Search

Abstract Background Marburg virus disease is an acute haemorrhagic fever caused by Marburg virus. Marburg virus is zoonotic, maintained in nature in Egyptian fruit bats, with occasional spillover infections into humans and nonhuman primates. Although rare, sporadic cases and outbreaks occur in Africa, usually associated with exposure to bats in mines or caves, and sometimes with secondary human-to-human transmission. Outbreaks outside of Africa have also occurred due to importation of infected monkeys. Although all previous Marburg virus disease outbreaks have been brought under control without vaccination, there is nevertheless the potential for large outbreaks when implementation of public health measures is not possible or breaks down. Vaccines could thus be an important additional tool, and development of several candidate vaccines is under way. Methods We developed a branching process model of Marburg virus transmission and investigated the potential effects of several prophylactic and reactive vaccination strategies in settings driven primarily by multiple spillover events as well as human-to-human transmission. Linelist data from the 15 outbreaks up until 2022, as well as an Approximate Bayesian Computational framework, were used to inform the model parameters. Results Our results show a low basic reproduction number which varied across outbreaks, from 0.5 [95% CI 0.05–1.8] to 1.2 [95% CI 1.0–1.9] but a high case fatality ratio. Of six vaccination strategies explored, the two prophylactic strategies (mass and targeted vaccination of high-risk groups), as well as a combination of ring and targeted vaccination, were generally most effective, with a probability of potential outbreaks being terminated within 1 year of 0.90 (95% CI 0.90–0.91), 0.89 (95% CI 0.88–0.90), and 0.88 (95% CI 0.87–0.89) compared with 0.68 (0.67–0.69) for no vaccination, especially if the outbreak is driven by zoonotic spillovers and the vaccination campaign initiated as soon as possible after onset of the first case. Conclusions Our study shows that various vaccination strategies can be effective in helping to control outbreaks of MVD, with the best approach varying with the particular epidemiologic circumstances of each outbreak.

Whilst it is now widely recognised that routine immunisation (RI) was disrupted by the COVID-19 pandemic in 2020, and further so in 2021, the extent of continued interruptions in 2022 and/or rebounds to previous trends remains unclear.We modelled country-specific RI trends using validated estimates of national coverage from the World Health Organisation and United Nation Children’s Fund for 182 countries (accounting for > 97% of children globally), to project expected diphtheria, tetanus, and pertussis-containing vaccine first-dose (DTP1), third-dose (DTP3) and measles-containing vaccine first-dose (MCV1) coverage for 2020–2022 based on pre-pandemic trends (from 2000 to 2019).We provide further evidence of peak pandemic immunisation disruption in 2021, followed by tentative recovery in 2022. We report a 3.4% (95 %CI: [2.5%; 4.4%]) decline in global DTP3 coverage in 2021 compared to 2000–2019 trends, from an expected 89.8% to reported 86.4%. This coverage gap reduced to a 2.7% (95 %CI: [1.8%; 3.6%]) decline in 2022, with reported coverage rising to 87.2%. Similar results were seen for DTP1 and MCV1. Whilst partial rebounds are encouraging, global coverage decline translates to a 17-year setback in RI to 2005 levels, and the majority of countries retain coverage at or lower than pre-pandemic levels. The Americas, Africa, and Asia were the most impacted regions; and low- and middle-income countries the most affected income groups.The number of annual Zero Dose (ZD) children – indicating those receiving no immunisations – increased from 12.1 million (M) globally in 2019 to a peak of 16.7 M in 2021, then reduced to 13.1 M in 2022. Overall, we estimate an excess of 8.8 M ZD children cumulatively in 2020–2022 compared to pre-pandemic levels.This work can be used as an objective baseline to inform future interventions to prioritise and target interventions, and facilitate catch-up of growing populations of under- and un-immunised children.

