Decision-Dependent Distributionally Robust Markov Decision Process Method in Dynamic Epidemic Control

In this paper, we present a Distributionally Robust Markov Decision Process (DRMDP) approach for addressing the dynamic epidemic control problem. The Susceptible-Exposed-Infectious-Recovered (SEIR) model is widely used to represent the stochastic spread of infectious diseases, such as COVID-19. While Markov Decision Processes (MDP) offers a mathematical framework for identifying optimal actions, such as vaccination and transmission-reducing intervention, to combat disease spreading according to the SEIR model. However, uncertainties in these scenarios demand a more robust approach that is less reliant on error-prone assumptions. The primary objective of our study is to introduce a new DRMDP framework that allows for an ambiguous distribution of transition dynamics. Specifically, we consider the worst-case distribution of these transition probabilities within a decision-dependent ambiguity set. To overcome the computational complexities associated with policy determination, we propose an efficient Real-Time Dynamic Programming (RTDP) algorithm that is capable of computing optimal policies based on the reformulated DRMDP model in an accurate, timely, and scalable manner. Comparative analysis against the classic MDP model demonstrates that the DRMDP achieves a lower proportion of infections and susceptibilities at a reduced cost. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The impact of individualism on the efficiency of epidemic control and the underlying computational and psychological mechanisms

Coronavirus disease 2019 (COVID-19) is a global health crisis, and some countries experience difficulties in controlling the infection and mortality of COVID-19. Based on previous findings, we argue that individualistic cultural values are not conducive to the control of the epidemic. The results of the cross-cultural analysis showed that the individualistic cultural values positively predicted the number of deaths, deaths per million, and mortality of COVID-19, and the independent self-construct negatively predicted the efficiency of epidemic control in the early phase. The evolutionary game model and cross-cultural experiment further suggested that individualistic culture reduced the efficiency of overall epidemic control by enhancing individuals' fear of death in the context of the epidemic and increased individuals' tendency to violate epidemic control. Our results support the natural-behavioral-cultural co-evolution model, suggesting the impact of culture on the control of virus transmission and deaths during COVID-19, and provide an important scientific reference for countries to respond to global public health crises. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

An adaptive testing strategy for efficient utilization of healthcare resources during an epidemic.

Lockdowns are found to be effective against rapidly spreading epidemics like COVID-19. Two downsides to strategies rooted in social distancing and lockdowns are that they adversely affect the economy and prolong the duration of the epidemic. The extended duration observed in these strategies is often due to the under-utilization of medical facilities. Even though an under-utilized health care system is preferred over an overwhelmed one, an alternate strategy could be to maintain medical facilities close to their capacity, with a factor of safety. We explore the practicality of this alternate mitigation strategy and show that it can be achieved by varying the testing rate. We present an algorithm to calculate the number of tests per day to maintain medical facilities close to their capacity. We illustrate the efficacy of our strategy by showing that it reduced the epidemic duration by 40% in comparison to lockdown-based strategies.

A model predictive control approach to optimally devise a two-dose vaccination rollout: A case study on COVID-19 in Italy.

The COVID-19 pandemic has led to the unprecedented challenge of devising massive vaccination rollouts, toward slowing down and eventually extinguishing the diffusion of the virus. The two-dose vaccination procedure, speed requirements, and the scarcity of doses, suitable spaces, and personnel, make the optimal design of such rollouts a complex problem. Mathematical modeling, which has already proved to be determinant in the early phases of the pandemic, can again be a powerful tool to assist public health authorities in optimally planning the vaccination rollout. Here, we propose a novel epidemic model tailored to COVID-19, which includes the effect of nonpharmaceutical interventions and a concurrent two-dose vaccination campaign. Then, we leverage nonlinear model predictive control to devise optimal scheduling of first and second doses, accounting both for the healthcare needs and for the socio-economic costs associated with the epidemics. We calibrate our model to the 2021 COVID-19 vaccination campaign in Italy. Specifically, once identified the epidemic parameters from officially reported data, we numerically assess the effectiveness of the obtained optimal vaccination rollouts for the two most used vaccines. Determining the optimal vaccination strategy is nontrivial, as it depends on the efficacy and duration of the first-dose partial immunization, whereby the prioritization of first doses and the delay of second doses may be effective for vaccines with sufficiently strong first-dose immunization. Our model and optimization approach provide a flexible tool that can be adopted to help devise the current COVID-19 vaccination campaign, and increase preparedness for future epidemics.

HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control.

