Epidemic Behavior Research Articles

A cross-sectional study applying the PRECEDE model to explore factors influencing epidemic prevention behaviors among preschool educators

BackgroundThis study investigates the epidemic prevention behaviors of preschool educators and the factors influencing these behaviors, applying the PRECEDE model as a framework for analysis.MethodsA cross-sectional survey was conducted among 190 preschool educators from public and private institutions in Taipei City and New Taipei City. A 64-item self-developed questionnaire was used to assess epidemic prevention behaviors and their determinants. The instrument’s reliability was supported by internal consistency (Cronbach’s α ranging from 0.85 to 0.92), while its validity was confirmed through expert review, item analysis, and confirmatory factor analysis (CFA). Statistical analyses included hierarchical regression to examine the influence of predisposing, reinforcing, and enabling factors on behavior.ResultsThe model explained 54% of the variance in epidemic prevention behaviors. Enabling factors had the strongest influence (β = 0.46, p < 0.001), followed by reinforcing factors (β = 0.15, p < 0.05) and predisposing attitudes (β = 0.14, p < 0.05). Background variables, such as age (β = 0.23, p < 0.001) and years of service, collectively explained 14% of the variance.ConclusionThe findings underscore the critical role of institutional support and professional training in enhancing epidemic prevention practices among preschool educators. Recommendations include integrating disease prevention training into professional development initiatives.

Don't say it's over: The perceived epidemic stage and COVID preventive behaviour.

Little is known about the perceptions of the COVID-19 pandemic's dynamic and its effect on self-protective behaviour. Using survey data from 1343 university students we explored how the perceived temporal distance to the COVID pandemic peak associates with risk perception and the adherence of preventive behaviours. Results show that individuals differ in their perception of the pandemic stage despite being in the same environment. The belief that the COVID peak is in the past was associated with less perceived risk and decreased self-protection. A high COVID-19 media involvement and trust in the authorities were associated with higher perceived risk and preventive behaviour implementation. Overall, the perception that the pandemic wave is in its final stages could be an independent predictor of more risky behaviour. Thus, the communication of the pandemic dynamic should be provided by policy makers with caution to avoid the possibility of discounting the risk.

SIR epidemics in interconnected networks: threshold curve and phase transition

For simplicity of mathematical modeling of epidemic spreading, the assumption is that hosts have identical rates of disease-causing contacts. However, in the real world, the scenario is different. The network-based framework allows us to capture the complex interdependencies and structural heterogeneity present in real-world systems. We examine two distinct scenarios involving the dynamics of susceptible-infected-recovered (SIR) in interconnected networks. In the first part, we show how the epidemic threshold of a contact network changes as a result of being coupled with another network for a fixed infection strength. The model employed in this work considers both the contact networks and interconnections as generic. We have depicted the epidemic threshold curve for interconnected networks, considering the assumption that the infection could be initially present in either one or both of the networks. If the normalized infection strengths are above the threshold curve, the infection spreads, whereas if the normalized infection strengths are below the threshold curve, the disease does not spread. This is true for any level of interconnection. In the second part, we investigate the spillover phenomenon, where the disease in a novel host population network comes from a reservoir network. We have observed a clear phase transition when the number of links or the inter-network infection rate exceeds a certain threshold, keeping all other parameters constant. We observe two regimes for spillover: a major spillover region and a minor spillover region based on interpopulation links (fraction of links between two networks) and inter-network infection strength (infection rate between reservoir and host network). If the interpopulation links and inter-network infection strength are in the major spillover region, the spillover probability is high, while if the former parameters are in the minor spillover region, the spillover probability is low. When the number of infected individuals within a reservoir network is nearly equal, and the inter-network infection strength remains constant, the threshold number of links required to achieve the spillover threshold condition varies based on the network topology. Overall, this work contributes to the understanding of SIR dynamics in interconnected networks and sheds light on the behavior of epidemics in complex systems.

