ABSTRACT
BACKGROUND:
Large gatherings often involve extended and intimate contact among individuals, creating
environments conducive to the spread of
infectious diseases. Despite this, there is limited
research utilizing outbreak
detection algorithms to analyze real
syndrome data from such events. This study sought to
address this gap by examining the implementation and
efficacy of outbreak
detection algorithms for
syndromic surveillance during
mass gatherings in
Iraq.
METHODS:
For the study, 10 data collectors conducted field
data collection over 10 days from August 25, 2023, to September 3, 2023. Data were gathered from 10
healthcare clinics situated along Ya Hussein Road, a major route from Najaf to Karbala in
Iraq. Various outbreak
detection algorithms, such as moving average, cumulative sum, and exponentially weighted moving average, were applied to analyze the reported
syndromes.
RESULTS:
During the 10 days from August 25, 2023, to September 3, 2023, 12202 pilgrims visited 10
health clinics along a route in
Iraq. Most pilgrims were between 20 and 59 years old (77.4%, n=9444), with more than half being
foreigners (58.1%, n=7092). Among the pilgrims, 40.5% (n=4938) exhibited
syndromes, with
influenza-like illness (ILI) being the most common (48.8%, n=2411). Other prevalent
syndromes included
food poisoning (21.2%, n=1048),
heatstroke (17.7%, n=875), febrile
rash (9.0%, n=446), and
gastroenteritis (3.2%, n=158). The cumulative sum (CUSUM)
algorithm was more effective than exponentially weighted moving average (EWMA) and moving average (MA)
algorithms for detecting small shifts.
CONCLUSION:
Effective
public health surveillance systems are crucial during
mass gatherings to swiftly identify and
address emerging
health risks. Utilizing advanced
algorithms and real-
time data analysis can empower authorities to improve their readiness and response capacity, thereby ensuring the
protection of
public health during these gatherings.