Clustering of contacts relevant to the spread of infectious disease

Social clustering of unvaccinated children in schools in the Netherlands

For the measles-mumps-rubella (MMR) vaccine, the World Health Organization-recommended coverage for herd protection is 95% for measles and 80% for rubella and mumps. However, a national vaccine coverage does not reflect social clustering of unvaccinated children, e.g. in schools of Orthodox Protestant or Anthroposophic identity in The Netherlands. To fully characterise this clustering, we estimated one-dose MMR vaccination coverages at all schools in the Netherlands. By combining postcode catchment areas of schools and school feeder data, each child in the Netherlands was characterised by residential postcode, primary and secondary school (referred to as school career). Postcode-level vaccination data were used to estimate vaccination coverages per school career. These were translated to coverages per school, stratified by school identity. Most schools had vaccine coverages over 99%, but major exceptions were Orthodox Protestant schools (63% in primary and 58% in secondary schools) and Anthroposophic schools (67% and 78%). School-level vaccine coverage estimates reveal strong clustering of unvaccinated children. The school feeder data reveal strongly connected Orthodox Protestant and Anthroposophic communities, but separated from one another. This suggests that even at a national one-dose MMR coverage of 97.5%, thousands of children per cohort are not protected by herd immunity.

Network structural metrics as early warning signals of widespread vaccine refusal in social-epidemiological networks

Sudden shifts in vaccine uptake, vaccine opinion, and infection incidence can occur in coupled behaviour-disease systems going through a bifurcation as the perceived risk of the vaccine increases. Literature shows that such regime shifts are sometimes foreshadowed by early warning signals (EWS). We propose and compare the performance of various measures of network structure as potential EWS indicators of epidemics and changes in population vaccine opinion. We construct a multiplex model coupling transmission of a vaccine-preventable childhood infectious disease and social dynamics concerning vaccine opinion. We find that the modularity of pro- and anti-vaccine network communities perform well as EWS, as do several measures of the number and size of opinion-based communities, and the size of pro-vaccine echo chambers. The number of opinion changes also gives early warnings, although the clustering coefficient and metrics concerning anti-vaccine echo chambers provide little warning. Stronger social norms are found to compromise the ability of all EWS metrics to provide advance warning. These exploratory results suggest that EWS indicators based on the network structure of online social media communities might assist public health preparedness by providing early warning of potential regime shifts.

Clustering of countries for COVID-19 cases based on disease prevalence, health systems and environmental indicators

The coronavirus has a high basic reproduction number ( R 0 ) and has caused the global COVID-19 pandemic. Governments are implementing lockdowns that are leading to economic fallout in many countries. Policy makers can take better decisions if provided with the indicators connected with the disease spread. This study is aimed to cluster the countries using social, economic, health and environmental related metrics affecting the disease spread so as to implement the policies to control the widespread of disease. Thus, countries with similar factors can take proactive steps to fight against the pandemic. The data is acquired for 79 countries and 18 different feature variables (the factors that are associated with COVID-19 spread) are selected. Pearson Product Moment Correlation Analysis is performed between all the feature variables with cumulative death cases and cumulative confirmed cases individually to get an insight of relation of these factors with the spread of COVID-19. Unsupervised k-means algorithm is used and the feature set includes economic, environmental indicators and disease prevalence along with COVID-19 variables. The learning model is able to group the countries into 4 clusters on the basis of relation with all 18 feature variables. We also present an analysis of correlation between the selected feature variables, and COVID-19 confirmed cases and deaths. Prevalence of underlying diseases shows strong correlation with COVID-19 whereas environmental health indicators are weakly correlated with COVID-19.

