Search Results (41)

Classical epidemic models treat vaccine uptake as an exogenous parameter, yet real-world coverage emerges from strategic choices made by individuals facing uncertain risks. During the last two decades, vaccination games, which combine epidemic dynamics with game theory, behavioural economics, and network science, have become a very important tool for analysing this problem. Here, we synthesise more than 80 theoretical, computational, and empirical studies to clarify how population structure, psychological perception, pathogen complexity, and policy incentives interact to determine vaccination equilibria and epidemic outcomes. Papers are organised along five methodological axes: (i) population topology (well-mixed, static and evolving networks, multilayer systems); (ii) decision heuristics (risk assessment, imitation, prospect theory, memory); (iii) additional processes (information diffusion, non-pharmacological interventions, treatment, quarantine); (iv) policy levers (subsidies, penalties, mandates, communication); and (v) pathogen complexity (multi-strain, zoonotic reservoirs). Common findings across these studies are that voluntary vaccination is almost always sub-optimal; feedback between incidence and behaviour can generate oscillatory outbreaks; local network correlations amplify free-riding but enable cost-effective targeted mandates; psychological distortions such as probability weighting and omission bias materially shift equilibria; and mixed interventions (e.g., quarantine + vaccination) create dual dilemmas that may offset one another. Moreover, empirical work surveys, laboratory games, and field data confirm peer influence and prosocial motives, yet comprehensive model validation remains rare. Bridging the gap between stylised theory and operational policy will require data-driven calibration, scalable multilayer solvers, and explicit modelling of economic and psychological heterogeneity. This review offers a structured roadmap for future research on adaptive vaccination strategies in an increasingly connected and information-rich world. Full article

Objective: This study seeks to forecast the global burden of diabetes-related mortality by type, age group, WHO region, and income classification through 2030, and to assess progress toward Sustainable Development Goal (SDG) 3.4, which aims to reduce premature mortality (among people age 30–70 years) from noncommunicable diseases (including diabetes) by one-third. Methods: We analyzed diabetes mortality data from the Institute for Health Metrics and Evaluation, Global Burden of Disease 2019, covering 30 years (1990–2019). Using this historical dataset, we generated 11-year prospective forecasts (2020–2030) globally and stratified by diabetes type (type 1, type 2), age groups, WHO regions, and World Bank income classifications. We employed multiple time series and epidemic modeling approaches to enhance predictive accuracy, including ARIMA, GAM, GLM, Facebook’s Prophet, n-sub-epidemic, and spatial wave models. We compared model outputs to identify consistent patterns and trends. Results: Our forecasts indicate a substantial increase in global diabetes-related mortality, with type 2 diabetes driving the majority of deaths. By 2030, annual diabetes mortality is projected to reach 1.63 million deaths (95% PI: 1.48–1.91 million), reflecting a 10% increase compared to 2019. Particularly concerning is the projected rise in mortality among adults aged 15–49 and 50–69 years, especially in Southeast Asia and low- and middle-income countries. Mortality in upper-middle-income countries is also expected to increase significantly, exceeding a 50% rise compared to 2019. Conclusions: Diabetes-related deaths are rising globally, particularly in younger and middle-aged adults in resource-limited settings. These trends jeopardize the achievement of SDG 3.4. Urgent action is needed to strengthen prevention, early detection, and management strategies, especially in Southeast Asia and low-income regions. Our findings provide data-driven insights to inform global policy and target public health interventions. Full article

