Biology subjects

Kucharski, A. J.

Publications and source records attributed to Kucharski, A. J..

3 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

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

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↗