El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers <s> ENSO and Health Forecasting <s> Effective prevention and control of epidemics has been a key element of global, regional and national disease control policies for many years. Epidemics are by their nature abnormal events and will clearly challenge the normal routine approaches to control and provision of treatment. Epidemics are caused by unusual changes in the existing equilibrium between the human host, the pathogen and its vector. While the level of risk may be exacerbated by social factors, climate variability plays an important role and indeed it is most often abnormality in meteorological and environmental conditions that ‘triggers’ epidemics of the climate sensitive diseases. <s> BIB001 </s> El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers <s> ENSO and Health Forecasting <s> Previous studies demonstrate statistically significant associations between disease and climate variations, highlighting the potential for developing climate-based epidemic early warning systems. However, limitations include failure to allow for non-climatic confounding factors, limited geographical/temporal resolution, or lack of evaluation of predictive validity. Here, we consider such issues for dengue in Southeast Brazil using a spatio-temporal generalised linear mixed model with parameters estimated in a Bayesian framework, allowing posterior predictive distributions to be derived in time and space. This paper builds upon a preliminary study by Lowe et al. but uses extended, more recent data and a refined model formulation, which, amongst other adjustments, incorporates past dengue risk to improve model predictions. For the first time, a thorough evaluation and validation of model performance is conducted using out-of-sample predictions and demonstrates considerable improvement over a model that mirrors current surveillance practice. Using the model, we can issue probabilistic dengue early warnings for pre-defined 'alert' thresholds. With the use of the criterion 'greater than a 50% chance of exceeding 300 cases per 100,000 inhabitants', there would have been successful epidemic alerts issued for 81% of the 54 regions that experienced epidemic dengue incidence rates in February-April 2008, with a corresponding false alarm rate of 25%. We propose a novel visualisation technique to map ternary probabilistic forecasts of dengue risk. This technique allows decision makers to identify areas where the model predicts with certainty a particular dengue risk category, to effectively target limited resources to those districts most at risk for a given season. <s> BIB002 </s> El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers <s> ENSO and Health Forecasting <s> Climate risk management has emerged over the last decade as a distinct area of activity within the wider field of climatology. Its focus is on integrating climate and non-climate information in order to enhance the decision-making process in a wide range of climate-sensitive sectors of society, the economy and the environment. Given the burgeoning pure and applied climate science literature that addresses a range of climate risks, the purpose of this progress report is to provide an overview of recent developments in the field of climatology that may contribute to the risk assessment component of climate risk management. Data rescue and climate database construction, hurricanes and droughts as examples of extreme climate events and seasonal climate forecasting are focused on in this report and are privileged over other topics because of either their fundamental importance for establishing event probability or scale of societal impact. The review of the literature finds that historical data rescue, climate... <s> BIB003 </s> El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers <s> ENSO and Health Forecasting <s> Using decadal prediction experiments from the WCRP/CMIP5 suite that were initialized every year from 1960-onward, we explore long-lead predictability of ENSO events. Both deterministic and probabilistic skill metrics are used to assess the ability of these decadal prediction systems to reproduce ENSO variability as represented by the NINO3.4 index (EN3.4). Several individual systems as well as the multi-model mean can predict ENSO events 3–4 years in advance, though not for every event during the hindcast period. This long-lead skill is beyond the previously documented predictability limits of initialized prediction systems. As part of the analysis, skill in reproducing the annual cycle of EN3.4, and the annual cycle of its interannual variability is examined. Most of the prediction systems reproduce the seasonal cycle of EN3.4, but are less able to capture the timing and magnitude of the variability. However, for the prediction systems used here, the fidelity of annual cycle characteristics does not appear to be related to the system’s ability to predict ENSO events. In addition, the performance of the multi-model ensemble mean is explored and compared to the multi-model mean based solely on the most skillful systems; the latter is found