The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
persons working for public health cannot treat the bursting 
events in time and effectively. 
The thoughts and methods of collaboration have been 
frequently applied to the fields of computer science, geography, 
epidemiology and so on in recent years. This paper designed a 
collaborative epidemical surveillance and response system, 
which is based on internet and mobile network and under C/S 
(Client/Server) mode, built up a whole work flow which is fit to 
epidemical surveillance and detection, event investigation, and 
emergency response, experimented in practical work, and 
reached a satisfying result in precision and efficiency. 
In the collaborative epidemical surveillance and response 
system designed in this paper, the broadly applied spatial and 
space-time scan statistic method (Kulldorff, 1997, 2001; 
Kulldorff, et al, 2005) was adopted in the disease detection 
module. This method analyzes the existed and current disease 
data, finds out the disease abnormal clusters in space and time, 
and finishes the epidemical space-time surveillance and 
detection. Bayesian analysis method was used for the function 
of assistant disease diagnosis in emergency response module. 
This function could finish the intelligent disease diagnosis 
according to the disease symptoms, patient characteristics, and 
clinical analysis information, and provides a rapid and scientific 
reference for the working people of event response. These 
methods above have reached a nice effect in practical 
experiments. 
2. EPIDEMICAL SURVEILLANCE AND RESPONSE 
2.1 Surveillance 
judge to the bursting cases, and complete the intelligent disease 
diagnosis on the basis of epidemical investigation, clinical 
examination of patients and examination results of laboratory. 
In addition, the response system should provide introduction of 
infectious disease characteristics and direction of fieldwork. 
Generally, epidemical response system could include the 
following modules: intelligent disease diagnosis, fieldwork 
treatment, response recommendation, and so on. 
3. ALGORITHMS OF SURVEILLANCE AND 
RESPONSE 
3.1 Spatial and space-time scan statistics 
Spatial and space-time scan statistics methods were proposed 
by Kulldorff of Harvard Medical School in 1997 and 2001, and 
are widely applied in the fields of medicine, biology and so on. 
Log likelihood ratio (LLR) is used as the evaluation norm in 
spatial scan statistics (Kulldorff, 1997). Null hypothesis (e.g. 
the possibility of persons in the studied regions is identical) is 
required before the spatial scan statistics. Firstly, a series of 
scan clusters are generated. Generally, these clusters are circles 
whose centre points are geographical centres of studied regions. 
The radiuses of these scan circles vary from zero to a specified 
maximum value. Secondly, LLR values of all scan circles are 
calculated. Several (4 to 5) regions with the maximum LLR 
values are chosen as the available hotspot regions of disease 
bursting. Finally, Monte Carlo hypothesis testing is used for the 
statistical significance evaluation of those available hotspot 
regions. Regions which have passed the testing are the detected 
hotspot regions. 
The detailed contents of epidemical surveillance include data 
collecting, data analysis, early detection, et al. The collecting 
data comprise disease reports, symptom information, medical 
cases, etc. Data analysis is classifying and analyzing the 
surveillance data statistically using spatial analysis methods, 
providing various ways (text, figure, electronic map, etc) for the 
epidemical investigation and inquiry, and predicting the 
probable hotspot regions of disease bursting. Early detection is 
analyzing the statistical data, publishing the detection signals 
automatically, and predicting the epidemical hotspot regions 
and spreading by the means of statistical models. 
GIS and information technique could provide strong help to 
epidemical surveillance. Visualization information technique 
can help researchers describe the complicated spatial and 
attribute data as visual geographical maps, accomplish the 
collaborative working mode of text and graph information, and 
study the epidemiology visually and çonveniently. Furthermore, 
GIS could provide various topological structures and visual 
spatial analysis methods for epidemical researches, determine 
the geographical characteristics of infectious diseases, analyze 
the surveillance and investigation data comprehensively, and 
furnish emergency response with helpful suggestions and 
directions. Meanwhile, GIS could distinguish multiple data 
types and structures, and help the collecting of disease data. 
2.2 Response 
Epidemical response is to determine the response methods and 
resource managements after infectious diseases burst. The 
response system can use the diagnosis algorithms, give a quick 
Models used in spatial scan statistics include Bernoulli model, 
Poisson model, Ordinal model, Exponential model, etc. For 
instance, LLR value of Poisson model is calculated as the 
following expression: 
n z Y Z f n G ~ n z 
p{Z)) [p(G)-p(Z) 
№G)) 
n z YY f*G)-n z Y c ~ nz 
p(Z)J lrfG)-rtZ)J 
where Z is a 3-dimensional vector, including the coordinates of 
the centre point and the radius of the scan circle, n z is the real 
disease case amount of the scan circle region, /¿(Z) is the 
population amount of the scan circle region, n G is the total 
disease case amount of the studied regions, and ^(Q) is the 
population amount of the studied regions. 
The main model of space-time scan statistics is space-time 
permutation model. The processing method used in space-time 
scan statistics is similar to spatial scan statistics. The scan 
cluster used in space-time permutation model is not circle but 
cylinder. The height of scan cylinder stands for time value (e.g. 
day amount), and the bottom surface of scan cylinder has the