Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data

Task-oriented machine learning surrogates for tipping points of agent-based models

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.

Morocco's population contact matrices: A crowd dynamics-based approach using aggregated literature data

Estimation of contact patterns is often based on questionnaires and time-use data. The results obtained using these methods have been used extensively over the years and recently to predict the spread of the COVID-19 pandemic. They have also been used to test the effectiveness of non-pharmaceutical measures such as social distance. The latter is integrated into epidemiological models by multiplying contact matrices by control functions. We present a novel method that allows the integration of social distancing and other scenarios such as panic. Our method is based on a modified social force model. The model is calibrated using data relating to the movements of individuals and their interactions such as desired walking velocities and interpersonal distances as well as demographic data. We used the framework to assess contact patterns in different social contexts in Morocco. The estimated matrices are extremely assortative and exhibit patterns similar to those observed in other studies including the POLYMOD project. Our findings suggest social distancing would reduce the numbers of contacts by 95%. Further, we estimated the effect of panic on contact patterns, which indicated an increase in the number of contacts of 11%. This approach could be an alternative to questionnaire-based methods in the study of non-pharmaceutical measures and other specific scenarios such as rush hours. It also provides a substitute for estimating children's contact patterns which are typically assessed through parental proxy reporting in surveys.

Modeling the impact of distancing measures on infectious disease spread: a case study of COVID-19 in the Moroccan population

This paper explores the impact of various distancing measures on the spread of infectious diseases, focusing on the spread of COVID-19 in the Moroccan population as a case study. Contact matrices, generated through a social force model, capture population interactions within distinct activity locations and age groups. These matrices, tailored for each distancing scenario, have been incorporated into an SEIR model. The study models the region as a network of interconnected activity locations, enabling flexible analysis of the effects of different distancing measures within social contexts and between age groups. Additionally, the method assesses the influence of measures targeting potential superspreaders (i.e., agents with a very high contact rate) and explores the impact of inter-activity location flows, providing insights beyond scalar contact rates or survey-based contact matrices. The results suggest that implementing intra-activity location distancing measures significantly reduces in the number of infected individuals relative to the act of imposing restrictions on individuals with a high contact rate in each activity location. The combination of both measures proves more advantageous. On a regional scale, characterized as a network of interconnected activity locations, restrictions on the movement of individuals with high contact rates was found to result in a $ 2 \% $ reduction, while intra-activity location-based distancing measures was found to achieve a $ 44 \% $ reduction. The combination of these two measures yielded a $ 48\% $ reduction.

A mixed-method approach to determining contact matrices in the Cox's Bazar refugee settlement

Contact matrices are an important ingredient in age-structured epidemic models to inform the simulated spread of the disease between subgroups of the population. These matrices are generally derived using resource-intensive diary-based surveys and few exist in the Global South or tailored to vulnerable populations. In particular, no contact matrices exist for refugee settlements-locations under-served by epidemic models in general. In this paper, we present a novel, mixed-method approach for deriving contact matrices in populations, which combines a lightweight, rapidly deployable survey with an agent-based model of the population informed by census and behavioural data. We use this method to derive the first set of contact matrices for the Cox's Bazar refugee settlement in Bangladesh. To validate our approach, we apply it to the UK population and compare our derived matrices with well-known contact matrices collected using traditional methods. Our findings demonstrate that our mixed-method approach successfully addresses some of the challenges faced by traditional and agent-based approaches to deriving contact matrices. It also shows potential for implementation in resource-constrained environments. This work therefore contributes to a broader aim of developing new methods and mechanisms of data collection for modelling disease spread in refugee and internally displaced person (IDP) settlements and better serving these vulnerable communities.

Quantifying social contact patterns in Minnesota during stay-at-home social distancing order

SARS-CoV-2 is primarily transmitted through person-to-person contacts. It is important to collect information on age-specific contact patterns because SARS-CoV-2 susceptibility, transmission, and morbidity vary by age. To reduce the risk of infection, social distancing measures have been implemented. Social contact data, which identify who has contact with whom especially by age and place are needed to identify high-risk groups and serve to inform the design of non-pharmaceutical interventions. We estimated and used negative binomial regression to compare the number of daily contacts during the first round (April-May 2020) of the Minnesota Social Contact Study, based on respondent's age, gender, race/ethnicity, region, and other demographic characteristics. We used information on the age and location of contacts to generate age-structured contact matrices. Finally, we compared the age-structured contact matrices during the stay-at-home order to pre-pandemic matrices. During the state-wide stay-home order, the mean daily number of contacts was 5.7. We found significant variation in contacts by age, gender, race, and region. Adults between 40 and 50 years had the highest number of contacts. The way race/ethnicity was coded influenced patterns between groups. Respondents living in Black households (which includes many White respondents living in inter-racial households with black family members) had 2.7 more contacts than respondents in White households; we did not find this same pattern when we focused on individual's reported race/ethnicity. Asian or Pacific Islander respondents or in API households had approximately the same number of contacts as respondents in White households. Respondents in Hispanic households had approximately two fewer contacts compared to White households, likewise Hispanic respondents had three fewer contacts than White respondents. Most contacts were with other individuals in the same age group. Compared to the pre-pandemic period, the biggest declines occurred in contacts between children, and contacts between those over 60 with those below 60.

Exploring human mixing patterns based on time use and social contact data and their implications for infectious disease transmission models

BACKGROUND

The increasing availability of data on social contact patterns and time use provides invaluable information for studying transmission dynamics of infectious diseases. Social contact data provide information on the interaction of people in a population whereas the value of time use data lies in the quantification of exposure patterns. Both have been used as proxies for transmission risks within in a population and the combination of both sources has led to investigate which contacts are more suitable to describe these transmission risks.

METHODS

We used social contact and time use data from 1707 participants from a survey conducted in Flanders, Belgium in 2010-2011. We calculated weighted exposure time and social contact matrices to analyze age- and gender-specific mixing patterns and to quantify behavioral changes by distance from home. We compared the value of both separate and combined data sources for explaining seroprevalence and incidence data on parvovirus-B19, Varicella-Zoster virus (VZV) and influenza like illnesses (ILI), respectively.

RESULTS

Assortative mixing and inter-generational interaction is more pronounced in the exposure matrix due to the high proportion of time spent at home. This pattern is less pronounced in the social contact matrix, which is more impacted by the reported contacts at school and work. The average number of contacts declined with distance. On the individual-level, we observed an increase in the number of contacts and the transmission potential by distance when travelling. We found that both social contact data and time use data provide a good match with the seroprevalence and incidence data at hand. When comparing the use of different combinations of both data sources, we found that the social contact matrix based on close contacts of at least 4 h appeared to be the best proxy for parvovirus-B19 transmission. Social contacts and exposure time were both on their own able to explain VZV seroprevalence data though combining both scored best. Compared with the contact approach, the time use approach provided the better fit to the ILI incidence data.

CONCLUSIONS

Our work emphasises the common and complementary value of time use and social contact data for analysing mixing behavior and analysing infectious disease transmission. We derived spatial, temporal, age-, gender- and distance-specific mixing patterns, which are informative for future modelling studies.