Infectious diseases, such as Black Death, Spanish Flu, and COVID-19, have accompanied human history and threatened public health, resulting in enormous infections and even deaths among citizens. Because of their rapid development and huge impact, laying out interventions becomes one of the most critical paths for policymakers to respond to the epidemic. However, the existing studies mainly focus on epidemic control with a single intervention, which makes the epidemic control effectiveness severely compromised. In view of this, we propose a Hierarchical Reinforcement Learning decision framework for multi-mode Epidemic Control with multiple interventions called HRL4EC. We devise an epidemiological model, referred to as MID-SEIR, to describe multiple interventions' impact on transmission explicitly, and use it as the environment for HRL4EC. Besides, to address the complexity introduced by multiple interventions, this work transforms the multi-mode intervention decision problem into a multi-level control problem, and employs hierarchical reinforcement learning to find the optimal strategies. Finally, extensive experiments are conducted with real and simulated epidemic data to validate the effectiveness of our proposed method. We further analyze the experiment data in-depth, conclude a series of findings on epidemic intervention strategies, and make a visualization accordingly, which can provide heuristic support for policymakers' pandemic response.

Deciphering the covid-19 epidemic and analysis of its controllability

This chapter discusses lessons from the Covid-19 crisis, based on the history of the disease in France and distribution throughout the world. The Covid-19 crisis raises many questions, in addition to those addressed in the deciphering of the epidemic. In addition to the pre-positioning of the epidemic control system, for which the best organization must be found, the tools for analyzing the emergence that have just been presented can be optimized through predictive modeling, propagation scenarios and the study of the consequences of anti-epidemic measures. While no one appears "especially guilty" of the occurrence of the Covid-19 crisis, it is highly unfortunate that real-time epidemic threat analysis systems, whose annual cost can be estimated at 1/10,000th the cost of the epidemic, were not used to contain severe acute respiratory syndrome coronavirus 2. © ISTE Ltd 2022.

Smart Urban Governance in Epidemic Control: Practices and Implications of Hangzhou

Appropriate governance tools can facilitate urban governments' effective responses to crises. Supported by information and communication technologies (ICTs), e-government infrastructure can be employed to achieve smart governance in epidemic control. Examining the case of Hangzhou, this paper discusses the Chinese megacity's adoption of e-government infrastructure as a means of combating the COVID-19 epidemic and stimulating recovering of the economy. This paper also summarizes several policy implications that may serve as points of reference for other cities when formulating their crisis response strategies. The paper concludes that smart governance rooted in the use of e-government infrastructure has exhibited great potential for public health crisis management.

On the Impact of Quarantine Policies and Recurrence Rate in Epidemic Spreading Using a Spatial Agent-Based Model

Pandemic outbreaks often determine swift global reaction, proven by for example the more recent COVID-19, H1N1, Ebola, or SARS outbreaks. Therefore, policy makers now rely more than ever on computational tools to establish various protection policies, including contact tracing, quarantine, regional or national lockdowns, and vaccination strategies. In support of this, we introduce a novel agent-based simulation framework based on: (i) unique mobility patterns for agents between their home location and a point of interest, and (ii) the augmented SICARQD epidemic model. Our numerical simulation results provide a qualitative assessment of how quarantine policies and the patient recurrence rate impact the society in terms of the infected population ratio. We investigate three possible quarantine policies (proactive, reactive, and no quarantine), a variable quarantine restrictiveness (0–100%), respectively, and three recurrence scenarios (short, long, and no recurrence). Overall, our results show that the proactive quarantine in correlation to a higher quarantine ratio (i.e., stricter quarantine policy) triggers a phase transition reducing the total infected population by over 90% compared to the reactive quarantine. The timing of imposing quarantine is also paramount, as a proactive quarantine policy can reduce the peak infected ratio by over (Formula presented.) times compared to a reactive quarantine, and by over (Formula presented.) times compared to no quarantine. Our framework can also reproduce the impactful subsequent epidemic waves, as observed during the COVID-19 pandemic, according to the adopted recurrence scenario. The suggested solution against residual infection hotspots is mobility reduction and proactive quarantine policies. In the end, we propose several nonpharmaceutical guidelines with direct applicability by global policy makers. © 2023 by the author.