Adherence strategy based on evolutionary games in epidemic spreading

In light of the dynamic interaction between epidemic spreading and behavior responses, the development of a comprehensive epidemic model is both crucial and challenging. In this paper, based on the evolutionary game theory, we investigate the co-evolution of adherence strategy decision-making and epidemic spreading, with a particular focus on the role of conformity. Considering individual self-determination and population imitation, we analyze two distinct strategy updating rules: profit-driven decision-making and conformity-driven decision-making. We also account for the influence of neighbors’ strategies on epidemic spread rates. Extensive simulations are conducted to analyze epidemic phase transitions and the distribution of infected individuals at the stationary state of the epidemic. By analyzing the adoption of strategies under different scenarios of conformity fractions, we find that conformity widens the disparity between different strategies over time. In conclusion, our results demonstrate that conformity promotes the adherence strategy, which helps reduce the size of the epidemic.

Leveraging advances in data-driven deep learning methods for hybrid epidemic modeling

Mathematical modeling of epidemic dynamics is crucial to understand its underlying mechanisms, quantify important parameters, and make predictions that facilitate more informed decision-making. There are three major types of models: mechanistic models including the SEIR-type paradigm, alternative data-driven (DD) approaches, and hybrid models that combine mechanistic models with DD approaches. In this paper, we summarize our work in the COVID-19 Scenario Modeling Hub (SMH) for more than 12 rounds since early 2021 for informed decision support. We emphasize the importance of deep learning techniques for epidemic modeling via a flexible DD framework that substantially complements the mechanistic paradigm to evaluate various future epidemic scenarios. We start with a traditional curve-fitting approach to model cumulative COVID-19 based on the underlying SEIR-type mechanisms. Hospitalizations and deaths are modeled as binomial processes of cases and hospitalization, respectively. We further formulate two types of deep learning models based on multivariate long short term memory (LSTM) to address the challenges of more traditional DD models. The first LSTM is structurally similar to the curve fitting approach and assumes that hospitalizations and deaths are binomial processes of cases. Instead of using a predefined exponential curve, LSTM relies on the underlying data to identify the most appropriate functions, and is capable of capturing both long-term and short-term epidemic behaviors. We then relax the assumption of dependent inputs among cases, hospitalizations, and death. Another type of LSTM that handles all input time series as parallel signals, the independent multivariate LSTM, is developed. Independent multivariate LSTM can incorporate a wide range of data sources beyond traditional case-based epidemiological surveillance. The DD framework unleashes its potential in big data era with previously neglected heterogeneous surveillance data sources, such as syndromic, environment, genomic, serologic, infoveillance, and mobility data. DD approaches, especially LSTM, complement and integrate with the mechanistic modeling paradigm, provide a feasible alternative approach to model today’s complex socio-epidemiological systems, and further leverage our ability to explore different scenarios for more informed decision-making during health emergencies.

Maternal concern and perception about child overweight risk, maternal feeding practices, and child eating behaviors: A cross-sectional study.

Abundant efforts have been directed to understand the global obesity epidemic and related obesogenic behaviors. However, the relationships of maternal concern and perception about child weight with child eating behaviors in Saudi Arabia have not been investigated. Therefore, this study aimed to examine the associations of maternal concern and perception about child overweight risk with maternal feeding practices and child eating behaviors among mothers and their children in Saudi Arabia. Mothers of 115 children aged 3-5 years old were recruited from eight preschools. Child eating behaviors (enjoyment of food, responsiveness to foods, food fussiness, satiety responsiveness, and slowness in eating) and maternal feeding practices (restriction, monitoring, and using food as a reward), maternal concern about child weight, and maternal perception about child overweight risk were assessed using validated questionnaires. Multiple linear regression models, adjusted for maternal body mass index (BMI) and child's BMI-z score and sex, were tested to examine the independent associations of maternal concern and perception about child overweight risk with child eating behaviors and maternal feeding practices. Maternal concern about child weight was positively associated with food enjoyment in children (B = 0.14 [95% confidence intervals = 0.02,0.27]) and responsiveness to food (B = 0.16 [0.03,0.29]), whereas a negative association with child food fussiness was observed (B = -0.19 [-0.33, -0.04]). Maternal perception about child overweight risk and child slowness in eating were negatively associated (B = -0.72 [-0.16 to -0.28]). Maternal concern and perception about child overweight risk were not associated with maternal feeding practices. In conclusion, maternal concern and perception about child overweight risk were found to be associated with food approach eating behavior traits but not with maternal feeding practices. Mothers who are concerned about child weight or perceive their children as overweight may need guidance and support to promote healthy eating behaviors among their children. Future cross-cultural studies are needed to understand the underlying mechanisms for the influence of maternal concern and perception about child overweight risk on maternal feeding practices, child eating behaviors, and weight status of children.

