METHODS: We conducted a systematic review by searching PubMed, EMBASE, and Scopus to identify articles reporting epidemiological models in snakebite envenoming from database inception to 31st December 2023. A narrative synthesis was performed to summarise types of models, methodologies, input parameters, model outputs, and associating factors.
RESULTS: Thirty-nine modelling studies were included from 2426 retrieved articles, comprising statistical models (76.9%) and mathematical models (23.1%). Most of the studies were conducted in South Asia, (35.9%) and Latin America (35.9%), and only a few (5.1%) were a global burden estimation. The eligible studies constructed 42 epidemiological models, of which 33 were statistical models that included regression, (60.6%) geostatistical (21.2%), and time series, (18.2%) while 9 mathematical models comprised compartmental, (44.4%) agent-based, (22.2%) transmission dynamics, (11.1%) network, (11.1%) and a simple mathematical model (11.1%). The outputs of the models varied across the study objectives. Statistical models analysed the relationship between incidence, (83.3%) mortality, (33.3%) morbidity (16.7%) and prevalence (10.0%) and their associating factors (environmental, [80%] socio-demographic [33.3%] and therapeutic [10.0%]). Mathematical models estimated incidence, (100%) mortality (33.3%), and morbidity (22.2%). Five mathematical modelling studies considered associating factors, including environmental (60%) and socio-demographic factors (40%).
CONCLUSION: Mathematical and statistical models are crucial for estimating the burden of snakebite envenoming, offering insights into risk prediction and resource allocation. Current challenges include low-quality data and methodological heterogeneity. Modelling studies are needed, and their continued improvement is vital for meeting WHO goals. Future research should emphasise standardised methodologies, high-quality community data, and stakeholder engagement to create accurate, applicable models for prevention and resource optimization in high-burden regions, including Africa and Asia.
METHODS: A multi-stage sampling design was adopted for the study and data collection took place in three phases in 2010, 2011, and 2012 in the Northern region of Malaysia. Face-to-face interviews involved respondents answering both 13 TTO and 15 VAS valuation tasks were carried out. Both additive and multiplicative model specifications were explored using the valuation data. Model performance was evaluated using out-of-sample predictive accuracy by applying the cross-validation technique. The distribution of the model values was also graphically compared on Bland-Altman plots and kernel density distribution curves.
RESULTS: Data from 630 and 611 respondents were included for analyses using TTO and VAS models, respectively. In terms of main-effects specifications, cross-validation results revealed a slight superiority of multiplicative models over its additive counterpart in modelling TTO values. However, both main-effects models had roughly equal predictive accuracy for VAS models. The non-linear multiplicative model with I32 term, MULT7_TTO, performed best for TTO models; while, the linear additive model with N3 term, ADD11_VAS, outperformed the other VAS models. Multiplicative modelling neither altered the dimensional rankings of importance nor did it change the distribution of values of the health states.
CONCLUSION: Using EQ-5D-3L valuation data, multiplicative modelling was shown to improve out-of-sample predictive accuracy of TTO models but not of VAS models.