High-resolution temporal contact networks are useful ingredients for realistic epidemic simulations. Existing solutions typically rely either on empirical studies that capture fine-grained interactions via Bluetooth or wearable sensors in confined settings or on large-scale simulation frameworks that model entire populations using generalized assumptions. However, for most realistic modeling of epidemic spread and the evaluation of countermeasures, there is a critical need for highly resolved, temporal contact networks that encompass multiple venues without sacrificing the intricate dynamics of real-world contacts. This paper presents an integrated approach for generating such networks by coupling Bayesian-optimized human mobility models (HuMMs) with a state-of-the-art epidemic simulation framework. Our primary contributions are twofold: First, we embed empirically calibrated HuMMs into an epidemic simulation environment to create a parameterizable, adaptive engine for producing synthetic, high-resolution, population-wide temporal contact network data. Second, we demonstrate through empirical evaluations that our generated networks exhibit realistic interaction structures and infection dynamics. In particular, our experiments reveal that while variations in population size do not affect the underlying network properties—a crucial feature for scalability—altering location capacities naturally influences local connectivity and epidemic outcomes. Additionally, sub-graph analyses confirm that different venue types display distinct network characteristics consistent with their real-world contact patterns. Overall, this integrated framework provides a scalable and empirically grounded method for epidemic simulation, offering a powerful tool for generating and simulating contact networks. Full article

With the acceleration of urbanization, citizens are facing more pandemic challenges. A deeper understanding of constructing more resilient cities can help citizens be better prepared for potential future pandemics or disasters. In this study, a static–dynamic analytical framework for urban health resilience evaluation and influencing factor exploration was proposed, which helped not only to analyze the basic static urban health resilience (SUHRI) under normal conditions but also to evaluate the dynamic urban health resilience (DURHI) under an external epidemic shock. The epidemic dynamic model could reasonably simulate the epidemic change trend and quantitatively evaluate resistance and recovery capacity, and the proposed influencing factor exploration model improved the model fitness by filtering out the influence of population flow autocorrelation existing in the residuals. SUHRI and DUHRI, and their corresponding key influencing factors, were compared and discussed. The results of the static–dynamic integration and difference score showed that 60.6% cities within the study area had a similar performance on SUHRI and DUHRI, but there was also a typical difference: some regional hubs exhibited high SUHRI but had only mid-level DUHRI, which was attributed to stronger external disturbances such as higher population mobility. The key influencing factors for static and dynamic urban health resilience also vary. Hospital capacity and income had the strongest influence on static urban health resilience but a relatively weaker or even non-significant correlation with dynamic urban health resilience sub-indices. The extracted population flow eigenvector collection had the strongest influence on dynamic urban health resilience, as it represents the population flow connection among cities, which could reflect the information of policy response, such as policy stringency and support intensity. We hope that our study will shed some light on constructing more resilient urban systems and being prepared for future public health emergencies. Full article

Syphilis and other sexually transmitted infections (STIs) are highly prevalent in most regions experiencing severe human immunodeficiency virus (HIV) epidemics. In sub-Saharan Africa, the region most heavily affected by HIV, the prevalence of syphilis among people living with HIV (PLWH) is notably high. This region accounts for 40% of global STIs and 70% of HIV cases. Despite the high prevalence of syphilis and other STIs among PLWH in the region, there are limited studies on the interplay between the two infections from the region. Most studies on the association between syphilis and HIV transmission/progression from the region are limited to specific groups of people, such as female sex workers or pregnant women. In this manuscript, we evaluated the association between the two infections using population-based surveys conducted in the region. Statistical methods (such as logistic regression models and propensity score matching) were employed to assess the interplay between the two infections. Our findings indicated that syphilis infection was associated with higher odds of HIV acquisition. Moreover, co-infection with syphilis was associated with higher odds of HIV disease progression among antiretroviral therapy (ART)-treated PLWH, though the association did not reach statistical significance. Our findings suggest that the recognition and treatment of syphilis to reduce the risk of HIV acquisition/progression should be a public health priority in sub-Saharan Africa, where ART may not be readily available. Full article

Sub-Saharan Africa continues to bear a disproportionate burden of the global HIV epidemic. Integrating HIV services into primary healthcare is a crucial strategy to accelerate progress towards ending the epidemic. However, several challenges hinder effective integration, including underfunding, human resource shortages, infrastructure limitations, weak health systems, and sociocultural factors. With this perspective, we discuss strategies to address these challenges. A comprehensive, multi-faceted approach is necessary to overcome these barriers. Investing in human resources, improving infrastructure, and strengthening health information systems are essential for strengthening the health system. Implementing patient-centered care, integrated service delivery models, and community engagement can optimize service delivery. Utilizing digital health tools, such as mobile health applications and electronic health records, can enhance service delivery and data management. Mobilizing for an increase in domestic resources, aligning donor funding, and using cost-effective approaches are crucial for effective financing. Finally, robust monitoring and evaluation systems are necessary to track progress, identify challenges, and inform decision-making. With these strategies, among many others, sub-Saharan African countries can significantly improve the integration of HIV services into primary healthcare, leading to better health outcomes for people living with HIV and more sustainable HIV programs. Full article

