Biology subjects

Edmunds, W. J.

Publications and source records attributed to Edmunds, W. J..

8 recordsLinked to original sources

bioRxiv2018

Real-time analysis of the diphtheria outbreak in forcibly displaced Myanmar nationals in Bangladesh

BackgroundBetween August and December 2017, more than 625,000 Rohingya from Myanmar fled into Bangladesh, settling in informal makeshift camps in Coxs Bazar district, joining 212,000 Rohingya already present. In early November, a diphtheria outbreak was reported in the camps, with 440 cases being reported during the first month. A rise in cases during early December led to a collaboration between teams from Medecins sans Frontieres - who were running a provisional diphtheria treatment centre - and the London School of Hygiene & Tropical Medicine with the goal to use transmission dynamic models to forecast the potential scale of the outbreak and the resulting resource needs.\n\nMethodsWe first adjusted for delays between symptoms onset and case presentation using the observed distribution of reporting delays from previously reported cases. We then fit a compartmental transmission model to the adjusted incidence stratified by age-group and location. Model forecasts with a lead-time of two weeks were issued on 12th, 20th, 26th and 30th December and communicated to decision-makers.\n\nResultsThe first forecast estimated that the outbreak would peak on 16th December in Balukhali camp with 222 (95% prediction interval 126-409) cases and would continue to grow in Kutupalong camp, requiring a bed capacity of 200 (95% PI 142-301). On 16th December, a total of 70 cases were reported, lower than forecasted. Subsequent forecasts were more accurate: on 20th December we predicted a total of 701 cases (95% PI 477-901) and 105 (95% PI 72-135) hospitalizations until the end of the year, with 616 cases actually reported during this period.\n\nConclusionsReal-time modelling enabled feedback of key information about the potential scale of the epidemic, resource needs, and mechanisms of transmission to decision-makers at a time when this information was largely unknown. By December 20th, the model generated reliable forecasts and helped support decision-making on operational aspects of the outbreak response, such as hospital bed and staff needs, and with advocacy for control measures. Although modelling is only one component of the evidence base for decision-making in outbreak situations, suitable analysis and forecasting techniques can be used to gain insights into an ongoing outbreak.

epidemiology↗

bioRxiv2017

Target immunity levels for achieving and maintaining measles elimination

BackgroundVaccination has reduced the global incidence of measles to the lowest rates in history. However, local interruption of measles virus transmission requires sustained high levels of population immunity that can be challenging to achieve and maintain. The herd immunity threshold for measles is typically stipulated at 90-95%. This figure does not easily translate into age-specific immunity levels required to interrupt transmission. Previous estimates of such levels were based on speculative contact patterns based on historical data from high-income countries. The aim of this study was to determine age-specific immunity levels that would ensure elimination of measles when taking into account empirically observed contact patterns.\n\nMethodsWe combined estimated immunity levels from serological data in 17 countries with studies of age-specific mixing patterns to derive contact-adjusted immunity levels. We then compared these to case data from the 10 years following the seroprevalence studies to establish a contact-adjusted immunity threshold for elimination. We lastly combined a range of hypothetical immunity profiles with contact data from a wide range of socioeconomic and demographic settings to determine whether they would be sufficient for elimination.\n\nResultsWe found that contact-adjusted immunity levels were able to predict whether countries would experience outbreaks in the decade following the serological studies in about 70% of countries. The corresponding threshold level of contact-adjusted immunity was found to be 93%, corresponding to an average basic reproduction number of approximately 14. Testing different scenarios of immunity with this threshold level using contact studies from around the world, we found that 95% immunity would have to be achieved by the age of five and maintained across older age groups to guarantee elimination. This reflects a greater level of immunity required in 5-9 year olds than established previously.\n\nConclusionsThe immunity levels we found necessary for measles elimination are higher than previous guidance. The importance of achieving high immunity levels in 5-9 year olds presents both a challenge and an opportunity. While such high levels can be difficult to achieve, school entry provides an opportunity to ensure sufficient vaccination coverage. Combined with observations of contact patterns, further national and sub-national serological studies could serve to highlight key gaps in immunity that need to be filled in order to achieve national and regional measles elimination.

epidemiology↗

bioRxiv2017

Assessing the performance of real-time epidemic forecasts

Real-time forecasts based on mathematical models can inform critical decision-making during infectious disease outbreaks. Yet, epidemic forecasts are rarely evaluated during or after the event, and there is little guidance on the best metrics for assessment. Here, we propose an evaluation approach that disentangles different components of forecasting ability using metrics that separately assess the calibration, sharpness and unbiasedness of forecasts. This makes it possible to assess not just how close a forecast was to reality but also how well uncertainty has been quantified. We used this approach to analyse the performance of weekly forecasts we generated in real time in Western Area, Sierra Leone, during the 2013-16 Ebola epidemic in West Africa. We investigated a range of forecast model variants based on the model fits generated at the time with a semi-mechanistic model, and found that good probabilistic calibration was achievable at short time horizons of one or two weeks ahead but models were increasingly inaccurate at longer forecasting horizons. This suggests that forecasts may have been of good enough quality to inform decision making requiring predictions a few weeks ahead of time but not longer, reflecting the high level of uncertainty in the processes driving the trajectory of the epidemic. Comparing forecasts based on the semi-mechanistic model to simpler null models showed that the best semi-mechanistic model variant performed better than the null models with respect to probabilistic calibration, and that this would have been identified from the earliest stages of the outbreak. As forecasts become a routine part of the toolkit in public health, standards for evaluation of performance will be important for assessing quality and improving credibility of mathematical models, and for elucidating difficulties and trade-offs when aiming to make the most useful and reliable forecasts.