The 10th Ebola virus disease (EVD) outbreak in the Democratic Republic of the Congo (DRC) drew substantial attention from the international community, which in turn invested more than US$1 billion in EVD control over two years (2018–2020). This is the first EVD outbreak to take place in a conflict area, which led to a shift in strategy from a pure public health response (PHR) to a multisectoral humanitarian response. A wide range of disease control and mitigation activities were implemented and were outlined in the five budgeted Strategic Response Plans used throughout the 26 months. This study used the budget/expenditure and output indicators for disease control and mitigation interventions compiled by the government of DRC and development and humanitarian partners to estimate unit costs of key Ebola control interventions. Of all the investment in EVD control, 68% was spent on PHR. The remaining 32% covered security, community support interventions for the PHR. The disbursement for the public health pillar was distributed as follows: (1) coordination (18.8%), (2), clinical management of EVD cases (18.4%), (3) surveillance and vaccination (15.9%), (4) infection prevention and control/WASH (13.8%) and (5) risk communication (13.7%). The unit costs of key EVD control interventions were as follows: US$66 182 for maintaining a rapid response team per month, US$4435 for contact tracing and surveillance per identified EVD case, US$1464 for EVD treatment per case, US$59.4 per EVD laboratory test, US$120.7 per vaccinated individual against EVD and US$175.0 for mental health and psychosocial support per beneficiary. The estimated unit costs of key EVD disease control interventions provide crucial information for future infectious disease control planning and budgeting, as well as prioritisation of disease control interventions.

Background: The serial interval is a key epidemiological measure that quantifies the time between the onset of symptoms in an infector-infectee pair. It indicates how quickly new generations of cases appear, thus informing on the speed of an epidemic. Estimating the serial interval requires to identify pairs of infectors and infectees. Yet, most studies fail to assess the direction of transmission between cases and assume that the order of infections - and thus transmissions - strictly follows the order of symptom onsets, thereby imposing serial intervals to be positive. Because of the long and highly variable incubation period of SARS-CoV-2, this may not always be true (i.e an infectee may show symptoms before their infector) and negative serial intervals may occur. This study aims to estimate the serial interval of different SARS-CoV-2 variants whilst accounting for negative serial intervals. Methods: This analysis included 5 842 symptomatic individuals with confirmed SARS-CoV-2 infection amongst 2 579 households from September 2020 to August 2022 across England & Wales. We used a Bayesian framework to infer who infected whom by exploring all transmission trees compatible with the observed dates of symptoms, based on a wide range of incubation period and generation time distributions compatible with estimates reported in the literature. Serial intervals were derived from the reconstructed transmission pairs, stratified by variants. Results: We estimated that 22% (95% credible interval (CrI) 8–32%) of serial interval values are negative across all VOC. The mean serial interval was shortest for Omicron BA5 (2.02 days, 1.26–2.84) and longest for Alpha (3.37 days, 2.52–4.04). Conclusions: This study highlights the large proportion of negative serial intervals across SARS-CoV-2 variants. Because the serial interval is widely used to estimate transmissibility and forecast cases, these results may have critical implications for epidemic control.

Background: Outbreaks of Marburg virus disease (MVD) are rare and small in size, with only 18 recorded outbreaks since 1967, only two of which involved more than 100 cases. It has been proposed, therefore, that Phase 3 trials for MVD vaccines should be held open over multiple outbreaks until sufficient end points accrue to enable vaccine efficacy (VE) to be calculated. Here we estimate how many outbreaks might be needed for VE to be estimated. Methods: We adapt a mathematical model of MVD transmission to simulate a Phase 3 individually randomised placebo controlled vaccine trial. We assume in the base case that vaccine efficacy is 70% and that 50% of individuals in affected areas are enrolled into the trial (1:1 randomisation). We further assume that the vaccine trial starts two weeks after public health interventions are put in place and that cases occurring within 10 days of vaccination are not included in VE calculations. Results: The median size of simulated outbreaks was 2 cases. Only 0.3% of simulated outbreaks were predicted to have more than 100 MVD cases. 95% of simulated outbreaks terminated before cases accrued in the placebo and vaccine arms. Therefore the number of outbreaks required to estimate VE was large: after 100 outbreaks, the estimated VE was 69% but with considerable uncertainty (95% CIs: 0%−100%) while the estimated VE after 200 outbreaks was 67% (95% CIs: 42%−85%). Altering base-case assumptions made little difference to the findings. In a sensitivity analysis, increasing R0 by 25% and 50% led to an estimated VE after 200 outbreaks of 69% (95% CIs: 53–85%) and 70% (95% CIs: 59–82%), respectively. Conclusions: It is unlikely that the efficacy of any candidate vaccine can be calculated before more MVD outbreaks have occurred than have been recorded to date. This is because MVD outbreaks tend to be small, public health interventions have been historically effective at reducing transmission, and vaccine trials are only likely to start after these interventions are already in place. Hence, it is expected that outbreaks will terminate before, or shortly after, cases start to accrue in the vaccine and placebo arms.

