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This paper focuses on the online anti-epidemic resource allocation between demanders and suppliers considering the epidemic spatial spread. The spatial crowdsourcing with sharing platform is an effective way for anti-epidemic resource allocation, and a reasonable online matching strategy can improve the efficiency of resource utilization. The paper proposes online matching heuristic strategy (HSTRF-LRLUF strategy) and designs an online batch bilateral matching algorithm for anti-epidemic resources, which considers the impact of grid spatial aggregation and diffusion risk of emerging infectious diseases. The population distribution within grids and the commuting patterns between grids can provide decision support for selecting online matching strategies of anti-epidemic resources. A larger matching time window focusing on the spatial transmission risk (TR) of the epidemic can obtain better matching results. However, with a smaller matching time window, the decision makers can focus on spatial agglomeration risk (OR) or spatial diffusion risk (IR). The paper effectively combines the spatial crowdsourcing model with the anti-epidemic resource allocation to achieve the allocation of emergency resources to individuals. A combined anti-epidemic resource online matching heuristic strategies is designed from the spatial agglomeration risk and the spatial diffusion risk. Decision makers can dynamically adjust the online matching strategies of anti-epidemic resources by evaluating the spatial agglomeration risk, the spatial diffusion risk, and the overall spatial transmission risk based on the real-time spread of the epidemic and the supply of anti-epidemic resources. [ABSTRACT FROM AUTHOR]

First two decades of the twenty-first century are characterised by epidemics of non-communicable diseases such as many hundreds of millions of patients diagnosed with cardiovascular diseases and the type 2 diabetes mellitus, breast, lung, liver and prostate malignancies, neurological, sleep, mood and eye disorders, amongst others. Consequent socio-economic burden is tremendous. Unprecedented decrease in age of maladaptive individuals has been reported. The absolute majority of expanding non-communicable disorders carry a chronic character, over a couple of years progressing from reversible suboptimal health conditions to irreversible severe pathologies and cascading collateral complications. The time-frame between onset of SHS and clinical manifestation of associated disorders is the operational area for an application of reliable risk assessment tools and predictive diagnostics followed by the cost-effective targeted prevention and treatments tailored to the person. This article demonstrates advanced strategies in bio/medical sciences and healthcare focused on suboptimal health conditions in the frame-work of Predictive, Preventive and Personalised Medicine (3PM/PPPM). Potential benefits in healthcare systems and for society at large include but are not restricted to an improved life-quality of major populations and socio-economical groups, advanced professionalism of healthcare-givers and sustainable healthcare economy. Amongst others, following medical areas are proposed to strongly benefit from PPPM strategies applied to the identification and treatment of suboptimal health conditions: Stress overload associated pathologies Male and female health Planned pregnancies Periodontal health Eye disorders Inflammatory disorders, wound healing and pain management with associated complications Metabolic disorders and suboptimal body weight Cardiovascular pathologies Cancers Stroke, particularly of unknown aetiology and in young individuals Sleep medicine Sports medicine Improved individual outcomes under pandemic conditions such as COVID-19. [ABSTRACT FROM AUTHOR]

Recent work by Volz (J Math Biol 56:293-310, 2008) has shown how to calculate the growth and eventual decay of an SIR epidemic on a static random network, assuming infection and recovery each happen at constant rates. This calculation allows us to account for effects due to heterogeneity and finiteness of degree that are neglected in the standard mass-action SIR equations. In this note we offer an alternate derivation which arrives at a simpler-though equivalent-system of governing equations to that of Volz. This new derivation is more closely connected to the underlying physical processes, and the resulting equations are of comparable complexity to the mass-action SIR equations. We further show that earlier derivations of the final size of epidemics on networks can be reproduced using the same approach, thereby providing a common framework for calculating both the dynamics and the final size of an epidemic spreading on a random network. Under appropriate assumptions these equations reduce to the standard SIR equations, and we are able to estimate the magnitude of the error introduced by assuming the SIR equations. [ABSTRACT FROM AUTHOR]

A theory to analyze complex scenarios facing threats with competing factors and limited resources has been introduced. The scenarios are modeled as closed systems. Hamilton's principle of stationary action is used to conceive a theory in which competing factors dispute available resources to minimize undesirable outcomes. The result indicates that the minimum response is obtained by a combination of the competing factors weighted by their corresponding criticalities. The theory has been applied to the COVID-19 pandemic with two competing factors: Health and Economy. As main result, to minimize the total number of deaths, the recommendation is to balance the emphasis on both factors. This implies to give more emphasis to the economic factor, by avoiding restrict interventions like lockdowns and business closures. The model may evolve from a qualitative to a quantitative status, allowing for computational simulations aimed at validations and forecasting. As such, this approach may become a useful tool for strategic decision-making regarding resources allocations to reduce guessing in scenarios full of uncertainties. [ABSTRACT FROM AUTHOR]

This paper studies an impact of media epidemic system with diffusion and linear source. We first derive the uniform bounds of solutions to impact on media reaction diffusion system. Then, the basic reproduction number is calculated and the threshold dynamics of impact media reaction diffusion system is also given and the Kuratowski measure κ of non-compactness is also considered. In addition, assume the spatial environment is homogeneous, it is shown that the unique endemic equilibrium of the system is global stability by constructing suitable Lyapunov function. Finally, we discuss the asymptotic profile of the system when the diffusion rate of the susceptible (infected) individuals for the system tends to zero or infinity. The main results show that the activities of infected individuals can only be at low risk, and then the virus eventually will be extinct, that is, to control the entry of viruses from abroad and increase the detection of domestic viruses. Finally, some numerical simulations are worked out to confirm the results obtained in this paper. [ABSTRACT FROM AUTHOR]

We determine the spreading speed of an epidemic model with nonlocal diffusion and free boundary. The model is evolved from a degenerate reaction-diffusion model of Capasso and Maddalena (J Math Biol 13:173–184, 1981), and was studied in Zhao et al. (Commun Pure Appl Anal 19:4599–4620, 2020) recently, where it was shown that as time goes to infinity, the population of the infective agents either vanishes or spreads successfully. In this paper, we show that when spreading is successful, the asymptotic spreading speed is finite or infinite depending on whether a threshold condition is satisfied by the kernel function governing the spatial dispersal of the agents. The proof relies on a rather complete understanding of the associated semi-wave problem and traveling wave problem. For free boundary models, the case of infinite spreading speed, also known as accelerated spreading, is only recently shown to happen in Du et al. (J Math Pure Appl 154:30–66, 2021) for a single species Fisher-KPP model; this paper is the first to show that it happens to a very different two species model with free boundary. This suggests that accelerated spreading is a rather common phenomenon for free boundary problems with nonlocal diffusion. In contrast, for the corresponding models with local diffusion, the spreading can only proceed with finite speed. [ABSTRACT FROM AUTHOR]