Novel estimates reveal subnational heterogeneities in disease-relevant contact patterns in the United States

Population contact patterns fundamentally determine the spread of directly transmitted airborne pathogens such as SARS-CoV-2 and influenza. Reliable quantitative estimates of contact patterns are therefore critical to modeling and reducing the spread of directly transmitted infectious diseases and to assessing the effectiveness of interventions intended to limit risky contacts. While many countries have used surveys and contact diaries to collect national-level contact data, local-level estimates of age-specific contact patterns remain rare. Yet, these local-level data are critical since disease dynamics and public health policy typically vary by geography. To overcome this challenge, we introduce a flexible model that can estimate age-specific contact patterns at the subnational level by combining national-level interpersonal contact data with other locality-specific data sources using multilevel regression with poststratification (MRP). We estimate daily contact matrices for all 50 US states and Washington DC from April 2020 to May 2021 using national contact data from the US. Our results reveal important state-level heterogeneities in levels and trends of contacts across the US over the course of the COVID-19 pandemic, with implications for the spread of respiratory diseases.

Improving Estimates of Social Contact Patterns for Airborne Transmission of Respiratory Pathogens

Data on social contact patterns are widely used to parameterize age-mixing matrices in mathematical models of infectious diseases. Most studies focus on close contacts only (i.e., persons spoken with face-to-face). This focus may be appropriate for studies of droplet and short-range aerosol transmission but neglects casual or shared air contacts, who may be at risk from airborne transmission. Using data from 2 provinces in South Africa, we estimated age mixing patterns relevant for droplet transmission, nonsaturating airborne transmission, and Mycobacterium tuberculosis transmission, an airborne infection where saturation of household contacts occurs. Estimated contact patterns by age did not vary greatly between the infection types, indicating that widespread use of close contact data may not be resulting in major inaccuracies. However, contact in persons >50 years of age was lower when we considered casual contacts, and therefore the contribution of older age groups to airborne transmission may be overestimated.

School Virus Infection Simulator for customizing school schedules during COVID-19

Even as the COVID-19 pandemic raged worldwide, schools strived to provide consistent education to their students. In such situations, schools require customized schedules that can address the health concerns and safety of the students to safely reopen and remain open. School schedules can be customized in many ways, and different approaches' impact on education and effectiveness in reducing infectious risks are different. To address this issue, we developed the School Virus Infection Simulation-Model (SVISM) for teachers and education policymakers. By taking into account the students' lesson schedules, classroom volume, air circulation rates in the classrooms, and infectability of the students, SVISM simulates the spread of infection at a school. We demonstrate the impact of several school schedules in self-contained and departmentalized classrooms and evaluate them in terms of the maximum number of students infected simultaneously, and the percentage of face-to-face lessons. The results show that the impact of increasing the classroom ventilation rate is not as stable as that of customizing school schedules. In addition, school schedules can differently impact the maximum number of students infected simultaneously, depending on whether classrooms are self-contained or departmentalized. We found that the maximum number of students infected simultaneously under a certain schedule with 50 percentage of face-to-face lessons in self-contained classrooms is higher than the maximum number of students infected simultaneously having schedules with a higher percentage of face-to-face lessons; this phenomenon was not found in departmentalized classrooms. These results show that the SVISM can help teachers and education policymakers plan school schedules appropriately to reduce the maximum number of students infected simultaneously, while also maintaining a certain rate of face-to-face lessons.

Individual's daily behaviour and intergenerational mixing in different social contexts of Kenya

We investigated contact patterns in diverse social contexts in Kenya and the daily behaviours that may play a pivotal role in infection transmission to the most vulnerable leveraging novel data from a 2-day survey on social contacts and time use (TU) from a sample of 1407 individuals (for a total of 2705 person days) from rural, urban formal, and informal settings. We used TU data to build six profiles of daily behaviour based on the main reported activities, i.e., Homestayers (71.1% of person days), Workers (9.3%), Schoolers (7.8%), or locations at increasing distance from home, i.e., Walkers (6.6%), Commuters (4.6%), Travelers (0.6%). In the rural setting, we observed higher daily contact numbers (11.56, SD 0.23) and percentages of intergenerational mixing with older adults (7.5% of contacts reported by those younger than 60 years vs. less than 4% in the urban settings). Overall, intergenerational mixing with older adults was higher for Walkers (7.3% of their reported contacts), Commuters (8.7%), and Homestayers (5.1%) than for Workers (1.5%) or Schoolers (3.6%). These results could be instrumental in defining effective interventions that acknowledge the heterogeneity in social contexts and daily routines, either in Kenya or other demographically and culturally similar sub-Saharan African settings.

Analyzing the demographic, spatial, and temporal factors influencing social contact patterns in U.S. and implications for infectious disease spread

Background

Diseases such as COVID-19 are spread through social contact. Reducing social contacts is required to stop disease spread in pandemics for which vaccines have not yet been developed. However, existing data on social contact patterns in the United States (U.S.) is limited.

Method

We use American Time Use Survey data from 2003–2018 to describe and quantify the age-pattern of disease-relevant social contacts. For within-household contacts, we construct age-structured contact duration matrices (who spends time with whom, by age). For both within-household and non-household contacts, we also estimate the mean number and duration of contact by location. We estimate and test for differences in the age-pattern of social contacts based on demographic, temporal, and spatial characteristics.

Results

The mean number and duration of social contacts vary by age. The biggest gender differences in the age-pattern of social contacts are at home and at work; the former appears to be driven by caretaking responsibilities.

Non-Hispanic Blacks have a shorter duration of contact and fewer social contacts than non-Hispanic Whites. This difference is largely driven by fewer and shorter contacts at home. Pre-pandemic, non-Hispanic Blacks have shorter durations of work contacts. Their jobs are more likely to require close physical proximity, so their contacts are riskier than those of non-Hispanic Whites. Hispanics have the highest number of household contacts and are also more likely to work in jobs requiring close physical proximity than non-Hispanic Whites.

With the exceptions of work and school contacts, the duration of social contact is higher on weekends than on weekdays. Seasonal differences in the total duration of social contacts are driven by school-aged respondents who have significantly shorter contacts during the summer months. Contact patterns did not differ by metro status. Age patterns of social contacts were similar across regions.

Conclusion

Social contact patterns differ by age, race and ethnicity, and gender. Other factors besides contact patterns may be driving seasonal variation in disease incidence if school-aged individuals are not an important source of transmission. Pre-pandemic, there were no spatial differences in social contacts, but this finding has likely changed during the pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06610-w.

Social contact patterns among employees in 3 U.S. companies during early phases of the COVID-19 pandemic, April to June 2020

We measured contact patterns using online diaries for 304 employees of 3 U.S. companies working remotely. The median number of daily contacts was 2 (IQR 1-4); majority were conversation (55 %), occurred at home (64 %) and lasted >4 h (38 %). These data are crucial for modeling outbreak control among the workforces.

Inferring Urban Social Networks from Publicly Available Data

The definition of suitable generative models for synthetic yet realistic social networks is a widely studied problem in the literature. By not being tied to any real data, random graph models cannot capture all the subtleties of real networks and are inadequate for many practical contexts—including areas of research, such as computational epidemiology, which are recently high on the agenda. At the same time, the so-called contact networks describe interactions, rather than relationships, and are strongly dependent on the application and on the size and quality of the sample data used to infer them. To fill the gap between these two approaches, we present a data-driven model for urban social networks, implemented and released as open source software. By using just widely available aggregated demographic and social-mixing data, we are able to create, for a territory of interest, an age-stratified and geo-referenced synthetic population whose individuals are connected by “strong ties” of two types: intra-household (e.g., kinship) or friendship. While household links are entirely data-driven, we propose a parametric probabilistic model for friendship, based on the assumption that distances and age differences play a role, and that not all individuals are equally sociable. The demographic and geographic factors governing the structure of the obtained network, under different configurations, are thoroughly studied through extensive simulations focused on three Italian cities of different size.

