Analysis of the impact of COVID-19 intervention on HFMD
incidence in Nanchang, China
Since HFMD has strong seasonality, a seasonal ARIMA model (p,d,q)
(P,D,Q)s was used for modeling, where p and P are autoregressive order
and seasonal autoregressive order respectively, and q and Q are the
moving average and seasonal moving average respectively. d and D are the
difference order and seasonal difference order respectively, and s is
the seasonal period. Based on the monthly onset number of HFMD cases, we
fit autoregressive integrated moving average (ARIMA) models for the
pre-COVID-19 period (2010-2019) and used these models to predict the
prevalence of HFMD in 2020. This was done by ARIMA Forecasting (v1.0.11)
in Free Statistics Software (v1.2.1), Office for Research Development
and Education, URL http://www.wessa.net/rwasp_arimaforecasting.wasp/.