Objectives: The rapid production of a large volume of literature during the early phase of the COVID-19 outbreak created a substantial burden for clinicians and scientists. Therefore, this manuscript aims to identify and describe the scientific literature addressing COVID-19 from a dental research perspective, in terms of the manuscript origin, research domain, study type, and level of evidence (LoE). Materials and methods: Data were retrieved from Web of Science, Scopus, and PubMed. A descriptive analysis of bibliographic data, collaboration network, and keyword co-occurrence analysis were performed. Articles were further classified according to the field of interest, main research question, type of study, and LoE. Results: The present study identified 296 dental scientific COVID-19 original papers, published in 89 journals, and co-authored by 1331 individuals affiliated with 429 institutions from 53 countries. Although 81.4% were single-country papers, extensive collaboration among the institutions of single countries (Italian, British, and Brazilian institutions) was observed. The main research areas were as follows: the potential use of saliva and other oral fluids as promising samples for COVID-19 testing, dental education, and guidelines for the prevention of COVID-19 transmission in dental practice. The majority of articles were narrative reviews, cross-sectional studies, and short communications. The overall LoE in the analyzed dental literature was low, with only two systematic reviews with the highest LoE I. Conclusion: The dental literature on the COVID-19 pandemic does not provide data relevant to the evidence-based decision-making process. Future studies with a high LoE are essential to gain precise knowledge on COVID-19 infection within the various fields of Dentistry. Clinical relevance: The published dental literature on COVID-19 consists principally of articles with a low level of scientific evidence which do not provide sufficient reliable high-quality evidence that is essential for decision making in clinical dental practice. [ABSTRACT FROM AUTHOR]

Since 2019n-CoV has swept the whole world, people's daily life is affected seriously and the public space system is facing to major challenges. Community public space should be re-evaluated and optimized as people change the way they use it. This research conducted a nationwide questionnaire survey in China, which collected the opinions of residents on community public space from the perspective of epidemic prevention and social needs to select research indicators. The questionnaire data were processed through the frequency analysis method, precedence chart and TOPSIS. We found: (1) under the background of the epidemic, people pay more attention to the satisfaction of leisure activities in community public space and the control of social spacing during activities; (2) the current assessment result of community public space risk of 2019-nCoV exposure is 0.386, which is relatively high; and (3) the exposure risk of the community can be effectively reduced by controlling for the two indicators of social distance and social facility sterilization. When the social distance is 1.8–3 m and the facility sterilization is once a day, the community public space minimizes exposure risk. In view of the above results, this paper selects the Wuhan start-up area of Wuhan, China, as an example for carrying out the optimization design of community public space based on the epidemic and the design the space optimization model for the two indicators of social distance and facility disinfection from both software and hardware aspects. [ABSTRACT FROM AUTHOR]

The modeling and control of epidemics, such as the novel Coronavirus, have become crucial on a global scale, for effective management of epidemic situations. This paper is focused on using the mean field approach for modeling and control of epidemics. The mean field approach is an effective alternative to the classical approach of using continuous time Markov decision process (CTMDP) models, which suffer from the curse of dimensionality and entail knowledge of global state information. The mean field approach captures the collective behavior of a dynamic system consisting of numerous interacting nodes representing individuals in the population. The objectives of this paper are twofold: (a) to provide an overview of the mean field approach to epidemic modeling and control, and (b) to present recent advances in this area. Emphasizing the importance of containing and suppressing epidemic spread through non-pharmaceutical interventions, the paper highlights the need to minimize loss of lives, reduce suffering, and alleviate the burden on the public healthcare system. A potential challenge here is the presence of a section of the population who act on their free will and deviate from recommended best practices, which could lead to a potential public health crisis. Motivated by this, the paper explores two specific threads to modeling and control. The first thread assumes that individual nodes comply with a socially optimal control policy mandated by a regulatory authority. The second thread allows for independent and strategic behavior by the individual nodes, modeled as a mean field game, where the strategies of rational agents are based on mean field Nash equilibria. The paper begins by discussing the modeling of epidemics using an extended SIVR (Susceptible-Infected-Vaccinated-Recovered) compartmental model, accompanied by an illustrative example. Next, a literature review is provided, focusing on the mean field approach for socially optimal control of epidemics and how a regulatory authority can effectively contain the spread. Subsequently, the paper presents an update on the use of mean field game-based approaches in studying epidemic spread and control. Finally, future research directions in this important area are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we explore the response of an aggregate measure of subjective wellbeing to the arrival and passage of the COVID-19 pandemic in a small, geographically separate economy in the South Pacific. Studies of national wellbeing and emotional responses to infection rates during a pandemic have been rare thus far. While several disciplines offer theoretical priors in the case of individuals, far less attention has been paid to the wellbeing and emotional response at a national level. Our paper contributes to the literature by applying a time-series approach to the relationship between wellbeing, emotions and the passage of a pandemic. As such we contribute to a wider literature on macro responses to exogenous shocks. Our analysis involves the use of a wellbeing index and emotional time-series derived from Big Data in the form of tweets originating within New Zealand. The index captures the daily evaluative mood of the country several weeks before the first domestic case of COVID-19 was recorded until several weeks of no new COVID-19 cases. We find distinct reactions to the pandemic: a initial fall in national wellbeing generated by a decrease in the emotions 'joy', 'anticipation' and 'trust'. Following a rapid and severe lockdown designed to limit domestic transmission of the virus national wellbeing recovered relatively quickly. Gaining insight into the wellbeing (happiness) reponse to pandemics at the national level is important because the average level of happiness within countries is known to be associated with a range of economic, social, health and political outcomes. [ABSTRACT FROM AUTHOR]

We propose, in this paper, a novel stochastic SIRS epidemic model to characterize the effect of uncertainty on the distribution of infectious disease, where the general incidence rate and Ornstein–Uhlenbeck process are also introduced to describe the complexity of disease transmission. First, the existence and uniqueness of the global nonnegative solution of our model is obtained, which is the basis for the discussion of the dynamical behavior of the model. And then, we derive a sufficient condition for exponential extinction of infectious diseases. Furthermore, through constructing a Lyapunov function and using Fatou's lemma, we obtain a sufficient criterion for the existence and ergodicity of a stationary distribution, which implies the persistence of the disease. In addition, the specific form of the density function of the model near the quasiendemic equilibrium is proposed by solving the corresponding Fokker–Planck equation and using some relevant algebraic equation theory. Finally, we explain the above theoretical results through some numerical simulations. [ABSTRACT FROM AUTHOR]

In epidemic modeling, interpretation of compartment quantities, such as s, i, and r in relevant equations, is not always straightforward. Ambiguities regarding whether these quantities represent numbers or fractions of individuals in each compartment rise questions about significance of the involved parameters. In this paper, we address these challenges by considering a density-dependent epidemic modelling by a birth-death process approach inspired by Kurtz from 1970s'. In contrast to existing literature, which employs population size scaling under constant population condition, we scale with respect to the area. Namely, under the assumption of spatial homogeneity of the population, we consider the quantities of susceptible, infective and recovered per unit area. This spatial scaling allows diffusion approximation for birth-death type epidemic models with varying population size. By adopting this approach, we anticipate to contribute to a clear and transparent description of compartment quantities and parameters in epidemic modeling. [ABSTRACT FROM AUTHOR]

Uncertain nonlinear time series analysis is a set of statistical techniques that use uncertainty theory to predict future values via nonlinear dynamics based on the previous observations. By assuming that the disturbance term is an uncertain variable, an uncertain nonlinear time series model is derived in this paper. In addition, this paper presents a method to estimate unknown parameters in an uncertain nonlinear time series model. Finally, some real examples (motion analysis and epidemic spreading) are provided to illustrate uncertain nonlinear time series analysis. As a result, it is shown that the uncertain nonlinear time series model may provide higher forecast accuracy than linear one. [ABSTRACT FROM AUTHOR]

Current link prediction strategies are about finding new probable strong relations to establish or weak ones to remove. An interesting strategy is utilizing link prediction to prioritize the edges in the network and finding newly probable established relations. In this paper we will introduce and explain RLP, reverse link prediction, as a new paradigm, and use popular basic scoring methods including CN, JC, AA, RA, and PA, as its core to examine. The test cases are nine datasets. Half of them are contact networks in different levels from personal contact to aviation, and another half is for covering different test situations. After reviewing the edge removal based epidemic mitigation methods, we show that RLP can be used to decrease the epidemics spreading speed as a general method with various link prediction algorithms, and here in this paper, preferential attachment (PA) has the best results overall. But the results heavily depend on the nature of the examined networks: regular, scale-free or small-world. We also propose an easy to understand criteria, path count, for comparing the efficacy of epidemics mitigation methods. RLP can be extended to use other link prediction scoring methods in various types of graphs as well. [ABSTRACT FROM AUTHOR]

The novel coronavirus is part of a series of infectious disease outbreaks that include: Ebola, Avian influenza, Middle East respiratory syndrome coronavirus, and Influenza A. This paper addresses the question of how do these epidemics and pandemics affect income inequality in countries around the world during the first two decades of this century. To achieve its objective, the paper develops a model that indicates a positive association between these health crises and income inequality. To empirically test our theoretical predictions, the paper explores the effect on the Gini coefficient of a dummy variable that indicates the occurrence of an epidemic or a pandemic in a country in a given year and the number of deaths per 100,000. To properly address potential endogeneity, we implement a Three-Stage-Least Squares technique. The estimation shows that the number of deaths per 100,000 population variable has a statistically significant positive effect on the Gini coefficient, especially when we incorporate COVID-19 data. This suggests that not only the occurrence, but also the health consequences of COVID-19 have a significant and economically important effect on income inequality. Background: The purpose of the study is to examine the effect of epidemics and pandemics on income inequality. This has important implications as the outcome of this study can guide policymakers into implementing policies that can mitigate the economic consequences of these health crises. Methods: The study is a cross country analysis using fixed effects estimation. To address potential endogeneity and determine causality, the paper uses the Three-Stage-Least-Squares estimation. Results: The paper finds that the number of epidemic deaths per 100,000 population variable has a statistically significant positive effect on the Gini coefficient, especially when we incorporate COVID-19 data. Conclusions: The paper finds that it is not only the occurrence of an epidemic, captured by the epidemics dummy variable, but also the health consequences, captured by the number of deaths per 100,000 population, that have a significant effect on income inequality. This is especially the case when we incorporate COVID-19 in our analysis. Trial registration: Not Applicable. [ABSTRACT FROM AUTHOR]

Epidemic spatial–temporal risk analysis, e.g., infectious number forecasting, is a mainstream task in the multivariate time series research field, which plays a crucial role in the public health management process. With the rise of deep learning methods, many studies have focused on the epidemic prediction problem. However, recent primary prediction techniques face two challenges: the overcomplicated model and unsatisfactory interpretability. Therefore, this paper proposes an Interpretable Spatial IDentity (ISID) neural network to predict infectious numbers at the regional weekly level, which employs a light model structure and provides post-hoc explanations. First, this paper streamlines the classical spatio-temporal identity model (STID) and retains the optional spatial identity matrix for learning the contagion relationship between regions. Second, the well-known SHapley Additive explanations (SHAP) method was adopted to interpret how the ISID model predicts with multivariate sliding-window time series input data. The prediction accuracy of ISID is compared with several models in the experimental study, and the results show that the proposed ISID model achieves satisfactory epidemic prediction performance. Furthermore, the SHAP result demonstrates that the ISID pays particular attention to the most proximate and remote data in the input sequence (typically 20 steps long) while paying little attention to the intermediate steps. This study contributes to reliable and interpretable epidemic prediction through a more coherent approach for public health experts. [ABSTRACT FROM AUTHOR]

In this paper, we address the controversies of epidemic control planning by developing a novel Simulation-Deep Reinforcement Learning (SiRL) model. COVID-19 reminded constituents over the world that government decision-making could change their lives. During the COVID-19 pandemic, governments were concerned with reducing fatalities as the virus spread but at the same time also maintaining a flowing economy. In this paper, we address epidemic decision-making regarding the interventions necessary given of the epidemic based on the purpose of the decision-maker. Further, we intend to compare different vaccination strategies, such as age-based and random vaccination, to shine a light on who should get priority in the vaccination process. To address these issues, we propose a simulation-deep reinforcement learning (DRL) framework. This framework is composed of an agent-based simulation model and a governor DRL agent that can enforce interventions in the agent-based simulation environment. Computational results show that our DRL agent can learn effective strategies and suggest optimal actions given a specific epidemic situation based on a multi-objective reward structure. We compare our DRL agent's decisions to government interventions at different periods of time during the COVID-19 pandemic. Our results suggest that more could have been done to control the epidemic. In addition, if a random vaccination strategy that allows super-spreaders to get vaccinated early were used, infections would have been reduced by 32% at the expense of 4% more deaths. We also show that a behavioral change of fully quarantining 10% of the risky individuals and using a random vaccination strategy leads to a reduction of the death toll by 14% and 27% compared to the age-based vaccination strategy that was implemented and the New Jersey reported data, respectively. We have also demonstrated the flexibility of our approach to be applied to other locations by validating and applying our model to the COVID-19 case in the state of Kansas. [ABSTRACT FROM AUTHOR]