A spatiotemporal simulation study on the transmission of harmful microorganisms through connected healthcare workers in a hospital ward setting

Background

Hand transmission of harmful microorganisms may lead to infections and poses a major threat to patients and healthcare workers in healthcare settings. The most effective countermeasure against these transmissions is the adherence to spatiotemporal hand hygiene policies, but adherence rates are relatively low and vary over space and time. The spatiotemporal effects on hand transmission and spread of these microorganisms for varying hand hygiene compliance levels are unknown. This study aims to (1) identify a healthcare worker occupancy group of potential super-spreaders and (2) quantify spatiotemporal effects on the hand transmission and spread of harmful microorganisms for varying levels of hand hygiene compliance caused by this group.

Methods

Spatiotemporal data were collected in a hospital ward of an academic hospital using radio frequency identification technology for 7 days. A potential super-spreader healthcare worker occupation group was identified using the frequency identification sensors’ contact data. The effects of five probability distributions of hand hygiene compliance and three harmful microorganism transmission rates were simulated using a dynamic agent-based simulation model. The effects of initial simulation assumptions on the simulation results were quantified using five risk outcomes.

Results

Nurses, doctors and patients are together responsible for 81.13% of all contacts. Nurses made up 70.68% of all contacts, which is more than five times that of doctors (10.44%). This identifies nurses as the potential super-spreader healthcare worker occupation group. For initial simulation conditions of extreme lack of hand hygiene compliance (5%) and high transmission rates (5% per contact moment), a colonised nurse can transfer microbes to three of the 17 healthcare worker or patients encountered during the 98.4 min of visiting 23 rooms while colonised. The harmful microorganism transmission potential for nurses is higher during weeknights (5 pm – 7 am) and weekends as compared to weekdays (7 am – 5 pm).

Conclusion

Spatiotemporal behaviour and social mixing patterns of healthcare can change the expected number of hand transmissions and spread of harmful microorganisms by super-spreaders in a closed healthcare setting. These insights can be used to evaluate spatiotemporal safety behaviours and develop infection prevention and control strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-05954-7.

Sensitivity analysis of the infection transmissibility in the UK during the COVID-19 pandemic

The coronavirus (COVID-19) outbreak started in December 2019 and rapidly spread around the world affecting millions of people. With the growth of infection rate, many countries adopted different policies to control the spread of the disease. The UK implemented strict rules instructing individuals to stay at home except in some special circumstances starting from 23 March 2020. Accordingly, this study focuses on sensitivity analysis of transmissibility of the infection as the effects of removing restrictions, for example by returning different occupational groups to their normal working environment and its effect on the reproduction number in the UK. For this reason, available social contact matrices are adopted for the population of UK to account for the average number of contacts. Different scenarios are then considered to analyse the variability of total contacts on the reproduction number in the UK as a whole and each of its four nations. Our data-driven retrospective analysis shows that if more than 38.5% of UK working-age population return to their normal working environment, the reproduction number in the UK is expected to be higher than 1. However, analysis of each nation, separately, shows that local reproduction number in each nation may be different and requires more adequate analysis. Accordingly, we believe that using statistical methods and historical data can provide good estimation of local transmissibility and reproduction number in any region. As a consequence of this analysis, efforts to reduce the restrictions should be implemented locally via different control policies. It is important that these policies consider the social contacts, population density, and the occupational groups that are specific to each region.

C, Galloway D, Schweizer ML, Guclu H, Cousins J, Lingle C, Yearwood GMH, Li K, Calderone P, Noble E, Gao H, Rainey J, Uzicanin A, Read JM. Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys

Comparisons of the utility and accuracy of methods for measuring social interactions relevant to disease transmission are rare. To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area. Although the number and type of contacts recorded by each participant differed between the two methods, we found good correspondence between the two methods in aggregate measures of age-specific interactions. Fewer, but longer, contacts were reported in surveys, relative to the generally short proximal interactions captured by wearable sensors. When adjusted for expectations of proportionate mixing, though, the two methods produced highly similar, assortative age-mixing matrices. These aggregate mixing matrices, when used in simulation, resulted in similar estimates of risk of infection by age. While proximity sensors and survey methods may not be interchangeable for capturing individual contacts, they can generate highly correlated data on age-specific mixing patterns relevant to the dynamics of respiratory virus transmission.

Inferring high-resolution human mixing patterns for disease modeling

Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.

Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys

Comparisons of the utility and accuracy of methods for measuring social interactions relevant to disease transmission are rare. To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area. Although the number and type of contacts recorded by each participant differed between the two methods, we found good correspondence between the two methods in aggregate measures of age-specific interactions. Fewer, but longer, contacts were reported in surveys, relative to the generally short proximal interactions captured by wearable sensors. When adjusted for expectations of proportionate mixing, though, the two methods produced highly similar, assortative age-mixing matrices. These aggregate mixing matrices, when used in simulation, resulted in similar estimates of risk of infection by age. While proximity sensors and survey methods may not be interchangeable for capturing individual contacts, they can generate highly correlated data on age-specific mixing patterns relevant to the dynamics of respiratory virus transmission.

What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization

BACKGROUND

COVID-19 has spread to 6 continents. Now is opportune to gain a deeper understanding of what may have happened. The findings can help inform mitigation strategies in the disease-affected countries.

METHODS

In this work, we examine an essential factor that characterizes the disease transmission patterns: the interactions among people. We develop a computational model to reveal the interactions in terms of the social contact patterns among the population of different age-groups. We divide a city's population into seven age-groups: 0-6 years old (children); 7-14 (primary and junior high school students); 15-17 (high school students); 18-22 (university students); 23-44 (young/middle-aged people); 45-64 years old (middle-aged/elderly people); and 65 or above (elderly people). We consider four representative settings of social contacts that may cause the disease spread: (1) individual households; (2) schools, including primary/high schools as well as colleges and universities; (3) various physical workplaces; and (4) public places and communities where people can gather, such as stadiums, markets, squares, and organized tours. A contact matrix is computed to describe the contact intensity between different age-groups in each of the four settings. By integrating the four contact matrices with the next-generation matrix, we quantitatively characterize the underlying transmission patterns of COVID-19 among different populations.

FINDINGS

We focus our study on 6 representative cities in China: Wuhan, the epicenter of COVID-19 in China, together with Beijing, Tianjin, Hangzhou, Suzhou, and Shenzhen, which are five major cities from three key economic zones. The results show that the social contact-based analysis can readily explain the underlying disease transmission patterns as well as the associated risks (including both confirmed and unconfirmed cases). In Wuhan, the age-groups involving relatively intensive contacts in households and public/communities are dispersedly distributed. This can explain why the transmission of COVID-19 in the early stage mainly took place in public places and families in Wuhan. We estimate that Feb. 11, 2020 was the date with the highest transmission risk in Wuhan, which is consistent with the actual peak period of the reported case number (Feb. 4-14). Moreover, the surge in the number of new cases reported on Feb. 12 and 13 in Wuhan can readily be captured using our model, showing its ability in forecasting the potential/unconfirmed cases. We further estimate the disease transmission risks associated with different work resumption plans in these cities after the outbreak. The estimation results are consistent with the actual situations in the cities with relatively lenient policies, such as Beijing, and those with strict policies, such as Shenzhen.

INTERPRETATION

With an in-depth characterization of age-specific social contact-based transmission, the retrospective and prospective situations of the disease outbreak, including the past and future transmission risks, the effectiveness of different interventions, and the disease transmission risks of restoring normal social activities, are computationally analyzed and reasonably explained. The conclusions drawn from the study not only provide a comprehensive explanation of the underlying COVID-19 transmission patterns in China, but more importantly, offer the social contact-based risk analysis methods that can readily be applied to guide intervention planning and operational responses in other countries, so that the impact of COVID-19 pandemic can be strategically mitigated.