The 2022–2023 mpox outbreak exhibited an uneven global distribution. While countries such as the UK, Brazil, and the USA were most heavily affected in 2022, many Asian countries, specifically China, Japan, South Korea, and Thailand, experienced the outbreak later, in 2023, with significantly fewer reported cases relative to their populations. This variation in timing and scale distinguishes the outbreaks in these Asian countries from those in the first wave. This study evaluates the predictability of mpox outbreaks with smaller case counts in Asian countries using popular epidemic forecasting methods, including the ARIMA, Prophet, GLM, GAM, n-Sub-epidemic, and Sub-epidemic Wave frameworks. Despite the fact that the ARIMA and GAM models performed well for certain countries and prediction windows, their results were generally inconsistent and highly dependent on the country, i.e., the dataset, as well as the prediction interval length. In contrast, n-Sub-epidemic Ensembles demonstrated more reliable and robust performance across different datasets and predictions, indicating the effectiveness of this model on small datasets and its utility in the early stages of future pandemics. Full article

Computational simulation models have been widely used to study the dynamics of COVID-19. Among those, bottom-up approaches such as agent-based models (ABMs) can account for population heterogeneity. While many studies have addressed COVID-19 spread at various scales, insufficient studies have investigated the spread of COVID-19 within closed indoor settings. This study aims to develop an ABM to simulate the spread of COVID-19 in a closed indoor setting using three transmission sub-models. Moreover, a comprehensive sensitivity analysis encompassing 4374 scenarios is performed. The model is calibrated using data from Calabria, Italy. The results indicated a decent consistency between the observed and predicted number of infected people (MAPE = 27.94%, RMSE = 0.87 and ${\chi }^2(1,N=34)=(44.11,p=0.11)$). Notably, the transmission distance was identified as the most influential parameter in this model. In nearly all scenarios, this parameter had a significant impact on the outbreak dynamics (total cases and epidemic peak). Also, the calibration process showed that the movement of agents and the number of initial asymptomatic agents are vital model parameters to simulate COVID-19 spread accurately. The developed model may provide useful insights to investigate different scenarios and dynamics of other similar infectious diseases in closed indoor settings. Full article

Cholera has been a global pandemic in past centuries, and its persistent emergence and spread pose a significant public health challenge globally. Despite efforts to contain the disease, recurrent cholera outbreaks in sub-Saharan Africa remain a major health threat. This has attracted substantial research interest, raising questions about the effectiveness of prevention and control methods of cholera spread in sub-Saharan Africa. Addressing this health challenge by adopting a sustainable, convenient, and cost-effective intervention will improve the health, well-being, and productivity of vulnerable populations in sub-Saharan Africa. Household-level solutions, which are characterized by relatively low-cost and independence from potentially insufficient public water supply infrastructure were examined to determine their effectiveness in reducing the incidence of cholera if widely adopted across the continent. We perform a mixed-methods retrospective analysis on the Cholera epidemic data obtained from 2010 to 2016 in sub-Saharan Africa. Using an empirical epidemiological model, we estimate the performance efficacy of a suite of household water treatment (HWT) technologies. We also develop economic estimations to perform benefit–cost analyses to determine the cost effectiveness, convenience of use and durability of these products. We find that—if universally adopted—the HWT technologies evaluated here offer comparable and effective microbiological potential for eradicating cholera disease in sub-Saharan Africa but are potentially not affordable for low-income households that reside in cholera hotspots. As such, household subsidies are necessary in lowering barriers to economic access to these products. This finding provides substantial insights on the efficacy and affordability of these household water treatment technologies—insights which can inform stakeholder decisions on the applicability of this intervention in eradicating cholera. Full article

