Forecasting local hospital bed demand for COVID-19 using on-request simulations

For hospitals, realistic forecasting of bed demand during impending epidemics of infectious diseases is essential to avoid being overwhelmed by a potential sudden increase in the number of admitted patients. Short-term forecasting can aid hospitals in adjusting their planning and freeing up beds in time. We created an easy-to-use online on-request tool based on local data to forecast COVID-19 bed demand for individual hospitals. The tool is flexible and adaptable to different settings. It is based on a stochastic compartmental model for estimating the epidemic dynamics and coupled with an exponential smoothing model for forecasting. The models are written in R and Julia and implemented as an R-shiny dashboard. The model is parameterized using COVID-19 incidence, vaccination, and bed occupancy data at customizable geographical resolutions, loaded from official online sources or uploaded manually. Users can select their hospital's catchment area and adjust the number of COVID-19 occupied beds at the start of the simulation. The tool provides short-term forecasts of disease incidence and past and forecasted estimation of the epidemic reproductive number at the chosen geographical level. These quantities are then used to estimate the bed occupancy in both general wards and intensive care unit beds. The platform has proven efficient, providing results within seconds while coping with many concurrent users. By providing ad-hoc, local data informed forecasts, this platform allows decision-makers to evaluate realistic scenarios for allocating scarce resources, such as ICU beds, at various geographic levels.Comment: 23 pages, 3 figures. Code available at https://github.com/QUPI-IUK/Bed-demand-forecas

Efficient surveillance for healthcare-associated infections spreading between hospitals

Early detection of new or novel variants of nosocomial pathogens is a public health priority. We show that, for healthcare-associated infections that spread between hospitals as a result of patient movements, it is possible to design an effective surveillance system based on a relatively small number of sentinel hospitals. We apply recently developed mathematical models to patient admission data from the national healthcare systems of England and The Netherlands. Relatively short detection times are achieved once 10-20% hospitals are recruited as sentinels and only modest reductions are seen as more hospitals are recruited thereafter. Using a heuristic optimization approach to sentinel selection, the same expected time to detection can be achieved by recruiting approximately half as many hospitals. Our study provides a robust evidence base to underpin the design of an efficient sentinel hospital surveillance system for novel nosocomial pathogens, delivering early detection times for reduced expenditure and effort

Probing empirical contact networks by simulation of spreading dynamics

Disease, opinions, ideas, gossip, etc. all spread on social networks. How these networks are connected (the network structure) influences the dynamics of the spreading processes. By investigating these relationships one gains understanding both of the spreading itself and the structure and function of the contact network. In this chapter, we will summarize the recent literature using simulation of spreading processes on top of empirical contact data. We will mostly focus on disease simulations on temporal proximity networks -- networks recording who is close to whom, at what time -- but also cover other types of networks and spreading processes. We analyze 29 empirical networks to illustrate the methods

Carbapenemase-producing enterobacterales colonisation status does not lead to more frequent admissions: a linked patient study

Background: Hospitals in any given region can be considered as part of a network, where facilities are connected to one another – and hospital pathogens potentially spread – through the movement of patients between them. We sought to describe the hospital admission patterns of patients known to be colonised with carbapenemase-producing Enterobacterales (CPE), and compare them with CPE-negative patient cohorts, matched on comorbidity information. Methods: We performed a linkage study in Victoria, Australia, including datasets with notifiable diseases (CPE notifications) and hospital admissions (admission dates and diagnostic codes) for the period 2011 to 2020. Where the CPE notification date occurred during a hospital admission for the same patient, we identified this as the ‘index admission’. We determined the number of distinct health services each patient was admitted to, and time to first admission to a different health service. We compared CPE-positive patients with four cohorts of CPE-negative patients, sampled based on different matching criteria. Results: Of 528 unique patients who had CPE detected during a hospital admission, 222 (42%) were subsequently admitted to a different health service during the study period. Among these patients, CPE diagnosis tended to occur during admission to a metropolitan public hospital (86%, 190/222), whereas there was a greater number of metropolitan private (23%, 52/222) and rural public (18%, 39/222) hospitals for the subsequent admission. Median time to next admission was 4 days (IQR, 0–75 days). Admission patterns for CPE-positive patients was similar to the cohort of CPE-negative patients matched on index admission, time period, and age-adjusted Charlson comorbidity index. Conclusions: Movement of CPE-positive patients between health services is not a rare event. While the most common movement is from one public metropolitan health service to another, there is also a trend for movement from metropolitan public hospitals into private and rural hospitals. After accounting for clinical comorbidities, CPE colonisation status does not appear to impact on hospital admission frequency or timing. These findings support the potential utility of a centralised notification and outbreak management system for CPE positive patients

Building a genomic framework for prospective MRSA surveillance in the United Kingdom and the Republic of Ireland.