Spatiotemporal Dynamics of Sporadic Shiga Toxin-Producing Escherichia coli Enteritis, Ireland, 2013-2017

The Republic of Ireland regularly reports the highest annual crude incidence rates of Shiga toxin–producing Escherichia coli (STEC) enteritis in the European Union, ≈10 times the average. We investigated spatiotemporal patterns of STEC enteritis in Ireland using multiple statistical tools. Overall, we georeferenced 2,755 cases of infection during January 2013–December 2017; we found >1 case notified in 2,340 (12.6%) of 18,641 Census Small Areas. We encountered the highest case numbers in children 0–5 years of age (n = 1,101, 39.6%) and associated with serogroups O26 (n = 800, 29%) and O157 (n = 638, 23.2%). Overall, we identified 17 space-time clusters, ranging from 2 (2014) to 5 (2017) clusters of sporadic infection per year; we detected recurrent clustering in 3 distinct geographic regions in the west and mid-west, all of which are primarily rural. Our findings can be used to enable targeted epidemiologic intervention and surveillance.

Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering

Some directly transmitted human pathogens, such as influenza and measles, generate sustained exponential growth in incidence and have a high peak incidence consistent with the rapid depletion of susceptible individuals. Many do not. While a prolonged exponential phase typically arises in traditional disease-dynamic models, current quantitative descriptions of nonstandard epidemic profiles are either abstract, phenomenological, or rely on highly skewed offspring distributions in network models. Here, we create large socio-spatial networks to represent contact behavior using human population-density data, a previously developed fitting algorithm, and gravity-like mobility kernels. We define a basic reproductive number [Formula: see text] for this system, analogous to that used for compartmental models. Controlling for [Formula: see text], we then explore networks with a household-workplace structure in which between-household contacts can be formed with varying degrees of spatial correlation, determined by a single parameter from the gravity-like kernel. By varying this single parameter and simulating epidemic spread, we are able to identify how more frequent local movement can lead to strong spatial correlation and, thus, induce subexponential outbreak dynamics with lower, later epidemic peaks. Also, the ratio of peak height to final size was much smaller when movement was highly spatially correlated. We investigate the topological properties of our networks via a generalized clustering coefficient that extends beyond immediate neighborhoods, identifying very strong correlations between fourth-order clustering and nonstandard epidemic dynamics. Our results motivate the observation of both incidence and socio-spatial human behavior during epidemics that exhibit nonstandard incidence patterns.

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

For a long time, various epidemics, such as lower respiratory infections and diarrheal diseases, have caused serious social losses and costs. Various methods for analyzing infectious disease occurrences have been proposed for effective prevention and proactive response to reduce such losses and costs. However, the results of the occurrence analyses were limited because numerous factors affect the outbreak of infectious diseases and there are complex interactions between these factors. To alleviate this limitation, we propose a cluster-based analysis scheme of infectious disease occurrences that can discover commonalities or differences between clusters by grouping elements with similar occurrence patterns. To do this, we collect and preprocess infectious disease occurrence data according to time, region, and disease. Then, we construct a tensor for the data and apply Tucker decomposition to extract latent features in the dimensions of time, region, and disease. Based on these latent features, we conduct k-means clustering and analyze the results for each dimension. To demonstrate the effectiveness of this scheme, we conduct a case study on data from South Korea and report some of the results.

Cluster-based dual evolution for multivariate time series: Analyzing COVID-19

This paper proposes a cluster-based method to analyze the evolution of multivariate time series and applies this to the COVID-19 pandemic. On each day, we partition countries into clusters according to both their cases and death counts. The total number of clusters and individual countries' cluster memberships are algorithmically determined. We study the change in both quantities over time, demonstrating a close similarity in the evolution of cases and deaths. The changing number of clusters of the case counts precedes that of the death counts by 32 days. On the other hand, there is an optimal offset of 16 days with respect to the greatest consistency between cluster groupings, determined by a new method of comparing affinity matrices. With this offset in mind, we identify anomalous countries in the progression from COVID-19 cases to deaths. This analysis can aid in highlighting the most and least significant public policies in minimizing a country's COVID-19 mortality rate.

