards the spatial areas with relatively high uncertainties at different temporal steps. This approach can be readily transferred to other application areas that are characterized with spatio-temporal uncertainty.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.

In an era of rapid technological advancements, generative artificial intelligence and foundation models are reshaping industries and offering new advanced solutions in a wide range of scientific areas, particularly in public and environmental health. However, foundation models have previously mostly focused on understanding and generating text, while geospatial features, interrelations, flows and correlations have been neglected. Thus, this paper outlines the importance of research into Geospatial Foundation Models, which have the potential to revolutionise digital health surveillance and public health. We examine the latest advances, opportunities, challenges, and ethical considerations of geospatial foundation models for research and applications in digital health. We focus on the specific challenges of integrating geospatial context with foundation models and lay out the future potential for multimodal geospatial foundation models for a variety of research avenues in digital health surveillance and health assessment.

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.

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

To control COVID-19, strict restrictions were implemented in China, leading to a decline in pollutant emissions. However, in the post-pandemic era, pollutants rebounded rapidly, particularly in the Central Plains region, without PM2.5 concentrations meeting national standard. This study analyzed Water-soluble inorganic ions (WSIIs) concentration and sources across different pandemic periods using long-term high-resolution PM2.5, WSIIs and meteorological data from Zhengzhou (ZZ), Anyang (AY), and Xinyang (XY). Results indicated that during the lockdown, WSIIs concentration in the three cities were significantly lower compared to other periods, but rebounded shortly after lifting of restrictions due to human activity resumption. Positive Matrix Factorization analysis showed that secondary aerosol sources were dominant in all cities. Simulations revealed a 90 % reduction of secondary aerosol sources in ZZ could lead to an additional decrease of 2.6 μg·m⁻³ of PM2.5 due to pH change. In AY, a 30 % reduction of secondary aerosol sources resulted in an extra reduction of 14.9 μg·m⁻³ in PM2.5. When the contribution of secondary aerosol sources in XY was decreased to 18 %, PM2.5 concentration decreased by 30 %, achieving an additional reduction of 9.5 μg·m⁻³ . This study offers strategies for achieving PM2.5 compliance and mitigating its impact on health and environment in the post-pandemic era.

Understanding the dynamics of infectious disease spread and predicting clinical outcomes are critical for managing large-scale epidemics and pandemics, such as COVID-19. Effective modeling of disease transmission in interconnected populations helps inform public health responses and interventions across regions.

We developed a novel metapopulation model for simulating respiratory virus transmission in the North America region, specifically for the 96 states, provinces, and territories of Canada, Mexico, and the United States. The model is informed by COVID-19 case data, which are assimilated using the Ensemble Adjustment Kalman filter (EAKF), a Bayesian inference algorithm. Additionally, commuting and mobility data are used to build and adjust the network and movement across locations on a daily basis.

This model-inference system provides estimates of transmission dynamics, infection rates, and ascertainment rates for each of the 96 locations from January 2020 to March 2021. The results highlight differences in disease dynamics and ascertainment among the three countries.

The metapopulation structure enables rapid simulation at a large scale, and the data assimilation method makes the system responsive to changes in system dynamics. This model can serve as a versatile platform for modeling other infectious diseases across the North American region.

We present a simple model of a crucial phenomenon in modern societies: The shift from local to centralized production, leading to economies of scale and mass production transported over long-distance networks. Agents combine two distinct (and limited) resources, time and raw materials, either to produce for self-consumption or to sell on the market. The model shows a rich landscape of diverse production regimes, including mixed regimes where agents optimize utility by combining time spent working for the market, for self-subsistence, or by taking time off.

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.

