Climate and health in Africa

Thomson, Madeleine C.; Mason, Simon J.; Platzer, Barbara L.; Mihretie, A.; Omumbo, Judith A.; Mantilla, Gilma; Ceccato, Pietro; Jancloes, Michel; Connor, Stephen J.

doi:10.1186/2194-6434-1-17

Cited by 10 publications

(6 citation statements)

References 66 publications

(45 reference statements)

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“…Numerous studies have investigated the potential of climate surveillance (Abeku et al, ; Ceccato et al, ; Grover‐Kopec et al, ; Ototo et al, ; Worrall et al, ) although to date there has been limited progress in sustainably operationalizing such approaches into health systems (Thomson et al, ). This is partly due to the challenge of effectively integrating climate into existing health planning procedures.…”

Section: Introductionmentioning

confidence: 99%

“…The ability to reliably predict the transmission of malaria for the season ahead sufficiently far in advance would be of significant benefit to health planners in Africa (Hay et al, ; Thomson & Connor, ; Thomson et al, ; Cox & Abeku, ). The lag between the rainy season and the peak of malaria transmission implies that monitoring weather conditions can provide warning of anomalous malaria transmission 1 to 2 months in advance, resulting in calls to improve climate monitoring capacity in Africa for health applications (Thomson et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, spatial disease modeling systems that accounted for climate were statistical in nature and relied on accurate and long‐term health data records. The quality, consistency, and availability of subnational level health data for malaria was often inadequate for the training and subsequent evaluation of disease prediction systems (Thomson et al, ). Paper‐based surveillance systems and limited confirmation of suspected cases through microscopy or diagnostic test kits lead to large health data uncertainties that compound those deriving from the use of imperfect climate and malaria modeling systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamical Malaria Forecasts Are Skillful at Regional and Local Scales in Uganda up to 4 Months Ahead

et al. 2019

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Malaria forecasts from dynamical systems have never been attempted at the health district or local clinic catchment scale, and so their usefulness for public health preparedness and response at the local level is fundamentally unknown. A pilot preoperational forecasting system is introduced in which the European Centre for Medium Range Weather Forecasts ensemble prediction system and seasonal climate forecasts of temperature and rainfall are used to drive the uncalibrated dynamical malaria model VECTRI to predict anomalies in transmission intensity 4 months ahead. It is demonstrated that the system has statistically significant skill at a number of sentinel sites in Uganda with high‐quality data. Skill is also found at approximately 50% of the Ugandan health districts despite inherent uncertainties of unconfirmed health reports. A cost‐loss economic analysis at three example sentinel sites indicates that the forecast system can have a positive economic benefit across a broad range of intermediate cost‐loss ratios and frequency of transmission anomalies. We argue that such an analysis is a necessary first step in the attempt to translate climate‐driven malaria information to policy‐relevant decisions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamical Malaria Forecasts Are Skillful at Regional and Local Scales in Uganda up to 4 Months Ahead

et al. 2019

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“…There has been limited progress in using subseasonal to seasonal climate forecasting to compute prospective forecasts on a routine basis. There are several challenges for implementing operational and sustainable subseasonal (henceforth seasonal) early warning systems [ 37 ]. Some of these challenges include the lack of multi-decadal health data sets with which to train and validate seasonal climate-driven early warning systems, the common mismatch of scales between climate data outputs and data used for decision-making, and a general lack of consensus as to how to communicate uncertainties to users [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…There has been limited progress in using subseasonal-to-seasonal climate forecasting to compute prospective forecasts on a routine basis. There are several challenges for implementing operational and sustainable subseasonal (henceforth seasonal) early warning systems ( Thomson etal., 2014 ) . Some of these challenges include the lack of multi-decadal health data sets with which to train and validate seasonal-climate-driven early warning systems, the common mismatch of scales between climate data outputs and data used for decision-making, and a general lack of consensus as to how to communicate uncertainties to users ( Tompkins et al, 2019 ) .…”

Section: Introductionmentioning

confidence: 99%

Probabilistic seasonal dengue forecasting in Vietnam using superensembles

Colón‐González

Bastos

Hofmann

et al. 2020

Preprint

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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.

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Climate and health in Africa

Cited by 10 publications

References 66 publications

Dynamical Malaria Forecasts Are Skillful at Regional and Local Scales in Uganda up to 4 Months Ahead

Dynamical Malaria Forecasts Are Skillful at Regional and Local Scales in Uganda up to 4 Months Ahead

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

Probabilistic seasonal dengue forecasting in Vietnam using superensembles

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