Changes in contents of PM2.5 components over Jinan city before and after implementation of epidemic control measures during the 2020 Spring Festival

To understand the influence of coronavirus disease control policies on changes in characteristics of particulate matter smaller than 2.5 μm (PM2.5), concentrations of various PM2.5 components in Jinan city before and after implementation of the epidemic control measures during the 2020 Spring Festival were studied using online monitoring data. Standardized multiple linear regression was used to analyze the contribution of meteorological factors to the variations in concentrations of PM2.5 components. After the epidemic control measures were implemented, the concentrations of PM2.5 components in the area decreased significantly, and the rate at which the daily average concentration was exceeded decreased by 24.8%. The concentrations of all PM2.5 components decreased to various degrees, with those of trace elements (TE), elemental carbon (EC), and nitrate (NO3−) having decreased significantly by 50.3%, 46.8%, and 31.5%, respectively. In terms of component proportions, those of TE and EC decreased after the epidemic control measures were initiated whereas those of ammonium (NH4+), organic matter (OM), sulfate (SO4 2− ), and mineral dust increased;the proportion of NO3 − changed slightly, and the total proportion of secondary ions SO4 2−, NO3−, and NH4 + increased by 14.3%. Comparison of the proportions of PM2.5 components showed that after the epidemic control measures were implemented, the proportions of NO3 − and EC in PM2.5 that cause a light pollution level decreased whereas those of OM, SO4 2−, and NH4 + increased. This indicated that people traveled less, motor vehicle emissions decreased, work at construction sites stopped, and NO3 − proportion was greatly reduced while epidemic control measures were in place. However, afterward, decrease in concentrations of PM2.5 components and increase in secondary transformation of volatile organic compounds led to an increase in OM concentration. Compared with those before the epidemic control measures were implemented, the NO2/SO2 and NO3– /SO4 2− ratios fell significantly, and their average values decreased by 30.0% and 14.0%, respectively, indicating that the contribution of mobile sources (e.g., automobile exhaust) to pollution had decreased during the epidemic control period. Under the influence of the control measures, the OC concentration also decreased for excellent, good, and mild pollution levels;however, the secondary organic carbon concentration increased, indicating that secondary conversions did not decrease under the epidemic control conditions. Standardized multiple linear regression analyses of meteorological factors showed that changes in the height of the boundary layer contributed the most (46.5%) to changes in concentrations of PM2.5 components before the epidemic control measures were implemented;afterward, humidity was the primary factor governing the increase in these concentrations. © 2022, The Science Press. All rights reserved.

An Adaptive Fusion Risk-Zone Detection Network and its Application

COVID-19 has caused a pandemic and adverse effects in many fields on a global scale. The city scale quarantine has demonstrated its effectiveness in controlling the epidemic. Conversely, it is costly and risky in inducing economic and social challenges. A compromised solution is to place quarantine measures at high-risk zones on a local scale. Therefore, it is important to investigate risk zones for conducting cost insensitive precautionary measures. The urban data depict the characteristics of different city zones, which offers an opportunity for detecting the high-risk zones. Yet, the high noise-to-signal ratio requires an efficient procedure to rule out irrelevant information in the informative raw urban data and adapt to the risk detection task. In this paper, we propose an Adaptive Fusion Risk-zone Detection Network (AFRDN), which fuses the static and dynamic multi-sourced urban data in an adaptive manner. Specifically, AFRDN first extracts diverse information-rich features from raw urban data with various encoders in the embedding learning module. Then, the AFRDN takes a hierarchical late fusion strategy by fusing the static embedding and the attentive hidden state of dynamic features in the deep latent space. To capture the most relevant information for risk-zone detection, the AFRDN adapts each dimension in the fused embedding with multi-head self-attention blocks. We have collected a real-world dataset including six Chinese cities and conducted extensive experiments to evaluate our framework. Simulation experiments and comparative analysis results show that the AFRDN is effective and feasible for early detection of infectious diseases high-risk zones. [ FROM AUTHOR] Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Informational value of visual nudges during crises: Improving public health outcomes through social media engagement amid COVID-19

In this study, we conceptualize and empirically evaluate how large-scale organizations can utilize the informational value of visual nudges on social media to promote safety among users and thus improve public health outcomes in the context of the coronovirus desease caused by the SARS-CoV-2 virus (COVID-19) pandemic. We construct a unique panel dataset combining data collected from multiple public and proprietary sources. To operationalize visual nudges from user-generated content, we engage in extensive manual classification of images collected from Instagram (IG), Twitter (TW), and Facebook (FB). To examine the relationship between visual nudging and COVID-19 positivity, we rely on a combination of econometric and epidemiological models. We find that when institutional actors share more images containing mask-related information on IG, their COVID-19 positivity rates decrease by up to 25%, on average. Also, given the fragmentary evidence behind FB and TW effects, our results provide suggestive evidence of the "boundary condition” of the visual nudge effect. Finally, empirical evidence indicates the dynamic and curvilinear effect of visual nudges on positivity over time, such that the informational value of visual nudging is most prominent if communicated 3 to 5 weeks ahead of time, on average. Our results demonstrate the informational value of visual nudges communicated through pertinent social media channels, as well as their capacity to improve public health outcomes. This suggests the feasibility of institutional actors using social media engagement to promote safe behaviors. We conclude by discussing how our findings may be used to develop more effective communication strategies regarding public perceptions of mask use and other relevant safety measures. © 2023 The Authors. Production and Operations Management published by Wiley Periodicals LLC on behalf of Production and Operations Management Society.

