In the absence of definitive treatments or vaccines, the primary strategy to mitigate the COVID-19 pandemic relied on non-pharmaceutical interventions. By the end of 2020, COVID-19 vaccines had been developed and initiated for preventive purposes. To better understand the association between various mitigation measures and their impact on the pandemic, we analyzed the effect of vaccination coverage, international travel, traveler positivity rates, and the stringency of public health measures on the incidence of COVID-19 cases and hospitalizations at the provincial level in Belgium. We identified several important interactions among the covariates that influence the incidence of COVID-19 confirmed cases. Specifically, the best-fitting model (AIC = 965.658) revealed significant interactions between lagged vaccination coverage and the stringency index, as well as between incoming travel rates and positivity rates. Additionally, when modeling COVID-19 hospitalizations, a significant interaction was observed between the incoming travel rate and the stringency index. Model performance improved substantially when incorporating the incidence of confirmed cases as a covariate (AIC = 1061.516 vs. AIC = 432.708), while highlighting key interactions between confirmed cases and traveler positivity rates, as well as between lagged vaccination coverage and incoming travel rates. These findings underscore the intricate interplay between public health interventions, population immunity, and mobility patterns in shaping the course of the COVID-19 pandemic.

Governments used travel bans during the COVID-19 pandemic to limit the introduction of new variant of concern (VoC). In the Netherlands, direct flights from South Africa were banned from 26 November 2021 onwards to curb Omicron (B.1.1.529) importation.

This study retrospectively evaluated the effect of the South African travel ban and the timing of its implementation on subsequent Omicron infections in the Netherlands and, in order to help inform future decision-making, assessed alternative scenarios in which the reproduction number (Re) and volume of indirectly imported cases were varied.

Descriptive analysis and modelling study.

Time (days) from 26 November 2021 to reach 10 000 cumulative Omicron infections in the Netherlands.

To benchmark the direct importation rate of Omicron from South Africa, we used the proportion (n/N, %) of passengers arriving on two direct flights from South Africa to the Netherlands on 26 November 2021 with a positive PCR sequencing result for Omicron VoC infection. We scaled the number of directly-imported Omicron infections before and after the travel ban to the incidence in South Africa. We assumed that 10% of all cases continued to arrive via indirect routes, a 'failure rate' of 2% (ie, incoming Dutch citizens not adhering to quarantine on arrival) and an effective reproduction number (Re) of Omicron of 1.3. In subsequent analyses, we varied, within plausible limits, the Re (1.1-2.0) and proportion of indirectly-imported cases (0-20%).

Compared with no travel ban, the travel ban achieved a 14-day delay in reaching 10 000 Omicron cases, with an additional day of delay if initiated 2 days earlier. If all indirect importation had been prevented (eg, European-wide travel ban), a 21-day delay could have been achieved. The travel ban's effect was negligible if Re was ≥2.0 and with a greater volume of ongoing importation.

Travel bans can delay the calendar timing of an outbreak but are substantially less effective for pathogens where importation cannot be fully controlled and tracing every imported case is unfeasible. When facing future disease outbreaks, we urge policy-makers to critically weigh up benefits against the known socioeconomic drawbacks of international travel restrictions.

With the acceleration of globalization, the connections between emerging market economies are becoming increasingly intricate, making it crucial to understand the mechanisms of risk transmission. This study employs the transfer entropy model to analyze risk diffusion and network resilience across ten emerging market countries. The findings reveal that Brazil, Mexico, and Saudi Arabia are the primary risk exporters, while countries such as India, South Africa, and Indonesia predominantly act as risk receivers. The research highlights the profound impact of major events such as the 2008 global financial crisis and the 2020 COVID-19 pandemic on risk diffusion, with risk diffusion peaking during the pandemic. Additionally, the study underscores the importance of network resilience, suggesting that certain levels of noise and shocks can enhance resilience and improve network stability. While the global economy gradually recovered following the 2008 financial crisis, the post-pandemic recovery has been slower, with external shocks and noise presenting long-term challenges to network resilience. This study emphasizes the importance of understanding network resilience and risk diffusion mechanisms, offering new insights for managing risk transmission in future global economic crises.

