Outbreak Size Research Articles

Epidemic outbreaks with adaptive prevention on complex networks

The adoption of prophylaxis attitudes, such as social isolation and use of face masks, to mitigate epidemic outbreaks strongly depends on the support of the population. In this work, we investigate a susceptible–infected–recovered (SIR) epidemic model, in which the epidemiological perception of the environment can adapt the behavior of susceptible individuals towards preventive behavior. Two rules, depending on local and global epidemic prevalence, for the spread of the epidemic in heterogeneous networks are investigated. We present the results of both heterogeneous mean-field theory and stochastic simulations. The former does not predict a shift of the epidemic threshold, neither with global nor with local awareness. In simulations, however, local awareness can significantly raise the epidemic threshold, delay the peak of prevalence, and reduce the outbreak size. Interestingly, we observed that increasing the local perception rate leads to less individuals recruited to the protected state, but still enhances the effectiveness in mitigating the outbreak. We also report that network heterogeneity substantially reduces the efficacy of local awareness mechanisms since hubs, the super-spreaders of the SIR dynamics, are little responsive to epidemic environments in the low epidemic prevalence regime. Our results indicate that strategies that improve the perception of who is socially very active can improve the mitigation of epidemic outbreaks.

SARS-CoV-2 transmission in university classes.

We investigate transmission dynamics for SARS-CoV-2 on a real network of classes at Simon Fraser University. Outbreaks are simulated over the course of one semester across numerous parameter settings, including moving classes above certain size thresholds online. Regression trees are used to analyze the effect of disease parameters on simulation outputs. We find that an aggressive class size thresholding strategy is required to mitigate the risk of a large outbreak, and that transmission by symptomatic individuals is a key driver of outbreak size. These findings provide guidance for designing control strategies at other institutions, as well as setting priorities and allocating resources for disease monitoring.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13721-022-00375-1.

Influence of heterogeneity of infection thresholds on epidemic spreading with neighbor resource supporting.

The spread of disease on complex networks has attracted wide attention in physics, mathematics, and epidemiology. Recent works have demonstrated that individuals always exhibit different criteria for disease infection in a network that significantly influences the epidemic dynamics. In this paper, considering the heterogeneity of node susceptibility, we proposed an infection threshold model with neighbor resource support. The infection threshold of an individual is associated with the degree, and a parameter follows the normal distribution. Based on improved heterogeneous mean-field theory and extensive numerical simulations, we find that the mean and standard deviation of the infection threshold model can affect the phase transition and epidemic outbreak size. As the mean of the normal distribution parameter increases from a small value to a large value, the system shows a change from a continuous phase transition to a discontinuous phase transition, and the disease even stops spreading. The disease spreads from a discontinuous phase transition to continuous for the sizeable mean value as the standard deviation increases. Furthermore, the standard deviation also varies in the outbreak size.

Interhospital transmission of vancomycin-resistant Enterococcus faecium in Aomori, Japan

BackgroundSpread of vancomycin-resistant Enterococcus (VRE) is a global concern as a significant cause of healthcare-associated infections. A series of VRE faecium (VREf) outbreaks caused by clonal propagation due to interhospital transmission occurred in six general hospitals in Aomori prefecture, Japan.MethodsThe number of patients with VREf was obtained from thirty seven hospitals participating in the local network of Aomori prefecture. Thirteen hospitals performed active screening tests for VRE. Whole genome sequencing analysis was performed.ResultsThe total number of cases with VREf amounted to 500 in fourteen hospitals in Aomori from Jan 2018 to April 2021. It took more than three years for the frequency of detection of VRE to return to pre-outbreak levels. The duration and size of outbreaks differed between hospitals according to the countermeasures available at each hospital. Whole genome sequencing analysis indicated vanA-type VREf ST1421 for most samples from six hospitals.ConclusionsThis was the first multi-jurisdictional outbreak of VREf sequence type 1421 in Japan. In addition to strict infection control measures, continuous monitoring of VRE detection in local medical regions and smooth and immediate communication among hospitals are required to prevent VREf outbreaks.

