Autoregressive time-series models
The autocorrelation function (ACF) plot (Figure S15) and statistical tests for the monthly seroprevalence of pigs of all age groups sampled in Gorakhpur district (n = 783) were consistent with stationarity (Box-Ljung Χ2 = 13.2, P = 0.35; ADF = -3.6, P = 0.03; KPSS Trend = 0.14, P = 0.05; KPSS Level = 0.28, P = 0.1). However, using systematic combinations of seasonality and differencing, the best fitting model, with relatively low AIC (1129), symmetrical residuals, with no autocorrelation demonstrated in the ACF plot was a model of order (p = 5, q = 1, P = 2, Q = 1) which incorporated seasonality (12) and differencing (d = 1, D = 1; Table 1; Figure S16). Trend lines of monthly total rainfall, mean minimum temperature, and mean relative humidity showed that highest rainfall and humidity was in the second half of the study period, and peaks in mean monthly temperature occurred in 2016 and 2022 (Figure S17). Cross-correlation function plots indicated no relationship between JEV seroprevalence and either monthly total rainfall or mean minimum temperature (Figures S18 and S19) but did indicate that JEV seroprevalence was negatively correlated with mean relative humidity at 6 months lag (Figure S20). Humidity was included with increasing lags in the ARIMA model (Model 4, Table 1). The model with the lowest AIC (1004.91) incorporated mean relative humidity at 12 months lag (residuals Figure S21) suggesting an inverse relationship between humidity and IgG seroprevalence in pigs. This likely reflects the broad pattern of higher IgG seroprevalence in Gorakhpur in the first half of the study period when humidity was lowest, and the reverse of this (seroprevalence low, humidity high) in the second half of the study period.