SARS-CoV-2 has resulted in multiple outbreaks in hospitals, but identifying transmission events is challenging. Here, the authors combine whole genome sequencing and epidemiological data from the first two waves of the pandemic at a UK hospital trust and characterise transmission chains.

Abstract The National Health Service (NHS) Pathways triage system collates data on enquiries to 111 and 999 services in England. Since the 18th of March 2020, these data have been made publically available for potential COVID-19 symptoms self-reported by members of the public. Trends in such reports over time are likely to reflect behaviour of the ongoing epidemic within the wider community, potentially capturing valuable information across a broader severity profile of cases than hospital admission data. We present a fully reproducible analysis of temporal trends in NHS Pathways reports until 14th May 2020, nationally and regionally, and demonstrate that rates of growth/decline and effective reproduction number estimated from these data may be useful in monitoring transmission. This is a particularly pressing issue as lockdown restrictions begin to be lifted and evidence of disease resurgence must be constantly reassessed. We further assess the correlation between NHS Pathways reports and a publicly available NHS dataset of COVID-19-associated deaths in England, finding that enquiries to 111/999 were strongly associated with daily deaths reported 16 days later. Our results highlight the potential of NHS Pathways as the basis of an early warning system. However, this dataset relies on self-reported symptoms, which are at risk of being severely biased. Further detailed work is therefore necessary to investigate potential behavioural issues which might otherwise explain our conclusions.

Abstract Background The COVID-19 pandemic has placed an unprecedented strain on health systems, with rapidly increasing demand for healthcare in hospitals and intensive care units (ICUs) worldwide. As the pandemic escalates, determining the resulting needs for healthcare resources (beds, staff, equipment) has become a key priority for many countries. Projecting future demand requires estimates of how long patients with COVID-19 need different levels of hospital care. Methods We performed a systematic review of early evidence on length of stay (LoS) of patients with COVID-19 in hospital and in ICU. We subsequently developed a method to generate LoS distributions which combines summary statistics reported in multiple studies, accounting for differences in sample sizes. Applying this approach, we provide distributions for total hospital and ICU LoS from studies in China and elsewhere, for use by the community. Results We identified 52 studies, the majority from China (46/52). Median hospital LoS ranged from 4 to 53 days within China, and 4 to 21 days outside of China, across 45 studies. ICU LoS was reported by eight studies—four each within and outside China—with median values ranging from 6 to 12 and 4 to 19 days, respectively. Our summary distributions have a median hospital LoS of 14 (IQR 10–19) days for China, compared with 5 (IQR 3–9) days outside of China. For ICU, the summary distributions are more similar (median (IQR) of 8 (5–13) days for China and 7 (4–11) days outside of China). There was a visible difference by discharge status, with patients who were discharged alive having longer LoS than those who died during their admission, but no trend associated with study date. Conclusion Patients with COVID-19 in China appeared to remain in hospital for longer than elsewhere. This may be explained by differences in criteria for admission and discharge between countries, and different timing within the pandemic. In the absence of local data, the combined summary LoS distributions provided here can be used to model bed demands for contingency planning and then updated, with the novel method presented here, as more studies with aggregated statistics emerge outside China.