At the end of 2022, a sudden policy shift in China triggered an unprecedented COVID-19 outbreak that led to a dramatic increase in the consumption of antipyretics. In this study, the occurrence of the two most commonly used antipyretics (ibuprofen and paracetamol) and their metabolites were analyzed in the wastewater of nine major cities in China, covering the periods before, during, and after the policy change. The remarkable surge after the policy change for ibuprofen and paracetamol reached 67 times (in Nanning) and 311 times (in Lanzhou) compared to pre-pandemic levels, respectively. The variation of increases was mainly affected by the availability and the sampling period. During the outbreak period, direct discharge of high-drug-load wastewater could cause even higher risks; the RQ values were 0.43 for invertebrates in Lanzhou and 0.30 for fish in Nanning. Furthermore, during the post-pandemic period, wastewater discharge might pose high risks (RQ value was 2.58 in Xining to algae) associated with ibuprofen chronic toxicity. Fortunately, wastewater treatments would significantly reduce this risk to a low level (RQ < 0.1). In some less developed areas, the lack of a comprehensive wastewater treatment system may lead to the direct discharge of untreated wastewater due to exfiltration of sewers, overflow of combined sewers, or lack of centralized or decentralized treatment facilities. Establishing a comprehensive wastewater treatment system is of great importance, especially in remote and impoverished areas. These results indicated that the potential ecological risks associated with epidemic outbreaks should not be overlooked. These risks may be heightened due to acute toxicity during health incidents, such as the COVID-19 outbreak, providing valuable insights for ecological management in future public health crises.

This manuscript introduces a new Erlang-distributed SEIR model. The model incorporates asymptomatic spread through a subdivided exposed class, distinguishing between asymptomatic ( E a ) and symptomatic ( E s ) cases. The model identifies two key parameters: relative infectiousness, β SA , and the percentage of people who become asymptomatic after being infected by a symptomatic individual, κ . Lower values of these parameters reduce the peak magnitude and duration of the infectious period, highlighting the importance of isolation measures. Additionally, the model underscores the need for strategies addressing both symptomatic and asymptomatic transmissions.

During the 2022 global mpox outbreak, the cumulative number of countries reporting their first imported case quickly rose in the early phase, but the importation rate subsequently slowed down, leaving many countries reporting no cases by the 2022 year-end.

We developed a mathematical model of international dissemination of mpox infections incorporating sexual networks and global mobility data. We used this model to characterize the mpox importation patterns observed in 2022 and to discuss the potential of further international spread.

Our proposed model better explained the observed importation patterns than models not assuming heterogeneity in sexual contacts. Estimated importation hazards decreased in most countries, surpassing the global case count decline, suggesting a reduced per-case risk of importation. We assessed each country's potential to export mpox cases until the end of an epidemic, identifying countries capable of contributing to the future international spread.

The accumulation of immunity among high-risk individuals over highly heterogeneous sexual networks may have contributed to the slowdown in the rate of mpox importations. Nevertheless, the existence of countries with the potential to contribute to the global spread of mpox highlights the importance of equitable resource access to prevent the global resurgence of mpox.

In mid-November, 2021, the SARS-CoV-2 omicron variant (B.1.1.529; BA.1 sublineage) was detected in southern Africa, prompting international travel restrictions. We aimed to investigate the spread of omicron BA.1 in Africa.

In this observational study, samples from patients infected with SARS-CoV-2 from 27 laboratories in 24 African countries, collected between June 1, 2021 and April 14, 2022, were tested for omicron BA.1 and delta (B.1.617.2) variants using real-time RT-PCR. Samples that tested positive for BA.1 by RT-PCR and were collected before estimated BA.1 emergence according to epidemiological properties were excluded from downstream analyses. The diagnostic precision of the assays was evaluated by high-throughput sequencing of samples from four countries. The observed spread of BA.1 was compared with mobility-based mathematical simulations and entries for SARS-CoV-2 in the Global Initiative on Sharing All Influenza Data (GISAID) genomic database. We estimated the effective reproduction number (Rt) at the country level considering the BA.1 fraction and the reported numbers of infections. Phylogeographical analyses were done in a Bayesian framework.