China's Zero-COVID Policy imposed stringent restrictions on citizens' mobility to curb the spread of COVID-19. While effective in reducing viral transmission, these measures may have inadvertently delayed or deterred vaccine uptake by fostering a heightened sense of security. This study examines the relationships between intra- and inter-regional travel mobility and individual hesitancy towards COVID-19 vaccines (HCV), leveraging the Baidu Mobility Index and data from a cross-sectional survey of 12,000 participants. Our descriptive analysis reveals that (a) individual attitudes toward COVID-19 vaccines are more polarized across regions with different mobility levels than toward vaccines in general and (b) regions with higher population mobility exhibit lower levels of hesitancy toward COVID-19 vaccines. Our OLS and IV results further demonstrate that a one-standard-deviation increase in inter-provincial travel rates is associated with a decrease of 0.0112-0.0195 standard deviations in HCV, whereas intra-provincial mobility is not correlated. Overall, this paper suggests prioritizing the roll-out of COVID-19 vaccines or similar initiatives in areas with higher mobility levels, where residents perceive greater risks and exhibit a higher likelihood of seeking vaccination.

It is now understood that the COVID-19 pandemic and its associated containment measures have affected the epidemiology of other respiratory viruses. This study aimed to investigate respiratory pathogen infections in Shenzhen during and after the COVID-19 pandemic.

A retrospective analysis was conducted on test data from 24,814 patients at Shenzhen Third People's Hospital between January 2021 and December 2023. The analysis focused on changes in detection rates, epidemiological characteristics, and clinical features of respiratory pathogens, including three viruses and eight bacteria.

The overall positivity rate for respiratory viruses increased after the COVID-19 epidemic (P < 0.05), whereas no significant difference was detected in the overall positivity rate of most respiratory bacteria. Notably, the detection rates of influenza A and B increased after the COVID-19 epidemic, with influenza A showing the most significant increase from 4.5 to 10.8% (P < 0.05). Conversely, the detection rates of PAE and MRSA decreased significantly (P < 0.05), whereas those of HIN and SMA increased significantly (P < 0.05). The seasonal patterns of influenza A changed markedly, with a shift in peak occurrence and extended periods of high positivity. The age distribution of infections also shifted, with adults showing higher detection rates after the pandemic than school-aged children and elderly individuals did.

The removal of non-pharmaceutical interventions following the COVID-19 pandemic has significantly affected the epidemiological and seasonal patterns of certain respiratory pathogens in Shenzhen. These findings highlight the need for continuous surveillance of multiple respiratory pathogens and adaptive public health strategies in the post-pandemic era.

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 serial interval (SI) distribution of an epidemic is used to approximate the generation time distribution, an essential parameter for inferring the transmissibility (${R}_t$) of an infectious disease. However, SI distributions may change as an epidemic progresses. We examined detailed contact tracing data on laboratory-confirmed cases of COVID-19 in Hong Kong, China, during the 5 COVID-19 waves from January 2020 to July 2022. We reconstructed the transmission pairs and estimated time-varying effective SI distributions and factors associated with longer or shorter intervals. Finally, we assessed the biases in estimating transmissibility using constant SI distributions. We found clear temporal changes in mean SI estimates within each epidemic wave studied and across waves, with mean SIs ranging from 5.5 days (95% credible interval, 4.4-6.6) to 2.7 days (95% credible interval, 2.2-3.2). The mean SIs shortened or lengthened over time, which was found to be closely associated with the temporal variation in COVID-19 case profiles and public health and social measures and could lead to biases in predicting ${R}_t$. Accounting for the impact of these factors, the time-varying quantification of SI distributions could lead to improved estimation of ${R}_t$, and could provide additional insights into the impact of public health measures on transmission.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widespread since 2020 and will likely continue to cause substantial recurring epidemics. However, understanding the underlying infection burden and dynamics, particularly since late 2021 when the Omicron variant emerged, is challenging. Here, we leverage extensive surveillance data available in New York City (NYC) and a comprehensive model-inference system to reconstruct SARS-CoV-2 dynamics therein through August 2023.

