Development and evaluation of temperature and surface hydrology schemes for dynamical vector-borne disease models

Surface hydrology and water temperature are two key factors for the life cycle of mosquito larvae, and thus their realistic representation is required for the latest generation of dynamical disease models. A new prognostic surface hydrology scheme based on diagnostic area-depth relations and nonlinear treatments of infiltration and run-o terms has been developed to simulate temporal evolution of the pond surface area and depth. The scheme is evaluated using in situ data from daily observations of potential mosquito developmental habitats in a suburb of Kumasi, Ghana. The ponds reveal a strong variability in their water persistence times, which ranged between 11 and 81 days. The pond persistence was strongly tied with rainfall, location and size of the puddles. Based on a range of evaluation metrics, the prognostic model is judged to provide a good representation of the in situ pond coverage evolution at most sites. It was further demonstrated that this developed prognostic equation can be generalized and applied to a grid-cell to derive a fractional pond coverage, and thus can be implemented in spatially distributed models for relevant vector-borne diseases such as malaria. Th new prognostic scheme is implemented in the vector-borne disease community model of the International Centre for Theoretical Physics, Trieste (VECTRI) model and in addition to the VECTRI default surface hydrology scheme are validated using a resolution Hydrology, Entomology, and Malaria Transmission Simulator (HYDREMATS) model. Based on multi-member ensemble Monte Carlo technique, the VECTRI model parameter setting that minimizes water fraction di erences was identified. Despite the simplicity of the two VECTRI surface hydrology parametrization schemes, they perform relatively well (NS E > 0.85) at reproducing the seasonal and intraseasonal variability in pond water fraction, with the prognostic scheme able to produce a closer match to the explicit benchmark model, HYDREMATS. However, the default VECTRI scheme tends to overestimate water fraction in 2005 and underestimate it in 2006, and also relatively overestimates water fraction during the monsoon onset period. This systematic error was improved by treating run-o and infiltration terms in the prognostic scheme. Simulations of vector densities with the prognostic scheme implemented in the VECTRI model were also close (NS E = 0.71) to the detailed agent based model contained in HYDREMATS. The results indicate that, with knowledge of local soil parameters and terrain, VECTRI schemes parameters could be adjusted to simulate malaria transmission on a local scale. Furthermore, VECTRI driven by satellites rainfall estimates produces a reasonable simulation of the sub-seasonal evolution of the pond fraction for the study area, thus indicating the possibility of driving the malaria model with satellite rainfall estimates in the absence of ground observations. In addition to the surface hydrology scheme, a new energy balance scheme that assumes a homogeneous mixed water column driven by empirically derived fluxes has been developed. The model shows good agreement at both diurnal and daily time scales with 10-minute temporal resolution observed water temperatures monitored between June and November 2013 within a peri-urban area of Kumasi, Ghana. In addition, there was a close match between larvae development times calculated using either the model-derived or observed water temperatures, with the modelled water temperature providing a significant improvement over simply assuming the water temperature to be equal to the 2-metre air temperature. Furthermore, the results show that diurnal variations in water temperature are important for simulation of aquatic-stage development times, however, e ect of sub-diurnal variations on larval development are similar to that of the diurnal. This highlights the potential of the model to predict mosquito developmental habitat water temperature, thus can be implemented in dynamical malaria models to predict larvae development times, especially in regions without observations of the input energy fluxes. Finally, VECTRI runs over Ghana reveal malaria transmission ranging from six to twelve months, with minimum intensity occurring between February and April. The correlation between mean annual model predicted entomological inoculation rate (EIR) and recorded national malaria cases from public health facilities was more than 0.5. On a local scale, the agreement between hospital recorded monthly malaria cases and VECTRI simulated EIR values was better relative to using only rainfall. This result demonstrates the potential ability of the VECTRI model to predict malaria transmission dynamics at both local and national scales. Thus VECTRI model can provide early warning information for malaria and in addition, provide useful information about intervention targeting aquatic and adult stages. The performance of the VECTRI model is likely to improve significantly when the developed temperature scheme is implemented.

Source: KNUST