to yield better results for the deterministic metrics. Finally, an analysis of the near-surface temperature and precipitation teleconnections reveals that the ability of the systems to detect ENSO events far in advance could translate into predictive skill over land for several lead years, though with reduced amplitudes compared to observations. <s> BIB004 </s> El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers <s> ENSO and Health Forecasting <s> The severity and timing of seasonal malaria epidemics is strongly linked with temperature and rainfall. Advance warning of meteorological conditions from seasonal climate models can therefore potentially anticipate unusually strong epidemic events, building resilience and adapting to possible changes in the frequency of such events. Here we present validation of a process-based, dynamic malaria model driven by hindcasts from a state-of-the-art seasonal climate model from the European Centre for Medium-Range Weather Forecasts. We validate the climate and malaria models against observed meteorological and incidence data for Botswana over the period 1982–2006; the longest record of observed incidence data which has been used to validate a modeling system of this kind. We consider the impact of climate model biases, the relationship between climate and epidemiological predictability and the potential for skillful malaria forecasts. Forecast skill is demonstrated for upper tercile malaria incidence for the Botswana malaria season (January–May), using forecasts issued at the start of November; the forecast system anticipates six out of the seven upper tercile malaria seasons in the observational period. The length of the validation time series gives confidence in the conclusion that it is possible to make reliable forecasts of seasonal malaria risk, forming a key part of a health early warning system for Botswana and contributing to efforts to adapt to climate change. <s> BIB005 </s> El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers <s> ENSO and Health Forecasting <s> Idealized model experiments investigate the advance warning for malaria that may be presently possible using temperature and rainfall predictions from state-of-the-art operational monthly and seasonal weatherprediction systems. The climate forecasts drive a dynamical malaria model for all of Africa, and the predictions are evaluated using reanalysis data. The regions and months for which climate is responsible for significant interannual malaria transmission variability are first identified. In addition to epidemic-prone zones these also include hyperendemic regions subject to high variability during specific months of the year, often associatedwith the monsoon onset.In manyof these areas,temperatureanomalies are predictablefrom 1 to 2 months ahead, and reliable precipitation forecasts are available in eastern and southern Africa 1 month ahead. The inherent lag between the rainy seasons and malaria transmission results in potential predictability in malaria transmission 3‐4 months in advance, extending the early warning available from environmental monitoring by 1‐2 months, although the realizable forecast skill will be less than this because of an imperfect malaria model.A preliminaryexaminationofthe forecastsfor the highlands ofUganda andKenyashows that the system is able to predict the years during the last two decades in which documented highland outbreaks occurred, in particular the major event of 1998, but that the timing of outbreaks was often imprecise and inconsistent across lead times. In addition to country-level evaluation with district health data, issues that need addressing to integrate such a climate-based prediction system into health-decision processes are briefly discussed. <s> BIB006 </s> El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers <s> ENSO and Health Forecasting <s> BACKGROUND ::: Southern Taiwan has been a hotspot for dengue fever transmission since 1998. During 2014 and 2015, Taiwan experienced unprecedented dengue outbreaks and the causes are poorly understood. This study aims to investigate the influence of regional and local climate conditions on the incidence of dengue fever in Taiwan, as well as to develop a climate-based model for future forecasting. ::: ::: ::: METHODOLOGY/PRINCIPLE FINDINGS ::: Historical time-series data on dengue outbreaks in southern Taiwan from 1998 to 2015 were investigated. Local climate variables were analyzed using a distributed lag non-linear model (DLNM), and the model of best fit was used to predict dengue incidence between 2013 and 2015. The cross-wavelet coherence approach was used to evaluate the regional El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) effects on dengue incidence and local climate variables. The DLNM results highlighted the important non-linear and lag effects of minimum temperature and precipitation. Minimum temperature above 23°C or below 17°C can increase dengue incidence rate with lag effects of 10 to 15 weeks. Moderate to high precipitation can increase dengue incidence rates with a lag of 10 or 20 weeks. The model of best fit successfully