The Motivation for COVID-19 Vaccination and Preventive Behavior.

COVID-19, a viral infection that emerged in late 2019, induces a severe acute respiratory syndrome marked by significant clinical symptoms, and the potential for progressive respiratory failure and death. People facing the threat of COVID-19 not only feared being infected, but were also worried about the side-effects of vaccination. This conflict affected their epidemic prevention behavior. To understand this issue, the present study explored whether infection anxiety affected the psychological avoidance or approach to getting vaccinated and the intention to take epidemic prevention measures. The study implemented a cross-sectional, web-based survey. We created questionnaires using Surveycake, an online e-form questionnaire platform. We used the snowball sampling method via a social media app to recruit participants. If individuals were willing to participate in the research, we emailed the e-form questionnaire link to them to collect data. After questionnaire collection, 288 questionnaires were returned, and 277 valid questionnaires were obtained for structural equation modeling analysis. According to the statistical results, it was found that infection anxiety was positively related to avoidance-avoidance conflict, and the power of infection anxiety on avoidance conflict was 23.0%. Infection anxiety was negatively related to approach-approach conflict regarding vaccination, and the power of infection anxiety on approach-approach conflict was 22.0%. Approach-approach conflict regarding vaccination was negatively related to prevention behavior, while avoidance-avoidance conflict regarding vaccination was positively related to prevention behavior. The two conflicts explained 12.5% of the total variance in prevention behavior. The study results highlight the long-term importance of achieving vaccine goals in order to prepare for future health emergencies similar to the recent COVID-19 pandemic.

Gamified Strength Recognition and Quiz to Enhance COVID-19 Prevention Knowledge and Behaviors, and Family Well-Being: Web-Based Pilot Intervention.

Background: Promoting COVID-19 prevention is key to pandemic control and innovative interventions can help communicate reliable science to the public. Under the Hong Kong Jockey Club SMART Family-Link Project, we developed and evaluated a pilot intervention for promoting COVID-19 prevention through a web-based family game, guided by the Theory of Planned Behavior and a strength-based approach. Methods: The "SMART Epidemic prevention" pilot theme was launched to the public on September 21, 2020 for 4 weeks. The game had two parts: (i) strength recognition and (ii) quiz questions on knowledge and behaviors about COVID-19 prevention. Simple baseline, in-game, and postgame evaluation assessed players' perceived knowledge, behaviors, family well-being, game satisfaction, and perceived benefits. Results: Of 86 registered families, 55 played actively, including 212 players (51% female, 35% aged below 18) who self-identified as children (44%), parents (39%), and grandparents (11%). In weeks 1 and 4, an average of 7 and 18 game rounds were played per family, and 86.6% and 75.9% of rounds had perfect (2) behavior matches. Postgame evaluation with 51 families showed improvements in epidemic prevention knowledge and behaviors, family communication, family happiness (all P < 0.001), and family relationship (P = 0.002) with small effect sizes (0.15-0.29). Overall game satisfaction was rated 4.49 (scale of 1-5). Ninety-four percent of families shared knowledge from the game with others. Conclusions: Our pilot web-based family game first showed preliminary evidence on enhancing COVID-19 prevention knowledge and behaviors, and family well-being, with participants recognizing family strengths, reporting high satisfaction and various perceived benefits, and showing sustained gameplay. Trial Registration: The research protocol was registered at the National Institutes of Health (Identifier No. NCT04550065) on September 16, 2020.