Through testing of 13 294 samples from patients infected with SARS-CoV-2, we established that, by November-December, 2021, omicron BA.1 had replaced the delta variant of SARS-CoV-2 in all African subregions, following a south-north gradient, with a median Rt of 2·60 (95% CI 2·46-2·71). This south-north spread, established on the basis of PCR data, was substantiated by phylogeographical reconstructions, ancestral state reconstructions, and GISAID data. PCR-based reconstructions of country-level BA.1 predominance and the availability of BA.1 genomic sequences in GISAID correlated significantly in time (p=0·0002, r=0·78). The first detections of BA.1 in high-income settings beyond Africa were predicted accurately in time by mobility-based mathematical simulations (p<0·0001). Comparing PCR-based reconstructions with mobility-based mathematical simulations suggested that SARS-CoV-2 infections in Africa were under-reported by approximately ten times. Inbound travellers infected with BA.1, departing from five continents, were identified in six African countries by early December, 2021.

Omicron BA.1 was widespread in Africa when travel bans were implemented, limiting their effectiveness. Combined with genomic surveillance and mobility-based mathematical modelling, PCR-based strategies can inform Rt and the geographical spread of emerging pathogens in a cost-effective and timely manner, and can guide evidence-based, non-pharmaceutical interventions such as travel restrictions or physical distancing.

Bill & Melinda Gates Foundation.

For the French, Portugese and Spanish translations of the abstract see Supplementary Materials section.

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI.

The coronavirus disease 2019 (COVID-19) interventions in interrupting transmission have paid heavy losses politically and economically. The Chinese government has replaced scaling up testing with monitoring focus groups and randomly supervising sampling, encouraging scientific research on the COVID-19 transmission curve to be confirmed by constructing epidemiological models, which include statistical models, computer simulations, mathematical illustrations of the pathogen and its effects, and several other methodologies. Although predicting and forecasting the propagation of COVID-19 are valuable, they nevertheless present an enormous challenge. This paper emphasis on pandemic simulation models by introduced respiratory-specific transmission to extend and complement the classical Susceptible-Exposed-(Asymptomatic)-Infected-Recovered SE(A)IR model to assess the significance of the COVID-19 transmission control features to provide an explanation of the rationale for the government policy. A novel epidemiological model is developed using mean-field theory. Utilizing the SE(A)IR extended framework, which is a suitable method for describing the progression of epidemics over actual or genuine landscapes, we have developed a novel model named SEIAPUFR. This model effectively detects the connections between various stages of infection. Subsequently, we formulated eight ordinary differential equations that precisely depict the population's temporal development inside each segment. Furthermore, we calibrated the transmission and clearance rates by considering the impact of various control strategies on the epidemiological dynamics, which we used to project the future course of COVID-19. Based on these parameter values, our emphasis was on determining the criteria for stabilizing the disease-free equilibrium (DEF). We also developed model parameters that are appropriate for COVID-19 outbreaks, taking into account varied population sizes. Ultimately, we conducted simulations and predictions for other prominent cities in China, such as Wuhan, Shanghai, Guangzhou, and Shenzhen, that have recently been affected by the COVID-19 outbreak. By integrating different control measures, respiratory-specific modeling, and disease supervision sampling into an expanded SEI (A) R epidemic model, we found that supervision sampling can improve early warning of viral activity levels and superspreading events, and explained the significance of containments in controlling COVID-19 transmission and the rationality of policy by the influence of different containment measures on the transmission rate. These results indicate that the control measures during the pandemic interrupted the transmission chain mainly by inhibiting respiratory transmission, and the proportion of supervision sampling should be proportional to the transmission rate, especially only aimed at preventing a resurgence of SARS-CoV-2 transmission in low-prevalence areas. Furthermore, The incidence hazard of Males and Females was 1.39(1.23-1.58), and 1.43(1.26-1.63), respectively. Our investigation found that the ratio of peak sampling is directly related to the transmission rate, and both decrease when control measures are implemented. Consequently, the control measures during the pandemic interrupted the transmission chain mainly by inhibiting respiratory transmission. Reasonable and effective interventions during the early stage can flatten the transmission curve, which will slow the momentum of the outbreak to reduce medical pressure.