Transmission routes of rare seasonal diseases: the case of norovirus infections

Norovirus (NoV) is the most commonly recognized cause of acute gastroenteritis, with over a million cases globally per year. While usually self-limiting, NoV poses a substantial economic burden because it is highly contagious and there are multiple transmission routes. Infection occurs through inhalation of vomitus; faecal-oral spread; and food, water and environmental contamination. While the incidence of the disease is predictably seasonal, much less is known about the relative contribution of the various exposure pathways in causing disease. Additionally, asymptomatic excretion and viral shedding make forecasting disease burden difficult. We develop a novel stochastic dynamic network model to investigate the contributions of different transmission pathways in multiple coupled social networks representing schools, hospitals, care-homes and family households in a community setting. We analyse how the networks impact on transmission. We used ward-level demographic data from Northumberland, UK to create a simulation cohort. We compared the results with extant data on NoV cases from the IID2 study. Connectivity across the simulated cohort was high. Cases of NoV showed marked seasonality, peaking in early winter and declining through the summer. For the first time, we show that fomites and food appear to be the most important exposure routes in determining the population burden of disease. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Age-group-targeted testing for COVID-19 as a new prevention strategy

Robust testing and tracing are key to fighting the menace of coronavirus disease 2019 (COVID-19). This outbreak has progressed with tremendous impact on human life, society and economy. In this paper, we propose an age-structured SIQR model to track the progression of the pandemic in India, Italy and USA, taking into account the different age structures of these countries. We have made predictions about the disease dynamics, identified the most infected age groups and analysed the effectiveness of social distancing measures taken in the early stages of infection. The basic reproductive ratio R 0 has been numerically calculated for each country. We propose a strategy of age-targeted testing, with increased testing in the most proportionally infected age groups. We observe a marked flattening of the infection curve upon simulating increased testing in the 15-40 year age groups in India. Thus, we conclude that social distancing and widespread testing are effective methods of control, with emphasis on testing and identifying the hot spots of highly infected populations. It has also been suggested that a complete lockdown, followed by lockdowns in selected regions, is more effective than the reverse.

How Physical Proximity Shapes Complex Social Networks

Social interactions among humans create complex networks and - despite a recent increase of online communication - the interactions mediated through physical proximity remain a fundamental way for people to connect. A common way to quantify the nature of the links between individuals is to consider repeated interactions: frequently occurring interactions indicate strong ties, such as friendships, while ties with low weights can indicate random encounters. Here we focus on a different dimension: rather than the strength of links, we study physical distance between individuals when a link is activated. The findings presented here are based on a dataset of proximity events in a population of approximately 500 individuals. To quantify the impact of the physical proximity on the dynamic network, we use a simulated epidemic spreading processes in two distinct networks of physical proximity. We consider the network of short-range interactions defined as d [Formula: see text] 1 meter, and the long-range which includes all interactions d [Formula: see text] 10 meters. Since these two networks arise from the same set of underlying behavioral data, we are able to quantitatively measure how the specific definition of the proximity network - short-range versus long-range - impacts the resulting network structure as well as spreading dynamics in epidemic simulations. We find that the short-range network - consistent with the literature - is characterized by densely-connected neighborhoods bridged by weak ties. More surprisingly, however, we show that spreading in the long-range network is quite different, mainly shaped by spurious interactions.

Varicella zoster virus transmission dynamics in Vojvodina, Serbia

This study aimed at establishing baseline key epidemiological parameters for varicella zoster virus (VZV) infection in Vojvodina, Serbia, with the ultimate goal to quantify the VZV transmission potential in the population. Seroprevalence data generated during the first large cross-sectional VZV serosurvey were modelled, using a two-tiered modelling approach to calculate age-specific forces of infection (FOI), the basic reproduction number (R₀) and herd immunity threshold (H). Seroprevalence and modelling data were compared with corresponding pre-vaccination epidemiological parameters from 11 countries participating in the European Sero-Epidemiology Network 2 (ESEN2) project. Serbia fits into the general dynamic VZV transmission patterns in Europe in the pre-vaccine era, with estimated R₀ = 4.12, (95% CI: 2.69–7.07) and H = 0.76 (95% CI: 0.63–0.86). The highest VZV transmission occurs among preschool children, as evidenced by the estimation of the highest FOI (0.22, 95% CI: 0.11–0.34) in the 0.5–4 age group, with a peak FOI of 0.25 at 2.23 years. Seroprevalence was consistently lower in 5–14 year-olds, resulting in considerable shares of VZV-susceptible adolescents (7.3%), and young adults (6%), resembling the situation in a minority of European countries. The obtained key epidemiological parameters showed most intense VZV transmission in preschool children aged <4 years, justifying the consideration of universal childhood immunization in the future. National immunization strategy should consider programs for VZV serologic screening and immunization of susceptible groups, including adolescents and women of reproductive age. This work is an important milestone towards the evaluation of varicella immunization policy options in Serbia.

Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong

The spread of many respiratory infections is determined by contact patterns between infectious and susceptible individuals in the population. There are no published data for quantifying social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong which is a hotspot for emerging infectious diseases due to its high population density and connectivity in the air transportation network. We adopted a commonly used diary-based design to conduct a social contact survey in Hong Kong in 2015/16 using both paper and online questionnaires. Participants using paper questionnaires reported more contacts and longer contact duration than those using online questionnaires. Participants reported 13 person-hours of contact and 8 contacts per day on average, which decreased over age but increased with household size, years of education and income level. Prolonged and frequent contacts, and contacts at home, school and work were more likely to involve physical contacts. Strong age-assortativity was observed in all age groups. We evaluated the characteristics of social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Our findings could help to improve the design of future social contact surveys, parameterize transmission models of respiratory infectious diseases, and inform intervention strategies based on model outputs.

Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong

The spread of many respiratory infections is determined by contact patterns between infectious and susceptible individuals in the population. There are no published data for quantifying social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong which is a hotspot for emerging infectious diseases due to its high population density and connectivity in the air transportation network. We adopted a commonly used diary-based design to conduct a social contact survey in Hong Kong in 2015/16 using both paper and online questionnaires. Participants using paper questionnaires reported more contacts and longer contact duration than those using online questionnaires. Participants reported 13 person-hours of contact and 8 contacts per day on average, which decreased over age but increased with household size, years of education and income level. Prolonged and frequent contacts, and contacts at home, school and work were more likely to involve physical contacts. Strong age-assortativity was observed in all age groups. We evaluated the characteristics of social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Our findings could help to improve the design of future social contact surveys, parameterize transmission models of respiratory infectious diseases, and inform intervention strategies based on model outputs.

Knowledge mapping visualization analysis of the military health and medicine papers published in the web of science over the past 10 years

BACKGROUND

Military medicine is a research field that seeks to solve the medical problems that occur in modern war conditions based on public medicine theory.

METHODS

We explore the main research topics of military health and medical research in the web of science™ core collection (WoSCC) from 2007 to 2016, and the goal of this work is to serve as a reference for orientation and development in military health and medicine. Based on CiteSpace III, a reference co-citation analysis is performed for 7921 papers published in the WoSCC from 2007 to 2016. In addition, a cluster analysis of research topics is performed with a comprehensive analysis of high-yield authors, outstanding research institutions and their cooperative networks.

RESULTS

Currently, the research topics in military health and medicine mainly focus on the following seven aspects: mental health diagnoses and interventions, an army study to assess risk and resilience in service members (STARRS), large-scale military action, brain science, veterans, soldier parents and children of wartime, and wound infection. We also observed that the annual publication rate increased with time. Wessely S, Greenberg N, Fear NT, Smith TC, Smith B, Jones N, Ryan MAK, Boyko EJ, Hull L, and Rona RJ were the top 10 authors in military health and medicine research. The top 10 institutes were the Uniformed Services University of the Health Sciences, the United States Army, the United States Navy, Kings College London, Walter Reed National Military Medical Center, Boston University, Walter Reed Army Institute of Research, Walter Reed Army Medical Center, Naval Health Research Center, and the VA Boston Healthcare System.

CONCLUSIONS

We are able to perform a comprehensive analysis of studies in military health and medicine research and summarize the current research climate and the developmental trends in the WoSCC. However, further studies and collaborations are needed worldwide. Overall, our findings provide valuable information and new perspectives and shape future research directions for further research in the area of military health and medicine.