We fit a metapopulation network SEIRSV (Susceptible-Exposed-Infectious-(re)Susceptible-Vaccination) model to age- and neighborhood-specific data of COVID-19 cases, emergency department visits, and deaths in NYC from the pandemic onset in March 2020 to August 2023. We further validate the model-inference estimates using independent SARS-CoV-2 wastewater viral load data.

The validated model-inference estimates indicate a very high infection burden-the number of infections (i.e., including undetected asymptomatic/mild infections) totaled twice the population size ( > 5 times documented case count) during the first 3.5 years. Estimated virus transmissibility increased around 3-fold, whereas estimated infection-fatality risk (IFR) decreased by >10-fold during this period. The detailed estimates also reveal highly complex variant dynamics and immune landscape, and higher infection risk during winter in NYC over the study period.

This study provides highly detailed epidemiological estimates and identifies key transmission dynamics and drivers of SARS-CoV-2 during its first 3.5 years of circulation in a large urban center (i.e., NYC). These transmission dynamics and drivers may be relevant to other populations and inform future planning to help mitigate the public health burden of SARS-CoV-2.

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.

The COVID-19 vaccine offers the most effective means to control the pandemic. Understanding vaccine acceptance is crucial due to high levels of vaccine hesitancy and relatively low vaccination coverage. The aim of this study is to assess the general population's knowledge and attitude toward COVID-19 vaccination.

An explanatory mixed-method approach was used among the general population attending outpatient services at a tertiary care hospital. A total of 369 eligible individuals encountered during the data collection period were included. Their knowledge and attitude regarding COVID-19 vaccination were assessed using a self-structured questionnaire. Additionally, in-depth interviews were conducted to explore reasons for vaccine hesitancy. Statistical analyses included mean with standard deviation (SD) or median with interquartile range (IQR), and correlation coefficients.

Among the 369 participants, 226 (61.2%) demonstrated moderate knowledge, while 241 (65.3%) exhibited a favorable attitude toward the COVID-19 vaccine. A positive correlation between knowledge and attitude was observed (r = 0.114, p = 0.029). Participants perceived the vaccine as a means to prevent infection, build immunity, and ensure safety. Hesitancy stemmed from concerns about side effects, age, health issues, fear of needles, lack of awareness, and media influence.

The primary factor driving vaccine hesitancy was fear of adverse effects following vaccination. Misinformation and fear are significant barriers to achieving global vaccination goals, requiring targeted interventions to enhance awareness and acceptance.

This study evaluates the impact of vehicular emissions on polycyclic aromatic hydrocarbons (PAHs) in roadside soils in Shanghai during the COVID-19 lockdown period. Soil samples from roadside lawns were collected, with PAH concentrations ranging from 153 to 5639 ng g-1. A significant reduction in PAH levels compared to their pre-COVID-19 levels (Kruskal-Wallis H test, p < 0.05) was observed in surface soil samples, highlighting the contribution of traffic and coal combustion to urban pollution. Source identification, using molecular diagnostic ratios and principal component analysis, revealed that vehicular emissions were the primary contributors to PAHs in Shanghai's roadside soils. The toxic equivalent quantity for benzo[a]pyrene concentrations in the soil samples was associated with these sources. The incremental lifetime cancer risk model indicated that adult exposure to PAHs in the soil posed health risks greater than 10-6 but lower than 10-4, suggesting a low-risk level. These findings suggest that targeted measures in the transportation sector could improve urban soil quality and reduce associated health risks.