Understanding the dispersal patterns of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) lineages is crucial to public health decision-making, especially in countries with limited access to viral genomic sequencing. This study provides a comprehensive epidemiological and phylodynamic perspective on SARS-CoV-2 lineage dispersal in Iran from February 2020 to July 2022. We explored the genomic epidemiology of SARS-CoV-2 combining 1281 genome sequences with spatial data in a phylogeographic framework. Our analyses shed light on multiple international imports seeding subsequent waves and on domestic dispersal dynamics. Lineage B.4 was identified to have been circulating in Iran, 29 days (95% highest probability density interval: 21-47) before non-pharmaceutical interventions were implemented. The importation dynamics throughout subsequent waves were primarily driven from the country or region where the variant was first reported and gradually shifted to other regions. At the national level, Tehran was the main source of dissemination across the country. Our study highlights the crucial role of continuous genomic surveillance and international collaboration for future pandemic preparedness and efforts to control viral transmission.

When considering airborne epidemic spreading in social systems, a natural connection arises between mobility and epidemic contacts. As individuals travel, possibilities to encounter new people either at the final destination or during the transportation process appear. Such contacts can lead to new contagion events. In fact, mobility has been a crucial target for early non-pharmaceutical containment measures against the recent COVID-19 pandemic, with a degree of intensity ranging from public transportation line closures to regional, city or even home confinements. Nonetheless, quantitative knowledge on the relationship between mobility-contagions and, consequently, on the efficiency of containment measures remains elusive. Here we introduce an agent-based model with a simple interaction between mobility and contacts. Despite its simplicity, our model shows the emergence of a critical mobility level, inducing major outbreaks when surpassed. We explore the interplay between mobility restrictions and the infection in recent intervention policies seen across many countries, and how interventions in the form of closures triggered by incidence rates can guide the epidemic into an oscillatory regime with recurrent waves. We consider how the different interventions impact societal well-being, the economy and the population. Finally, we propose a mitigation framework based on the critical nature of mobility in an epidemic, able to suppress incidence and oscillations at will, preventing extreme incidence peaks with potential to saturate health care resources.

To synthesise the role of digital technologies in epidemic control and prevention, focussing on Ebola and COVID-19.

A scoping review.

A systematic search was done on PubMed, HINARI, Web of Science, Google Scholar and a direct Google search until 10 September 2024.

We included all qualitative and quantitative studies, conference papers or abstracts, anonymous reports, editorial reports and viewpoints published in English.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews checklist was used to select the included study. Data analysis was performed using Gale's framework thematic analysis method, resulting in the identification of key themes.

A total of 64 articles that examined the role of digital technology in the Ebola and COVID-19 pandemics were included in the final review. Five main themes emerged: digital epidemiological surveillance (using data visualisation tools and online sources for early disease detection), rapid case identification, community transmission prevention (via digital contact tracing and assessing interventions with mobility data), public education messages and clinical care. The identified barriers encompassed legal, ethical and privacy concerns, as well as organisational and workforce challenges.

Digital technologies have proven good for disease prevention and control during pandemics. While the adoption of these technologies has lagged in public health compared with other sectors, tools such as artificial intelligence, telehealth, wearable devices and data analytics offer significant potential to enhance epidemic responses. However, barriers to widespread implementation remain, and investments in digital infrastructure, training and strong data protection are needed to build trust among users. Future efforts should focus on integrating digital solutions into health systems, ensuring equitable access and addressing ethical concerns. As public health increasingly embraces digital innovations, collaboration among stakeholders will be crucial for effective pandemic preparedness and management.

