Descriptive analyses
Descriptive and time series analyses were conducted in R (R Core Team,
2022) using packages plyr (Wickham, 2011), tidyverse (Wickham, 2017),
DescTools (Signorell et al., 2019), lubridate (Grolemund & Wickham,
2011), irr (Gamer, Lemon, Gamer, Robinson, & Kendall’s, 2012), and
tseries (Trapletti & Hornik, 2016).
Frequency distributions and descriptive statistics were generated to
describe sampling effort for JEV sero-surveillance (IgG and IgM antibody
testing) by pig age group and district throughout the study period.
Frequency distributions and
descriptive statistics were generated to explore temporal and spatial
patterns in aggregated (monthly and annually) IgM seroprevalence (all
age groups combined), and IgG seroprevalence in each age group then in
all age groups combined.
Apparent seroprevalence was adjusted to true seroprevalence to account
for imperfect test specificity and sensitivity (Rogan & Gladen, 1978)
with 95% confidence intervals according to Blaker’s method (Blaker,
2000) in the R package, epi-R (Stevenson et al., 2017). Linear
regression was used to quantify the secular trend and seasonality,
followed by time series decomposition to visualize temporal components
including trend, seasonality (annual cyclical variation), and the
remainder, or random, component that could not be accounted for by trend
or cyclical variation. A heat plot (function ‘geom_tile’ in R package
ggplot2; Wickham (2009)) was generated to visualise the rolling mean of
quarterly IgG seroprevalence (the mean IgG seroprevalence across
current, prior and subsequent quarters) by district throughout the study
period.