FUNDING

General Research Fund of the Hong Kong Research Grants Council; Key Project Grants of the National Natural Science Foundation of China.

Evaluating the impact of the weather conditions on the influenza propagation

Background

Predicting the details of how an epidemic evolves is highly valuable as health institutions need to better plan towards limiting the infection propagation effects and optimizing their prediction and response capabilities. Simulation is a cost- and time-effective way of predicting the evolution of the infection as the joint influence of many different factors: interaction patterns, personal characteristics, travel patterns, meteorological conditions, previous vaccination, etc. The work presented in this paper extends EpiGraph, our influenza epidemic simulator, by introducing a meteorological model as a modular component that interacts with the rest of EpiGraph’s modules to refine our previous simulation results. Our goal is to estimate the effects of changes in temperature and relative humidity on the patterns of epidemic influenza based on data provided by the Spanish Influenza Sentinel Surveillance System (SISSS) and the Spanish Meteorological Agency (AEMET).

Methods

Our meteorological model is based on the regression model developed by AB and JS, and it is tuned with influenza surveillance data obtained from SISSS. After pre-processing this data to clean it and reconstruct missing samples, we obtain new values for the reproduction number of each urban region in Spain, every 10 minutes during 2011. We simulate the propagation of the influenza by setting the date of the epidemic onset and the initial influenza-illness rates for each urban region.

Results

We show that the simulation results have the same propagation shape as the weekly influenza rates as recorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from 2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show that a diminishing relative humidity of 10% produces an increment of about 1.6% in the final infection rate. The effect of temperature changes on the infection spread is also noticeable, with a decrease of 1.1% per extra degree.Conclusions: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and would permit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We make EpiGraph source code and epidemic data publicly available.

Next Generation Technology for Epidemic Prevention and Control: Data-Driven Contact Tracking

Contact tracking is one of the key technologies in prevention and control of infectious diseases. In the face of a sudden infectious disease outbreak, contact tracking systems can help medical professionals quickly locate and isolate infected persons and high-risk individuals, preventing further spread and a large-scale outbreak of infectious disease. Furthermore, the transmission networks of infectious diseases established using contact tracking technology can aid in the visualization of actual virus transmission paths, which enables simulations and predictions of the transmission process, assessment of the outbreak trend, and further development and deployment of more effective prevention and control strategies. Exploring effective contact tracking methods will be significant. Governments, academics, and industries have all given extensive attention to this goal. In this paper, we review the developments and challenges of current contact tracing technologies regarding individual and group contact from both static and dynamic perspectives, including static individual contact tracing, dynamic individual contact tracing, static group contact tracing, and dynamic group contact tracing. With the purpose of providing useful reference and inspiration for researchers and practitioners in related fields, directions in multi-view contact tracing, multi-scale contact tracing, and AI-based contact tracing are provided for next-generation technologies for epidemic prevention and control.

Modeling the impact of changes in day-care contact patterns on the dynamics of varicella transmission in France between 1991 and 2015

Annual incidence rates of varicella infection in the general population in France have been rather stable since 1991 when clinical surveillance started. Rates however show a statistically significant increase over time in children aged 0-3 years, and a decline in older individuals. A significant increase in day-care enrolment and structures' capacity in France was also observed in the last decade. In this work we investigate the potential interplay between an increase of contacts of young children possibly caused by earlier socialization in the community and varicella transmission dynamics. To this aim, we develop an age-structured mathematical model, informed with historical demographic data and contact matrix estimates in the country, accounting for longitudinal linear increase of early childhood contacts. While the reported overall varicella incidence is well reproduced independently of mixing variations, age-specific empirical trends are better captured by accounting for an increase in contacts among pre-school children in the last decades. We found that the varicella data are consistent with a 30% increase in the number of contacts at day-care facilities, which would imply a 50% growth in the contribution of 0-3y old children to overall yearly infections in 1991-2015. Our findings suggest that an earlier exposure to pathogens due to changes in day-care contact patterns, represents a plausible explanation for the epidemiological patterns observed in France. Obtained results suggest that considering temporal changes in social factors in addition to demographic ones is critical to correctly interpret varicella transmission dynamics.

Scabies in residential care homes: Modelling, inference and interventions for well-connected population sub-units

In the context of an ageing population, understanding the transmission of infectious diseases such as scabies through well-connected sub-units of the population, such as residential care homes, is particularly important for the design of efficient interventions to mitigate against the effects of those diseases. Here, we present a modelling methodology based on the efficient solution of a large-scale system of linear differential equations that allows statistical calibration of individual-based random models to real data on scabies in residential care homes. In particular, we review and benchmark different numerical methods for the integration of the differential equation system, and then select the most appropriate of these methods to perform inference using Markov chain Monte Carlo. We test the goodness-of-fit of this model using posterior predictive intervals and propagate forward the resulting parameter uncertainty in a Bayesian framework to consider the economic cost of delayed interventions against scabies, quantifying the benefits of prompt action in the event of detection. We also revisit the previous methodology used to assess the safety of treatments in small population sub-units-in this context ivermectin-and demonstrate that even a very slight relaxation of the implicit assumption of homogeneous death rates significantly increases the plausibility of the hypothesis that ivermectin does not cause excess mortality based upon the data of Barkwell and Shields.

Projecting social contact matrices in 152 countries using contact surveys and demographic data

Heterogeneities in contact networks have a major effect in determining whether a pathogen can become epidemic or persist at endemic levels. Epidemic models that determine which interventions can successfully prevent an outbreak need to account for social structure and mixing patterns. Contact patterns vary across age and locations (e.g. home, work, and school), and including them as predictors in transmission dynamic models of pathogens that spread socially will improve the models' realism. Data from population-based contact diaries in eight European countries from the POLYMOD study were projected to 144 other countries using a Bayesian hierarchical model that estimated the proclivity of age-and-location-specific contact patterns for the countries, using Markov chain Monte Carlo simulation. Household level data from the Demographic and Health Surveys for nine lower-income countries and socio-demographic factors from several on-line databases for 152 countries were used to quantify similarity of countries to estimate contact patterns in the home, work, school and other locations for countries for which no contact data are available, accounting for demographic structure, household structure where known, and a variety of metrics including workforce participation and school enrolment. Contacts are highly assortative with age across all countries considered, but pronounced regional differences in the age-specific contacts at home were noticeable, with more inter-generational contacts in Asian countries than in other settings. Moreover, there were variations in contact patterns by location, with work-place contacts being least assortative. These variations led to differences in the effect of social distancing measures in an age structured epidemic model. Contacts have an important role in transmission dynamic models that use contact rates to characterize the spread of contact-transmissible diseases. This study provides estimates of mixing patterns for societies for which contact data such as POLYMOD are not yet available.

Characterizing and Discovering Spatiotemporal Social Contact Patterns for Healthcare

During an epidemic, the spatial, temporal and demographic patterns of disease transmission are determined by multiple factors. In addition to the physiological properties of the pathogens and hosts, the social contact of the host population, which characterizes the reciprocal exposures of individuals to infection according to their demographic structure and various social activities, are also pivotal to understanding and predicting the prevalence of infectious diseases. How social contact is measured will affect the extent to which we can forecast the dynamics of infections in the real world. Most current work focuses on modeling the spatial patterns of static social contact. In this work, we use a novel perspective to address the problem of how to characterize and measure dynamic social contact during an epidemic. We propose an epidemic-model-based tensor deconvolution framework in which the spatiotemporal patterns of social contact are represented by the factors of the tensors. These factors can be discovered using a tensor deconvolution procedure with the integration of epidemic models based on rich types of data, mainly heterogeneous outbreak surveillance data, socio-demographic census data and physiological data from medical reports. Using reproduction models that include SIR/SIS/SEIR/SEIS models as case studies, the efficacy and applications of the proposed framework are theoretically analyzed, empirically validated and demonstrated through a set of rigorous experiments using both synthetic and real-world data.