Preparedness, response, and preventive behaviors for a pandemic disaster caused by COVID-19 among primary school students in Krabi province, Thailand.

The global impact of the Coronavirus Disease 2019 (COVID-19) pandemic has been enormous. The primary school's role and responsibilities include providing students with education and training, cooperating and supporting educational management, and monitoring, following up, and evaluating the school's operations. The goals of this study were to evaluate the conditions of preparation for the COVID-19 epidemic's prevention and response, as well as to investigate the relationship between predisposing, enabling, and reinforcing factors in preventing the spread of COVID-19 among primary school students in the educational area. A cross-sectional analytical study was used as the research design. The informants are kids in grades 4-6 at Anuban Krabi School in Krabi province. Data is gathered in a computational quantitative manner. Purposive sampling was used to choose the informants. The researchers employed a questionnaire with a content validity index (CVI) of 0.89 and a reliability coefficient (Cronbach's Alpha Coefficient) of 0.89. For quantitative data analysis, the Pearson Correlation Coefficient was used. In preparation for the COVID-19 outbreak, there is a high degree of knowledge, behaviors, and self-conduct (x=2.78, 2.55, 2.66, and 2.78). The perception and awareness of the COVID-19 epidemic were statistically significant at P < 0.01 in terms of the relationship between perception, awareness, behavior, and self-conducting in stopping the spread of COVID-19. Conclusion: At P < 0.01, regarding the relationship between COVID-19 epidemic perceptions, behaviors, and self-conducting. The correlation between awareness, behaviors, and self-conducting in relation to the COVID-19 outbreak was statistically significant at P<0.01. To prepare and response for COVID-19 enhance and concern perceived policy and COVID-19 information, participation with stakeholders or communities emphasize COVID-19 prevention and all of organization and family.

Analysis of Epidemic Models in Complex Networks and Node Isolation Strategie Proposal for Reducing Virus Propagation

Many models of virus propagation in Computer Networks inspired by epidemic disease propagation mathematical models that can be found in the epidemiology field (SIS, SIR, SIRS, etc.) have been proposed in the last two decades. The purpose of these models has been to determine the conditions under which a virus becomes rapidly extinct in a network. The most common models of virus propagation in networks are SIS-type models or their variants. In such models, the conditions that lead to a rapid extinction of the spread of a computer virus have been calculated and its dependence on some parameters inherent to the mathematical model has been observed. In this article, we will try to analyze a particular SIS-type model proposed in the past by Chakrabarti as well as an SIRS-type variation of this model proposed in the past by myself. I will show through simulations the influence that the topology of a network has on the dynamics of the spread of a virus in different network types. In the recent past, there have been interesting articles that demonstrate the relationship between the eigenvalue λ1 of the adjacency matrix and the reduction in the spread of a virus in a network. From this, the minimization of the spectral radius strategies by edge suppression has been proposed. This problem is NP-complete in its general case and for this reason, heuristic algorithms have been proposed. In this article, I will perform simulations of an SIS-type model in topologies with the same number of nodes but with different structures to compare their epidemic behavior. The simulations will show that regular topologies with small node degrees, i.e., of degree 4, as is the case of the topology that I call Lattice4, have favorable behavior in terms of the fast extinction property, with respect to other denser and less regular topologies such as the binomial topologies as well as Power law topologies. Based on the results of the simulations, my contribution will consist of proposing, as a node isolation strategy, a transformation of the original topology into an approximately regular topology by edge elimination. Although such a transformed topology is not optimal in terms of reducing the propagation of a virus, it induces the rapid extinction of the virus in the network.