The emerging field of outbreak analytics calls attention to the need for data from multiple sources to inform evidence-based decision making in managing infectious diseases outbreaks. To date, these approaches have not systematically integrated evidence from social and behavioural sciences. During the 2018–2020 Ebola outbreak in Eastern Democratic Republic of the Congo, an innovative solution to systematic and timely generation of integrated and actionable social science evidence emerged in the form of the Cellulle d’Analyse en Sciences Sociales (Social Sciences Analytics Cell) (CASS), a social science analytical cell. CASS worked closely with data scientists and epidemiologists operating under the Epidemiological Cell to produce integrated outbreak analytics (IOA), where quantitative epidemiological analyses were complemented by behavioural field studies and social science analyses to help better explain and understand drivers and barriers to outbreak dynamics. The primary activity of the CASS was to conduct operational social science analyses that were useful to decision makers. This included ensuring that research questions were relevant, driven by epidemiological data from the field, that research could be conducted rapidly (ie, often within days), that findings were regularly and systematically presented to partners and that recommendations were co-developed with response actors. The implementation of the recommendations based on CASS analytics was also monitored over time, to measure their impact on response operations. This practice paper presents the CASS logic model, developed through a field-based externally led consultation, and documents key factors contributing to the usefulness and adaption of CASS and IOA to guide replication for future outbreaks.

Summary: Background: In most countries, contacts of confirmed COVID-19 cases are asked to quarantine for 14 days after exposure to limit asymptomatic onward transmission. While theoretically effective, this policy places a substantial social and economic burden on both the individual and wider society, which might result in low adherence and reduced policy effectiveness. We aimed to assess the merit of testing contacts to avert onward transmission and to replace or reduce the length of quarantine for uninfected contacts. Methods: We used an agent-based model to simulate the viral load dynamics of exposed contacts, and their potential for onward transmission in different quarantine and testing strategies. We compared the performance of quarantines of differing durations, testing with either PCR or lateral flow antigen (LFA) tests at the end of quarantine, and daily LFA testing without quarantine, against the current 14-day quarantine strategy. We also investigated the effect of contact tracing delays and adherence to both quarantine and self-isolation on the effectiveness of each strategy. Findings: Assuming moderate levels of adherence to quarantine and self-isolation, self-isolation on symptom onset alone can prevent 37% (95% uncertainty interval [UI] 12–56) of onward transmission potential from secondary cases. 14 days of post-exposure quarantine reduces transmission by 59% (95% UI 28–79). Quarantine with release after a negative PCR test 7 days after exposure might avert a similar proportion (54%, 95% UI 31–81; risk ratio [RR] 0·94, 95% UI 0·62–1·24) to that of the 14-day quarantine period, as would quarantine with a negative LFA test 7 days after exposure (50%, 95% UI 28–77; RR 0·88, 0·66–1·11) or daily testing without quarantine for 5 days after tracing (50%, 95% UI 23–81; RR 0·88, 0·60–1·43) if all tests are returned negative. A stronger effect might be possible if individuals isolate more strictly after a positive test and if contacts can be notified faster. Interpretation: Testing might allow for a substantial reduction in the length of, or replacement of, quarantine with a small excess in transmission risk. Decreasing test and trace delays and increasing adherence will further increase the effectiveness of these strategies. Further research is required to empirically evaluate the potential costs (increased transmission risk, false reassurance) and benefits (reduction in the burden of quarantine, increased adherence) of such strategies before adoption as policy. Funding: National Institute for Health Research, UK Research and Innovation, Wellcome Trust, EU Horizon 2021, and the Bill & Melinda Gates Foundation.