Place-based social contact and mixing: a typology of generic meeting places of relevance for infectious disease transmission

This study aims to develop a typology of generic meeting places based on social contact and mixing of relevance for infectious disease transmission. Data were collected by means of a contact diary survey conducted on a representative sample of the Swedish population. The typology is derived from a cluster analysis accounting for four dimensions associated with transmission risk: visit propensity and its characteristics in terms of duration, number of other persons present and likelihood of physical contact. In the analysis, we also study demographic, socio-economic and geographical differences in the propensity of visiting meeting places. The typology identifies the family venue, the fixed activity site, the family vehicle, the trading plaza and the social network hub as generic meeting places. The meeting place typology represents a spatially explicit account of social contact and mixing relevant to infectious disease modelling, where the social context of the outbreak can be highlighted in light of the actual infectious disease.

Modelling the transmission and control strategies of varicella among school children in Shenzhen, China

Objectives

Varicella (chickenpox) is a highly transmissible childhood disease. Between 2010 and 2015, it displayed two epidemic waves annually among school populations in Shenzhen, China. However, their transmission dynamics remain unclear and there is no school-based vaccination programme in Shenzhen to-date. In this study, we developed a mathematical model to compare a school-based vaccination intervention scenario with a baseline (i.e. no intervention) scenario.

Methods

Data on varicella reported cases were downloaded from the Infectious Disease Reporting Information Management System. We obtained the population size, age structure of children aged 15 or under, the class and school distribution from Shenzhen Education Bureau. We developed an Agent-Based Susceptible-Exposed-Infectious-Recovered (ABM-SEIR) Model that considered within-class, class-to-class and out-of-school transmission modes. The intervention scenario was that school-wide vaccination intervention occurred when an outbreak threshold was reached within a school. We varied this threshold level from five to ten cases. We compared the reduction of disease outbreak size and estimated the key epidemiological parameters under the intervention strategy.

Results

Our ABM-SEIR model provided a good model fit to the two annual varicella epidemic waves from 2013 to 2015. The transmission dynamics displayed strong seasonality. Our results suggested that a school-based vaccination strategy could effectively prevent large outbreaks at different thresholds.

Conclusions

There was a considerable increase in reported varicella cases from 2013 to 2015 in Shenzhen. Our modelling study provided important theoretical support for disease control decision making during school outbreaks and the development of a school-based vaccination programme.

Estimating contact patterns relevant to the spread of infectious diseases in Russia

Understanding human mixing patterns is the key to provide public health decision makers with model-based evaluation of strategies for the control of infectious diseases. Here we conducted a population-based survey in Tomsk, Russia, asking participants to record all their contacts in physical person during the day. We estimated 9.8 contacts per person per day on average, 15.2 when including additional estimated professional contacts. We found that contacts were highly assortative by age, especially for school-age individuals, and the number of contacts negatively correlated with the age of the participant. The network of contacts was quite clustered, with the majority of contacts (about 72%) occurring between family members, students of the same school/university, and work colleagues. School represents the location where the largest number of contacts was recorded - students contacted about 7 individuals per day at school. Our modeling analysis based on the recorded contact patterns supports the importance of modeling age-mixing patterns - we show that, in the case of an epidemic caused by a novel influenza virus, school-age individuals would be the most affected age group, followed by adults aged 35-44 years. In conclusion, this study reveals an age-mixing pattern in general agreement with that estimated for European countries, although with several quantitative differences. The observed differences can be attributable to sociodemographic and cultural differences between countries. The age- and setting-specific contact matrices provided in this study could be instrumental for the design of control measures for airborne infections, specifically targeted on the characteristics of the Russian population.

Social Contact Structures and Time Use Patterns in the Manicaland Province of Zimbabwe

BACKGROUND

Patterns of person-to-person contacts relevant for infectious diseases transmission are still poorly quantified in Sub-Saharan Africa (SSA), where socio-demographic structures and behavioral attitudes are expected to be different from those of more developed countries.

METHODS AND FINDINGS

We conducted a diary-based survey on daily contacts and time-use of individuals of different ages in one rural and one peri-urban site of Manicaland, Zimbabwe. A total of 2,490 diaries were collected and used to derive age-structured contact matrices, to analyze time spent by individuals in different settings, and to identify the key determinants of individuals' mixing patterns. Overall 10.8 contacts per person/day were reported, with a significant difference between the peri-urban and the rural site (11.6 versus 10.2). A strong age-assortativeness characterized contacts of school-aged children, whereas the high proportion of extended families and the young population age-structure led to a significant intergenerational mixing at older ages. Individuals spent on average 67% of daytime at home, 2% at work, and 9% at school. Active participation in school and work resulted the key drivers of the number of contacts and, similarly, household size, class size, and time spent at work influenced the number of home, school, and work contacts, respectively. We found that the heterogeneous nature of home contacts is critical for an epidemic transmission chain. In particular, our results suggest that, during the initial phase of an epidemic, about 50% of infections are expected to occur among individuals younger than 12 years and less than 20% among individuals older than 35 years.

CONCLUSIONS

With the current work, we have gathered data and information on the ways through which individuals in SSA interact, and on the factors that mostly facilitate this interaction. Monitoring these processes is critical to realistically predict the effects of interventions on infectious diseases dynamics.

Clustering of contacts relevant to the spread of infectious disease

OBJECTIVE

Infectious disease spread depends on contact rates between infectious and susceptible individuals. Transmission models are commonly informed using empirically collected contact data, but the relevance of different contact types to transmission is still not well understood. Some studies select contacts based on a single characteristic such as proximity (physical/non-physical), location, duration or frequency. This study aimed to explore whether clusters of contacts similar to each other across multiple characteristics could better explain disease transmission.

METHODS

Individual contact data from the POLYMOD survey in Poland, Great Britain, Belgium, Finland and Italy were grouped into clusters by the k medoids clustering algorithm with a Manhattan distance metric to stratify contacts using all four characteristics. Contact clusters were then used to fit a transmission model to sero-epidemiological data for varicella-zoster virus (VZV) in each country.

RESULTS AND DISCUSSION

Across the five countries, 9-15 clusters were found to optimise both quality of clustering (measured using average silhouette width) and quality of fit (measured using several information criteria). Of these, 2-3 clusters were most relevant to VZV transmission, characterised by (i) 1-2 clusters of age-assortative contacts in schools, (ii) a cluster of less age-assortative contacts in non-school settings. Quality of fit was similar to using contacts stratified by a single characteristic, providing validation that single stratifications are appropriate. However, using clustering to stratify contacts using multiple characteristics provided insight into the structures underlying infection transmission, particularly the role of age-assortative contacts, involving school age children, for VZV transmission between households.