This paper focuses on the characterization of viability zones in compartmental models with varying population size, due both to deaths caused by epidemics and to natural demography. This is achieved with the use of viscosity characterizations of viability and extensively illustrated on several models. An example taking into consideration real data is provided. The paper is completed with a viscosity approach to the optimality of minimal ("greedy") non-pharmaceutical interventions. [ABSTRACT FROM AUTHOR]

Vaccination is an important tool in disease control to suppress disease, and vaccine-influenced diseases no longer conform to the general pattern of transmission. In this paper, by assuming that the infection rate is affected by the Ornstein–Uhlenbeck process, we obtained a stochastic SIRV model. First, we prove the existence and uniqueness of the global positive solution. Sufficient conditions for the extinction and persistence of the disease are then obtained. Next, by creating an appropriate Lyapunov function, the existence of the stationary distribution for the model is proved. Further, the explicit expression for the probability density function of the model around the quasi-equilibrium point is obtained. Finally, the analytical outcomes are examined by numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, the full information about the existence and nonexistence of a time-periodic traveling wave solution of a reaction–diffusion Zika epidemic model with seasonality, which is non-monotonic, is investigated. More precisely, if the basic reproduction number, denoted by R 0 , is larger than one, there exists a minimal wave speed c ∗ > 0 satisfying for each c > c ∗ , the system admits a nontrivial time-periodic traveling wave solution with wave speed c, and for c < c ∗ , there exist no nontrivial time-periodic traveling waves such that if R 0 ⩽ 1 , the system admits no nontrivial time-periodic traveling waves. [ABSTRACT FROM AUTHOR]

This paper aims to establish a novel explicit method for the stochastic SIS epidemic model, which can preserve the bounded positive domain and asymptotic properties. The proposed new method is based on combining a logarithmic transformation with a truncated Euler-Maruyama method, and it has the first-order rate of convergence for the pth-moment with p > 0 . Moreover, without additional restriction conditions except those necessary to guarantee the extinction of the exact solution, the approximation of the extinction is achieved for the stochastic SIS model whose coefficients violate the global monotonicity condition. Some numerical experiments are given to illustrate the theoretical results and testify to the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

Background: Shortages of mechanical ventilation have become a constant problem in Emergency Departments (EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES) to design effective interventions ensuring the high availability of ventilators for patients needing these devices. Methods: First, we applied Random Forest (RF) to estimate the mechanical ventilation probability of respiratory-affected patients entering the emergency wards. Second, we introduced the RF predictions into a DES model to diagnose the response of EDs in terms of mechanical ventilator availability. Lately, we pretested two different interventions suggested by decision-makers to address the scarcity of this resource. A case study in a European hospital group was used to validate the proposed methodology. Results: The number of patients in the training cohort was 734, while the test group comprised 315. The sensitivity of the AI model was 93.08% (95% confidence interval, [88.46 − 96.26%]), whilst the specificity was 85.45% [77.45 − 91.45%]. On the other hand, the positive and negative predictive values were 91.62% (86.75 − 95.13%) and 87.85% (80.12 − 93.36%). Also, the Receiver Operator Characteristic (ROC) curve plot was 95.00% (89.25 − 100%). Finally, the median waiting time for mechanical ventilation was decreased by 17.48% after implementing a new resource capacity strategy. Conclusions: Combining AI and DES helps healthcare decision-makers to elucidate interventions shortening the waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics. [ABSTRACT FROM AUTHOR]

The primary motivation of this paper is the lack of consensus on the impact of renewable energy (RE) and research and development (R&D) expenditure on CO2 emissions in the literature. Current literature has mostly ignored the spillover effect of R&D on CO2 emissions by increasing the intensity effect of technology, leading to biased results. Further, little is known about the impact of previous epidemics on CO2 emissions. This study fills these gaps by evaluating the spillover effects of RE and R&D on CO2 emissions in a global panel of 54 countries from 2003 to 2017. Using a two-way time- and spatial-fixed-effects panel analysis, we find both income-induced and scale effects of economic growth are present in our panel, though the scale effect is the dominant one. Our findings indicate that economic growth increases CO2 emissions at a decreasing rate, validating the Environmental Kuznets Curve hypothesis, and that urbanization and foreign trade worsen the environment. We also find that epidemic episodes before COVID-19 had a nonsignificant impact on CO2 emissions internationally. More importantly, our results confirm the presence of both the intensity and scale effects of R&D, with the intensity effect being the dominant one. We find overwhelming evidence that global R&D investment led to an overall (direct plus spillover) reduction of CO2 emissions, driven by its spillover effect, through two channels: RE and economic growth. Finally, we find that RE installations assist with reducing CO2 emissions internationally, though RE composition and state of R&D can lead to different findings. Our findings have significant policy implications for sustainable development. Our RE and R&D-spillover results support the policy recommendation of shifting to high-tech clean energy sources. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that vaccinating at maximal effort in the face of an ongoing epidemic is the best strategy to minimise infections and deaths from the disease. Despite this, no one has proved that this is guaranteed to be true if the disease follows multi-group SIR (Susceptible–Infected–Recovered) dynamics. This paper provides a novel proof of this principle for the existing SIR framework, showing that the total number of deaths or infections from an epidemic is decreasing in vaccination effort. Furthermore, it presents a novel model for vaccination which assumes that vaccines assigned to a subgroup are distributed randomly to the unvaccinated population of that subgroup. It suggests, using COVID-19 data, that this more accurately captures vaccination dynamics than the model commonly found in the literature. However, as the novel model provides a strictly larger set of possible vaccination policies, the results presented in this paper hold for both models. [ABSTRACT FROM AUTHOR]