Mathematical Analysis of Malaria Epidemic: Asymptotic Stability With Cost‐Effectiveness Study

Malaria is an old, curable vector‐borne disease that is devastating in the tropics and subtropical regions of the world. The disease has unmatched complications in the human host, especially in children. Mathematical models of infectious diseases have been the steering wheel, driving scientists towards elucidation of the dynamic behaviour of epidemics and providing tailored strategic management of diseases. With the ongoing vaccination programs for vector‐borne diseases, the research proposes a nonlinear differential equation model for the malaria disease that provides public health with a shift from the classical understanding of nonpharmaceutical preventive malaria control to pharmaceutical measures of vaccines. The asymptotic dynamic behaviour of the model is studied at the model’s equilibria. The bifurcation type invoked at the disease‐free state is analysed, and the result revealed that the convention that is the condition for eradicating the disease is not always sufficient when the system undergoes backward bifurcation. Furthermore, sensitivity analysis was investigated to quantify the amount of influence each parameter has on . With the Latin hypercube sampling and partial rank correlation coefficient method, the uncertainty in is computed with a 95% confidence interval, with the mean, and 5th and 95th percentiles, respectively, simulated as 0.143788, 0.01545, and 0.41491. An intervention model was derived from the nonintervention model to experiment with and evaluate the respective effects of the various pairings of interventions on the dynamics of the disease. Lastly, an in‐depth cost analysis was studied to identify the most cost‐effective intervention regarding rewarding the desired outcome. From the analysis, we recommend that besides the nonpharmaceutical measure of bed nets and insecticide spray, public health should target the pharmaceutical intervention of vaccine as it can close the gap in malaria prevention.

Representation of degree correlation using eigenvalue decomposition and its application to epidemic models

Abstract Degree correlation plays a crucial role in studying network structures; however, its varied forms pose challenges to understanding its impact on network dynamics. In this study, a method is devised that uses eigenvalue decomposition to characterize degree correlations. Additionally, the applicability of this method is demonstrated by approximating the basic and type reproduction numbers in an epidemic network model. The findings elucidate the interplay between degree correlations and epidemic behavior, thus contributing to a deeper understanding of social networks and their dynamics.

Construction of influencing factors model for public information avoidance behavior in major infectious disease outbreaks based on meta-ethnography

ObjectiveMajor infectious disease outbreaks are highly susceptible to diffuse outbreaks due to their sudden and more widespread nature. Compared to previous outbreaks such as the Spanish flu and SARS in China, COVID-19 has greatly affected the health of citizens and the economic development of countries worldwide, and is representative of major infectious disease outbreaks in many ways. Information avoidance, a common information behaviour during major infectious disease outbreaks, can alleviate the stress caused by information overload as a strategy to reduce negative emotions and maintain optimism. However, it can also bias risk perceptions and avoid content of greater value. Therefore, a deeper understanding of public information behaviour, particularly how and why relevant information is circumvented, places a demand on researchers. MethodsA meta-ethnographic qualitative research methodology was used, and the seven steps of the methodology were strictly followed, including identifying integration themes, defining the connotations of integration themes, reading original studies, identifying relationships between studies, inter-translation between studies, synthetic translation, and presenting integration results. 26 original studies were integrated in a unified research framework, with a macro perspective that integrates consistent as well as complex and even contradictory findings and identifies dominant factors. ConclusionsIdentify demographic factors, information literacy, risk perception, cognitive structure, information quality, information sources, external characteristics of information, and environmental characteristics sub-dimensions around the dimensions of ‘individual’, ‘information’ and ‘environment’. The study also explored the factors under each sub-dimension. The study finally identified three dimensions, nine sub-dimensions and 26 factors, and obtained a more complete theoretical framework to construct a “model of factors influencing public information avoidance behaviour in major infectious disease epidemics”, with a view to providing a theoretical basis and practical reference for relevant departments in guiding public information behaviour and health practices in major infectious disease epidemics.

