Barbara Hofmann

Bio: Barbara Hofmann is an academic researcher from HR Wallingford. The author has contributed to research in topics: Warning system & Medicine. The author has an hindex of 2, co-authored 2 publications receiving 7 citations.

Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles.

Abstract: Background With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. Methods and findings We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002-2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6-148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5-80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102-575) than those made with the baseline model (CRPS = 125, 95% CI 120-168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data. Conclusions This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.

Probabilistic seasonal dengue forecasting in Vietnam using superensembles

Abstract: Timely information is key for decision-making. The ability to predict dengue transmission ahead of time would significantly benefit planners and decision-makers. Dengue is climate-sensitive. Monitoring climate variability could provide advance warning about dengue risk. Multiple dengue early warning systems have been proposed. Often, these systems are based on deterministic models that have limitations for quantifying the probability that a public health event may occur. We introduce an operational seasonal dengue forecasting system where Earth observations and seasonal climate forecasts are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to six months ahead. We demonstrate that the system has skill and relative economic value at multiple forecast horizons, seasons, and locations. The superensemble generated, on average, more accurate forecasts than those obtained from the models used to create it. We argue our system provides a useful tool for the development and deployment of targeted vector control interventions, and a more efficient allocation of resources in Vietnam.

Iconographies supplémentaires de l'article : The global economic burden of dengue: a systematic analysis

Abstract: BACKGROUND Dengue is a serious global burden. Unreported and unrecognised apparent dengue virus infections make it difficult to estimate the true extent of dengue and current estimates of the incidence and costs of dengue have substantial uncertainty. Objective, systematic, comparable measures of dengue burden are needed to track health progress, assess the application and financing of emerging preventive and control strategies, and inform health policy. We estimated the global economic burden of dengue by country and super-region (groups of epidemiologically similar countries). METHODS We used the latest dengue incidence estimates from the Institute for Health Metrics and Evaluation's Global Burden of Disease Study 2013 and several other data sources to assess the economic burden of symptomatic dengue cases in the 141 countries and territories with active dengue transmission. From the scientific literature and regressions, we estimated cases and costs by setting, including the non-medical setting, for all countries and territories. FINDINGS Our global estimates suggest that in 2013 there were a total of 58·40 million symptomatic dengue virus infections (95% uncertainty interval [95% UI] 24 million-122 million), including 13 586 fatal cases (95% UI 4200-34 700), and that the total annual global cost of dengue illness was US$8·9 billion (95% UI 3·7 billion-19·7 billion). The global distribution of dengue cases is 18% admitted to hospital, 48% ambulatory, and 34% non-medical. INTERPRETATION The global cost of dengue is substantial and, if control strategies could reduce dengue appreciably, billions of dollars could be saved globally. In estimating dengue costs by country and setting, this study contributes to the needs of policy makers, donors, developers, and researchers for economic assessments of dengue interventions, particularly with the licensure of the first dengue vaccine and promising developments in other technologies. FUNDING Sanofi Pasteur.

Climate change and dengue: a critical and systematic review of quantitative modelling approaches

Abstract: Background Many studies have found associations between climatic conditions and dengue transmission. However, there is a debate about the future impacts of climate change on dengue transmission. This paper reviewed epidemiological evidence on the relationship between climate and dengue with a focus on quantitative methods for assessing the potential impacts of climate change on global dengue transmission. Methods A literature search was conducted in October 2012, using the electronic databases PubMed, Scopus, ScienceDirect, ProQuest, and Web of Science. The search focused on peer-reviewed journal articles published in English from January 1991 through October 2012. Results Sixteen studies met the inclusion criteria and most studies showed that the transmission of dengue is highly sensitive to climatic conditions, especially temperature, rainfall and relative humidity. Studies on the potential impacts of climate change on dengue indicate increased climatic suitability for transmission and an expansion of the geographic regions at risk during this century. A variety of quantitative modelling approaches were used in the studies. Several key methodological issues and current knowledge gaps were identified through this review. Conclusions It is important to assemble spatio-temporal patterns of dengue transmission compatible with long-term data on climate and other socio-ecological changes and this would advance projections of dengue risks associated with climate change. Keywords: Climate; Dengue; Models; Projection; Scenarios

Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines.

Abstract: BACKGROUND: The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. METHODS AND FINDINGS: We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. CONCLUSIONS: These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.

Deep learning models for forecasting dengue fever based on climate data in Vietnam

Abstract: Background Dengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam. Objective This study aimed to develop an accurate DF prediction model in Vietnam using a wide range of meteorological factors as inputs to inform public health responses for outbreak prevention in the context of future climate change. Methods Convolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. Models were developed using lagged DF incidence and meteorological variables (measures of temperature, humidity, rainfall, evaporation, and sunshine hours) as inputs for 20 provinces throughout Vietnam. Data from 1997–2013 were used to train models, which were then evaluated using data from 2014–2016 by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results and discussion LSTM-ATT displayed the highest performance, scoring average places of 1.60 for RMSE-based ranking and 1.95 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 13 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreak months up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. To the best of our knowledge, this is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich meteorological features. Conclusion This study demonstrates the usefulness of deep learning models for meteorological factor-based DF forecasting. LSTM-ATT should be further explored for mitigation strategies against DF and other climate-sensitive diseases in the coming years.