Influenza in workplaces: transmission, workers' adherence to sick leave advice and European sick leave recommendations

Background: Knowledge about influenza transmission in the workplace and whether staying home from work when experiencing influenza-like illness can reduce the spread of influenza is crucial for the design of efficient public health initiatives. Aim: This review synthesizes current literature on sickness presenteeism and influenza transmission in the workplace and provides an overview of sick leave recommendations in Europe for influenza. Methods: A search was performed on Medline, Embase, PsychINFO, Cinahl, Web of Science, Scopus and SweMed to identify studies related to workplace contacts, -transmission, -interventions and compliance with recommendations to take sick leave. A web-based survey on national recommendations and policies for sick leave during influenza was issued to 31 European countries. Results: Twenty-two articles (9 surveys; 13 modelling articles) were eligible for this review. Results from social mixing studies suggest that 20–25% of weekly contacts are made in the workplace, while modelling studies suggest that on average 16% (range 9–33%) of influenza transmission occurs in the workplace. The effectiveness of interventions to reduce workplace presenteeism is largely unknown. Finally, estimates from studies reporting expected compliance with sick leave recommendations ranged from 71 to 95%. Overall, 18 countries participated in the survey of which nine (50%) had issued recommendations encouraging sick employees to stay at home during the 2009 A(H1N1) pandemic, while only one country had official recommendations for seasonal influenza. Conclusions: During the 2009 A(H1N1) pandemic, many European countries recommended ill employees to take sick leave. Further research is warranted to quantify the effect of reduced presenteeism during influenza illness.

Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh, PA

Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools.

The impact of demographic changes on the epidemiology of herpes zoster: Spain as a case study

Varicella zoster virus (VZV) causes varicella upon first exposure and may reactivate later in life into herpes zoster (HZ), with a risk that is thought to be reduced by re-exposures to VZV. Given the decades-long time scales of reactivation and its dependence on the accumulation of re-exposure episodes, adopting a long-term perspective may be useful to correctly interpret current epidemiological trends of VZV. In this study, we investigate the possible impact of demographic changes on varicella and HZ in Spain, using an age-structured mathematical model informed with historical demographic data and calibrated against age-specific profiles of varicella seroprevalence and HZ incidence data. The model qualitatively reproduces the remarkable growth of HZ incidence observed in Spain between 1997 and 2004, before the introduction of varicella vaccination programmes. We demonstrate that this growth may be partially ascribed to the reduction of varicella circulation that followed the overall decline of the birth rate in the twentieth century. Model predictions further suggest that, even under the most optimistic projections, HZ incidence will continue its rise until at least 2040. Considering the effect of demographic changes can help interpreting variations in epidemiological trends of HZ, contributing to a more accurate evaluation of vaccination programmes against VZV.

Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys

Given their importance in shaping social networks and determining how information or transmissible diseases propagate in a population, interactions between individuals are the subject of many data collection efforts. To this aim, different methods are commonly used, ranging from diaries and surveys to decentralised infrastructures based on wearable sensors. These methods have each advantages and limitations but are rarely compared in a given setting. Moreover, as surveys targeting friendship relations might suffer less from memory biases than contact diaries, it is interesting to explore how actual contact patterns occurring in day-to-day life compare with friendship relations and with online social links. Here we make progresses in these directions by leveraging data collected in a French high school and concerning (i) face-to-face contacts measured by two concurrent methods, namely wearable sensors and contact diaries, (ii) self-reported friendship surveys, and (iii) online social links. We compare the resulting data sets and find that most short contacts are not reported in diaries while long contacts have a large reporting probability, and that the durations of contacts tend to be overestimated in the diaries. Moreover, measured contacts corresponding to reported friendship can have durations of any length but all long contacts do correspond to a reported friendship. On the contrary, online links that are not also reported in the friendship survey correspond to short face-to-face contacts, highlighting the difference of nature between reported friendships and online links. Diaries and surveys suffer moreover from a low sampling rate, as many students did not fill them, showing that the sensor-based platform had a higher acceptability. We also show that, despite the biases of diaries and surveys, the overall structure of the contact network, as quantified by the mixing patterns between classes, is correctly captured by both networks of self-reported contacts and of friendships, and we investigate the correlations between the number of neighbors of individuals in the three networks. Overall, diaries and surveys tend to yield a correct picture of the global structural organization of the contact network, albeit with much less links, and give access to a sort of backbone of the contact network corresponding to the strongest links, i.e., the contacts of longest cumulative durations.

Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions

Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use contact diaries, architectural distance measures, and institutional structures to estimate social contact networks within a Swiss research institute. Some contact reports were inconsistent, indicating reporting errors. We adjust for this with a latent variable model, jointly estimating the true (unobserved) network of contacts and duration-specific reporting probabilities. We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0-5 min contacts. Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions. Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models.

Inferring a district-based hierarchical structure of social contacts from census data

Researchers have recently paid attention to social contact patterns among individuals due to their useful applications in such areas as epidemic evaluation and control, public health decisions, chronic disease research and social network research. Although some studies have estimated social contact patterns from social networks and surveys, few have considered how to infer the hierarchical structure of social contacts directly from census data. In this paper, we focus on inferring an individual’s social contact patterns from detailed census data, and generate various types of social contact patterns such as hierarchical-district-structure-based, cross-district and age-district-based patterns. We evaluate newly generated contact patterns derived from detailed 2011 Hong Kong census data by incorporating them into a model and simulation of the 2009 Hong Kong H1N1 epidemic. We then compare the newly generated social contact patterns with the mixing patterns that are often used in the literature, and draw the following conclusions. First, the generation of social contact patterns based on a hierarchical district structure allows for simulations at different district levels. Second, the newly generated social contact patterns reflect individuals social contacts. Third, the newly generated social contact patterns improve the accuracy of the SEIR-based epidemic model.

The role of different social contexts in shaping influenza transmission during the 2009 pandemic

Evaluating the relative importance of different social contexts in which infection transmission occurs is critical for identifying optimal intervention strategies. Nonetheless, an overall picture of influenza transmission in different social contexts has yet to emerge. Here we provide estimates of the fraction of infections generated in different social contexts during the 2009 H1N1 pandemic in Italy by making use of a highly detailed individual-based model accounting for time use data and parametrized on the basis of observed age-specific seroprevalence. We found that 41.6% (95%CI: 39-43.7%) of infections occurred in households, 26.7% (95%CI: 21-33.2) in schools, 3.3% (95%CI: 1.7-5%) in workplaces, and 28.4% (95%CI: 24.6-31.9%) in the general community. The above estimates strongly depend on the lower susceptibility to infection of individuals 19+ years old compared to younger ones, estimated to be 0.2 (95%CI 0.12-0.28). We also found that school closure over the weekends contributed to decrease the effective reproduction number of about 8% and significantly affected the pattern of transmission. These results highlight the pivotal role played by schools in the transmission of the 2009 H1N1 influenza. They may be relevant in the evaluation of intervention options and, hence, for informing policy decisions.

Contact patterns among high school students

Face-to-face contacts between individuals contribute to shape social networks and play an important role in determining how infectious diseases can spread within a population. It is thus important to obtain accurate and reliable descriptions of human contact patterns occurring in various day-to-day life contexts. Recent technological advances and the development of wearable sensors able to sense proximity patterns have made it possible to gather data giving access to time-varying contact networks of individuals in specific environments. Here we present and analyze two such data sets describing with high temporal resolution the contact patterns of students in a high school. We define contact matrices describing the contact patterns between students of different classes and show the importance of the class structure. We take advantage of the fact that the two data sets were collected in the same setting during several days in two successive years to perform a longitudinal analysis on two very different timescales. We show the high stability of the contact patterns across days and across years: the statistical distributions of numbers and durations of contacts are the same in different periods, and we observe a very high similarity of the contact matrices measured in different days or different years. The rate of change of the contacts of each individual from one day to the next is also similar in different years. We discuss the interest of the present analysis and data sets for various fields, including in social sciences in order to better understand and model human behavior and interactions in different contexts, and in epidemiology in order to inform models describing the spread of infectious diseases and design targeted containment strategies.