The COVID-19 lockdown had a positive control effect on urban air quality. However, this effect remains uncertain after the epidemic enters regular management, and furthermore, only limited data are available regarding urban PM2.5 (aerodynamic diameter ≤ 2.5μm) under the impact of the epidemic. We used daily ambient PM2.5 concentration data in Beijing to compare and analyze the changes in urban PM2.5 concentrations before and after the COVID-19 epidemic and to estimate the healthy effects and economic burden associated with PM2.5 before and after the epidemic. The study found that COVID-19 has a significant impact on the urban environmental PM2.5 concentration, which is manifested by the decrease in the PM2.5 concentration in Beijing during the epidemic by 27.8%. Exposure-response models estimated 56.443 (95% CI: 43.084–69.893) thousand people die prematurely in Beijing during the COVID-19 epidemic attributed to long-term PM2.5 exposure, with a 13.3% decrease in the number of premature deaths year-on-year. The total healthy economic losses attributable to PM2.5 in Beijing during the COVID-19 epidemic were 35.76 (95% CI: 28.41–42.44) billion yuan, with a per capita loss of 816.8 yuan. Strict control measures throughout the COVID-19 epidemic had a positive impact on air quality in Beijing, with a decrease in both premature deaths and economic healthy losses attributable to fine particles. This paper helps to enrich and expand the research on the impact of COVID-19 on the urban environment and provides a basis for formulating policies related to air quality improvement in the post-epidemic era. [ABSTRACT FROM AUTHOR]

Healthcare social networks have a significant role in providing connected and personalized healthcare environment with real-time capabilities. However, building resilient, robust and technology-driven healthcare 5.0 has its own barriers. Especially with social media's high susceptibility to rumors and fake news, these networks can harm the society. Many researchers have been investigating the process of information diffusion, and it has been one of the most intriguing issues in network analysis. Modeling rumor propagation is one of the prominent researched topics in recent years. Traditional models assume that rumor propagation happens only in one direction, where only supporters are supposed to be active, whereas, in a real-life situation, both supporters and deniers of the information operate simultaneously. In this paper, we introduce a model for the recovery of nodes in a setting where rumor propagation and rumor control happen simultaneously. We propose the Susceptible-Infected-Recovered-Anti-spreader model based on the notion of spreading of epidemics and also its applications to modeling the propagation of rumors and control of rumor. Our model assumes people have multiple forms of reactions to rumor, either posting it, deleting it or announcing the rumor as fake. This paper also suggests how the model can act as a simulation method to compare two node centrality algorithms where spreaders chosen from one centrality algorithm try to spread the rumor, and the anti-spreaders chosen from other centrality try to dispel the rumor and vice versa. We simulate the proposed algorithm on different weighted and unweighted real-world network datasets and establish that the experimental results agrees with the proposed model. [ABSTRACT FROM AUTHOR]

Public opinion is the abbreviation of public ideas, which is the sum of the beliefs, attitudes, opinions and emotions expressed by the masses about various phenomena in the society. Network public opinion is not only the mapping of public opinion in the network, but also the direct response of social public opinion. Weibo public opinion has gradually become an important medium for people to obtain public opinion in time. The outbreak of Weibo public opinion is very likely to cause social repercussions, which will even affect people's trust in the government and even social stability. The scale of the Internet is expanding, the number of users is increasing, and the applications provided by the Internet are becoming more and more popular. In this context, Weibo public opinion analysis has become an important research topic. Facing the rapid growth of micro blog information, how to obtain the hot topics of micro blog public opinion timely, comprehensively and accurately is the primary problem to be solved in the process of micro blog public opinion analysis. Based on the infectious disease model, this paper proposes an analysis model of Weibo public opinion communication. The speech analysis model is proposed to extract the information for the processing, and the representation of the data is applied to improve the algorithm efficiency. The experimental results compared with the state-of-the-art have proven the satisfactory performance. [ABSTRACT FROM AUTHOR]

The article focuses on the research on the role of climate data in predicting malaria outbreaks in Botswana. Researchers was able to identify the malaria season which starts during the rainy season in the country. Their model could help them determine which areas will be hit hardest by the epidemic. This advance information could help curb the epidemics by implementing preventive action.

In order to understand the influencing factors affecting the COVID-19 propagation, and analyze the development trend of the epidemic situation in the world, COVID-19 propagation model to simulate the COVID-19 propagation in the population is proposed in this paper. First of all, this paper analyzes the economic factors and interventions affecting the COVID-19 propagation in various different countries. Then, the touch number for COVID-19 High-risk Population Dynamic Network in this paper was redefined, and it predicts and analyzes the development trend of the epidemic situation in different countries. The simulation data and the published confirmed data by the world health organization could fit well, which also verified the reliability of the model. Finally, this paper also analyzes the impact of public awareness of prevention on the control of the epidemic. The analysis shows that increasing the awareness of prevention, timely and early adoption of protective measures such as wearing masks, and reducing travel can greatly reduce the risk of infection and the outbreak scale. [ABSTRACT FROM AUTHOR]

In this paper we propose a time-space economic model to control the evolution and the spread of a disease. The underlying epidemiological model is formulated as a reaction-diffusion integro-differential partial differential equation. This specific model formulation, supported by empirical data, contains three different terms: a pure diffusion term, a linear growth term, and an integral term. These three terms capture different diffusion channels of a transmissible disease: a local diffusion effect, a temporal effect, and a global diffusion effect. The decision maker aims at deciding the optimal effort to be implemented in order to control the number of infections and, at the same time, minimize the cost of treatment. We analyze the finite horizon case in detail and we provide the closed-form expression of the optimal policy to be implemented to control the epidemic while sustaining economic growth. We also propose two different extensions: The first one considers an infinite horizon model while, the second one, is related to a multi-period framework. [ABSTRACT FROM AUTHOR]

This paper studies a two-age structured SIRS epidemic model with logistic growth of susceptible population and two-time delays. We simultaneously introduce two-time delays, i.e., the immunity and incubation periods, into this dynamic system and investigate their impact on different dynamic behaviors for the model. By means of the C 0 -semigroup theory, the model is transformed into a non-densely defined abstract Cauchy problem, and the condition of the existence and uniqueness of the endemic equilibrium is obtained. Following the spectral analysis, the characteristic equation technique, and the Hopf bifurcation theorem, we show that different combinations of the two delays perform a vital role in the instability/stability as well as the Hopf bifurcation results of equilibrium solutions. We numerically provide some graphical representations to check the main theoretical results and show the rich dynamics by varying the two delay parameters. [ABSTRACT FROM AUTHOR]