epidemiology↗

bioRxiv2017

Identifying Human Encounters That Shape The Transmission Of Streptococcus Pneumoniae And Other Respiratory Infections

Although patterns of social contacts are believed to be an important determinant of infectious disease transmission, there is little empirical evidence to back this up. Indeed, no previous study has linked individuals risk of respiratory infection with their current pattern of social contacts. We explored whether the frequency of different types of social encounters were associated with current pneumococcal carriage and self-reported acute respiratory symptoms (ARS), though a survey in Uganda in 2014. In total 566 participants were asked about their daily social encounters and about symptoms of ARS in the last two weeks. A nasopharyngeal specimen was also taken from each participant. We found that the frequency of physical (i.e. skin-to-skin), long ([≥]1h) and household contacts - which capture some measure of close (i.e. relatively intimate) contact -, was higher among pneumococcal carriers than non-carriers, and among people with ARS compared to those without, irrespective of their age. With each additional physical encounter the age-adjusted risk of carriage and ARS increased by 6% (95%CI 2-9%) and 9% (1-18%) respectively. In contrast, the number of casual contacts (<5 minutes long) was not associated with either pneumococcal carriage or ARS. A detailed analysis by age of contacts showed that the number of close contacts with young children (<5 years) was particularly higher among older children and adult carriers than non-carriers, while the higher number of contacts among people with ARS was more homogeneous across contacts of all ages. Our findings provide key evidence that the frequency of close interpersonal contact is important for transmission of respiratory infections, but not that of casual contacts. Such results strengthen the evidence for public health measures based upon assumptions of what contacts are important for transmission, and are important to improve disease prevention and control efforts, as well as inform research on infectious disease dynamics.\n\nAuthor summaryAlthough social contacts are an important determinant for the transmission of many infectious diseases it is not clear how the nature and frequency of contacts shape individual infection risk. We explored whether frequency, duration and type of social encounters were associated with someones risk of respiratory infection, using nasopharyngeal carriage (NP) of Streptococcus pneumoniae and acute respiratory symptoms as endpoints. To do so, we conducted a survey in South-West Uganda collecting information on peoples social encounters, respiratory symptoms, and pneumococcal carriage status. Our results show that both pneumococcal carriage and respiratory symptoms are independently associated with a higher number of social encounters, irrespective of a persons age. More specifically, our findings strongly suggest that the frequency of close contacts is important for transmission of respiratory infections, particularly pneumococcal carriage. In contrast, our study showed no association with the frequency of short casual contacts. Those results are essential for both improving disease prevention and control efforts as well as informing research on infectious disease dynamics and transmission models.

epidemiology↗

bioRxiv2017

Predicting The Impact Of Pneumococcal Conjugate Vaccine Programme Options In Vietnam: A Dynamic Transmission Model

BackgroundCatch-up campaigns (CCs) at the introduction of the pneumococcal conjugate vaccines (PCVs) may accelerate the impact of PCVs. However, limited vaccine supplies may delay vaccine introduction if additional doses are needed for such campaigns. We studied the relative impact of introducing PCV13 with and without catch-up campaign, and the implications of potential introduction delays.\n\nMethodsWe used a dynamic transmission model applied to the population of Nha Trang in Sout central Vietnam. Four strategies were considered: routine vaccination (RV) only, and RV alongside catch-up campaigns among <1y olds (CC1), <2y olds (CC2) and <5y olds (CC5). The model was parameterised with local data on human social contact rates, and was fitted to local carriage data. Post-PCV predictions were based on best estimates of parameters governing post-PCV dynamics, including serotype competition, vaccine efficacy and duration of protection.\n\nResultsOur model predicts elimination of vaccine-type (VT) carriage across all age groups within 10 years of introduction in all scenarios with near-complete replacement by non-VT. Most of the benefit of CCs is predicted to occur within the first 3 years after introduction, with the highest impact in the first year, when IPD incidence is predicted to be 11% (95%CrI 9 - 14%) lower than RV with CC1, 25% (21 - 30 %) lower with CC2 and 38% (32 - 46%) lower with CC5.\n\nHowever, CCs would only prevent more cases of IPD insofar such campaigns do not delay introduction by more than 31 (95%CrI 30 - 32) weeks with CC1, 58 (53 - 63) weeks with CC2 and 89 (78 - 101) weeks for CC5.\n\nConclusionCCs are predicted to offer a substantial additional reduction in pneumococcal disease burden over RV alone, if their implementation does not result in much introduction delay. Those findings are important to help guide vaccine introduction in countries that have not yet introduced PCV, particularly in Asia.