This study analyzed the association between social vulnerability indicators and the incidence rate of severe COVID-19 in pregnant women in Pernambuco State, Brazil, between 2020 and 2021. It is an ecological study that assessed severe cases of COVID-19 in pregnant women reported to the Influenza Surveillance System in Brazil. To determine such association, the zero adjusted Gamma (ZAGA) regression model was applied due to the large number of zeros in the response variable. Variables available in the Demographic Census were used, representing socioenvironmental conditions, household characteristics, and urban services. In the study period, 475 severe cases of COVID-19 were reported in pregnant women, with an incidence rate of 1.40 cases per 1,000 live births. Modeling with ZAGA showed that the mean incidence rate is affected by the illiteracy rate, with the average increasing by a relative 5.1% for every 1% (p = 0.024). The ZAGA model also estimates the chance of a municipality having a zero rate, with these values increasing by 2.7% for every 1% of the proportion of Family Health Strategy coverage, by 19.3% for every 0.01 of the Municipal Human Development Index (M-HDI) education dimension, and by 21.3% for every 0.01 of the M-HDI longevity dimension. When the M-HDI increases, the chance of the municipality having a zero rate decreases by 33.8% for every 0.01. Population density reduces the chance by 4.5% for every 10 inhabitants/km2. This study highlighted the influence of social vulnerability indicators on the incidence of severe COVID-19 cases in pregnant women, showing that some aspects of social and demographic characteristics are related to such influence.

ObjectivesCommunity pharmacies play a crucial role in China's primary health care system. This study aimed to assess the impact of stringent COVID-19 policy responses - such as lockdowns, travel restrictions and operational closures - on unannounced standardized patients' (USPs) satisfaction with community pharmacy services.MethodsA cross-sectional study was conducted from April 2021 to September 2022, using an USPs approach in community pharmacies across China. USPs' satisfaction was measured using validated tools, with closure policies related to COVID-19 as the primary exposure variable.ResultsThe study included 1076 eligible USP visits to community pharmacies. Results indicated that stricter closure policies had a significant negative impact on USPs' satisfaction (β = -0.18, p = 0.019). This negative effect may be attributed to worsened capability of pharmaceutical service providers (β = -0.17, p = 0.002) and accessibility (β = -0.12, p = 0.019). Subgroup analyses demonstrated a negative correlation between stricter closure policies and lower satisfaction levels with regard to accessibility, capability, and communication.ConclusionsCOVID-19 closure policies in China had adverse consequences for the quality of pharmacy services. These findings highlight that governments must act with urgency when addressing abrupt infectious diseases or public health emergencies. Enhancing access to pharmacy services and capability of providers are critical strategies to ensure an effective response to sudden public health crises.

Human mobility is becoming increasingly complex in urban environments. However, our fundamental understanding of urban population dynamics, particularly the pulsating fluctuations occurring across different locations and timescales, remains limited. Here, we use mobile device data from large cities and regions worldwide combined with a detrended fractal analysis to uncover a universal spatiotemporal scaling law that governs urban population fluctuations. This law reveals the scale invariance of these fluctuations, spanning from city centers to peripheries over both time and space. Moreover, we show that at any given location, fluctuations obey a time-based scaling law characterized by a spatially decaying exponent, which quantifies their relationship with urban structure. These interconnected discoveries culminate in a robust allometric equation that links population dynamics with urban densities, providing a powerful framework for predicting and managing the complexities of urban human activities. Collectively, this study paves the way for more effective urban planning, transportation strategies, and policies grounded in population dynamics, thereby fostering the development of resilient and sustainable cities.

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.

The COVID-19 pandemic has underscored the critical need for effective public health strategies to combat infectious diseases. This study examines the epidemiological characteristics and spatial distribution of COVID-19 incidence and mortality in Zanjan Province, northwest Iran, to inform future epidemic preparedness. Using data from 39,739 hospitalized COVID-19 cases recorded between February 2020 and September 2021, sourced from the Medical Care Monitoring Center, we conducted descriptive and geospatial analyses. Demographic, clinical, and spatial variables were analyzed using logistic regression and advanced spatial techniques, including Kernel Density Estimation and Local Moran's I, to identify risk factors and disease hotspots. Results revealed that women accounted for 52% of cases, with higher incidence rates, while men exhibited higher mortality rates (7.86% vs. 7.80%). Urban areas, particularly the provincial capital, were identified as hotspots, with the highest patient density (20,384 cases per 10 km²). Comorbidities such as HIV/AIDS (OR: 4.85), chronic liver disease (OR: 3.6), chronic blood diseases (OR: 2.8), and cancer (OR: 2.5) significantly increased mortality risk, with ventilator use showing the highest odds ratio for death (OR = 91). Vaccination significantly reduced mortality, with fully vaccinated individuals experiencing a 6.3% mortality rate compared to 8.1% in unvaccinated individuals. Spatial analysis highlighted population density and mobility as key drivers of disease spread. These findings emphasize the importance of integrating spatial and epidemiological data to enhance pandemic preparedness. Targeted interventions in urban hotspots, early detection systems, and prioritizing vaccination for high-risk populations are critical for mitigating future outbreaks. This study provides a foundation for evidence-based public health strategies to strengthen global epidemic response and improve preparedness for future health crises.