In this paper, we will revisit the model studied in Lou and Zhao (J Math Biol 62:543–568, 2011), where the model takes the form of a nonlocal and time-delayed reaction–diffusion model arising from the fixed incubation period. We consider the infection age to be a continuous variable but without the limitation of the fixed incubation period, leading to an age-space structured malaria model in a bounded domain. By performing the elementary analysis, we investigate the well-posedness of the model by proving the global existence of the solution, define the explicit formula of basic reproduction number when all parameters remain constant. By analyzing the characteristic equations and designing suitable Lyapunov functions, we also establish the threshold dynamics of the constant disease-free and positive equilibria. Our theoretical results are also validated by numerical simulations for 1-dimensional and 2-dimensional domains. [ABSTRACT FROM AUTHOR]

This special issue contains papers describing the role AI plays in various stages of a disease outbreak, with COVID-19 as a case study. Leveraging artificial intelligence (AI) capabilities for COVID-19 containment. In the last year, most of the world's activities stopped due to the COVID-19 pandemic, but not research: we especially observed a fast race for new insights in COVID-19 and also in applying new technical approaches for COVID-19. [Extracted from the article]

Targeted vaccination policies can have a significant impact on the number of infections and deaths in an epidemic. However, optimising such policies is complicated, and the resultant solution may be difficult to explain to policy-makers and to the public. The key novelty of this paper is a derivation of the leading-order optimal vaccination policy under multi-group susceptible–infected–recovered dynamics in two different cases. Firstly, it considers the case of a small vulnerable subgroup in a population and shows that (in the asymptotic limit) it is optimal to vaccinate this group first, regardless of the properties of the other groups. Then, it considers the case of a small vaccine supply and transforms the optimal vaccination problem into a simple knapsack problem by linearising the final size equations. Both of these cases are then explored further through numerical examples, which show that these solutions are also directly useful for realistic parameter values. Moreover, the findings of this paper give some general principles for optimal vaccination policies which will help policy-makers and the public to understand the reasoning behind optimal vaccination programs in more generic cases. [ABSTRACT FROM AUTHOR]

With the outbreaks of the COVID-19 epidemics in several provinces of China, government takes prevention and control measures to contain the epidemics. It is more difficult to make the long-term prediction of the sporadic COVID-19 epidemics than widespread ones in that the former cannot obey the laws of the infectious disease well like the latter. In this paper, we make long-term predictions including end time and final size, peak and peak time of current confirmed cases and the number of accumulative removed cases of the sporadic COVID-19 epidemics in different regions of China by a novel non-autonomous delayed SIR compartment model (S—susceptible, I—infected, R—removed). The key contribution of this paper is that under the rigorous containments, we find transmission rate β (t) is approximately an exponential decreasing function with respect to time t, rather than a fixed constant. In addition, the removed rate γ (t) is approximately a piecewise linear increasing function instead of a linear increasing function which is (at + b)heaviside (t-14). First, according to the few data in the early stage, i.e., roughly the first 7 days, issued by the National Health Commission of China and local Health Commissions, we can accurately estimate these parameters, i.e., transmission and removed rates of the model. Then, by them, we accurately predict the evolution of the COVID-19 there. On the basis of them to predict Category A of the sporadic COVID-19 epidemics since July 20th, 2021 in this summer. The results agree very well to the actual ones. It is also adopted to predict Category B - - - the tour group epidemics since October 17th, 2021 and Category C - - - other sporadic epidemics since October 27th, 2021. The results show that although our method is simple and the needed data are very few, the long-term prediction of the sporadic COVID-19 epidemics in China is quite effective. We can use this novel non-autonomous delayed SIR model to accurately predict its end time and final size, peak and peak time of current confirmed cases and the number of accumulative removed cases in China. This work can help governments and policy-makers make optimal prevention and control policies for all cities and provinces to contain the COVID-19 epidemics, and prepare well for the resumption of work, production and classes in advance to reduce the economic and social losses. [ABSTRACT FROM AUTHOR]

Among the most important questions that await an answer in seismology, perhaps one is whether there is a correlation between the magnitudes of two successive seismic events. The answer to this question is considered of fundamental importance given the potential effect in forecasting models, such as Epidemic Type Aftershock Sequence models. After a meta-analysis of 29 papers, we speculate that given the lack of studies carried out with realistic physical models and given the possible bias due to the lack of events recorded in the experimental seismic catalogs, important improvements are necessary on both fronts to be sure to provide a statistically relevant answer. [ABSTRACT FROM AUTHOR]

The aim of this paper is twofold. On one hand, we strive to give a simpler proof of the optimality of greedy controls when the cost of interventions is control-affine and the dynamics follow a state-constrained controlled SIR model. This is achieved using the Hamilton–Jacobi characterization of the value function, via the verification argument and explicit trajectory-based computations. Aside from providing an alternative to the Pontryagin complex arguments in Avram et al. (Appl Math Comput 418:126816, 2022) (see also Avram et al. in Appl Math Comput 423:127012, 2022), this method allows one to consider more general classes of costs; in particular state-dependent ones. On the other hand, the paper is completed by linear programming methods allowing one to deal with possibly discontinuous costs. In particular, we propose a brief exposition of classes of linearized dynamic programming principles based on our previous work and ensuing dual linear programming algorithms. We emphasize the particularities of our state space and possible generations of forward scenarios using the description of reachable sets. [ABSTRACT FROM AUTHOR]

Zoonotic viruses have sacrificed hundreds of millions of people throughout human history. There are currently 1.7 million unidentified viruses estimated to be circulating in mammal and bird populations. It is foreseeable that in the near future, another of these will transmit to people, heralding the start of the next pandemic—one potentially more deadly than COVID-19. At the core of this article is a call for pre-emptive protection of the natural environment and its regenerative systems as the first fundamental step in the prevention of future epidemics and pandemics. While zoonoses originate in nature, the predominant legal discipline, managing these crises, is international health law which is invoked reactively once an outbreak has been reported. In this paper, we identify the need for a legal shift in epidemic and pandemic responses. In particular, we call for the incorporation of international environmental agreements to prevent the initial viral spillover from animal to human populations. We propose a strategy of strengthening existing agreements and a coupling of legal disciplines, such as health and environmental law, emphasizing the need for synergies across legal disciplines to enhance the emergence and management of future pandemics and epidemics. We introduce Coupled Human and Natural Systems (CHANS) Law to frame the required integration across legal instruments to regulate inextricably human-nature connections and advocate for the development of a Convention on Epidemics and Pandemics. [ABSTRACT FROM AUTHOR]