As an essential component of urban natural sources, isoprene has strong interactions and synergies with anthropogenic precursors (volatile organic compounds and nitrogen oxides) of ozone (O3), influencing O3 formation in urban areas. However, the variability of these effects under different anthropogenic emission scenarios has not been fully understood. This study, utilizing observational data from Dezhou (a medium-sized city in the center of North China Plain) from May to September in both 2019 and 2020, and incorporating four future scenarios based on Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5), unravels the mechanisms of O3 formation and the impact of isoprene on O3 production using an observation-based box model. Our observation results showed that O3 concentrations were lower in 2020 compared to 2019. The box model analysis suggests a shift in O3 formation from a VOC-limited regime in 2019 to a transition regime in 2020, primarily due to a significant reduction in anthropogenic emissions. Isoprene photochemistry contributed to 7% and 11% of the daytime average net O3 production rates in 2019 and 2020, respectively. The increase can be attributed to a notable rise in RO2 radicals, from 14% in 2019 to 19% in 2020. Under the future scenario with the lowest projected anthropogenic emissions (SSP1-2.6), isoprene-derived RO2 radicals (generated through isoprene oxidation) are expected to account for 36% of the total RO2 radicals. We also use a quasi-EKMA diagram to illustrate the contribution of isoprene to O3 concentrations, highlighting the nonlinear amplification or reduction of its contribution depending on changes in anthropogenic VOCs and NOx concentrations. This nonlinear relationship is influenced by NOx concentrations, with the impact of isoprene being reduced at lower NOx levels. To effectively mitigate long-term O3 pollution, especially given the growing contribution of biogenic precursors, it is crucial to focus on reducing NOx emissions.

This study delves into the parenting cognition perspectives on COVID-19 in children, exploring symptoms, transmission modes, and protective measures. It aims to correlate these perspectives with sociodemographic factors and employ advanced machine-learning techniques for comprehensive analysis.

Data collection involved a semi-structured questionnaire covering parental knowledge and attitude on COVID-19 symptoms, transmission, protective measures, and government satisfaction. The analysis utilised the Generalised Linear Regression Model (GLM), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and AdaBoost (AB).

The study revealed an average knowledge score of 18.02 ± 2.9, with 43.2 and 52.9% of parents demonstrating excellent and good knowledge, respectively. News channels (85%) emerged as the primary information source. Commonly reported symptoms included cough (96.47%) and fever (95.6%). GLM analysis indicated lower awareness in rural areas (β = -0.137, p < 0.001), lower attitude scores in males compared to females (β = -0.64, p = 0.025), and a correlation between lower socioeconomic status and attitude scores (β = -0.048, p = 0.009). The SVM classifier achieved the highest performance (66.70%) in classification tasks.

This study offers valuable insights into parental attitudes towards COVID-19 in children, highlighting symptom recognition, transmission awareness, and preventive practices. Correlating these insights with sociodemographic factors underscores the need for tailored educational initiatives, particularly in rural areas, and for addressing gender and socioeconomic disparities. The efficacy of advanced analytics, exemplified by the SVM classifier, underscores the potential for informed decision-making in public health communication and targeted interventions, ultimately empowering parents to safeguard their children's well-being amidst the ongoing pandemic.

The COVID-19 outbreak has significantly impacted human lifestyles and life patterns. Therefore, data related to human social life may tell us the increase or decrease in the number of confirmed COVID-19 cases. However, although the number of confirmed cases is affected by social life, it is difficult to find studies that attempt to predict the number of confirmed cases using various lifestyle data. This paper attempted an exploratory data analysis to see if the number of confirmed cases could be predicted more accurately by including various lifestyle data.

We included taking public transportation, watching a movie at the cinema, and accommodation at a motel in the lifestyle data. Finally, a 'lifestyle addition' set was constructed that added lifestyle data to the number of past confirmed cases and search term frequency data. The deep learning algorithms used in the analysis are deep neural networks (DNNs) and recurrent neural networks (RNNs). Performance differences across data sets and between deep learning models were tested to be statistically significant.