The COVID-19 pandemic was one of the most serious public health events of the 21st century, which had a profound impact on the entire human society and sparked extensive debate and research on public health crisis management. To clarify the development path of the issue and to discover the structure and internal logic of related studies, this study conducted a scientometric analysis (co-citation analysis, co-occurrence analysis, cooperation network analysis, knowledge domain migration analysis) of 8814 publications from the Web of Science Core Collection and PubMed using CiteSpace, and drew the following conclusions: (1) The research focuses on empirical studies in medicine and other fields, and expands to non-medical fields such as "social media", "COVID-19 lockdown", and "air quality"; (2) The USA, UK, Italy and other major developed countries in Europe and America are leading the research trend, while developing countries, notably China, India and Brazil have become the important contributors to the study of this issue in different ways; (3) The research results at this stage are mainly in the fields of medicine, health and biology and are cited internally, but are also developing in the direction of economics, political, environmental and other fields. Finally, this study summarises some of the issues that should be of concern to public health crisis management in the post-pandemic era, in the hope of providing some insight for researchers on this issue.

An epidemiological model with a minimal number of parameters is introduced and its structural and practical identifiabity is investigated both analytically and numerically. The model is useful when a high percentage of unreported cases is suspected, hence only hospitalization data are used to fit the model parameters and calculate the basic reproductive number R0 and the effective reproductive number Re. As a case study, the model is used to study the initial surge and the Omicron wave of the COVID-19 epidemic in Belgium. It was found that the reported cases largely underestimate the actual cases, and the estimated values of R0 are consistent with other studies. The exact number of people initially in each epidemiological class is also considered unknown and was estimated directly and not considered as additional parameters to be fitted. Furthermore, the parameter fitting was performed with two different available data sets, in order to improve confidence. The methodology presented here can be easily modified to study outbreaks of diseases for which little information on confirmed cases is known a priori or when the available information is largely unreliable.

Emerging infectious disease agents represent pathogens that may evade current screening protocols while posing significant transfusion transmission risks regionally. This study investigated the prevalence of SARS-CoV-2 and other respiratory viruses among 633 blood donors at the MT-Hemocentro from November 2021 to February 2023. Nucleic acid obtained from nasopharyngeal swabs were tested by RT-qPCR for SARS-CoV-2, RSV, FLU-A, and FLU-B. Serum from positive samples was also tested for nucleic acid. The prevalence of SARS-CoV-2 was 6.48 % (41/633); 2 of the 41 blood donors had SARSCoV-2 detectable in their serum. All positive samples were collected between January 2022 and March 2023, coinciding with the third epidemic wave in Brazil; 97.6 % of these SARS-CoV-2-positive donors were vaccinated with at least two doses. SARS-CoV-2 genomes recovered from six nasopharyngeal samples were classified into BA.1.1.1, BA.1.14.1, BA.2, BA.5.1, BA.5.2.1 sublineages. Phylogeographic analysis across Brazil's five regions revealed that the Northeast acted as the main exporter of Omicron sublineages, while the South and Southeast regions were more frequently importers.

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.

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 COVID-19 pandemic caused significant disruptions to daily life, affecting regular physical activity (PA) and health behaviors worldwide. This study investigates the associations between PA domains and changes in health behaviors and mental health outcomes among middle-aged and old Chinese adults.

Using wave 5 cross-sectional data from the 2020 China Health and Retirement Longitudinal Study, we analyzed 17,180 adults aged 45 and above, focusing on health behavior changes such as smoking, alcohol consumption, dietary adjustments, and panic purchasing, as well as mental health outcomes like anxiety and fear. PA was classified by intensity levels-light, moderate, and vigorous-and by activity purposes-total, leisure, and occupational.

