Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall

Shi Zhao; Salihu S. Musa; Hao Fu; Daihai He; Jing Qin

We fit all models to the weekly reported LF cases in different regions evaluate the fitting performance by the Akaike information criterion (AIC). We adopt the standard nonlinear least squares (NLS) approach for model fitting and parameter estimation, following [16, 18] . A pvalue < 0.05 is regarded as statistically significant, and the 95% confidence intervals (CIs) are estimated for all unknown parameters. The models are selected by comparing the AIC to that of the baseline model. Only the models with an AIC lower than the AIC of the baseline model are considered for further analysis. Since the epidemic curves of an infectious disease commonly exhibit autocorrelations [19], we use autoregression (AR) models with a degree of 2, i.e., AR(2), as the baseline models for growth model selection. We also adopt the coefficient of determination (R-squared) and the coefficient of partial determination (partial R-squared) to evaluate goodness-of-fit and fitting improvement, respectively. For partial Rsquared, the AR(2) model is treated as the baseline model.