Epidemic outbreak (COVID-19, SARS-CoV-2) is an exceptional scenario of agri-food supply chain (AFSC) risk at the globalised level which is characterised by logistics' network breakdown (ripple effects), demand mismatch (uncertainty), and sustainable issues. Thus, the aim of this research is the modelling of the sustainable based multi-tier system for AFSC, which is managed through the different emerging application of Internet of things (IoT) technology. Different IoT technologies, viz., Blockchain, robotics, Big data analysis, and cloud computing, have developed a competitive AFSC at the global level. Competitive AFSC needs cautious incorporation of multi-tiers suppliers, specifically during dealing with globalised sustainability issues. Firms have been advancing towards their multi suppliers for driving social, environments and economical practices. This paper also studies the interrelationship of 14 enablers and their cause and effect magnitude as contributing to IoT-based food secure model. The methodology used in the paper is interpretative structural modelling (ISM) for establishing interrelationship among the enablers and Fuzzy-Decision-Making Trial and Evaluation Laboratory (F-DEMATEL) to provide the magnitude of the cause-effect strength of the hierarchical framework. This paper also provides some theoretical contribution supported by information processing theory (IPT) and dynamic capability theory (DCT). This paper may guide the organisation's managers in their strategic planning based on enabler's classification into cause and effect groups. This paper may also encourage the mangers for implementing IoT technologies in AFSC. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive dataset on West Nile virus outbreaks that have occurred in Italy from September 2012 to November 2022. We have digitized bulletins published by the Italian National Institute of Health to demonstrate the potential utilization of this data for the research community. Our aim is to establish a centralized open access repository that facilitates analysis and monitoring of the disease. We have collected and curated data on the type of infected host, along with additional information whenever available, including the type of infection, age, and geographic details at different levels of spatial aggregation. By combining our data with other sources of information such as weather data, it becomes possible to assess potential relationships between West Nile virus outbreaks and environmental factors. We strongly believe in supporting public oversight of government epidemic management, and we emphasize that open data play a crucial role in generating reliable results by enabling greater transparency. [ABSTRACT FROM AUTHOR]

Purpose: Despite substantial vaccination progress, persistent measles outbreaks challenge global elimination efforts, particularly within healthcare settings. In this paper, we critically review the factors contributing to measles outbreak and effective control measures for nosocomial transmission of measles. Methods: We systematically searched electronic databases for articles up to 17th May, 2023. This was performed by two independent reviewers, with any disagreements resolved by a third reviewer. We also searched governmental and international health agencies for relevant studies. Results: Forty relevant articles were systematically reviewed, revealing key factors fuelling measles outbreak in healthcare settings, including high transmissibility capability; high intensity exposure; delayed care; failure to use protective equipment and implement control measures; vaccine failure; unclear immunisation history and lack of registries; and lacking recommendation on healthcare workers' (HCWs) measles vaccination. To combat these challenges, successful control strategies were identified which include early notification of outbreak and contact tracing; triaging all cases and setting up dedicated isolation unit; strengthening protective equipment use and physical measures; improving case detection; determining immunity status of HCWs; establishing policy for measles vaccination for HCWs; management of exposed personnel; and developing a pre-incident response plan. Conclusion: A coordinated and comprehensive approach is essential to promptly identify and manage measles cases within healthcare settings, necessitating multifactorial strategies tailored to individual settings. These findings provide a valuable foundation for refining strategies to achieve and maintain measles elimination status in healthcare environments. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the notion of practically susceptible population, which is a fraction of the biologically susceptible population. Assuming that the fraction depends on the severity of the epidemic and the public's level of precaution (as a response of the public to the epidemic), we propose a general framework model with the response level evolving with the epidemic. We firstly verify the well-posedness and confirm the disease's eventual vanishing for the framework model under the assumption that the basic reproduction number R 0 < 1 . For R 0 > 1 , we study how the behavioural response evolves with epidemics and how such an evolution impacts the disease dynamics. More specifically, when the precaution level is taken to be the instantaneous best response function in literature, we show that the endemic dynamic is convergence to the endemic equilibrium; while when the precaution level is the delayed best response, the endemic dynamic can be either convergence to the endemic equilibrium, or convergence to a positive periodic solution. Our derivation offers a justification/explanation for the best response used in some literature. By replacing "adopting the best response" with "adapting toward the best response", we also explore the adaptive long-term dynamics. [ABSTRACT FROM AUTHOR]

This paper focuses on the numerical threshold stability of a nonlinear age-space structured SIR epidemic model under Neumann boundary condition. The space domain is firstly discretized to derive a numerical semi-discrete system in accordance with the method of lines. It is shown that the semi-discrete system shares the same stability threshold value (basic reproduction number R 0 ) of the model. A fully discrete positivity-preserving numerical scheme is established by applying an Implicit–Explicit (IMEX) numerical method to discretize the semi-discrete system. A numerical stability threshold value R 0 h is proposed and named as numerical basic reproduction number. Moreover, it is proved that R 0 h converges to the basic reproduction number R 0 of the original model while the stepsize vanishing, which implies R 0 h provides a realistic approximation to R 0 , besides the role plays in numerical stability analysis. Eventually, the numerical conclusions are testified through some numerical simulations. [ABSTRACT FROM AUTHOR]

In the late nineteenth century, Sulina, a settlement of about 10,000 inhabitants, was Romania's busiest port. Located at the mouth of the only navigable branch of the Danube, the town held a strategic position along South-Eastern European transportation corridors, being the gateway of Lower Danubian trade and shipping. But Sulina was also a hydrobiological melting pot of natural and anthropogenic water flows carried by the Danube, the Black Sea's currents, and the tanks and bilges of the thousands of ships that came to load their cargoes in the local harbour and roadstead. With advances in the science of bacteriology, provisioning Sulina with safe urban water became a Romanian and international public health priority. Investments in the town's water supply and sanitation are a fascinating, yet little-known episode of sanitary internationalism, in which several actors in Romania and Europe cooperated – institutionally, technologically and financially – in the attempt to bring sanitary civilisation to one of Europe's most crucial commercial and epidemiological gateways. In line with similar interest for water, disease and urban infrastructure in a peripheral (quasi-colonial) context, this paper will be illustrative for the growing debates in Romania around the quality of water in the context of the larger hygiene movement. The rhetoric of 'improvement' and 'progress' in providing access to safe drinking water, stemming from the idea that 'uncleanliness with all its consequences comes mainly from lack of water', was accompanied by calls for the construction of modern water infrastructure. [ABSTRACT FROM AUTHOR]