Long-run analysis of a perturbed HIV/AIDS model with antiretroviral therapy and heavy-tailed increments performed by tempered stable Lévy jumps

The novelty of this article lies in providing a new framework for modeling the dynamics of HIV/AIDS infection under antiretroviral therapy (ART) which aims to reduce a person's viral load to an undetectable level. Since HIV/AIDS transmission can exhibit very tailed behavior (unpredictable jumps) due to the fact that the risk of spreading can vary greatly depending on the behavior and characteristics of individuals, we probe the effect of α-stable jumps on its asymptotic dynamics. Our proposed model is a compartmental step-wise formulation that takes the form of a system of Itô-Lévy differential equations with α-stable process. First, we check its well-posedness and we give the necessary assumptions needed for the analysis. Then, and based on a new approach, we prove two main asymptotic scenarios: extinction and stationary persistence of HIV/AIDS infection. The theoretical outcomes show that the kinetic behavior of our HIV/AIDS system is principally induced by certain underlying parameters, which are accurately correlated to the Lévy noise amplitudes. Finally, we reinforce our study with two numerical experiments to test the impact of the innovative mathematical techniques adopted to obtain the main results. Moreover, we numerically highlight the influence of α-stable jumps in epidemic state transition and dynamic behavior change.

Critical behaviors of nonlinear contagion models with recurrent mobility patterns

Recently, there has been a lot of discussion about the nonlinearity property of contagion processes in epidemic spreading on social networks with various structures. In this paper, we propose a nonlinear contagion model in networked metapopulations to investigate the critical behavior of epidemics with recurrent mobility patterns. First, we build up a discrete-time Markovian chain model to formulate the spreading of susceptible-infected-susceptible-like diseases. Additionally, we develop a practicable framework to analyze the impact of mobility on the epidemic threshold and derive the theoretical condition for the transition of an epidemic from a local to a global scale. This transition is associated with multiple discontinuous phase changes. We validate our analytical results through extensive numerical simulations on both regular and heterogeneous networks. Our findings offer a useful tool to discuss the implementation of prevention strategies such as quarantine and lockdown.

Implication of close contact testing in eliminating an epidemic: Application to COVID-19 epidemic in South Korea and New York City

After the first wave of the coronavirus epidemic, temporary stability in the disease spread dynamics was observed in many regions. This behavior can potentially be attributed to the measures implemented to contain the spread, particularly close contact testing. We propose a deterministic mathematical model that simulates the dynamic spread of the disease while considering the actions of the public health system. The developed model achieves a non-forced balance in daily confirmed cases, reproducing the observed epidemic behavior during the COVID-19 outbreak in South Korea and New York City. Our finding indicated that, although the quasi-steady state behavior can only be attained within a certain range of model parameters, an increase in the health system&amp;rsquo;s interventions does not eliminate the epidemic. We conclude that the observed stationary state of daily COVID-19 cases does not result from setting the basic reproductive number to one. Instead, it emerges as a natural consequence of the policies implemented by authorities to mitigate its spread.

Comparison of Some Numerical Simulation Techniques for COVID-19 Model in Iraq

The aim of our study is to solve a nonlinear epidemic model, which is the COVID-19 epidemic model in Iraq, through the application of initial value problems in the current study. The model has been presented as a system of ordinary differential equations that has parameters that change with time. Two numerical simulation methods are proposed to solve this model as suitable methods for solving systems whose coefficients change over time. These methods are the Mean Monte Carlo Runge-Kutta method (MMC_RK) and the Mean Latin Hypercube Runge-Kutta method (MLH_RK). The results of numerical simulation methods are compared with the results of the numerical Runge-Kutta 4th order method (RK4) from 2021 to 2025 using the absolute error, which proves that the MLH_RK method is the best and closest to the expected values. The results have been discussed after being tabulated and represented graphically. Epidemic behavior for the next two years until 2025 has been projected using the proposed methods.

Co-circulation of Dengue and Zika viruses: A modelling approach applied to epidemics data

Since the 1980s, the prevalence of diseases caused by arboviruses, in particular, those transmitted by Aedes Aegypti mosquitoes have increased in the Americas. The diseases transmitted by this mosquito include Dengue, Zika, and Chikungunya, which are endemic diseases in the region between the tropics. It is relevant to investigate the effect of virus co-circulation in the transmission of these diseases because there are evidences of both cross-protection and cross-enhancement among the viruses causing Zika and Dengue. In order to understand that effect, in this work we formulate an stochastic model, based on a previous deterministic model for co-circulation of Dengue serotypes. We applied both the deterministic and stochastic versions of the model to epidemics data of Dengue and Zika in Brazilian cities during 2015–2016. We use the deterministic model to calculate the basic reproductive number associated to the co-circulation of both diseases, and we show a non-trivial epidemic behavior associated to the entrance of Zika disease in 2015 in Brazil. Based on the stochastic model we evaluated the importance of the population size and the initial number of cases for the subsequent development of both diseases. Using actual data of simultaneous epidemics of dengue and Zika from Brazilian cities, we mainly find regions of the parameters compatible to cross protection of the population against the infections.