Among metropolitan cities in South Korea, Seoul (9.6 million) with the largest population and Busan (3.4 million) with the second largest population had the lowest error rate in 'lifestyle addition' set. When predicting with the 'lifestyle addition' set, in Seoul, the error rate was reduced to 20.1%, and in Busan, the graph of the actual number of confirmed cases and the predicted graph were almost identical.

Through this study, we were able to identify three notable results that could contribute to predicting the number of patients infected with epidemic in the future.

This paper theoretically investigates externalities and policy interventions in travel during a pandemic. We develop a tractable static model of two regions from a short-run perspective. The model shows that the externalities can be both negative and positive, depending on regional asymmetry. Thus, even when infectious diseases are widespread, travel restrictions do not necessarily reduce infections and do not necessarily improve social welfare. A formula for the optimal policy intervention is derived and shown to be the weighted average of four types of externalities defined by the direction of travel and the epidemiological status of a traveler.

Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological, and virological data, integrating different data sources. We propose a novel-combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic, and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across countries simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn-winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn-winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power. The multiscale approach enables model selection yielding a novel computational framework for describing global influenza dynamics at different scales-local transmission and national epidemics vs. international coupling through mobility and imported cases. Our findings have important implications to improve preparedness against seasonal influenza epidemics. The approach can be generalized to other epidemic contexts, such as emerging disease outbreaks to improve the flexibility and predictive power of modeling.

The presence of erratic or unstable paths in standard kinetic Monte Carlo simulations significantly undermines the accurate simulation and sampling of transition pathways. While typically reliable methods, such as the Gillespie algorithm, are employed to simulate such paths, they encounter challenges in efficiently identifying rare events due to their sequential nature and reliance on exact Monte Carlo sampling. In contrast, the weighted-ensemble method effectively samples rare events and accelerates the exploration of complex reaction pathways by distributing computational resources among multiple replicas, where each replica is assigned a weight reflecting its importance, and evolves independently from the others. Here, we implement the highly efficient and robust weighted-ensemble method to model susceptible-infected-susceptible dynamics on large heterogeneous population networks, and explore the interplay between stochasticity and contact heterogeneity, which ultimately gives rise to disease clearance. Studying a wide variety of networks characterized by fat-tailed asymmetric degree distributions, we are able to compute the mean time to extinction and quasistationary distribution around it in previously inaccessible parameter regimes.

This paper describes Epihiper, a state-of-the-art, high performance computational modeling framework for epidemic science. The Epihiper modeling framework supports custom disease models, and can simulate epidemics over dynamic, large-scale networks while supporting modulation of the epidemic evolution through a set of user-programmable interventions. The nodes and edges of the social-contact network have customizable sets of static and dynamic attributes which allow the user to specify intervention target sets at a very fine-grained level; these also permit the network to be updated in response to nonpharmaceutical interventions, such as school closures. The execution of interventions is governed by trigger conditions, which are Boolean expressions formed using any of Epihiper's primitives (e.g. the current time, transmissibility) and user-defined sets (e.g. people with work activities). Rich expressiveness, extensibility, and high-performance computing responsiveness were central design goals to ensure that the framework could effectively target realistic scenarios at the scale and detail required to support the large computational designs needed by state and federal public health policymakers in their efforts to plan and respond in the event of epidemics. The modeling framework has been used to support the CDC Scenario Modeling Hub for COVID-19 response, and was a part of a hybrid high-performance cloud system that was nominated as a finalist for the 2021 ACM Gordon Bell Special Prize for high performance computing-based COVID-19 Research.

