E-contact tracing of hospital infections – validation of SIRSZ automatic risk assessment

Aim. Contact networks play a crucial role in infectious disease propagation and position in the network mediate risk of acquiring or sending infections. We studied the spread of hospital-associated infections through computer simulations and validated our ‘computer assisted’ risk assessment with ‘human’ risk assessment in a prospective study.Concept. We collected time-varying structure of contacts and covariates reconstructed from Polish Hospitals:1. The organisational structure is mapped by a set of questionnaires, CAD maps integration, functional paths annotation and local vision. It is done mostly by surveys within medical staff through an interactive web application.2. The Cohabitation layer processes data from the registry of patient admissions and discharges from each hospital unit (wards, clinics, etc.) and medical shift register. With simulated infection paths, we were able to compute network centrality measures for patients. We obtained the risk of getting infected, based on the patient’s incoming connections, and the risk of spreading infections resulting from outgoing connections. We compare various standard centrality measures – position of patients and staff in contact networks (‘computer assisted’ risk assessment) of both contacts and paths networks, with a predictor of ‘human’ risk perception (based on 190 patients).Results. We showed that the best predictor of HAI risk is Adjusted Rage Rank on paths (r= 0.42, p < 0.01). However, surprisingly good predictive power in risk assessment was found in the betweenness centrality of the underlying network of contacts (r = 0.30, p < 0.01).Conclusion: We conclude that epidemiology of a given pathogen in a given place and time could be explained only with the contact network only to a large extent. However, further possibility of the collection, processing and storage of the data on individual persons, translated to mathematical modelling could lead in future to satisfactory improvement in risk assessment.

Revealing mechanisms of infectious disease spread through empirical contact networks

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.

Analyses of Contact Networks of Community Dogs on a University Campus in Nakhon Pathom, Thailand

Free-roaming dogs have been identified as an important reservoir of rabies in many countries including Thailand. There is a need for novel insights to improve current rabies control strategies in these countries. Network analysis is commonly used to study the interactions between individuals or organizations and has been applied in preventive veterinary medicine. However, contact networks of domestic free-roaming dogs are mostly unexplored. The objective of this study was to explore the contact network of free-roaming dogs residing on a university campus. Three one-mode networks were created using co-appearances of dogs as edges. A two-mode network was created by associating the dog with the pre-defined area it was seen in. The average number of contacts a dog had was 6.74. The normalized degree for the weekend network was significantly higher compared to the weekday network. All one-mode networks displayed small-world network characteristics. Most dogs were observed in only one area. The average number of dogs which shared an area was 8.67. In this study, we demonstrated the potential of observational methods to create networks of contacts. The network information acquired can be further used in network modeling and designing targeted disease control programs.

Topology-Aware Optimisation of Vaccination Strategy for Minimising Virus Spreading

Vaccination is currently the primary way for mitigating the COVID-19 outbreak without severe lockdown. Despite its importance, the available number of vaccines worldwide is insufficient, and the production rates are hard to be increased in a short time. Therefore, vaccination needs to follow strict prioritization criteria. In this regard, almost all countries have prioritized similar classes of exposed workers: healthcare professionals and the elderly obtaining to maximize the survival of patients and years of life saved. Nevertheless, the virus is currently spreading at high rates, and any prioritization criterion so far adopted did not show to account for the topology of the contact networks. We consider that a network in which nodes are people while the edges represent their contacts may model the virus's spreading efficiently. In such a model, it is already known that spreading may be efficiently stopped by disconnecting the network, i.e., by vaccinating more central or relevant nodes, therefore, eliminating "bridge edges". Consequently, we introduce such a model and discuss the use of a topology-aware versus an age-based vaccination strategy.

Pruning Digital Contact Networks for Meso-scale Epidemic Surveillance Using Foursquare Data

With the recent boom in human sensing, the push to incorporate human mobility tracking with epidemic modeling highlights the lack of groundwork at the meso-scale (e.g., city-level) for both contact tracing and transmission dynamics. Although GPS data has been used to study city-level outbreaks, current approaches fail to capture the path of infection at the individual level. Consequently, in this paper, we extend the usefulness of epidemics prediction from estimating the size of an outbreak at the population level to estimating the individuals who may likely get infected within a finite period of time. To this end, we propose a network-based method to first build and then prune the dynamic contact networks for recurring interactions; these networks can serve as the backbone topology for mechanistic epidemics modeling. We test our method using Foursquare’s Points of Interest (POI) smart-phone geolocation data from over 1.3 million devices and show that we can recreate the COVID-19 infection curves for two major (yet very different) US cities (i.e., Austin and New York City) while maintaining the granularity of individual transmissions and reducing model uncertainty. Our method provides a foundation for building a disease prediction framework at the meso-scale that can help both policy makers and individuals of their estimated state of health and help with pandemic planning.

Predicting hospital-onset COVID-19 infections using dynamic networks of patient contacts: an observational study

ABSTRACTBackgroundReal-time prediction is key to prevention and control of healthcare-associated infections. Contacts between individuals drive infections, yet most prediction frameworks fail to capture the dynamics of contact. We develop a real-time machine learning framework that incorporates dynamic patient contact networks to predict patient-level hospital-onset COVID-19 infections (HOCIs), which we test and validate on international multi-site datasets spanning epidemic and endemic periods.MethodsOur framework extracts dynamic contact networks from routinely collected hospital data and combines them with patient clinical attributes and background contextual hospital data to forecast the infection status of individual patients. We train and test the HOCI prediction framework using 51,157 hospital patients admitted to a UK (London) National Health Service (NHS) Trust from 01 April 2020 to 01 April 2021, spanning UK COVID-19 surges 1 and 2. We then validate the framework by applying it to data from a non-UK (Geneva) hospital site during an epidemic surge (40,057 total inpatients) and to data from the same London Trust from a subsequent period post surge 2, when COVID-19 had become endemic (43,375 total inpatients).FindingsBased on the training data (London data spanning surges 1 and 2), the framework achieved high predictive performance using all variables (AUC-ROC 0·89 [0·88-0·90]) but was almost as predictive using only contact network variables (AUC-ROC 0·88 [0·86-0·90]), and more so than using only hospital contextual (AUC-ROC 0·82 [0·80-0·84]) or patient clinical (AUC-ROC 0·64 [0·62-0·66]) variables. The top three risk factors we identified consisted of one hospital contextual variable (background hospital COVID-19 prevalence) and two contact network variables (network closeness, and number of direct contacts to infectious patients), and together achieved AUC-ROC 0·85 [0·82-0·88]. Furthermore, the addition of contact network variables improved performance relative to hospital contextual variables on both the non-UK (AUC-ROC increased from 0·84 [0·82–0·86] to 0·88 [0·86–0·90]) and the UK validation datasets (AUC-ROC increased from 0·52 [0·49–0·53] to 0·68 [0·64-0·70]).InterpretationOur results suggest that dynamic patient contact networks can be a robust predictor of respiratory viral infections spreading in hospitals. Their integration in clinical care has the potential to enhance individualised infection prevention and early diagnosis.FundingMedical Research Foundation, World Health Organisation, Engineering and Physical Sciences Research Council, National Institute for Health Research, Swiss National Science Foundation, German Research Foundation.

Targeted pandemic containment through identifying local contact network bottlenecks

Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts–between individuals or between population centres–are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods.