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

Abstract

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.

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

“…The system showed ability in predicting spatiotemporal variations in dengue cases and outbreak occurrence at forecast horizons of up to 6 months ahead. Predictions deteriorated, and uncertainty increased with lead time, as previously observed in other settings and diseases [ 36 , 42 , 75 , 90 ]. Forecasts improved, and the credible intervals decreased as time progressed and dengue data increased, likely due to an improvement in the associations learned by the superensemble.…”

“…Multiple studies have highlighted the potential usefulness of seasonal climate-driven epidemiological surveillance for decision-making and planning [32,[34][35][36]. These studies have used subseasonal (i.e., between 2 weeks and 2 months ahead) forecasts to inform disease models and compute predictions of dengue risk.…”

“…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 ].…”

Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania

“…Mathematical models have been applied for various countries at varying resolutions, examples include sub-Saharan African (SSA) countries [15,[20][21][22], Ghana [23,24], Kenya [25,26], Mozambique [27,28], Nigeria [16,17,29], Uganda [30], South Africa [31], Zambia [32][33][34][35], and the Asia-Pacific Region [36,37]. In those examples, modelling was used to investigate relevant transmission dynamics, intervention effectiveness or for stratification.…”

Probabilistic seasonal dengue forecasting in Vietnam using superensembles

“…Multiple studies have highlighted the potential usefulness of seasonal-climate-driven epidemiological surveillance for decision-making and planning ( Lowe et al, 2014; Lauer et al, 2018; Tompkins and Di Giuseppe, 2015; Tompkins et al, 2019 ) . These studies have used subseasonal (i.e.…”