Household crowding, social mixing patterns and respiratory symptoms in seven countries of the African meningitis belt

Objectives

To describe the variation in household crowding and social mixing patterns in the African meningitis belt and to assess any association with self-reported recent respiratory symptoms.

Methods

In 2010, the African Meningococcal Carriage Consortium (MenAfriCar) conducted cross-sectional surveys in urban and rural areas of seven countries. The number of household members, rooms per household, attendance at social gatherings and meeting places were recorded. Associations with self-reported recent respiratory symptoms were analysed by univariate and multivariate regression models.

Results

The geometric mean people per room ranged from 1.9 to 2.8 between Ghana and Ethiopia respectively. Attendance at different types of social gatherings was variable by country, ranging from 0.5 to 1.5 per week. Those who attended 3 or more different types of social gatherings a week (frequent mixers) were more likely to be older, male (OR 1.27, p<0.001) and live in urban areas (OR 1.45, p<0.001). Frequent mixing and young age, but not increased household crowding, were associated with higher odds of self-reported respiratory symptoms (aOR 2.2, p<0.001 and OR 2.8, p<0.001 respectively). A limitation is that we did not measure school and workplace attendance.

Conclusion

There are substantial variations in household crowding and social mixing patterns across the African meningitis belt. This study finds a clear association between age, increased social mixing and respiratory symptoms. It lays the foundation for designing and implementing more detailed studies of social contact patterns in this region.

Network epidemiology and plant trade networks

Models of epidemics in complex networks are improving our predictive understanding of infectious disease outbreaks. Nonetheless, applying network theory to plant pathology is still a challenge. This overview summarizes some key developments in network epidemiology that are likely to facilitate its application in the study and management of plant diseases. Recent surveys have provided much-needed datasets on contact patterns and human mobility in social networks, but plant trade networks are still understudied. Human (and plant) mobility levels across the planet are unprecedented-there is thus much potential in the use of network theory by plant health authorities and researchers. Given the directed and hierarchical nature of plant trade networks, there is a need for plant epidemiologists to further develop models based on undirected and homogeneous networks. More realistic plant health scenarios would also be obtained by developing epidemic models in dynamic, rather than static, networks. For plant diseases spread by the horticultural and ornamental trade, there is the challenge of developing spatio-temporal epidemic simulations integrating network data. The use of network theory in plant epidemiology is a promising avenue and could contribute to anticipating and preventing plant health emergencies such as European ash dieback.

The relative importance of frequency of contacts and duration of exposure for the spread of directly transmitted infections

The recent availability of survey data on social contact patterns has made possible important advances in the understanding of the social determinants of the spread of close-contact infections, and of the importance of long-lasting contacts for effective transmission to occur. Still, little is known about the relationship between two of the most critical identified factors (frequency of contacts and duration of exposure) and how this relationship applies to different types of infections. By integrating data from two independently collected social surveys (Polymod and time use), we propose a model that combines these two transmission determinants into a new epidemiologically relevant measure of contacts: the number of "suitable" contacts, which is the number of contacts that involve a sufficiently long exposure time to allow for transmission. The validity of this new epidemiological measure is tested against Italian serological data for varicella and parvovirus-B19, with uncertainty evaluated using the Bayesian melding technique. The model performs quite well, indicating that the interplay between time of exposure and contacts is critical for varicella transmission, while for B19 it is the duration of exposure that matters for transmission.

Pandemic influenza A/H1N1pdm in Italy: age, risk and population susceptibility

Background

A common pattern emerging from several studies evaluating the impact of the 2009 A/H1N1 pandemic influenza (A/H1N1pdm) conducted in countries worldwide is the low attack rate observed in elderly compared to that observed in children and young adults. The biological or social mechanisms responsible for the observed age-specific risk of infection are still to be deeply investigated.

Methods

The level of immunity against the A/H1N1pdm in pre and post pandemic sera was determined using left over sera taken for diagnostic purposes or routine ascertainment obtained from clinical laboratories. The antibody titres were measured by the haemagglutination inhibition (HI) assay. To investigate whether certain age groups had higher risk of infection the presence of protective antibody (≥1∶40), was calculated using exact binomial 95% CI on both pre- and post- pandemic serological data in the age groups considered. To estimate age-specific susceptibility to infection we used an age-structured SEIR model.

Results

By comparing pre- and post-pandemic serological data in Italy we found age- specific attack rates similar to those observed in other countries. Cumulative attack rate at the end of the first A/H1N1pdm season in Italy was estimated to be 16.3% (95% CI 9.4%-23.1%). Modeling results allow ruling out the hypothesis that only age-specific characteristics of the contact network and levels of pre-pandemic immunity are responsible for the observed age-specific risk of infection. This means that age-specific susceptibility to infection, suspected to play an important role in the pandemic, was not only determined by pre-pandemic levels of H1N1pdm antibody measured by HI.

Conclusions

Our results claim for new studies to better identify the biological mechanisms, which might have determined the observed pattern of susceptibility with age. Moreover, our results highlight the need to obtain early estimates of differential susceptibility with age in any future pandemics to obtain more reliable real time estimates of critical epidemiological parameters.

A lattice model for influenza spreading

We construct a stochastic SIR model for influenza spreading on a D-dimensional lattice, which represents the dynamic contact network of individuals. An age distributed population is placed on the lattice and moves on it. The displacement from a site to a nearest neighbor empty site, allows individuals to change the number and identities of their contacts. The dynamics on the lattice is governed by an attractive interaction between individuals belonging to the same age-class. The parameters, which regulate the pattern dynamics, are fixed fitting the data on the age-dependent daily contact numbers, furnished by the Polymod survey. A simple SIR transmission model with a nearest neighbors interaction and some very basic adaptive mobility restrictions complete the model. The model is validated against the age-distributed Italian epidemiological data for the influenza A(H1N1) during the [Formula: see text] season, with sensible predictions for the epidemiological parameters. For an appropriate topology of the lattice, we find that, whenever the accordance between the contact patterns of the model and the Polymod data is satisfactory, there is a good agreement between the numerical and the experimental epidemiological data. This result shows how rich is the information encoded in the average contact patterns of individuals, with respect to the analysis of the epidemic spreading of an infectious disease.

Hope-Simpson's progressive immunity hypothesis as a possible explanation for herpes zoster incidence data

Varicella-zoster virus (VZV) is the causative agent of both varicella (chickenpox) and herpes zoster (HZ) (shingles). After varicella infection, the virus remains dormant in the host's dorsal ganglia and can reactivate due to waning cell-mediated immunity, causing HZ. Exposure of varicella-immune persons to VZV may boost the host's immune response, resulting in a protective effect against HZ. In this study, we used mathematical models of VZV transmission and HZ development to test the biological hypothesis of "progressive immunity," originally proposed by Hope-Simpson (Proc R Soc Med. 1965;58:9-20), that cell-mediated protection against HZ increases after each episode of exposure to VZV. Predictions from a model incorporating such a hypothesis were compared with those of other concurrent models proposed for explaining HZ epidemiology. The progressive immunity model fits significantly better the age profile of HZ incidence for Finland (years 2000-2006), Italy (2003-2005), Spain (1997-2004), and the United Kingdom (1991-1992), suggesting that this mechanism may be critical in shaping HZ patterns. The model thus validated is an alternative to VZV models currently used to evaluate the impact of mass immunization programs for varicella and therefore extends the range of tools available to assist policy-makers with the present decision paralysis on the introduction of vaccination.

