Locating irregularly shaped clusters of infection intensity.
Patterns of disease may take on irregular geographic shapes, especially when features of the physical environment influence risk. Identifying these patterns can be important for planning, and also identifying new environmental or social factors associated with high or low risk of illness. Until recently, cluster detection methods were limited in their ability to detect irregular spatial patterns, and limited to finding clusters that were roughly circular in shape. This approach has less power to detect irregularly-shaped, yet important spatial anomalies, particularly at high spatial resolutions. We employ a new method of finding irregularly-shaped spatial clusters at micro-geographical scales using both simulated and real data on Schistosoma mansoni and hookworm infection intensities. This method, which we refer to as the "greedy growth scan", is a modification of the spatial scan method for cluster detection. Real data are based on samples of hookworm and S. mansoni from Kitengei, Makueni district, Kenya. Our analysis of simulated data shows how methods able to find irregular shapes are more likely to identify clusters along rivers than methods constrained to fixed geometries. Our analysis of infection intensity identifies two small areas within the study region in which infection intensity is elevated, possibly due to local features of the physical or social environment. Collectively, our results show that the "greedy growth scan" is a suitable method for exploratory geographical analysis of infection intensity data when irregular shapes are suspected, especially at micro-geographical scales.
Yiannakoulias
Niko
N
School of Geography and Earth Sciences, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada. yiannan@mcmaster.ca
Wilson
Shona
S
Kariuki
H Curtis
HC
Mwatha
Joseph K
JK
Ouma
John H
JH
Muchiri
Eric
E
Kimani
Gachuhi
G
Vennervald
Birgitte J
BJ
Dunne
David W
DW
D000818:Animals; D016010:Binomial Distribution; D003198:Computer Simulation; D003710:Demography; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D005843:Geography; D006725:Hookworm Infections / Q000453:epidemiology* / Q000635:transmission; D006801:Humans; D007630:Kenya / Q000453:epidemiology; D016012:Poisson Distribution; D018570:Risk Assessment; D012307:Risk Factors; D012555:Schistosomiasis mansoni / Q000453:epidemiology* / Q000635:transmission; D013029:Space-Time Clustering