The findings indicate that leisure PA is associated with healthier behaviors, including lower odds of increased smoking (OR = 0.71, 95% CI: 0.57-0.90) and alcohol consumption (OR = 0.70, 95% CI: 0.54-0.90), whereas occupational PA is linked to adverse behavioral outcomes, such as higher odds of smoking (OR = 1.45, 95% CI: 1.15-1.83) and alcohol use (OR = 1.43, 95% CI: 1.10-1.86). Additionally, participants engaged in all domains of PA were more likely to experience anxiety and fear compared to those who were physically inactive.

Our limited understanding of the role PA has on behavioral and mental health during public health crises highlights the importance of having tailored strategies to enhance resilience in similar future scenarios.

The recent COVID-19 pandemic offers a rare opportunity to understand how citizens attribute responsibility for governments' responses to unanticipated negative-and in this case, systemic-exogenous shocks. Classical accounts of responsibility are complicated when crises are pervasive, involve multiple valence dimensions, and where individuals can make relative assessments of performance. We fielded a conjoint experiment in 16 countries with 22,147 respondents. In this experiment, subjects made re-election decisions regarding 178,184 randomly assigned incumbent profiles. We find that incumbents' performance along both health and economic dimensions drives these hypothetical reelection decisions. Using machine learning techniques, we find only muted heterogeneity in the magnitude and distribution of these treatment effects. This result suggests that these widely reported performance signals have consistent political effects across countries. In a complementary analysis, we also find that subjects' intentions to vote for incumbent governments are positively correlated with subjective and relative evaluations of the government's pandemic performance, along both health and economic dimensions. These results provide consistent evidence that evaluations of pandemic performance matter politically.

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.

The coronavirus disease 2019 (COVID-19) pandemic has altered daily behavioral patterns based on government healthcare policies, including consumption and movement patterns. We aimed to examine the extent to which changes in the government's healthcare policy have affected people's lives, primarily focusing on changes in consumption and population movements.

We collected consumption data using weekly credit card transaction data from the Hana Card Corporation and population mobility data using mobile phone data from SK Telecom in Seoul, South Korea. Interrupted time-series analysis was used to calculate the relative risk ratio and perform the intervention effects when government healthcare policy changes.

We found that leisure and outside movements were the most immediately affected by changes in government healthcare policies. It took over 2 years and 11 months, respectively, for these sectors to return to their pre-COVID-19 routines.

Enhancing healthcare policies presents advantages and disadvantages. Although such policies help prevent the spread of COVID-19, they also reduce consumption and mobility, extending the time needed to return to pre-COVID-19 levels. Government healthcare policymakers should consider not only disease prevention but also the impact of these policies on social behaviors, economic activity, and mobility.

We analyse the relationship between population influx and the effective reproduction number in the 23 wards of Tokyo during the COVID-19 pandemic to estimate hotspots of infection. We identify some patterns of population influx via factor analysis and estimate specific areas as infection-related hotspots by focusing on influx patterns that are highly correlated with the effective reproduction number. As a result, several influx patterns are assumed to be directly related to the subsequent spread of the infection. This analytical method has the potential to detect unknown hotspots related to pandemics in the future.

Digital data sources such as mobile phone call detail records (CDRs) are increasingly being used to estimate population mobility fluxes and to predict the spatiotemporal dynamics of infectious disease outbreaks. Differences in mobile phone operators' geographic coverage, however, may result in biased mobility estimates.

We leverage a unique dataset consisting of CDRs from three mobile phone operators in Bangladesh and digital trace data from Meta's Data for Good program to compare mobility patterns across these sources. We use a metapopulation model to compare the sources' effects on simulated outbreak trajectories, and compare results with a benchmark model with data from all three operators, representing around 100 million subscribers across the country.

We show that mobility sources can vary significantly in their coverage of travel routes and geographic mobility patterns. Differences in projected outbreak dynamics are more pronounced at finer spatial scales, especially if the outbreak is seeded in smaller and/or geographically isolated regions. In some instances, a simple diffusion (gravity) model was better able to capture the timing and spatial spread of the outbreak compared to the sparser mobility sources.