The paper investigates the spread pattern and dynamics of Covid-19 propagation based on SIR model. Using the model dynamics, an analytical estimation has been obtained for virus span, its longevity, growing pattern, etc. Experimental simulations are carried out on the data of four regions of India over a period of two months of country-wide lockdown. The analysis illustrates the effect of lockdown on the contact rate and its implication. Simulation results illustrate that there is a cut-down in effective contact rate by a considerable factor ranging from 2 to 4 for the selected regions. Further, the estimates for the vaccines to be developed, maximum range and span of the disease can be also estimated. Results portray that the SIR model is a significant tool to cast the dynamics and predictions of Covid-19 outbreak in comparison to other epidemic models. The study demonstrates the progression of real time data in accordance with the SIR model with high accuracy. [ABSTRACT FROM AUTHOR]

In 2022, COVID-19 solutions in China have entered a normal stage, and the solutions imported from ports have been transformed from emergency prevention and control measures to investigative long-term prevention and control measures. Therefore, it is necessary to study solutions for COVID-19 at border ports. In this study, 170 research papers related to the prevention and control measures of COVID-19 at ports from 2020 to September 2022 were retrieved from Wanfang database, HowNet database, Wip database, and WoS core collection. Citespace 6.1.R2 software was used to research institutions visualize and analyze researchers and keywords to explore their research hotspots and trends. After analysis, the overall volume of documents issued in the past 3 years was stable. The major contributors are scientific research teams such as the Chinese Academy of Inspection and Quarantine Sciences (Han Hui et al.) and Beijing Customs (Sun Xiaodong et al.), with less cross-agency cooperation. The top five high-frequency keywords with cumulative frequency are as follows: COVID-19 (29 times), epidemic prevention and control (29 times), ports (28 times), health quarantine (16 times), and risk assessment (16 times). The research hotspots in the field of prevention and control measures for COVID-19 at ports are constantly changing with the progress of epidemic prevention and control. Cooperation between research institutions needs to be strengthened urgently. The research hotspots are the imported epidemic prevention and control, risk assessment, port health quarantine, and the normalized epidemic prevention and control mechanism, which is the trend of research and needs further exploration in the future. [ABSTRACT FROM AUTHOR]

This paper provides an analysis on global dynamics of a diffusive cholera model. We formulate the model by a reaction–diffusion system with space-dependent parameters and bacterial hyperinfectivity, where susceptible and infectious humans disperse with distinct dispersal rates and the hyper-infectious and lower-infectious vibrios in contaminated water are assumed to be immobile in the domain. We first establish the well-posedness of the model. To cope with the lack of compactness of solution semiflow, we verify the asymptotic smoothness of semiflow implied with κ -contraction condition. The basic reproduction number, R 0 , is identified as a threshold, predicting whether or not the disease extinction and persistence will occur. R 0 is also equivalently characterized by some principle spectral conditions of an associated elliptic eigenvalue problem. The asymptotic profiles of the positive steady state are investigated for the cases when the dispersal rate of the susceptible humans is small and large. Our theoretical results indicate that: (1) cholera epidemics will be extinct through restricting the flow of susceptible humans within some extend; (2) the risk of infection will be underestimated if hyperinfectivity is not considered. In a homogeneous case, we also confirm the global attractivity of a unique positive equilibrium by utilizing the technique of Lyapunov function. [ABSTRACT FROM AUTHOR]

The use of internet-based query data offers a novel approach to improve disease surveillance and provides timely disease information. This paper systematically reviewed the literature on infectious disease predictions using internet-based query data and climate factors, discussed the current research progress and challenges, and provided some recommendations for future studies. We searched the relevant articles in the PubMed, Scopus, and Web of Science databases between January 2000 and December 2019. We initially included studies that used internet-based query data to predict infectious disease epidemics, then we further filtered and appraised the studies that used both internet-based query data and climate factors. In total, 129 relevant papers were included in the review. The results showed that most studies used a simple descriptive approach (n=80; 62%) to detect epidemics of influenza (including influenza-like illness (ILI)) (n=88; 68%) and dengue (n=9; 7%). Most studies (n=61; 47%) purely used internet search metrics to predict the epidemics of infectious diseases, while only 3 out of the 129 papers included both climate variables and internet-based query data. Our research shows that including internet-based query data and climate variables could better predict climate-sensitive infectious disease epidemics; however, this method has not been widely used to date. Moreover, previous studies did not sufficiently consider the spatiotemporal uncertainty of infectious diseases. Our review suggests that further research should use both internet-based query and climate data to develop predictive models for climate-sensitive infectious diseases based on spatiotemporal models. [ABSTRACT FROM AUTHOR]

Timely and high-resolution estimates of the home locations of a sufficiently large subset of the population are critical for effective disaster response and public health intervention, but this is still an open problem. Conventional data sources, such as census and surveys, have a substantial time lag and cannot capture seasonal trends. Recently, social media data has been exploited to address this problem by leveraging its large user-base and real-time nature. However, inherent sparsity and noise, along with large estimation uncertainty in home locations, have limited their effectiveness. Consequently, much of previous research has aimed only at a coarse spatial resolution, with accuracy being limited for high-resolution methods. In this paper, we develop a consensus deep-learning solution that uses two deep neural networks to deal with sparse and noisy social media data. In the first step, high accuracy is achieved by implementing a deep neural network that has more balanced home location candidates, using batch normalization, and duplicating home location records. We obtained over 92% accuracy for large subsets on a commonly used dataset. Compared to other high-resolution methods, our approach yields up to 60% error reduction by reducing high-resolution home prediction error from over 21% to less than 8%. Systematic comparisons show that our method gives the highest accuracy both for the entire sample and for subsets. Evaluation on a real-world public health problem further validates the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In this paper, an SEIRS epidemic model is proposed, incorporating appropriate compartments as isolated exposed class and diagnosed class, relevant to interventions. In the scenario of constant isolated proportion, the qualitative analyses are carried out in terms of the basic reproduction number R 0 . The sensitivity of R 0 with respect to model parameters is discussed. The Pontryagin's Maximum Principle is applied to characterize the optimal control problem analytically, aiming at finding the optimal value of the control to minimize the cost of interventions. A general explicit expression for the optimal control is obtained. Numerical simulations are performed to illustrate analytical results. [ABSTRACT FROM AUTHOR]