Estimating transmission dynamics of SARS-CoV-2 at different intraspatial levels in an institutional outbreak

IntroductionLarge, localised outbreaks of COVID-19 have been repeatedly reported in high-density residential institutions. Understanding the transmission dynamics will inform outbreak response and the design of living environments that are more resilient to future outbreaks. MethodsWe developed an individual-based, multilevel transmission dynamics model using case, serology and symptom data from a 60-day cluster randomised trial of prophylaxes in a densely populated foreign worker dormitory in Singapore. Using Bayesian data augmentation, we estimated the basic reproduction number and the contribution that within-room, between-level and across-block transmission made to it, and the prevalence of infection over the study period across different spatial levels. We then simulated the impact of changing the building layouts in terms of floors and blocks on outbreak size. ResultsWe found that the basic reproduction number was 2.76 averaged over the different putative prophylaxes, with substantial contributions due to transmission beyond the residents’ rooms. By the end of ~60 days of follow up, prevalence was 64.4 % (95 % credible interval 64.2–64.6 %). Future outbreak sizes could feasibly be halved by reducing the density to include additional housing blocks, or taller buildings, while retaining the overall number of men in the complex. DiscussionThe methods discussed can potentially be utilised to estimate transmission dynamics at any high-density accommodation site with the availability of case and serology data. The restructuring of infrastructure to reduce the number of residents per room can dramatically slow down epidemics, and therefore should be considered by policymakers as a long-term intervention.

Impact of joint interactions with humans and social interactions with conspecifics on the risk of zooanthroponotic outbreaks among wildlife populations

Pandemics caused by pathogens that originate in wildlife highlight the importance of understanding the behavioral ecology of disease outbreaks at human–wildlife interfaces. Specifically, the relative effects of human–wildlife and wildlife-wildlife interactions on disease outbreaks among wildlife populations in urban and peri-urban environments remain unclear. We used social network analysis and epidemiological Susceptible-Infected-Recovered models to simulate zooanthroponotic outbreaks, through wild animals’ joint propensities to co-interact with humans, and their social grooming of conspecifics. On 10 groups of macaques (Macaca spp.) in peri-urban environments in Asia, we collected behavioral data using event sampling of human–macaque interactions within the same time and space, and focal sampling of macaques’ social interactions with conspecifics and overall anthropogenic exposure. Model-predicted outbreak sizes were related to structural features of macaques’ networks. For all three species, and for both anthropogenic (co-interactions) and social (grooming) contexts, outbreak sizes were positively correlated to the network centrality of first-infected macaques. Across host species and contexts, the above effects were stronger through macaques’ human co-interaction networks than through their grooming networks, particularly for rhesus and bonnet macaques. Long-tailed macaques appeared to show intraspecific variation in these effects. Our findings suggest that among wildlife in anthropogenically-impacted environments, the structure of their aggregations around anthropogenic factors makes them more vulnerable to zooanthroponotic outbreaks than their social structure. The global features of these networks that influence disease outbreaks, and their underlying socio-ecological covariates, need further investigation. Animals that consistently interact with both humans and their conspecifics are important targets for disease control.

Effectiveness of case-area targeted interventions including vaccination on the control of epidemic cholera: protocol for a prospective observational study

IntroductionCholera outbreaks in fragile settings are prone to rapid expansion. Case-area targeted interventions (CATIs) have been proposed as a rapid and efficient response strategy to halt or substantially reduce the...

Modelling the role of predation on disease burdens of prey.

Research Highlight: Brandell, E. E., Cross, P. C., Smith, D. W., Rogers, W., Galloway, N. L., MacNulty, D. R., Stahler, D. R., Treanor, J. & Hudson, P. J. (2022). Examination of the interaction between age-specific predation and chronic disease in the Greater Yellowstone Ecosystem. Journal of Animal Ecology, 00, 1-12. https://doi.org/10.1111/1365-2656.13661. Predation can alter disease dynamics in prey. If predators select for infected individuals, they can reduce disease burdens. In other cases, predators can increase disease burdens via various mechanisms such as altered prey behaviour. The influence of predation on disease dynamics is a result of interactions among various traits of the predators, prey and the pathogen itself. For example, pathogens tend to vary with age and predators typically select for certain age classes. Thus, the overlap between ages selected by predators and those infected will likely contribute to any effects of predation on reducing disease burdens. In this paper, Brandell et al. (2022) develop a model to evaluate the predator cleansing effect given age-based variation in pathogens and predation. The model was developed for Chronic Wasting Disease (CWD) infections in deer and elk facing predation by cougars and grey wolves in the Greater Yellowstone Ecosystem. The results indicate that predators can reduce CWD outbreak size, especially if selecting for infected individuals. CWD is an always fatal disease and this work suggests that predators could reduce disease burdens in cervids. The model is also applicable to other systems and promises to further our understanding of the role of predation on disease in prey, as well as drive future empirical studies.