predicted dengue transmission between 2013 and 2015. The prediction accuracy ranged from 0.7 to 0.9, depending on the number of weeks ahead of the prediction. ENSO and IOD were associated with nonstationary inter-annual patterns of dengue transmission. IOD had a greater impact on the seasonality of local climate conditions. ::: ::: ::: CONCLUSIONS/SIGNIFICANCE ::: Our findings suggest that dengue transmission can be affected by regional and local climatic fluctuations in southern Taiwan. The climate-based model developed in this study can provide important information for dengue early warning systems in Taiwan. Local climate conditions might be influenced by ENSO and IOD, to result in unusual dengue outbreaks. <s> BIB007 </s> El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers <s> ENSO and Health Forecasting <s> Seasonal climate forecasts (SCF) can support decision-making and thus help society cope with and prepare for climate variability and change. The demand for understanding the value and benefits of using SCF in decision-making processes can be associated with different logics. Two of these would be the need to justify public and private investment in the provision of SCF and demonstrating the gains and benefits of using SCF in specific decision-making contexts. This paper reviews the main factors influencing how SCF is (or can be) valued in supporting decision-making and the main methods and metrics currently used to perform such valuations. Our review results in four key findings: (a) there is a current emphasis on economic ex ante studies and the quantification of SCF value; (b) there are fundamental differences in how the value of SCF is defined and estimated across methods and approaches; (c) most valuation methods are unable to capture the differential benefits and risks of using SCF across spatiotemporal scales and groups; and (d) there is limited involvement of the decision-makers in the valuation process. The paper concludes by providing some guiding principles towards more effective valuations of SCF, notably the need for a wider diversity and integration of methodological approaches. These should particularly embrace ex-post, qualitative, and participatory approaches which allow co-evaluation with decision-makers so that more comprehensive and equitable SCF valuations can be developed in future. This article is categorized under: Vulnerability and Adaptation to Climate Change > Institutions for Adaptation <s> BIB008
Due to an improvement in the climate science community's' understanding of the largescale mechanisms that influence climate, plus rapid advances in computing technology, seasonal to inter-annual to decadal climate forecasts have become a real prospect BIB003 BIB008 This, coupled with an increasing knowledge of the nature of climate -health associations, has spawned a number of attempts to construct disease early warning systems based on seasonal predictions of health-sensitive climate fields, so that potential health threats may be anticipated several months in advance. Because a key source of the seasonal predictability of health-sensitive climate variables is ENSO, much effort has been invested in improving ENSO predictability BIB004 100,  with the hope that seasonal forecasting of health becomes operationally possible  . Attempts to develop seasonal health forecasts based on health sensitive climate predictors closely tied to ENSO include malaria warning systems for parts of Africa 103, BIB001 BIB005 BIB006 and India  and forecasts of dengue for Ecuador and Brazil BIB002  and Taiwan BIB007 . While the potential for ENSO-based health forecasting is clear, developers of seasonal health forecasts need to be conscious of some of the issues associated with this endeavor. For example Tomkins and Di Giuseppe BIB006 , in an assessment of a seasonal malaria forecast system for the highlands of Uganda, commented on the difficulty associated with achieving consistent forecasts of the timing of malaria outbreaks despite obtaining accurate forecasts of the years in which outbreaks occur. Increasingly, seasonal health forecasts are couched in probabilistic terms that have been found to pose communication and uptake problems, making it imperative for forecast developers to think carefully how forecasts are provided to end users  . In the context of climate services based on climate forecasts, Ballester et al.  provided a sobering review of some of the challenges related to the construction of seasonal health forecasts. These include the capital and human resources and the associated governance arrangements required for development and implementation; the need for forecasting tools to master the complexity of the interactions between climate, disease transmission, socioeconomic disparities and vulnerability; the imperative for integrated climate and health data sets; and acknowledgement that early warning systems and the climate forecasts on which they are based may only be effective when certain windows of opportunity present themselves such as during ENSO events when there is a clear climate signature in a range of health responses.