Despite over two decades of progress against HIV/AIDS in adjacent sub-Saharan Africa, HIV rates and deaths due to AIDS are exponentially rising in Madagascar. Furthermore, a growing body of evidence suggests that, due to a scarcity of general-population screening data, even the startling increase demonstrated by official models vastly underestimates the true population prevalence of HIV. We aimed to implement a real-world HIV screening and treatment protocol to serve a general population stemming from across northern Madagascar. In collaboration with the Malagasy Ministry of Health, we provided point-of-care HIV screening and confirmatory testing for over 1000 participants from 73 towns, villages, and cities. We recorded an overall HIV prevalence of 2.94%. Notably, we observed a 13.1% HIV prevalence rate among urban populations and showed that proximity to a major route of travel was significantly associated with HIV risk. We also observed a link between HIV risk and various occupations, including those associated with increased mobility (such as mining). Importantly, all HIV-positive individuals were initiated on antiretroviral therapy in concordance with local health authorities. To our knowledge, this study marks the largest primary test data-based HIV study to date among Madagascar’s general population, showing a greatly higher HIV prevalence (2.9%) than previously reported modeling-based figures (0.4%). Our rates aligned with the pattern of higher prevalence demonstrated in smaller general-population screening studies occurring more commonly prior to political strife in the mid-2000s. These findings demonstrate evidence of a growing HIV epidemic in northern Madagascar and underscore the need for future investment into more comprehensive HIV screening and control initiatives in Madagascar. Full article

Diet-related diseases and mortalities are assuming epidemic proportions globally. It is more worrisome in the Global South, especially in Africa, where the healthcare system is not resilient to the public health burden. There is a growing effort around the world to foster urban food system policies that would checkmate the failing health of the public and ensure improved quality of life. However, these efforts seem non-existent in many African regions. Therefore, there is a need for heightened efforts in these areas to address the food system and ensure a global healthy society. This study identified Nnewi, Nigeria, in sub-Saharan Africa, a typical urban area in Nigeria, and analyzed the public health challenges attributed to the non-existent food system policy and poor nutritional practices. The Milan Urban Food Policy Pact model, which has been successfully implemented in many cities, was adopted to propose a sustainable food system policy for Nnewi. Key policies proposed include autonomous local government power, government-assisted programs, clean and sustainable amenities, agricultural reforms, nutrition education, and reductions in food wastage to achieve a circular economy. An evaluation tool for implementing the food system policy was also developed. Overall, implementing the food system policies proposed herein would improve the quality of life of Nnewi residents. Other urban areas could also adopt similar food system policies to achieve the Sustainable Development Goals of a healthy and resilient global society. Full article

In the spring of 2022, a new round of epidemic broke out in Shanghai, causing a shock to the Shanghai carbon trading market. Against this background, this paper studied the impact of the new epidemic on the price of Shanghai carbon emission rights and tried to explore the prediction model under the unexpected event. First, because a model based on point value data cannot capture the information hidden in inter-day price fluctuation, based on the interval price of Shanghai carbon emission rights (SHEA) and its influencing factors, an autoregressive conditional interval model with jumping and exogenous variables (ACIXJ) was established to explore the influence of the Russian–Ukrainian conflict and COVID-19 on the interval price of SHEA, respectively. The empirical results show that the conflict between Russia and Ukraine has no obvious influence on the price of SHEA, but COVID-19 led to a decline in the price trend of SHEA over four days before the city was closed, and the volatility changed significantly on the day before the city was closed. The price fluctuation was the strongest within 3 days after the city was closed; In addition, in order to accurately predict the interval data of SHEA against the background of COVID-19, based on the interval data decomposition algorithm (BEMD), a hybrid forecasting model of NDGM-ACIXJ/CNN-LSTM was constructed, in which the discrete gray model of approximate nonhomogeneous exponential series (NDGM) combined with the ACIXJ model is used to predict the high-frequency sub-interval, and the convolution neural network long-term and short-term memory model (CNN-LSTM) is used to predict the low-frequency sub-interval. The empirical results show that the prediction model proposed in this article has higher prediction precision than the reference models (ACIX, ACIXJ, NDGM-ACIXJ, BEMD-ACIX/CNN-LSTM, BEMD-ACIXJ/CNN-LSTM). Full article