Contact Tracing and Epidemic Intervention via Deep Reinforcement Learning

The recent outbreak of COVID-19 poses a serious threat to people's lives. Epidemic control strategies have also caused damage to the economy by cutting off humans' daily commute. In this article, we develop an Individual-based Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart epidemic control strategies that can simultaneously minimize infections and the cost of mobility intervention. IDRLECA first hires an infection probability model to calculate the current infection probability of each individual. Then, the infection probabilities together with individuals' health status and movement information are fed to a novel GNN to estimate the spread of the virus through human contacts. The estimated risks are used to further support an RL agent to select individual-level epidemic-control actions. The training of IDRLECA is guided by a specially designed reward function considering both the cost of mobility intervention and the effectiveness of epidemic control. Moreover, we design a constraint for control-action selection that eases its difficulty and further improve exploring efficiency. Extensive experimental results demonstrate that IDRLECA can suppress infections at a very low level and retain more than 95% of human mobility. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

The COVID-19 pandemic in various restriction policy scenarios based on the dynamic social contact rate.

The social contact rate has influenced the transmission of COVID-19, with more social contact resulting in more contagion cases. We chose 18 countries with the most confirmed cases in the first 200 days after the Wuhan lockdown. This was the first study using the dynamic social contact rate to simulate the epidemic under diverse restriction policies over 500 days since the COVID-19 outbreak. The developed General Dynamic Model suggested that the probability of contagion ranged from 12.52% to 39.39% in the epidemic. The geometric mean of the social contact rates differed from 18.21% to 96.00% between countries. The restriction policies in developed economies were 3.5 times more efficient than in developing economies. We compare the effectiveness of different policies for disease prevention and discuss the influence of policy adjustment frequency for each country. Maintaining the tightest restriction or alternate tightening and loosening restrictions was recommended, with each having an average 72.45% and 79.78% reduction in maximum active cases, respectively.

Layered vaccine allocation for spatio-temporal vaccination of COVID-19

Optimal allocation of vaccine doses is a major challenge faced by the health authorities especially in the case of an ever-growing pandemic expansion and a limited supply availability. Based on a spatio-temporal compartmental virus propagation model applied to the case of SARS-CoV-2 virus, we investigate a layered vaccine allocation strategy for the subpopulations of a given country or a geographical region based on the prevalence of susceptible individuals as a prioritization metric. Our findings show that a relaxed layered allocation prioritization, where a maximum of regions benefit from vaccine doses is more effective in controlling the epidemic than a strict prioritization, focused only on the few most prioritized regions. These results are consistent among different vaccine rollout speeds for various limiting values of the priority list. [ FROM AUTHOR] Copyright of International Journal of Modern Physics C: Computational Physics & Physical Computation is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Human Mobility-based Individual-level Epidemic Simulation Platform

COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in an airborne way, and human-to-human transmission is the primary cause of the fast spread of COVID-19. Thus, mobility should be restricted to control the epidemic, and many governments worldwide have succeeded in curbing the spread by means of control policies like city lockdowns. Against this background, we propose a novel fine-grained transmission model based on real-world human mobility data and develop a platform that helps the researcher or governors to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform can also support users to determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential virus carriers. © 2022 held by the owner/author(s). Publication rights licensed to ACM.

Research on Big Data Empower Epidemic Governance Based on Knowledge Map

Since the current outbreak of COVID-19, in two years, it is necessary to comprehensively review and sort out the logical approach, value dependence and technical path of big data empowered epidemic governance and strengthen the systematic and comprehensive research on big data empowered epidemic control. Based on the perspective of bibliometric analysis, CiteSpace is used to analyze China's big data enabled epidemic governance research and mine the high-frequency keywords, clustering topics and time zone evolution of big data enabled epidemic governance research. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Capacity of local authority and community on epidemic response in Vietnam: Implication for COVID-19 preparedness.