Lives saved by public health restrictions over the Victorian COVID-19 Delta variant epidemic wave, Aug-Nov 2021

BackgroundPrior to mid-2021, Australia’s approach to COVID-19 was to eliminate community transmission. However, between August-November 2021, the state of Victoria, Australia, experienced an outbreak of the Delta variant that continued to grow despite extensive lockdowns and public health measures in place. While these public health restrictions were ultimately unable to stop community transmission, they likely had a major impact reducing transmission and adverse health outcomes relative to voluntary risk-mitigation only (e.g., in response to rising cases and deaths, some people may avoid crowded settings, hospitality, retail, social occasions, or indoor settings). This study aims to estimate the impact of the August-November 2021 enforced public health restrictions in Victoria, compared to voluntary risk-mitigation only. MethodsAn agent-based model was calibrated to Victorian epidemiological, health and behavioural data from 1 August to 30 November 2021, as well as policies that were implemented over that period. Two counter-factual scenarios were run for the same period with (a) no restrictions in place; or (b) voluntary risk-mitigation only, based on behaviour measured over the December-January Omicron BA.1 epidemic wave when restrictions were not in place. ResultsOver August-November 2021, the baseline model scenario resulted in 97,000 (91,000−102,000) diagnoses, 9100 (8500−9700) hospital admissions, and 480 (430−530) deaths. Without any restrictions in place, there were 3,228,000 (3,200,000−3,253,000) diagnoses, 375,100 (370,200−380,900) hospital admissions, and 16,700 (16,000−17,500) deaths. With voluntary risk-mitigation equal to those observed during the Omicron BA.1 epidemic wave, there were 1,507,000 (1,469,000−1,549,000) diagnoses, 130,300 (124,500−136,000) hospital admissions, and 5500 (5000−6100) deaths. ConclusionPublic health restrictions implemented in Victoria over August-November 2021 are likely to have averted more than 120,000 hospitalizations and 5000 deaths relative to voluntary risk-mitigation only. During a COVID-19 epidemic wave voluntary behaviour change can reduce transmission substantially, but not to the same extent as enforced restrictions.

Narrative minireview of the spatial epidemiology of substance use disorder in the United States: Who is at risk and where?

Drug overdose is the leading cause of death by injury in the United States. The incidence of substance use disorder (SUD) in the United States has increased steadily over the past two decades, becoming a major public health problem for the country. The drivers of the SUD epidemic in the United States have changed over time, characterized by an initial heroin outbreak between 1970 and 1999, followed by a painkiller outbreak, and finally by an ongoing synthetic opioid outbreak. The nature and sources of these abused substances reveal striking differences in the socioeconomic and behavioral factors that shape the drug epidemic. Moreover, the geospatial distribution of the SUD epidemic is not homogeneous. The United States has specific locations where vulnerable communities at high risk of SUD are concentrated, reaffirming the multifactorial socioeconomic nature of this epidemic. A better understanding of the SUD epidemic under a spatial epidemiology framework is necessary to determine the factors that have shaped its spread and how these patterns can be used to predict new outbreaks and create effective mitigation policies. This narrative minireview summarizes the current records of the spatial distribution of the SUD epidemic in the United States across different periods, revealing some spatiotemporal patterns that have preceded the occurrence of outbreaks. By analyzing the epidemic of SUD-related deaths, we also describe the epidemic behavior in areas with high incidence of cases. Finally, we describe public health interventions that can be effective for demographic groups, and we discuss future challenges in the study and control of the SUD epidemic in the country.