Intervention of resource allocation strategies on spatial spread of epidemics.

Medical resources are crucial in mitigating epidemics, especially during pandemics such as the ongoing COVID-19. Thereby, reasonable resource deployment inevitably plays a significant role in suppressing the epidemic under limited resources. When an epidemic breaks out, people can produce resources for self-protection or donate resources to help others for treatment. That is, the exchange of resources also affects the transmission between individuals, thus, altering the epidemic dynamics. To understand factors on resource deployment and the interplay between resource and transmission we construct a metapopulation network model with resource allocation. Our results indicate actively or promptly donating resources is not helpful to suppress the epidemic under both homogeneous population distribution and heterogeneous population distribution. Besides, strengthening the speed of resources production can significantly increase the recovery rate so that they reduce the final outbreak size. These results may provide policy guidance toward epidemic containment.

Cooperative epidemic spreading in simplicial complex

Higher-order interaction exists widely in real-world networks and dramatically affects the system’s phase transition and critical phenomena. In the study of epidemic spreading dynamics on higher-order networks, the discontinuous phase transition and bistable physical phenomena have attracted the attention of many scholars. However, epidemics are rarely spread individually but are spread on high-order networks so that multiple epidemics interact mutually. The dynamics of promoting transmission on higher-order networks still lack research. This paper explored the cooperative spreading of two epidemics in the simplicial complex, which simultaneously includes structural and dynamical reinforcement effects. We use the Markov chain and mean-field methods to predict the outbreak size and threshold for theoretical analysis. Increase the structural or dynamical reinforcement effect, and the spreading dynamics are promoted, i.e., a more significant outbreak size and smaller threshold. When the two reinforcement effects are strong, the system will have a discontinuous phase transition and hysteresis loop, and a large initial seed size will lead to the easy outbreak of the epidemic. For the greater initial seed size, the dynamical reinforcement effect affects the outbreak size in most cases.

An ensemble forecast system for tracking dynamics of dengue outbreaks and its validation in China.

As a common vector-borne disease, dengue fever remains challenging to predict due to large variations in epidemic size across seasons driven by a number of factors including population susceptibility, mosquito density, meteorological conditions, geographical factors, and human mobility. An ensemble forecast system for dengue fever is first proposed that addresses the difficulty of predicting outbreaks with drastically different scales. The ensemble forecast system based on a susceptible-infected-recovered (SIR) type of compartmental model coupled with a data assimilation method called the ensemble adjusted Kalman filter (EAKF) is constructed to generate real-time forecasts of dengue fever spread dynamics. The model was informed by meteorological and mosquito density information to depict the transmission of dengue virus among human and mosquito populations, and generate predictions. To account for the dramatic variations of outbreak size in different seasons, the effective population size parameter that is sequentially updated to adjust the predicted outbreak scale is introduced into the model. Before optimizing the transmission model, we update the effective population size using the most recent observations and historical records so that the predicted outbreak size is dynamically adjusted. In the retrospective forecast of dengue outbreaks in Guangzhou, China during the 2011–2017 seasons, the proposed forecast model generates accurate projections of peak timing, peak intensity, and total incidence, outperforming a generalized additive model approach. The ensemble forecast system can be operated in real-time and inform control planning to reduce the burden of dengue fever.

Impact of assortative mixing by mask-wearing on the propagation of epidemics in networks

In this study, we discuss the impacts of assortative mixing by mask-wearing on the effectiveness of mask use in suppressing the propagation of epidemics. We employ the mask model, which is an epidemic model involving mask wearers and non-mask wearers. We derive the occurrence probability and mean size of large outbreaks, epidemic threshold, and average epidemic size for the mask model in an assortatively mixed random network that follows an arbitrary degree distribution. Applying our analysis to the Poisson random networks, we find that the assortative (disassortative) mixing by mask-wearing decreases (increases) the epidemic threshold. Assortative mixing, the tendency for (non-)mask wearers to prefer to connect with (non-)mask wearers, is not effective in containing epidemics in that the transmissibility required for large outbreaks to occur is small. On the other hand, in high-transmissibility cases, mask use is most effective in decreasing the occurrence probability and mean size of large outbreaks, as well as the average epidemic size, when the mixing pattern is strongly assortative. Strongly assortative mixing, resulting in the separation of mask wearers and non-mask wearers, reduces the probability and degree of a large outbreak in high-transmissibility cases, although it allows a large outbreak to occur even in low-transmissibility cases. In scale-free networks, mask use is most effective when the mixing pattern is strongly assortative and when it is maximally disassortative, provided that the mask coverage is not low. Both for the Poisson random and scale-free networks, all analytical treatments are in good agreement with simulation results.

