Table 1.

A summary of time series methods for describing, comparing and explaining the seasonality of the 14 most cited gastrointestinal infections from our review

Pathogen	Total citations	Discrete seasons			Seasonal curves as continuous processes
		Two seasons	Four seasons	Monthly records	Average smoothers	Cubic splines	STL models	SARIMA models	Harmonic regressions	Spectral analyses
Salmonella	66	5	25	22	1			2	12
Campylobacter	45	2	20	9	1	1	1		9	2
Gastroenteritis	38	1	15	15		1		2	4
Vibrio	20	2	7	4		1		2	4
Norovirus	19	3	9	6
E. coli	18		14	1			1		2
Cryptosporidium	17	2	1	10		1			3
Yersinia	11		7	3					1
Shigella	11		7	2					2
Giardia	10	1	3	3				1	2
Listeria	9		6	2					1
Cyclospora	6	1	1	4					1
Rotavirus	6	2	1						3	1
Clostridium	2			2
Total	215	17	76	74	2	4	2	7	31	2

We ranked pathogens in descending order by total citations. We divided methods by comparisons of discrete seasons and the construction of seasonal curves. Discrete seasons methods included comparisons by two seasons, four seasons or calendar months. Seasonal curve methods included average smoothers, cubic splines, seasonal trend decomposition (STL), seasonal autoregressive integrated moving average (SARIMA) models, harmonic regression models and spectral analyses. Column and row totals are less than the sum of all rows and columns, respectively, as many publications investigated multiple pathogens and used multiple methodologies to describe, compare and explain seasonality features.