Local authority's response and community adaptive capacity are critically important for the prevention and control of infectious diseases, especially for the disease with an astonishing speed of spreading like COVID-19. This study aims to examine the perception on the capability of local authority's response and community adaptation among core workforces in responding to acute events in Vietnam. Health professionals, medical students, and community workers in all regions of Vietnam were invited to participate in a web-based survey from December 2019 to February 2020. The snowball sampling technique was utilized to recruit respondents. The Tobit multivariable regression model was used to identify associated factors. The results showed that based on a 0-10 numeric rating scale, the mean scores of the capacity of local agencies and community adaptation were 6.2 ± 2 and 6.0 ± 1.8, respectively. Regarding local authority competencies, the lowest score went to "Adequate equipment, infrastructures and funding for disease prevention". For community adaptation, the respondents evaluated the capacity on "Periodic training, equipment and drills to prepare for epidemic and disaster response" competency" with the lowest mark (5.2 ± 2.5). Overall, there were significant differences in the assessment of community adaptive capacity between urban and rural areas (p < 0.01). This study indicated the moderate capacity of the local authority and community adaptation on epidemics and disasters in Vietnam. It is critically necessary to develop the action plan, response scenario and strategies to optimize the utilization of equipment and human resources in combating epidemics for each setting.

Effective contact tracing for COVID-19: A systematic review.

Contact tracing is commonly recommended to control outbreaks of COVID-19, but its effectiveness is unclear. Following PRISMA guidelines, we searched four databases using a range of terms related to contact tracing effectiveness for COVID-19. We found 343 papers; 32 were included. All were observational or modelling studies. Observational studies (n = 14) provided consistent, very-low certainty evidence that contact tracing (alone or in combination with other interventions) was associated with better control of COVID-19 (e.g. in Hong Kong, only 1084 cases and four deaths were recorded in the first 4.5 months of the pandemic). Modelling studies (n = 18) provided consistent, high-certainty evidence that under assumptions of prompt and thorough tracing with effective quarantines, contact tracing could stop the spread of COVID-19 (e.g. by reducing the reproduction number from 2.2 to 0.57). A cautious interpretation indicates that to stop the spread of COVID-19, public health practitioners have 2-3 days from the time a new case develops symptoms to isolate the case and quarantine at least 80% of its contacts.

Socio-demographic variables can guide prioritized testing strategies for epidemic control in resource-limited contexts.

BACKGROUND: Targeted surveillance allows public health authorities to implement testing and isolation strategies when diagnostic resources are limited, and can be implemented via the consideration of social network topologies. Yet, it remains unclear how to implement such surveillance and control when network data are unavailable. METHODS: We evaluated the ability of socio-demographic proxies of degree centrality to guide prioritized testing of infected individuals compared to known degree centrality. Proxies were estimated via readily-available socio-demographic variables (age, gender, marital status, educational attainment, and household size). We simulated SARS-CoV-2 epidemics via a SEIR individual-based model on two contact networks from rural Madagascar to further test the applicability of these findings to low-resource contexts. RESULTS: Targeted testing using socio-demographic proxies performed similarly to targeted testing using known degree centralities. At a low testing capacity, using the proxies reduced the infection burden by 22-33% while using 20% fewer tests, compared to random testing. By comparison, using known degree centrality reduced the infection burden by 31-44% while using 26-29% fewer tests. CONCLUSIONS: We demonstrate that incorporating social network information into epidemic control strategies is an effective countermeasure to low testing capacity and can be implemented via socio-demographic proxies when social network data are unavailable.

Interacting With COVID-19: How Population Behavior, Feedback and Memory Shaped Recurrent Waves of the Epidemic

Until the approval of vaccines at the end of 2020, societies relied on non-pharmaceutical interventions (NPIs) in order to control the COVID-19 pandemic. Spontaneous changes in individual behavior might have contributed to or counteracted epidemic control due to NPIs. For example, the population compliance to NPIs may have varied over time as people developed 'epidemic fatigue' or altered their perception of the risk and severity of COVID-19. Whereas official measures are well documented, the behavioral response of the citizens is harder to capture. We propose a mathematical model of the societal response, taking into account three main effects: the citizen response dynamics, the authorities' NPIs, and the occurrence of unpreventable events that significantly alter the virus transmission rate. A key assumption is that a society has a waning memory of the epidemic effects, which reflects on both the severity of the authorities' NPIs and on the citizens' compliance to the prescribed rules. This, in turn, feeds back onto the transmission rate of the disease, such that a higher number of hospitalizations decreases the probability of transmission. We show that the model is able to reproduce the COVID-19 dynamics in terms of hospital admissions for several European countries during 2020 over surprisingly long time scales. Also, it is capable of capturing the effects of disturbances (for example the emergence of new virus variants) and can be exploited for implementing control actions to limit such effects. A possible application, illustrated in this letter, consists of exploiting the estimations based on the data of one country, to predict and control the evolution in another country, where the virus spreading is still in an earlier phase. © 2017 IEEE.