Spatiotemporal Shifts in Tornadic Activity of 1958–2017 in the Central United States

Research in the last several years has revealed a number of interesting trends, such as a eastward shift in tornadic activity away from ‘Tornado Alley’ southeast into ‘Dixie Alley’ and a decrease in the number of tornado days paired with an increase in the sizes of tornado outbreaks. The purpose of this study seeks to reaffirm some of those findings. It accomplishes this by aggregating the tornado records of 1958–2017 into multiple time periods and finding the centroid of the tornado distributions during each of those time periods and compares them to one another. In doing so, this study reaffirms the significant eastward shifts in tornadic activity found by the literature, finds little consistent evidence of a northward shift in tornadic activity, and reveals interesting monthly and annual differences in the total variability of tornadoes that has not been substantially discussed within the literature thus far.

Relating Eulerian and Lagrangian spatial models for vector-host disease dynamics through a fundamental matrix.

We explore the relationship between Eulerian and Lagrangian approaches for modeling movement in vector-borne diseases for discrete space. In the Eulerian approach we account for the movement of hosts explicitly through movement rates captured by a graph Laplacian matrix L. In the Lagrangian approach we only account for the proportion of time that individuals spend in foreign patches through a mixing matrix P. We establish a relationship between an Eulerian model and a Lagrangian model for the hosts in terms of the matrices L and P. We say that the two modeling frameworks are consistent if for a given matrix P, the matrix L can be chosen so that the residence times of the matrix P and the matrix L match. We find a sufficient condition for consistency, and examine disease quantities such as the final outbreak size and basic reproduction number in both the consistent and inconsistent cases. In the special case of a two-patch model, we observe how similar values for the basic reproduction number and final outbreak size can occur even in the inconsistent case. However, there are scenarios where the final sizes in both approaches can significantly differ by means of the relationship we propose.

Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics.

The fraction of cases reported, known as 'reporting', is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an epidemic which is, by definition, not observed. We introduce a simple statistical method for estimating reporting, initially developed for the response to Ebola in Eastern Democratic Republic of the Congo (DRC), 2018-2020. This approach uses transmission chain data typically gathered through case investigation and contact tracing, and uses the proportion of investigated cases with a known, reported infector as a proxy for reporting. Using simulated epidemics, we study how this method performs for different outbreak sizes and reporting levels. Results suggest that our method has low bias, reasonable precision, and despite sub-optimal coverage, usually provides estimates within close range (5-10%) of the true value. Being fast and simple, this method could be useful for estimating reporting in real-time in settings where person-to-person transmission is the main driver of the epidemic, and where case investigation is routinely performed as part of surveillance and contact tracing activities.

The Role of Staff in Transmission of SARS-CoV-2 in Long-term Care Facilities.

Background:US long-term care facilities (LTCFs) have experienced a disproportionate burden of COVID-19 morbidity and mortality.Methods:We examined SARS-CoV-2 transmission among residents and staff in 60 LTCFs in Fulton County, Georgia, from March 2020 to September 2021. Using the Wallinga-Teunis method to estimate the time-varying reproduction number, R(t), and linear-mixed regression models, we examined associations between case characteristics and R(t).Results:Case counts, outbreak size and duration, and R(t) declined rapidly and remained low after vaccines were first distributed to LTCFs in December 2020, despite increases in community incidence in summer 2021. Staff cases were more infectious than resident cases (average individual reproduction number, Ri = 0.6 [95% confidence intervals [CI] = 0.4, 0.7] and 0.1 [95% CI = 0.1, 0.2], respectively). Unvaccinated resident cases were more infectious than vaccinated resident cases (Ri = 0.5 [95% CI = 0.4, 0.6] and 0.2 [95% CI = 0.0, 0.8], respectively), but estimates were imprecise.Conclusions:COVID-19 vaccines slowed transmission and contributed to reduced caseload in LTCFs. However, due to data limitations, we were unable to determine whether breakthrough vaccinated cases were less infectious than unvaccinated cases. Staff cases were six times more infectious than resident cases, consistent with the hypothesis that staff were the primary drivers of SARS-CoV-2 transmission in LTCFs.

