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Supplementary tables of full model results and an assortment of supplementary figures referred to throughout the main text.

The dengue virus is now the most widespread arbovirus affecting human populations, causing significant economic and social impact in South America and South‐East Asia. Increasing urbanization and globalization, coupled with insufficient resources for control, misguided policies or lack of political will, and expansion of its mosquito vectors are some of the reasons why interventions have so far failed to curb this major public health problem. Computational approaches have elucidated on dengue's population dynamics with the aim to provide not only a better understanding of the evolution and epidemiology of the virus but also robust intervention strategies. It is clear, however, that these have been insufficient to address key aspects of dengue's biology, many of which will play a crucial role for the success of future control programmes, including vaccination. Within a multiscale perspective on this biological system, with the aim of linking evolutionary, ecological and epidemiological thinking, as well as to expand on classic modelling assumptions, we here propose, discuss and exemplify a few major computational avenues—real‐time computational analysis of genetic data, phylodynamic modelling frameworks, within‐host model frameworks and GPU‐accelerated computing. We argue that these emerging approaches should offer valuable research opportunities over the coming years, as previously applied and demonstrated in the context of other pathogens.

This document is taken from the Met Office Numerical Weather Prediction (NWP) Wiki. It describes some of the technical changes included in the global upgrade at PS39., Previously curated at: http://cedadocs.ceda.ac.uk/1327/ This item was not refereed before the publication Main files in this record: GlobalOPCHANGE.pdf Item originally deposited with Centre for Environmental Data Analysis (CEDA) document repository by Miss Kate Winfield. Transferred to CEDA document repository community on Zenodo on 2022-11-24

Sea‐surface temperature (SST) variations of the oceans surrounding southern Africa are associated with seasonal rainfall variability, especially during austral summer when the tropical atmospheric circulation is dominant over the region. Because of instabilities in the linear association between summer rainfall over southern Africa and SSTs of the tropical Indian Ocean, the skilful prediction of seasonal rainfall may best be achieved using physically based models. A two‐tiered retro‐active forecast procedure for the December–February (DJF) season is employed over a 10‐year period starting from 1987/1988. Rainfall forecasts are produced for a number of homogeneous regions over part of southern Africa. Categorized (below‐normal, near‐normal and above‐normal) statistical DJF rainfall predictions are made for the region to form the baseline skill level that has to be outscored by more elaborate methods involving general circulation models (GCMs). The GCM used here is the Centre for Ocean–Land–Atmosphere Studies (COLA) T30, with predicted global SST fields as boundary forcing and initial conditions derived from the National Centres for Environmental Prediction (NCEP) reanalysis data. Bias‐corrected GCM simulations of circulation and moisture at certain standard pressure levels are downscaled to produce rainfall forecasts at the regional level using the perfect prognosis approach. In the two‐tiered forecasting system, SST predictions for the global oceans are made first. SST anomalies of the equatorial Pacific (NIÑO3.4) and Indian oceans are predicted skilfully at 1‐ and 3‐month lead‐times using a statistical model. These retro‐active SST forecasts are accurate for pre‐1990 conditions, but predictability seems to have weakened during the 1990s. Skilful multi‐tiered rainfall forecasts are obtained when the amplitudes of large events in the global oceans (such as El Niño and La Niña episodes) are described adequately by the predicted SST fields. GCM simulations using persisted August SST anomalies instead of forecast SSTs produce skill levels similar to those of the baseline for longer lead‐times. Given high‐skill SST forecasts, the scheme has the potential to provide climate forecasts that outscore the baseline skill level substantially.

Background The required efforts, feasibility and predicted success of an intervention strategy against an infectious disease are partially determined by its basic reproduction number, R0. In its simplest form R0 can be understood as the product of the infectious period, the number of infectious contacts and the per-contact transmission probability, which in the case of vector-transmitted diseases necessarily extend to the vector stages. As vectors do not usually recover from infection, they remain infectious for life, which places high significance on the vector’s life expectancy. Current methods for estimating the R0 for a vector-borne disease are mostly derived from compartmental modelling frameworks assuming constant vector mortality rates. We hypothesised that some of the assumptions underlying these models can lead to unrealistic high vector life expectancies with important repercussions for R0 estimates. Methodology and principal findings Here we used a stochastic, individual-based model which allowed us to directly measure the number of secondary infections arising from one index case under different assumptions about vector mortality. Our results confirm that formulas based on age-independent mortality rates can overestimate R0 by nearly 100% compared to our own estimate derived from first principles. We further provide a correction factor that can be used with a standard R0 formula and adjusts for the discrepancies due to erroneous vector age distributions. Conclusion Vector mortality rates play a crucial role for the success and general epidemiology of vector-transmitted diseases. Many modelling efforts intrinsically assume these to be age-independent, which, as clearly demonstrated here, can lead to severe over-estimation of the disease’s reproduction number. Our results thus re-emphasise the importance of obtaining field-relevant and species-dependent vector mortality rates, which in turn would facilitate more realistic intervention impact predictions., Author summary Many infectious diseases of public health concern, such as dengue, Zika and malaria, are transmitted by insect vectors. Control effort required to curb their continued spread or even prevent their establishment in the first place are partially determined by the disease’s basic reproduction number, R0. Of particular importance for estimating R0 is the duration at which the vector can transmit the disease, which is limited by its life expectancy. Many R0 estimation methods are based on mathematical frameworks that assume constant vector mortality rates. Here we demonstrate how the resulting exponential distribution in life-expectancy can lead to significant over-estimations. By means of an individual-based model we elucidate the effect of vector mortality on R0 estimates and derive a correction factor that alleviates some of the discrepancies due to erroneous vector age distributions. Our results clearly demonstrate the need to obtain more realistic, i.e. field-relevant vector mortality rates in order to generate robust estimates of a disease’s reproduction number and provide guidance for setting-specific disease control.