The correct interpretation of microbial sequencing data applied to surveillance and outbreak investigation depends on accessible genomic databases to provide vital genetic context. Our aim was to construct and describe a United Kingdom MRSA database containing over 1000 methicillin-resistant Staphylococcus aureus (MRSA) genomes drawn from England, Northern Ireland, Wales, Scotland, and the Republic of Ireland over a decade. We sequenced 1013 MRSA submitted to the British Society for Antimicrobial Chemotherapy by 46 laboratories between 2001 and 2010. Each isolate was assigned to a regional healthcare referral network in England and was otherwise grouped based on country of origin. Phylogenetic reconstructions were used to contextualize MRSA outbreak investigations and to detect the spread of resistance. The majority of isolates (n = 783, 77%) belonged to CC22, which contains the dominant United Kingdom epidemic clone (EMRSA-15). There was marked geographic structuring of EMRSA-15, consistent with widespread dissemination prior to the sampling decade followed by local diversification. The addition of MRSA genomes from two outbreaks and one pseudo-outbreak demonstrated the certainty with which outbreaks could be confirmed or refuted. We identified local and regional differences in antibiotic resistance profiles, with examples of local expansion, as well as widespread circulation of mobile genetic elements across the bacterial population. We have generated a resource for the future surveillance and outbreak investigation of MRSA in the United Kingdom and Ireland and have shown the value of this during outbreak investigation and tracking of antimicrobial resistance.We are grateful for assistance from the library construction, sequencing and core informatics teams at the Wellcome Trust Sanger Institute. We acknowledge David Harris and Martin Aslett for their help in submitting the sequenced isolates to public databases. The study was supported by grants from the UKCRC Translational Infection Research Initiative, and the Medical Research Council (Grant Number G1000803) with contributions to the Grant from the Biotechnology and Biological Sciences Research Council, the National Institute for Health Research on behalf of the Department of Health, and the Chief Scientist Office of the Scottish Government Health Directorate (to Prof. Peacock); by Wellcome Trust grant number 098051 awarded to the Wellcome Trust Sanger Institute; and by a Healthcare Infection Society Major Reasearch Grant. MET is a Clinician Scientist Fellow, supported by the Academy of Medical Sciences and the Health Foundation and the NIHR Cambridge Biomedical Research Centre. BGS was supported by Wellcome Trust grant number 089472. The study was approved by the University of Cambridge Human Biology Research Ethics Committee (reference HBREC.2013.05), and by the Cambridge University Hospitals NHS Foundation Trust Research and Development Department (reference A092869). Isolates were supplied by the BSAC Resistance Surveillance Project.This is the final version of the article. It first appeared from Cold Spring Harbor Laboratory Press via http://dx.doi.org/10.1101/gr.196709.11

Patient Referral Patterns and the Spread of Hospital-Acquired Infections through National Health Care Networks

Rates of hospital-acquired infections, such as methicillin-resistant Staphylococcus aureus (MRSA), are increasingly used as quality indicators for hospital hygiene. Alternatively, these rates may vary between hospitals, because hospitals differ in admission and referral of potentially colonized patients. We assessed if different referral patterns between hospitals in health care networks can influence rates of hospital-acquired infections like MRSA. We used the Dutch medical registration of 2004 to measure the connectedness between hospitals. This allowed us to reconstruct the network of hospitals in the Netherlands. We used mathematical models to assess the effect of different patient referral patterns on the potential spread of hospital-acquired infections between hospitals, and between categories of hospitals (University medical centers, top clinical hospitals and general hospitals). University hospitals have a higher number of shared patients than teaching or general hospitals, and are therefore more likely to be among the first to receive colonized patients. Moreover, as the network is directional towards university hospitals, they have a higher prevalence, even when infection control measures are equally effective in all hospitals. Patient referral patterns have a profound effect on the spread of health care-associated infections like hospital-acquired MRSA. The MRSA prevalence therefore differs between hospitals with the position of each hospital within the health care network. Any comparison of MRSA rates between hospitals, as a benchmark for hospital hygiene, should therefore take the position of a hospital within the network into account