Summary: Background: The risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk. Methods: We estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies. Findings: We estimated that 1·7 billion (UI 1·0–2·4) people, comprising 22% (UI 15–28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from 66% of those aged 70 years or older). We estimated that 349 million (186–787) people (4% [3–9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from

Summary: Background: Non-pharmaceutical interventions have been implemented to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the UK. Projecting the size of an unmitigated epidemic and the potential effect of different control measures has been crucial to support evidence-based policy making during the early stages of the epidemic. This study assesses the potential impact of different control measures for mitigating the burden of COVID-19 in the UK. Methods: We used a stochastic age-structured transmission model to explore a range of intervention scenarios, tracking 66·4 million people aggregated to 186 county-level administrative units in England, Wales, Scotland, and Northern Ireland. The four base interventions modelled were school closures, physical distancing, shielding of people aged 70 years or older, and self-isolation of symptomatic cases. We also modelled the combination of these interventions, as well as a programme of intensive interventions with phased lockdown-type restrictions that substantially limited contacts outside of the home for repeated periods. We simulated different triggers for the introduction of interventions, and estimated the impact of varying adherence to interventions across counties. For each scenario, we projected estimated new cases over time, patients requiring inpatient and critical care (ie, admission to the intensive care units [ICU]) treatment, and deaths, and compared the effect of each intervention on the basic reproduction number, R0. Findings: We projected a median unmitigated burden of 23 million (95% prediction interval 13–30) clinical cases and 350 000 deaths (170 000–480 000) due to COVID-19 in the UK by December, 2021. We found that the four base interventions were each likely to decrease R0, but not sufficiently to prevent ICU demand from exceeding health service capacity. The combined intervention was more effective at reducing R0, but only lockdown periods were sufficient to bring R0 near or below 1; the most stringent lockdown scenario resulted in a projected 120 000 cases (46 000–700 000) and 50 000 deaths (9300–160 000). Intensive interventions with lockdown periods would need to be in place for a large proportion of the coming year to prevent health-care demand exceeding availability. Interpretation: The characteristics of SARS-CoV-2 mean that extreme measures are probably required to bring the epidemic under control and to prevent very large numbers of deaths and an excess of demand on hospital beds, especially those in ICUs. Funding: Medical Research Council.

We estimate the number of COVID-19 cases from newly reported deaths in a population without previous reports. Our results suggest that by the time a single death occurs, hundreds to thousands of cases are likely to be present in that population. This suggests containment via contact tracing will be challenging at this point, and other response strategies should be considered. Our approach is implemented in a publicly available, user-friendly, online tool.

Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola epidemic in West Africa, a forecasting exercise was convened by a network of infectious disease modellers. The challenge was to forecast unseen “future” simulated data for four different scenarios at five different time points. In a similar method to that used during the recent Ebola epidemic, we estimated current levels of transmissibility, over variable time-windows chosen in an ad hoc way. Current estimated transmissibility was then used to forecast near-future incidence. We performed well within the challenge and often produced accurate forecasts. A retrospective analysis showed that our subjective method for deciding on the window of time with which to estimate transmissibility often resulted in the optimal choice. However, when near-future trends deviated substantially from exponential patterns, the accuracy of our forecasts was reduced. This exercise highlights the urgent need for infectious disease modellers to develop more robust descriptions of processes – other than the widespread depletion of susceptible individuals – that produce non-exponential patterns of incidence. Keywords: Forecasting, Rapid response, Branching process, Renewal equation, MCMC

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease. epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease. epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

The epidemiological curve (epicurve) is one of the simplest yet most useful tools used by field epidemiologists, modellers, and decision makers for assessing the dynamics of infectious disease epidemics. Here, we present the free, open-source package incidence for the R programming language, which allows users to easily compute, handle, and visualise epicurves from unaggregated linelist data. This package was built in accordance with the development guidelines of the R Epidemics Consortium (RECON), which aim to ensure robustness and reliability through extensive automated testing, documentation, and good coding practices. As such, it fills an important gap in the toolbox for outbreak analytics using the R software, and provides a solid building block for further developments in infectious disease modelling. incidence is available from https://www.repidemicsconsortium.org/incidence.

ABSTRACT The emergence of azole resistance in the pathogenic fungus Aspergillus fumigatus has continued to increase, with the dominant resistance mechanisms, consisting of a 34-nucleotide tandem repeat (TR34)/L98H and TR46/Y121F/T289A, now showing a structured global distribution. Using hierarchical clustering and multivariate analysis of 4,049 A. fumigatus isolates collected worldwide and genotyped at nine microsatellite loci using analysis of short tandem repeats of A. fumigatus (STRAf), we show that A. fumigatus can be subdivided into two broad clades and that cyp51A alleles TR34/L98H and TR46/Y121F/T289A are unevenly distributed across these two populations. Diversity indices show that azole-resistant isolates are genetically depauperate compared to their wild-type counterparts, compatible with selective sweeps accompanying the selection of beneficial mutations. Strikingly, we found that azole-resistant clones with identical microsatellite profiles were globally distributed and sourced from both clinical and environmental locations, confirming that azole resistance is an international public health concern. Our work provides a framework for the analysis of A. fumigatus isolates based on their microsatellite profile, which we have incorporated into a freely available, user-friendly R Shiny application (AfumID) that provides clinicians and researchers with a method for the fast, automated characterization of A. fumigatus genetic relatedness. Our study highlights the effect that azole drug resistance is having on the genetic diversity of A. fumigatus and emphasizes its global importance upon this medically important pathogenic fungus. IMPORTANCE Azole drug resistance in the human-pathogenic fungus Aspergillus fumigatus continues to emerge, potentially leading to untreatable aspergillosis in immunosuppressed hosts. Two dominant, environmentally associated resistance mechanisms, which are thought to have evolved through selection by the agricultural application of azole fungicides, are now distributed globally. Understanding the effect that azole resistance is having on the genetic diversity and global population of A. fumigatus will help mitigate drug-resistant aspergillosis and maintain the azole class of fungicides for future use in both medicine and crop protection.

Epidemiological outbreak data is often captured in line list and contact format to facilitate contact tracing for outbreak control. epicontacts is an R package that provides a unique data structure for combining these data into a single object in order to facilitate more efficient visualisation and analysis. The package incorporates interactive visualisation functionality as well as network analysis techniques. Originally developed as part of the Hackout3 event, it is now developed, maintained and featured as part of the R Epidemics Consortium (RECON). The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub.

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease. epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

Epidemiological outbreak data is often captured in line list and contact format to facilitate contact tracing for outbreak control. epicontacts is an R package that provides a unique data structure for combining these data into a single object in order to facilitate more efficient visualisation and analysis. The package incorporates interactive visualisation functionality as well as network analysis techniques. Originally developed as part of the Hackout3 event, it is now developed, maintained and featured as part of the R Epidemics Consortium (RECON). The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub.

Epidemiology relies on data but the divergent ways data are recorded and transferred, both within and between outbreaks, and the expanding range of data-types are creating an increasingly complex problem for the discipline. There is a need for a consistent, interpretable and precise way to transfer data while maintaining its fidelity. We introduce ‘EpiJSON’, a new, flexible, and standards-compliant format for the interchange of epidemiological data using JavaScript Object Notation. This format is designed to enable the widest range of epidemiological data to be unambiguously held and transferred between people, software and institutions. In this paper, we provide a full description of the format and a discussion of the design decisions made. We introduce a schema enabling automatic checks of the validity of data stored as EpiJSON, which can serve as a basis for the development of additional tools. In addition, we also present the R package ‘repijson’ which provides conversion tools between this format, line-list data and pre-existing analysis tools. An example is given to illustrate how EpiJSON can be used to store line list data. EpiJSON, designed around modern standards for interchange of information on the internet, is simple to implement, read and check. As such, it provides an ideal new standard for epidemiological, and other, data transfer to the fast-growing open-source platform for the analysis of disease outbreaks.