epidemiology↗

bioRxiv2017

Characteristics Of Human Encounters And Social Mixing Patterns Relevant To Infectious Diseases Spread By Close Contact: A Survey In Southwest Uganda

Quantification of human interactions relevant to infectious disease transmission through social contact is central to predict disease dynamics, yet data from low-resource settings remain scarce. We undertook a social contact survey in rural Uganda, whereby participants were asked to recall details about the frequency, type, and socio-demographic characteristics of any conversational encounter that lasted for [≥]5 minutes (henceforth defined as contacts) during the previous day. An estimate of the number of casual contacts (i.e. <5 minutes) was also obtained. A total of 568 individuals were included. On average participants reported having routine contact with 7.2 individuals (range 1-25). Children aged 5-14 years had the highest frequency of contacts and the elderly ([≥]65 years) the fewest (P<0.001). A strong age-assortative pattern was seen, particularly outside the household and increasingly so for contacts occurring further away from home. Adults aged 25-64 years tended to travel more and further than others, and males travelled more frequently than females. Our study provides detailed information on contact patterns and their spatial characteristics in an African setting. It therefore fills an important knowledge gap that will help more accurately predict transmission dynamics and the impact of control strategies in such areas.

epidemiology↗

bioRxiv2017

Assessing The Efficiency Of Catch-Up Campaigns For Introduction Of Pneumococcal Conjugate Vaccine; A Modelling Study Based On Data From Kilifi, Kenya

BackgroundThe World Health Organisation recommends the use of catch-up campaigns as part of the introduction of pneumococcal conjugate vaccines (PCVs) to accelerate herd protection and hence PCV impact. The value of a catch-up campaign is a trade-off between the costs of vaccinating additional age groups and the benefit of additional direct and indirect protection. There is a paucity of observational data, particularly from low-middle income countries to quantify the optimal breadth of such catch-up campaigns.\n\nMethodsIn Kilifi, Kenya PCV10 was introduced in 2011 using the 3-dose EPI infant schedule and a catch-up campaign in children <5 years old. We fitted a transmission dynamic model to detailed local data including nasopharyngeal carriage and invasive pneumococcal disease (IPD) to infer the marginal impact of the PCV catch-up campaign over hypothetical routine cohort vaccination in that setting, and to estimate the likely impact of alternative campaigns and their dose-efficiency.\n\nResultsWe estimated that, within 10 years of introduction, the catch-up campaign among <5y olds prevents an additional 65 (48 to 84) IPD cases, compared to PCV cohort introduction alone. Vaccination without any catch-up campaign prevented 155 (121 to 193) IPD cases and used 1321 (1058 to 1698) PCV doses per IPD case prevented. In the years after implementation, the PCV programme gradually accrues herd protection and hence its dose-efficiency increases: 10 years after the start of cohort vaccination alone the programme used 910 (732 to 1184) doses per IPD case averted. We estimated that a two-dose catch-up among <1y olds uses an additional 910 (732 to 1184) doses per additional IPD case averted. Furthermore, by extending a single dose catch-up campaign to children 1 to <2y old and subsequently to 2 to <5y olds the campaign uses an additional 412 (296 to 606) and 543 (403 to 763) doses per additional IPD case averted. These results were not sensitive to vaccine coverage, serotype competition, the duration of vaccine protection or the relative protection of infants.\n\nConclusionsWe find that catch-up campaigns are a highly dose-efficient way to accelerate population protection against pneumococcal disease.

epidemiology↗

bioRxiv2017

Vaccination of health care workers to control Ebola virus disease

BackgroundHealth care workers (HCW) are at risk of infection during Ebola virus disease outbreaks and therefore may be targeted for vaccination before or during outbreaks. The effect of these strategies depends on the role of HCW in transmission which is understudied.\n\nMethodsTo evaluate the effect of HCW-targeted or community vaccination strategies, we used a transmission model to explore the relative contribution of HCW and the community to transmission. We calibrated the model to data from multiple Ebola outbreaks. We quantified the impact of ahead-of-time HCW-targeted strategies, and reactive HCW and community vaccination.\n\nResultsWe found that for some outbreaks (we call \"type 1\") HCW amplified transmission both to other HCW and the community, and in these outbreaks prophylactic vaccination of HCW decreased outbreak size. Reactive vaccination strategies had little effect because type 1 outbreaks ended quickly. However, in outbreaks with longer time courses (\"type 2 outbreaks\"), reactive community vaccination decreased the number of cases, with or without prophylactic HCW-targeted vaccination. For both outbreak types, we found that ahead-of-time HCW-targeted strategies had an impact at coverage of 30%.\n\nConclusionsThe optimal vaccine strategy depends on the dynamics of the outbreak and the impact of other interventions on transmission. Although we will not know the characteristics of a new outbreak, ahead-of-time HCW-targeted vaccination can decrease the total outbreak size, even at low vaccine coverage.\n\nsummaryTargeting health care workers for Ebola virus disease vaccination can decrease the size of outbreaks, and the number of health care workers infected. The impact of these strategies decrease depends on timing, coverage, and the dynamics of the outbreak.

epidemiology↗