Assessment of the Effectiveness of Omicron Transmission Mitigation Strategies for European Universities Using an Agent-Based Network Model.

Returning universities to full on-campus operations while the coronavirus disease 2019 pandemic is ongoing has been a controversial discussion in many countries. The risk of large outbreaks in dense course settings is contrasted by the benefits of in-person teaching. Transmission risk depends on a range of parameters, such as vaccination coverage and efficacy, number of contacts, and adoption of nonpharmaceutical intervention measures. Owing to the generalized academic freedom in Europe, many universities are asked to autonomously decide on and implement intervention measures and regulate on-campus operations. In the context of rapidly changing vaccination coverage and parameters of the virus, universities often lack sufficient scientific insight on which to base these decisions. To address this problem, we analyzed a calibrated, data-driven agent-based simulation of transmission dynamics among 13 284 students and 1482 faculty members in a medium-sized European university. Wed use a colocation network reconstructed from student enrollment data and calibrate transmission risk based on outbreak size distributions in education institutions. We focused on actionable interventions that are part of the already existing decision process of universities to provide guidance for concrete policy decisions. Here we show that, with the Omicron variant of the severe acute respiratory syndrome coronavirus 2, even a reduction to 25% occupancy and universal mask mandates are not enough to prevent large outbreaks, given the vaccination coverage of about 85% reported for students in Austria. Our results show that controlling the spread of the virus with available vaccines in combination with nonpharmaceutical intervention measures is not feasible in the university setting if presence of students and faculty on campus is required.

Epidemiological characteristics of varicella outbreaks in the Republic of Korea, 2016-2020.

ObjectivesWe described the trends and epidemiological characteristics of varicella outbreaks from 2016 to 2020 in the Republic of Korea.Methods We investigated variables such as the outbreak setting, age of patients, vaccination status, and lesion count. The collected data were analyzed with the Cochrane-Armitage trend test and Kruskal-Wallis test. These statistical tests were performed using R ver. 4.0.3.ResultsThe number of varicella outbreaks increased from 2016 to 2018; however, after a slight decrease in the number of outbreaks in 2019, the number fell sharply in 2020. The median size of outbreaks decreased from 8 to 9 cases during 2016−2019 to 6 cases in 2020. The median duration of outbreaks was 18 days during 2016−2017, 28 days in 2018, 29 days in 2019, and 15 days in 2020. Varicella outbreaks occurred most frequently in elementary schools, and vaccination coverage of patients increased from 89.4% in 2016 to 97.2% in 2019. The median age of patients with outbreak-related varicella decreased from 8 years in 2016 to 6 years in 2020.ConclusionSignificant changes were observed in the age of patients with outbreak-related varicella. Ongoing monitoring of varicella outbreaks should be conducted. Further research will be needed to measure the disease burden of varicella and enable evidence-based policy decisions.

Assessing the spread risk of COVID-19 associated with multi-mode transportation networks in China

The spatial spread of COVID-19 during early 2020 in China was primarily driven by outbound travelers leaving the epicenter, Wuhan, Hubei province. Existing studies focus on the influence of aggregated out-bound population flows originating from Wuhan; however, the impacts of different modes of transportation and the network structure of transportation systems on the early spread of COVID-19 in China are not well understood. Here, we assess the roles of the road, railway, and air transportation networks in driving the spatial spread of COVID-19 in China. We find that the short-range spread within Hubei province was dominated by ground traffic, notably, the railway transportation. In contrast, long-range spread to cities in other provinces was mediated by multiple factors, including a higher risk of case importation associated with air transportation and a larger outbreak size in hub cities located at the center of transportation networks. We further show that, although the dissemination of SARS-CoV-2 across countries and continents is determined by the worldwide air transportation network, the early geographic dispersal of COVID-19 within China is better predicted by the railway traffic. Given the recent emergence of multiple more transmissible variants of SARS-CoV-2, our findings can support a better assessment of the spread risk of those variants and improve future pandemic preparedness and responses.

Assortative mixing among vaccination groups and biased estimation of reproduction numbers

Assortative mixing among vaccination groups and biased estimation of reproduction numbers