In this work, we first introduce a class of deterministic epidemic models with varying populations inspired by Arino et al. (2007), the parameterization of two matrices, demography, the waning of immunity, and vaccination parameters. Similar models have been focused on by Julien Arino, Fred Brauer, Odo Diekmann, and their coauthors, but mostly in the case of “closed populations” (models with varying populations have been studied in the past only in particular cases, due to the difficulty of this endeavor). Our Arino–Brauer models contain SIR–PH models of Riano (2020), which are characterized by the phase-type distribution $(\overrightarrow{\alpha },A)$, modeling transitions in “disease/infectious compartments”. The A matrix is simply the Metzler/sub-generator matrix intervening in the linear system obtained by making all new infectious terms 0. The simplest way to define the probability row vector $\overrightarrow{\alpha }$ is to restrict it to the case where there is only one susceptible class $\mathsf{s}$, and when matrix B (given by the part of the new infection matrix, with respect to $\mathsf{s}$) is of rank one, with $B=b\overrightarrow{\alpha }$. For this case, the first result we obtained was an explicit formula (12) for the replacement number (not surprisingly, accounting for varying demography, waning immunity and vaccinations led to several nontrivial modifications of the Arino et al. (2007) formula). The analysis of $(A,B)$ Arino–Brauer models is very challenging. As obtaining further general results seems very hard, we propose studying them at three levels: (A) the exact model, where only a few results are available—see Proposition 2; and (B) a “first approximation” (FA) of our model, which is related to the usually closed population model often studied in the literature. Notably, for this approximation, an associated renewal function is obtained in (7); this is related to the previous works of Breda, Diekmann, Graaf, Pugliese, Vermiglio, Champredon, Dushoff, and Earn. (C) Finally, we propose studying a second heuristic “intermediate approximation” (IA). Perhaps our main contribution is to draw attention to the importance of $(A,B)$ Arino–Brauer models and that the FA approximation is not the only way to tackle them. As for the practical importance of our results, this is evident, once we observe that the $(A,B)$ Arino–Brauer models include a large number of epidemic models (COVID, ILI, influenza, illnesses, etc.). Full article

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Not only do the epidemiologic determinants and distributions of patients with ROP vary worldwide, but clinical differences have also been described. The Third Edition of the International Classification of ROP (ICROP3) acknowledges that aggressive ROP (AROP) can occur in larger preterm infants and involve areas of the more anterior retina, particularly in low-resource settings with unmonitored oxygen supplementation. As sub-specialty training programs are underway to address an epidemic of ROP in sub-Saharan Africa, recognizing characteristic retinal pathology in preterm infants exposed to unmonitored supplemental oxygen is important to proper diagnosis and treatment. This paper describes specific features associated with various ROP presentations: oxygen-induced retinopathy in animal models, traditional ROP seen in high-income countries with modern oxygen management, and ROP related to excessive oxygen supplementation in low- and middle-income countries: oxygen-associated ROP (OA-ROP). Full article

Some deterministic models deal with environmental conditions and use parameter estimations to obtain experimental parameters, but they do not consider anthropogenic or environmental disturbances, e.g., chemical control or climatic conditions. Even more, they usually use theoretical or measured in-lab parameters without worrying about uncertainties in initial conditions, parameters, or changes in control inputs. Thus, in this study, we estimate parameters (including chemical control parameters) and confidence contours under uncertainty conditions using data from the municipality of Bello (Colombia) during 2010–2014, which includes two epidemic outbreaks. Our study shows that introducing non-periodic pulse inputs into the mathematical model allows us to: (i) perform parameter estimation by fitting real data of consecutive dengue outbreaks, (ii) highlight the importance of chemical control as a method of vector control, and (iii) reproduce the endemic behavior of dengue. We described a methodology for parameter and sub-contour box estimation under uncertainties and performed reliable simulations showing the behavior of dengue spread in different scenarios. Full article