Our results highlight the potential biases in predicted outbreak dynamics from a metapopulation model parameterized with non-population representative data, and the limits to the generalizability of models built on these types of novel human behavioral data.

Understanding the impact of non-pharmaceutical COVID-19 interventions (NPIs) on road safety has become increasingly important to uncover the unintended consequences of the pandemic. This study explores how NPIs influenced alcohol-related and speed-related traffic collisions, including fatalities and serious injuries, in five cities of the province of Québec, Canada: Montréal, Québec, Laval, Longueuil and Sherbrooke.

We performed Poisson interrupted time-series analyses using daily traffic fatality and injury data from 2015 to 2022, to assess the change in rate expressed per 10 000 population. A Québec COVID-19 NPIs Index was applied, incorporating 58 interventions enacted from March 2020 to March 2022 in these cities. We accounted for weather conditions and seasonal patterns and divided the pandemic period into four semesters to better understand changes over time.

The analysis revealed a nuanced interaction between NPIs and road safety. Alcohol-related injuries decreased during stringent NPIs, particularly in Montréal, Québec city and Longueuil. In contrast, the effects on speed-related incidents were mixed, with Montréal and Laval, showing increases and the other three cities displaying no meaningful changes across the four semesters.

These findings highlight the necessity for ad hoc road safety strategies that address specific patterns of alcohol consumption and speeding risks during future pandemic-related disruptions.

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.

Human mobility between different regions is a major factor in large-scale outbreaks of infectious diseases. Deep learning models incorporating infectious disease transmission dynamics for predicting the spread of multi-regional outbreaks due to human mobility have become a hot research topic. In this study, we incorporate the Graph Transformer Neural Network and graph learning mechanisms into a metapopulation SIR model to build a hybrid framework, Metapopulation Graph Transformer Neural Network (M-Graphormer), for high-dimensional parameter estimation and multi-regional epidemic prediction. The framework effectively solves the problem that existing models may lose some hidden spatial dependencies in the data when dealing with the dynamic graph structure of the network due to human mobility. We performed multi-wave infectious disease prediction in multiple regions based on real epidemic data. The results show that the framework is capable of performing high-dimensional parameter estimation and accurately predicting epidemic transmission dynamics in multiple regions even with low data quality. In addition, we retrospectively extrapolate the temporal evolution patterns of contact rate under different interventions implemented in different regions, reflecting the dynamics of intervention intensity and the need for flexibility in adjusting interventions in different regions. To provide early warning of infectious disease transmission, we retrospectively predicted the arrival time of infectious diseases using data from the early stages of outbreaks.

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.

Italy was significantly affected by the COVID-19 pandemic, experiencing multiple waves of infection following the sequential emergence of new variants. Understanding the transmission patterns and evolution of SARS-CoV-2 is vital for future preparedness.

We conducted an analysis of viral genome sequences, integrating epidemiological and phylodynamic approaches, to characterize how SARS-CoV-2 variants have spread within the country.

Our findings indicate bidirectional international transmission, with Italy transitioning between importing and exporting the virus. Italy experienced four distinct epidemic waves, each associated with a significant reduction in fatalities from 2021 to 2023. These waves were primarily driven by the emergence of VOCs such as Alpha, Delta, and Omicron, which were reflected in observed transmission dynamics and effectiveness of public health measures.

The changing patterns of viral spread and variant prevalence throughout Italy's pandemic response underscore the continued importance of flexible public health strategies and genomic surveillance, both of which are crucial for tracking the evolution of variants and adapting control measures effectively to ensure preparedness for future outbreaks.