The emergence of SARS-CoV-2 variants of concern (VOCs) punctuated the dynamics of the COVID-19 pandemic in multiple occasions. The stages subsequent to their identification have been particularly challenging due to the hurdles associated with a prompt assessment of transmissibility and immune evasion characteristics of the newly emerged VOC. Here, we retrospectively analyze the performance of a modeling strategy developed to evaluate, in real-time, the risks posed by the Alpha and Omicron VOC soon after their emergence. Our approach utilized multi-strain, stochastic, compartmental models enriched with demographic information, age-specific contact patterns, the influence of non-pharmaceutical interventions, and the trajectory of vaccine distribution. The models' preliminary assessment about Omicron's transmissibility and immune evasion closely match later findings. Additionally, analyses based on data collected since our initial assessments demonstrate the retrospective accuracy of our real-time projections in capturing the emergence and subsequent dominance of the Alpha VOC in seven European countries and the Omicron VOC in South Africa. This study shows the value of relatively simple epidemic models in assessing the impact of emerging VOCs in real time, the importance of timely and accurate data, and the need for regular evaluation of these methodologies as we prepare for future global health crises.

Pre-COVID-19, most of the supply chains functioned with more capacity than demand. However, COVID-19 changed traditional supply chains' dynamics, resulting in more demand than their production capacity. This article presents a multiobjective and multiperiod supply chain network design along with customer prioritization, keeping in view price discounts and outsourcing strategies to deal with the situation when demand exceeds the production capacity. Initially, a multiperiod, multiobjective supply chain network is designed that incorporates prices discounts, customer prioritization, and outsourcing strategies. The main objectives are profit and prioritization maximization and time minimization. The introduction of the prioritization objective function having customer ranking as a parameter and considering less capacity than demand and outsourcing differentiates this model from the literature. A four-valued neutrosophic multiobjective optimization method is introduced to solve the model developed. To validate the model, a case study of the supply chain of a surgical mask is presented as the real-life application of research. The research findings are useful for the managers to make price discounts and preferred customer prioritization decisions under uncertainty and imbalance between supply and demand. In future, the logic in the proposed model can be used to create web application for optimal decision-making in supply chains.

The COVID-19 pandemic has highlighted the crucial role of health sector decision-makers in establishing and evaluating effective treatment and prevention policies. To inform sound decisions, it is essential to simultaneously monitor multiple pandemic characteristics, including transmission rates, infection rates, recovery rates (which indicate treatment efficacy), and fatality rates. This study introduces an innovative application of existing methodologies: the Multivariate Exponentially Weighted Moving Average (MEWMA) and Multivariate Cumulative Sum (MCUSUM) control charts (CCs), used for monitoring the parameters of the Susceptible, Exposed, Infected, Recovered, Death, and Vaccination (SEIRDV) model. The methodology is applied to COVID-19 data from the State of Qatar, offering new insights into the pandemic's dynamics. By monitoring changes in the model parameters, this study aims to assess the effectiveness of interventions and track the impact of emerging variants. The results underscore the practical utility of these methodologies for decision-making during similar pandemics. Additionally, this study employs an augmented particle Markov chain Monte Carlo scheme that enables real-time monitoring of SEIRDV model parameters, offering improved estimation accuracy and robustness compared to traditional approaches. The results demonstrate that MEWMA and MCUSUM charts are effective tools for monitoring SEIRDV model parameters and can support decision-making in any similar pandemic.

Personal mobility data from mobile phones and other sensors are increasingly used to inform policymaking during pandemics, natural disasters, and other humanitarian crises. However, even aggregated mobility traces can reveal private information about individual movements to potentially malicious actors. This paper develops and tests an approach for releasing private mobility data, which provides formal guarantees over the privacy of the underlying subjects. Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. Taken together, these results can help enable the responsible use of private mobility data in humanitarian response.