Building a genomic framework for prospective MRSA surveillance in the United Kingdom and the Republic of Ireland

The correct interpretation of microbial sequencing data applied to surveillance and outbreak investigation depends on accessible genomic databases to provide vital genetic context. Our aim was to construct and describe a United Kingdom MRSA database containing over 1000 methicillin-resistant Staphylococcus aureus (MRSA) genomes drawn from England, Northern Ireland, Wales, Scotland, and the Republic of Ireland over a decade. We sequenced 1013 MRSA submitted to the British Society for Antimicrobial Chemotherapy by 46 laboratories between 2001 and 2010. Each isolate was assigned to a regional healthcare referral network in England and was otherwise grouped based on country of origin. Phylogenetic reconstructions were used to contextualize MRSA outbreak investigations and to detect the spread of resistance. The majority of isolates (n = 783, 77%) belonged to CC22, which contains the dominant United Kingdom epidemic clone (EMRSA-15). There was marked geographic structuring of EMRSA-15, consistent with widespread dissemination prior to the sampling decade followed by local diversification. The addition ofMRSAgenomes fromtwo outbreaks and one pseudo-outbreak demonstrated the certainty with which outbreaks could be confirmed or refuted. Weidentified local and regional differences in antibiotic resistance profiles, with examples of local expansion, as well as widespread circulation of mobile genetic elements across the bacterial population. Wehave generated a resource for the future surveillance and outbreak investigation ofMRSAin the United Kingdom and Ireland and have shown the value of this during outbreak investigation and tracking of antimicrobial resistance.</p

Model-based evaluation of admission screening strategies for the detection and control of carbapenemase-producing Enterobacterales in the English hospital setting

BackgroundGlobally, detections of carbapenemase-producing Enterobacterales (CPE) colonisations and infections are increasing. The spread of these highly resistant bacteria poses a serious threat to public health. However, understanding of CPE transmission and evidence on effectiveness of control measures is severely lacking. This paper provides evidence to inform effective admission screening protocols, which could be important in controlling nosocomial CPE transmission.MethodsCPE transmission within an English hospital setting was simulated with a data-driven individual-based mathematical model. This model was used to evaluate the ability of the 2016 England CPE screening recommendations, and of potential alternative protocols, to identify patients with CPE-colonisation on admission (including those colonised during previous stays or from elsewhere). The model included nosocomial transmission from colonised and infected patients, as well as environmental contamination. Model parameters were estimated using primary data where possible, including estimation of transmission using detailed epidemiological data within a Bayesian framework. Separate models were parameterised to represent hospitals in English areas with low and high CPE risk (based on prevalence).ResultsThe proportion of truly colonised admissions which met the 2016 screening criteria was 43% in low-prevalence and 54% in high-prevalence areas respectively. Selection of CPE carriers for screening was improved in low-prevalence areas by adding readmission as a screening criterion, which doubled how many colonised admissions were selected. A minority of CPE carriers were confirmed as CPE positive during their hospital stay (10 and 14% in low- and high-prevalence areas); switching to a faster screening test pathway with a single-swab test (rather than three swab regimen) increased the overall positive predictive value with negligible reduction in negative predictive value.ConclusionsUsing a novel within-hospital CPE transmission model, this study assesses CPE admission screening protocols, across the range of CPE prevalence observed in England. It identifies protocol changes—adding readmissions to screening criteria and a single-swab test pathway—which could detect similar numbers of CPE carriers (or twice as many in low CPE prevalence areas), but faster, and hence with lower demand on pre-emptive infection-control resources. Study findings can inform interventions to control this emerging threat, although further work is required to understand within-hospital transmission sources

Nowcasting pandemic influenza A/H1N1 2009 hospitalizations in the Netherlands

During emerging epidemics of infectious diseases, it is vital to have up-to-date information on epidemic trends, such as incidence or health care demand, because hospitals and intensive care units have limited excess capacity. However, real-time tracking of epidemics is difficult, because of the inherent delay between onset of symptoms or hospitalizations, and reporting. We propose a robust algorithm to correct for reporting delays, using the observed distribution of reporting delays. We apply the algorithm to pandemic influenza A/H1N1 2009 hospitalizations as reported in the Netherlands. We show that the proposed algorithm is able to provide unbiased predictions of the actual number of hospitalizations in real-time during the ascent and descent of the epidemic. The real-time predictions of admissions are useful to adjust planning in hospitals to avoid exceeding their capacity