The COVID-19 pandemic presented unparalleled challenges to prompt and adaptive responses from nations worldwide. This review examines China's multifaceted approach to the crisis, focusing on five key areas of response: infrastructure and system design, medical care and treatment, disease prevention and control, economic and social resilience, and China's engagement in global health. This review demonstrates the effectiveness of a top-down command system at the national level, intersectoral coordination, a legal framework, and public social governance. This study also examines medical care and treatment strategies, highlighting the importance of rapid emergency response, evidence-based treatment, and well-planned vaccination rollout. Further discussion on disease prevention and control measures emphasizes the importance of adaptive measures, timely infection control, transmission interruption, population herd immunity, and technology applications. Socioeconomic impact was also assessed, detailing strategies for disease prevention, material supply, livelihood preservation, and social economy revival. Lastly, we examine China's contributions to the global health community, with a focus on knowledge-sharing, information exchange, and multilateral assistance. While it is true that each nation's response must be tailored to its own context, there are universal lessons to be drawn from China's approach. These insights are pivotal for enhancing global health security, especially as the world navigates evolving health crises.

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.

In the face of an infectious disease, a key epidemiological measure is the basic reproduction number, which quantifies the average secondary infections caused by a single case in a susceptible population. In practice, the effective reproduction number, denoted as R t , is widely used to assess the transmissibility of the disease at a given time t. Real-time estimating this metric is vital for understanding and managing disease outbreaks. Traditional statistical inference often relies on two assumptions. One is that samples are assumed to be drawn from a homogeneous population distribution, neglecting significant variations in individual transmission rates. The other is the ideal case reporting assumption, disregarding time delays between infection and reporting. In this paper, we thoroughly investigate these critical factors and assess their impact on estimating R t . We first introduce negative binomial and Weibull distributions to characterize transmission rates and reporting delays, respectively, based on which observation and state equations are formulated. Then, we employ a Bayesian filtering for estimating R t . Finally, validation using synthetic and empirical data demonstrates a significant improvement in estimation accuracy compared to conventional methods that ignore these factors.

To control the COVID-19 epidemic, the Japanese government and the local governments have repeatedly implemented non-pharmaceutical interventions (NPIs) throughout 2020-2022. Using Bayesian state-space mediation models, we examined the effect of repeated NPIs on infection spread mitigation, mediated by human mobility changes in each prefecture during three epidemic phases: from April 1, 2020 to February 28, 2021; from March 1, 2021 to December 16, 2021; and from December 17, 2021 to December 31, 2022. In the first phase, controlling downtown populations at nighttime was effective in mitigating the infection spread in almost all prefectures. In the second and third phases, the effect was not clear, especially in metropolitan prefectures. Controlling visitors from the central prefectures of metropolitan areas was effective in mitigating infection spread in the surrounding prefectures during all phases. These results suggest that the local spread of infection can be mitigated by focusing on nighttime human mobility control in downtown areas before the epidemic spreads widely and transmission routes become more diverse, and that the geospatial spread of infection can be prevented by controlling the flows of people from large cities to other areas.

A novel coronavirus discovered in late 2019 (COVID-19) quickly spread into a global epidemic and, thankfully, was brought under control by 2022. Because of the virus's unknown mutations and the vaccine's waning potency, forecasting is still essential for resurgence prevention and medical resource management. Computational efficiency and long-term accuracy are two bottlenecks for national-level forecasting. This study develops a novel multivariate time series forecasting model, the densely connected highly flexible dendritic neuron model (DFDNM) to predict daily and weekly positive COVID-19 cases. DFDNM's high flexibility mechanism improves its capacity to deal with nonlinear challenges. The dense introduction of shortcut connections alleviates the vanishing and exploding gradient problems, encourages feature reuse, and improves feature extraction. To deal with the rapidly growing parameters, an improved variation of the adaptive moment estimation (AdamW) algorithm is employed as the learning algorithm for the DFDNM because of its strong optimization ability. The experimental results and statistical analysis conducted across three Japanese prefectures confirm the efficacy and feasibility of the DFDNM while outperforming various state-of-the-art machine learning models. To the best of our knowledge, the proposed DFDNM is the first to restructure the dendritic neuron model's neural architecture, demonstrating promising use in multivariate time series prediction. Because of its optimal performance, the DFDNM may serve as an important reference for national and regional government decision-makers aiming to optimize pandemic prevention and medical resource management. We also verify that DFDMN is efficiently applicable not only to COVID-19 transmission prediction, but also to more general multivariate prediction tasks. It leads us to believe that it might be applied as a promising prediction model in other fields.