Biology subjects

Funk, S.

Publications and source records attributed to Funk, S..

7 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↗

bioRxiv2018

Projecting the end of the Zika virus epidemic in Latin America: a modelling analysis

Background Zika virus (ZIKV) emerged in Latin America & the Caribbean (LAC) region in 2013, and has had serious implications for population health in the region. In 2016, the World Health Organization declared the ZIKV outbreak a Public Health Emergency of International Concern following a cluster of associated neurological disorders and neonatal malformations. In 2017, Zika cases declined, but future incidence in LAC remains uncertain due to gaps in our understanding, considerable variation in surveillance and a lack of a comprehensive collation of data from affected countries.\n\nMethods Our analysis combines information on confirmed and suspected Zika cases across LAC countries and a spatio-temporal dynamic transmission model for ZIKV infection to determine key transmission parameters and projected incidence in 91 major cities within 35 countries. Seasonality was determined by spatio-temporal estimates of Aedes aegypti vector capacity. We used country and state-level data from 2015 to mid-2017 to infer key model parameters, country-specific disease reporting rates, and the 2018 projected incidence. A 10-fold cross-validation approach was used to validate parameter estimates to out-of-sample epidemic trajectories.\n\nResults There was limited transmission in 2015, but in 2016 and 2017 there was sufficient opportunity for wide-spread ZIKV transmission in most cities, resulting in the depletion of susceptible individuals. We predict that the highest number of cases in 2018 within some Brazilian States (Sao Paulo and Rio de Janeiro), Colombia and French Guiana, but the estimated number of cases were no more than a few hundred. Model estimates of the timing of the peak in incidence were correlated (p<0.05) with the reported peak in incidence. The reporting rate varied across countries, with lower reporting rates for those with only confirmed cases compared to those who reported both confirmed and suspected cases.\n\nConclusions The findings suggest that the ZIKV epidemic is by and large over, with incidence projected to be low in most cities in LAC in 2018. Local low levels of transmission are probable but the estimated rate of infection suggests that most cities have a population with high levels of herd immunity.

epidemiology↗

bioRxiv2018

Genomic and epidemiological monitoring of yellow fever virus transmission potential

The yellow fever virus (YFV) epidemic that began in Dec 2016 in Brazil is the largest in decades. The recent discovery of YFV in Brazilian Aedes sp. vectors highlights the urgent need to monitor the risk of re-establishment of domestic YFV transmission in the Americas. We use a suite of epidemiological, spatial and genomic approaches to characterize YFV transmission. We show that the age- and sex-distribution of human cases in Brazil is characteristic of sylvatic transmission. Analysis of YFV cases combined with genomes generated locally using a new protocol reveals an early phase of sylvatic YFV transmission restricted to Minas Gerais, followed in late 2016 by a rise in viral spillover to humans, and the southwards spatial expansion of the epidemic towards previously YFV-free areas. Our results establish a framework for monitoring YFV transmission in real-time, contributing to the global strategy of eliminating future yellow fever epidemics.

epidemiology↗

bioRxiv2018

Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji

Dengue is a major health burden, but it can be challenging to examine transmission dynamics and evaluate control measures because outbreaks depend on multiple factors, including human population structure, prior immunity and climate. We combined population-representative paired sera collected before and after the major 2013/14 dengue-3 outbreak in Fiji with surveillance data to determine how such factors influence dengue virus transmission and control in island settings. Our results suggested the 10-19 year-old age group had the highest risk of acquiring infection, but we did not find strong evidence that other demographic or environmental risk factors were linked to seroconversion. Mathematical modelling showed that temperature-driven variation in transmission and herd immunity could not fully explain observed dynamics. However, there was evidence of an additional reduction in transmission coinciding with a vector clean-up campaign, which may have contributed to the decline in cases and prevented transmission continuing into the following season.

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

The relative fitness of drug resistant Mycobacterium tuberculosis: a modelling study of household transmission in Lima, Peru

The relative fitness of drug resistant versus susceptible bacteria in an environment dictates resistance prevalence. Estimates for the relative fitness of resistant Mycobacterium tuberculosis (Mtb) strains are highly heterogeneous and mostly derived from in-vitro experiments. Measuring fitness in the field allows us to determine how the environment influences resistance spread.\n\nWe designed a household structured, stochastic mathematical model to estimate the fitness costs associated with multi-drug resistance (MDR) carriage in Mtb in Lima, Peru between 2010-2013. By fitting the model to data from a large prospective cohort study of TB disease in household contacts we estimated the fitness, relative to susceptible strains with a fitness of 1, of MDR-Mtb to be 0.33 (95% credible interval: 0.17-0.54) or 0.39 (0.26-0.58), if only transmission or progression to disease, respectively, was affected. The relative fitness of MDR-Mtb increased to 0.57 (0.43-0.73) when the fitness cost influenced both transmission and progression to disease equally.\n\nWe found the average relative fitness of MDR-Mtb circulating within households in Lima, Peru between 2010-2013 to be significantly lower than concurrent susceptible-Mtb. If these fitness levels do not change, then existing TB control programmes are likely to keep MDR-TB prevalence at current levels in Lima, Peru.

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↗