Perspectives on the impact of varicella immunization on herpes zoster. A model-based evaluation from three European countries

The introduction of mass vaccination against Varicella-Zoster-Virus (VZV) is being delayed in many European countries because of, among other factors, the possibility of a large increase in Herpes Zoster (HZ) incidence in the first decades after the initiation of vaccination, due to the expected decline of the boosting of Cell Mediated Immunity caused by the reduced varicella circulation. A multi-country model of VZV transmission and reactivation, is used to evaluate the possible impact of varicella vaccination on HZ epidemiology in Italy, Finland and the UK. Despite the large uncertainty surrounding HZ and vaccine-related parameters, surprisingly robust medium-term predictions are provided, indicating that an increase in HZ incidence is likely to occur in countries where the incidence rate is lower in absence of immunization, possibly due to a higher force of boosting (e.g. Finland), whereas increases in HZ incidence might be minor where the force of boosting is milder (e.g. the UK). Moreover, a convergence of HZ post vaccination incidence levels in the examined countries is predicted despite different initial degrees of success of immunization policies. Unlike previous model-based evaluations, our investigation shows that after varicella immunization an increase of HZ incidence is not a certain fact, rather depends on the presence or absence of factors promoting a strong boosting intensity and which might or not be heavily affected by changes in varicella circulation due to mass immunization. These findings might explain the opposed empirical evidences observed about the increases of HZ in sites where mass varicella vaccination is ongoing.

Pneumococcal transmission and disease in silico: a microsimulation model of the indirect effects of vaccination

Background

The degree and time frame of indirect effects of vaccination (serotype replacement and herd immunity) are key determinants in assessing the net effectiveness of vaccination with pneumococcal conjugate vaccines (PCV) in control of pneumococcal disease. Using modelling, we aimed to quantify these effects and their dependence on coverage of vaccination and the vaccine's efficacy against susceptibility to pneumococcal carriage.

Methods and Findings

We constructed an individual-based simulation model that explores the effects of large-scale PCV programmes and applied it in a developed country setting (Finland). A population structure with transmission of carriage taking place within relevant mixing groups (families, day care groups, schools and neighbourhoods) was considered in order to properly assess the dependency of herd immunity on coverage of vaccination and vaccine efficacy against carriage. Issues regarding potential serotype replacement were addressed by employing a novel competition structure between multiple pneumococcal serotypes. Model parameters were calibrated from pre-vaccination data about the age-specific carriage prevalence and serotype distribution. The model predicts that elimination of vaccine-type carriage and disease among those vaccinated and, due to a substantial herd effect, also among the general population takes place within 5–10 years since the onset of a PCV programme with high (90%) coverage of vaccination and moderate (50%) vaccine efficacy against acquisition of carriage. A near-complete replacement of vaccine-type carriage by non-vaccine-type carriage occurs within the same time frame.

Conclusions

The changed patterns in pneumococcal carriage after PCV vaccination predicted by the model are unequivocal. The overall effect on disease incidence depends crucially on the magnitude of age- and serotype-specific case-to-carrier ratios of the remaining serotypes relative to those of the vaccine types. Thus the availability of reliable data on the incidence of both pneumococcal carriage and disease is essential in assessing the net effectiveness of PCV vaccination in a given epidemiological setting.

Behavioral Epidemiology of Infectious Diseases: An Overview

The focus of the growing discipline of behavioral epidemiology (BE) of infectious diseases is on individual behavior as a key determinant of infection trajectories. This overview departs from the central, but static, role of human behavior in traditional mathematical models of infection to motivate the importance of including behavior into epidemiological models. Our aim is threefold. First, we attempt to motivate the historical and cultural background underpinning the BE revolution, focusing on the issue of rational opposition to vaccines as a natural endpoint of the changed relation between man and disease in modern industrialized countries. Second, we review those contributions, from both mathematical epidemiology and economics, that forerun the current “epidemic” of studies on BE. Last, we offer a more detailed overview of the current epidemic phase of BE studies and, still motivated by the issue of immunization choices, introduce some baseline ideas and models.

Temporal aggregation impacts on epidemiological simulations employing microcontact data

Background

Microcontact datasets gathered automatically by electronic devices have the potential augment the study of the spread of contagious disease by providing detailed representations of the study population’s contact dynamics. However, the impact of data collection experimental design on the subsequent simulation studies has not been adequately addressed. In particular, the impact of study duration and contact dynamics data aggregation on the ultimate outcome of epidemiological models has not been studied in detail, leaving the potential for erroneous conclusions to be made based on simulation outcomes.

Methods

We employ a previously published data set covering 36 participants for 92 days and a previously published agent-based H1N1 infection model to analyze the impact of contact dynamics representation on the simulated outcome of H1N1 transmission. We compared simulated attack rates resulting from the empirically recorded contact dynamics (ground truth), aggregated, typical day, and artificially generated synthetic networks.

Results

No aggregation or sampling policy tested was able to reliably reproduce results from the ground-truth full dynamic network. For the population under study, typical day experimental designs – which extrapolate from data collected over a brief period – exhibited too high a variance to produce consistent results. Aggregated data representations systematically overestimated disease burden, and synthetic networks only reproduced the ground truth case when fitting errors systemically underestimated the total contact, compensating for the systemic overestimation from aggregation.

Conclusions

The interdepedendencies of contact dynamics and disease transmission require that detailed contact dynamics data be employed to secure high fidelity in simulation outcomes of disease burden in at least some populations. This finding serves as motivation for larger, longer and more socially diverse contact dynamics tracing experiments and as a caution to researchers employing calibrated aggregate synthetic representations of contact dynamics in simulation, as the calibration may underestimate disease parameters to compensate for the overestimation of disease burden imposed by the aggregate contact network representation.

Inferring the structure of social contacts from demographic data in the analysis of infectious diseases spread

Social contact patterns among individuals encode the transmission route of infectious diseases and are a key ingredient in the realistic characterization and modeling of epidemics. Unfortunately, the gathering of high quality experimental data on contact patterns in human populations is a very difficult task even at the coarse level of mixing patterns among age groups. Here we propose an alternative route to the estimation of mixing patterns that relies on the construction of virtual populations parametrized with highly detailed census and demographic data. We present the modeling of the population of 26 European countries and the generation of the corresponding synthetic contact matrices among the population age groups. The method is validated by a detailed comparison with the matrices obtained in six European countries by the most extensive survey study on mixing patterns. The methodology presented here allows a large scale comparison of mixing patterns in Europe, highlighting general common features as well as country-specific differences. We find clear relations between epidemiologically relevant quantities (reproduction number and attack rate) and socio-demographic characteristics of the populations, such as the average age of the population and the duration of primary school cycle. This study provides a numerical approach for the generation of human mixing patterns that can be used to improve the accuracy of mathematical models in the absence of specific experimental data.

The dynamics of infectious diseases caused by pathogens transmissible from human to human strongly depends on contact patterns between individuals. High quality observational data on contact patterns, usually presented in the form of age-specific contact matrices, are difficult to gather and are currently available only for few countries worldwide. Here we propose a computational approach, based on the simulation of a virtual society of agents, allowing the estimation of contact patterns by age for 26 European countries. We validate the estimated contact matrices against those obtained by the most extensive field study on contact patterns, with data collected in eight European countries. We show that our contact matrices share some common features, e.g. individuals tend to mix preferentially with individuals their own age, and country-specific differences, which can be partly explained by differences in population structures due to different demographic trajectories followed after WWII. Our analysis highlights well defined correlations between epidemiological parameters and socio-demographic features of the populations. This study provides the first estimates of contact matrices for many European countries where specific experimental data are still not available.