The COVID-19 pandemic has significantly impacted communities worldwide, and effective management strategies are critical to reduce transmission rates and minimize the impact of the disease. In this study, we modeled and analyzed the COVID-19 transmission dynamics and derived relevant epidemiological values for three regions of the Philippines, namely, the National Capital Region (NCR), Davao City, and Baguio City, under different community quarantine implementations. The unique features and differences of these regions-of-interest were accounted for in simulating the disease spread and in estimating key epidemiological parameters fitted to the reported COVID-19 cases. Results support the robustness of the model formulated and provides insights into the effect of the government's implemented intervention protocols. With a forecasting feature, this modeling framework is beneficial for science-based decision support, policy making, and assessment for recent and future pandemics wherever regions-of-interest.

The COVID-19 pandemic has generated a demand for timely data, resulting in a surge of mobile phone surveys for tracking the impacts of and responses to the pandemic. Mobile phone surveys have become a preferred mode of data collection across low- and middle-income countries.

This study piloted 2 population-based, cross-sectional mobile phone surveys among Sri Lankan residents in 2020 and 2021 during the COVID-19 pandemic. The surveys aimed to gather data on knowledge, attitudes, and practices, vaccine acceptability, availability, and barriers to COVID-19 testing, and use of a medicine distribution service.

The study used Surveda, an open-source survey tool developed by the NCD (noncommunicable disease) Mobile Phone Survey Data 4 Health Initiative, for data collection and management. The surveys were conducted through interactive voice response using automated, prerecorded messages in Sinhala, Tamil, and English. The sample design involved random sampling of mobile phone numbers, stratified by sex, proportional to the general population. Eligibility criteria varied between surveys, targeting adults aged 35 years and older with any noncommunicable disease for the first survey and all adults for the second survey. The data were adjusted to population estimates, and statistical analysis was conducted using SAS (SAS Institute) and R software (R Core Team). Descriptive statistics, Rao-Scott chi-square tests, and z tests were used to analyze the data. Response rates, cooperation rates, and productivity of the sampling approach were calculated.

In the first survey, n=5001, the overall response rate was 7.5%, with a completion rate of 85.6%. In the second survey, n=1250, the overall response rate was 10.9%, with a completion rate of 61.9%. Approximately 3 out of 4 adults reported that they avoided public places (888/1175, 75.6%), more than two-thirds avoided public transportation (808/1173, 68.9%), and 9 out of 10 practiced physical distancing (1046/1167, 89.7%). Approximately 1 out of 10 Sri Lankan persons reported being tested for COVID-19, and the majority of those received a polymerase chain reaction test (112/161, 70%). Significantly more males than females reported being tested for COVID-19 (98/554, 17.8% vs 61/578, 10.6%, respectively; P<.001). Finally, the majority of adult Sri Lankan people reported that they definitely or probably would get the COVID-19 vaccination (781/1190, 65.7%).

The surveys revealed that, overall, the adult Sri Lankan population adhered to COVID-19 mitigation strategies. These findings underscore the use of mobile phone surveys in swiftly and easily providing essential data to inform a country's response during the COVID-19 pandemic, obviating the need for face-to-face data collection.

This research examines the correlation between the COVID-19 pandemic and the desire to engage in compensatory consuming behaviors, specifically emphasizing emotional eating as a psychological coping strategy, particularly with respect to snacks and sweets. Conducting sentiment analysis by using a Natural Language Processing (NLP) method on posts from Sina Weibo, a leading Chinese social media platform, the research identifies three distinct phases of consumer behavior during the pandemic: anxiety, escapism, and compensatory periods. These stages are marked by varying degrees of emotional eating tendencies, illustrating a psychological trajectory from initial shock to seeking comfort through food as a means of regaining a sense of normalcy and control. The analysis reveals a notable increase in posts expressing a desire for compensatory consumption of snacks and sweets in 2020 compared to 2019, indicating a significant shift towards emotional eating amid the pandemic. This shift reflects the broader psychological impacts of the crisis, offering insights into consumer behavior and the role of digital platforms in capturing public sentiment during global crises. The findings have implications for policymakers, health professionals, and the food industry, suggesting the need for strategies to address the psychological and behavioral effects of natural disasters.