Recent years have seen the development of numerous methodologies for reconstructing transmission trees in infectious disease outbreaks from densely sampled whole genome sequence data. However, a fundamental and as of yet poorly addressed limitation of such approaches is the requirement for genetic diversity to arise on epidemiological timescales. Specifically, the position of infected individuals in a transmission tree can only be resolved by genetic data if mutations have accumulated between the sampled pathogen genomes. To quantify and compare the useful genetic diversity expected from genetic data in different pathogen outbreaks, we introduce here the concept of 'transmission divergence', defined as the number of mutations separating whole genome sequences sampled from transmission pairs. Using parameter values obtained by literature review, we simulate outbreak scenarios alongside sequence evolution using two models described in the literature to describe transmission divergence of ten major outbreak-causing pathogens. We find that while mean values vary significantly between the pathogens considered, their transmission divergence is generally very low, with many outbreaks characterised by large numbers of genetically identical transmission pairs. We describe the impact of transmission divergence on our ability to reconstruct outbreaks using two outbreak reconstruction tools, the R packages outbreaker and phybreak, and demonstrate that, in agreement with previous observations, genetic sequence data of rapidly evolving pathogens such as RNA viruses can provide valuable information on individual transmission events. Conversely, sequence data of pathogens with lower mean transmission divergence, including Streptococcus pneumoniae, Shigella sonnei and Clostridium difficile, provide little to no information about individual transmission events. Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios, and demonstrate the need to expand the toolkit of outbreak reconstruction tools to integrate other types of epidemiological data.

Abstract Multidrug‐resistant (MDR) Klebsiella pneumoniae has become a leading cause of nosocomial infections worldwide. Despite its prominence, little is known about the genetic diversity of K. pneumoniae in resource‐poor hospital settings. Through whole‐genome sequencing (WGS), we reconstructed an outbreak of MDR K. pneumoniae occurring on high‐dependency wards in a hospital in Kathmandu during 2012 with a case‐fatality rate of 75%. The WGS analysis permitted the identification of two MDR K. pneumoniae lineages causing distinct outbreaks within the complex endemic K. pneumoniae. Using phylogenetic reconstruction and lineage‐specific PCR, our data predicted a scenario in which K. pneumoniae, circulating for 6 months before the outbreak, underwent a series of ward‐specific clonal expansions after the acquisition of genes facilitating virulence and MDR. We suggest that the early detection of a specific NDM‐1 containing lineage in 2011 would have alerted the high‐dependency ward staff to intervene. We argue that some form of real‐time genetic characterisation, alongside clade‐specific PCR during an outbreak, should be factored into future healthcare infection control practices in both high‐ and low‐income settings.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

BackgroundThe ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved.Methods and findingsOver 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola ("cases") were asked if they had exposure to other potential Ebola cases ("potential source contacts") in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO's response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = -0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible misclassifications).ConclusionsAchieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population.

One of the UN sustainable development goals is to achieve universal access to safe and affordable drinking water by 2030. It is locations like Kathmandu, Nepal, a densely populated city in South Asia with endemic typhoid fever, where this goal is most pertinent. Aiming to understand the public health implications of water quality in Kathmandu we subjected weekly water samples from 10 sources for one year to a range of chemical and bacteriological analyses. We additionally aimed to detect the etiological agents of typhoid fever and longitudinally assess microbial diversity by 16S rRNA gene surveying. We found that the majority of water sources exhibited chemical and bacterial contamination exceeding WHO guidelines. Further analysis of the chemical and bacterial data indicated site-specific pollution, symptomatic of highly localized fecal contamination. Rainfall was found to be a key driver of this fecal contamination, correlating with nitrates and evidence of S. Typhi and S. Paratyphi A, for which DNA was detectable in 333 (77%) and 303 (70%) of 432 water samples, respectively. 16S rRNA gene surveying outlined a spectrum of fecal bacteria in the contaminated water, forming complex communities again displaying location-specific temporal signatures. Our data signify that the municipal water in Kathmandu is a predominant vehicle for the transmission of S. Typhi and S. Paratyphi A. This study represents the first extensive spatiotemporal investigation of water pollution in an endemic typhoid fever setting and implicates highly localized human waste as the major contributor to poor water quality in the Kathmandu Valley.