Using Google Trends and ambient temperature to predict seasonal influenza outbreaks

2018 ◽  
Vol 117 ◽  
pp. 284-291 ◽  
Author(s):  
Yuzhou Zhang ◽  
Hilary Bambrick ◽  
Kerrie Mengersen ◽  
Shilu Tong ◽  
Wenbiao Hu
2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Yuzhou Zhang ◽  
Hilary Bambrick ◽  
Kerrie Mengersen ◽  
Shilu Tong ◽  
Wenbiao Hu

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
V. Marmara ◽  
D. Marmara ◽  
P. McMenemy ◽  
A. Kleczkowski

Abstract Background Seasonal influenza has major implications for healthcare services as outbreaks often lead to high activity levels in health systems. Being able to predict when such outbreaks occur is vital. Mathematical models have extensively been used to predict epidemics of infectious diseases such as seasonal influenza and to assess effectiveness of control strategies. Availability of comprehensive and reliable datasets used to parametrize these models is limited. In this paper we combine a unique epidemiological dataset collected in Malta through General Practitioners (GPs) with a novel method using cross-sectional surveys to study seasonal influenza dynamics in Malta in 2014–2016, to include social dynamics and self-perception related to seasonal influenza. Methods Two cross-sectional public surveys (n = 406 per survey) were performed by telephone across the Maltese population in 2014–15 and 2015–16 influenza seasons. Survey results were compared with incidence data (diagnosed seasonal influenza cases) collected by GPs in the same period and with Google Trends data for Malta. Information was collected on whether participants recalled their health status in past months, occurrences of influenza symptoms, hospitalisation rates due to seasonal influenza, seeking GP advice, and other medical information. Results We demonstrate that cross-sectional surveys are a reliable alternative data source to medical records. The two surveys gave comparable results, indicating that the level of recollection among the public is high. Based on two seasons of data, the reporting rate in Malta varies between 14 and 22%. The comparison with Google Trends suggests that the online searches peak at about the same time as the maximum extent of the epidemic, but the public interest declines and returns to background level. We also found that the public intensively searched the Internet for influenza-related terms even when number of cases was low. Conclusions Our research shows that a telephone survey is a viable way to gain deeper insight into a population’s self-perception of influenza and its symptoms and to provide another benchmark for medical statistics provided by GPs and Google Trends. The information collected can be used to improve epidemiological modelling of seasonal influenza and other infectious diseases, thus effectively contributing to public health.


2007 ◽  
Vol 13 (6) ◽  
pp. 429-431 ◽  
Author(s):  
Kozo Yasui ◽  
Yoshiro Amano ◽  
Isaki Minami ◽  
Shinichi Nakamura ◽  
Yohei Akazawa ◽  
...  

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S248-S248
Author(s):  
Jonathan Temte ◽  
Yenlik Zheteyeva ◽  
Shari Barlow ◽  
Maureen Goss ◽  
Emily Temte ◽  
...  

Abstract Background Schools are purported to be primary venues of influenza transmission and amplification with secondary spread to communities. We assessed K—12 student absenteeism monitoring as a means for early detection of influenza activity in the community. Methods. We conducted a 3-year, prospective observational study of all-cause (a-TOT), illness-associated (a-I), and influenza-like illness-associated (a-ILI) absenteeism within the Oregon School District, Oregon, WI (OSD: enrollment = 3,900 students). Absenteeism reporting was facilitated by automated processes within OSD’s electronic student information system. Students were screened for ILI, and, if eligible, visited at home, where pharyngeal specimens were collected for influenza RT-PCR (IVD CDC Human Influenza Virus RT-PCR Diagnostic Panel) and multipathogen testing (Luminex NxTAG RPP). The study definition of a-ILI was validated for 700 children with acute respiratory infections using binomial logistic regression. Surveillance of medically attended laboratory-confirmed influenza (MAI) occurred in five primary care clinics in and adjoining OSD as part of the Wisconsin Influenza Incidence Surveillance Project using the same laboratory testing. Poisson general additive log linear regression models of daily counts of absenteeism and MAI were compared using correlation analysis. Results. Influenza A and B were detected in 54 and 51 of the 700 visited students, respectively. Influenza was significantly associated with a-ILI status (OR = 4.74; 95% CI: 2.78—8.18; P < 0.001). Of MAI patients, 371 had influenza A and 143 had influenza B. a-I was significantly correlated with MAI in the community (r = 0.472; P < 0.001) with a 15-day lead time. a-ILI was significantly correlated with MAI in the community (r = 0.480; P < 0.001) with a 1-day lead time. a-TOT performed poorly (r = 0.278; P < 0.001), following MAI by 9 days (Figure 1). Conclusion. Surveillance using cause-specific absenteeism was feasible to implement in OSD and performed well over a 3-year period marked by diverse presentations of seasonal influenza. Monitoring a-I and a-ILI can detect influenza outbreaks in the community, providing early warning in time for community mitigation efforts for seasonal and pandemic influenza. Disclosures All authors: No reported disclosures.


2020 ◽  
Vol 186 ◽  
pp. 109546 ◽  
Author(s):  
Kirran N. Mohammad ◽  
Emily Ying Yang Chan ◽  
Martin Chi Sang Wong ◽  
William Bernard Goggins ◽  
Ka Chun Chong

2013 ◽  
Vol 18 ◽  
pp. 2187-2192
Author(s):  
Milton Soto-Ferrari ◽  
Peter Holvenstot ◽  
Diana Prieto ◽  
Elise de Doncker ◽  
John Kapenga

2018 ◽  
Author(s):  
Thilo Reich ◽  
Marcin Budka

ABSTRACTThe introduction of electronic patient records in the ambulance service provides new opportunities to monitor the population. Most patients presenting to British ambulance services are discharged at scene. Ambulance records are therefore an ideal data source for syndromic early event detection systems to monitor infectious disease in the prehospital population. It has been previously found that tympanic temperature records can be used to detect influenza outbreaks in emergency departments. This study investigated whether routine tympanic temperature readings collected by ambulance crews can be used to detect seasonal influenza. Here we show that these temperature readings do allow the detection of seasonal influenza before methods applied to conventional data sources. The counts of pyretic patients were used to calculate a sliding case ratio (CR) as a measurement to detect seasonal influenza outbreaks. This method does not rely on conventional thresholds and can be adapted to the data. The data collected correlated with seasonal influenza. The 2016/17 outbreak was detected with high specificity and sensitivity, up to 9 weeks before other surveillance programs. An unanticipated outbreak of E. coli was detected in the same dataset. Our results show that ambulance records can be a useful data source for biosurveillance systems. Two outbreaks caused by different infectious agents have been successfully detected. The routine ambulance records allowed to use tympanic temperature readings that can be used as surveillance tool for febrile diseases. Therefore, this method is a valuable addition to the current surveillance tools.


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