Modeling Zika Virus Spread in Colombia Using Google Search Queries and Logistic Power Models

Abstract

Public health agencies generally have a small window to respond to burgeoning disease outbreaks in order to mitigate the potential impact. There has been significant interest in developing forecasting models that can predict how and where a disease will spread. However, since clinical surveillance systems typically publish data with a lag of two or more weeks, there is a need for complimentary data streams that can close this gap. We examined the usefulness of Google Trends search data for analyzing the 2016 Zika epidemic in Colombia and evaluating their ability to predict its spread. We calculated the correlation and the time delay between the reported case data and the Google Trends data using variations of the logistic growth model, and showed that the data sets were systematically offset from each other, implying a lead time in the Google Trends data. Our study showed how Internet data can potentially complement clinical surveillance data and may be used as an effective early detection tool for disease outbreaks.2 mosquitoes. The symptoms of ZIKV are typically mild and may include fever, rash, 3 joint pain, and conjunctivitis (red eyes). These symptoms hardly warrant a visit to the 4 hospital, and some infected persons may not exhibit any symptoms at all. The 5 declaration of ZIKV to be a public health emergency was largely due to the correlation 6 between ZIKV outbreaks and increased clusters of the neurological birth defect, 7 Microcephaly [1]. In addition, ZIKV also poses a neurological threat to adults due to its 8 link to the Guillain-Barre Syndrome [2]. Similar to other mosquito borne diseases, ZIKV 9 appears to have a seasonal pattern. As of January 2018, the end of the mosquito season 10 in many countries has slowed the spread, thus, the World Health Organization (WHO) 11 has determined that ZIKV is no longer in a state of emergency. Nevertheless, ZIKV still 12 poses a viable threat for upcoming seasons. Therefore, the WHO is developing a 13 long-term response plan to minimize and ultimately prevent future outbreaks [3]. 14 PLOS1/20 Traditional methods of case-counting during outbreaks result in long processing 15 times, so case counts obtained using these methods typically lag behind real-time 16 incidence by up to several weeks [4]. Recently, researchers have examined the potential 17 Due to the prevalence of Internet usage, it is expected that physical phenomena 23 would be expressed in Internet search patterns. If Internet searches of a disease are 24 testament to an individual's interest in that disease, it may be possible to quickly detect 25 outbreaks and provide real-time information to health workers weeks before traditional 26 methods do. Some attempts have been made to use real-time Internet search data to 27 track outbreaks more effectively [5-7]. The goal of our study is to analyze the 2016 28 ZIKV outbreak in Colombia retroactively and determine whether Google Trends search 29 queries might have served as a faster, up-to-date indicator of the ZIKV outbreak than 30 traditional methods of ...

Analysing the Propagation of the Coronavirus Epidemic: The Case of Wuhan in Hubei Province, China

“…But as it became clearer that the strategy was working, they praised the Chinese efforts. 3 The WHO Director-General Tedros Adhanom Ghebreyesus stated, "The Chinese government is to be congratulated for the extraordinary measures it has taken to contain the outbreak, despite the severe social and economic impact those measures are having on the Chinese people." Dr. Gauden Galea, the WHO representative in China, also commended the Chinese efforts in containing the spread of Covid-19.…”

Measuring differences between phenomenological growth models applied to epidemiology

“…We herein focus on phenomenological growth models (PGMs) that only require a small number of parameters are commonly used to describe epidemic growth patterns, and which can be expressed by an ordinary differential equation (ODE) of the type where is time, is the total size of the epidemic (the cumulative number of cases) at time , is the initial number of cases, is an incidence function that is specific to each PGM under study, and is a vector of parameters. Such models have been used to study the epidemics of influenza [16] , [17] , [18] , Ebola [19] , [20] , [21] , [22] , Zika [23] , [24] , [25] , Chikungunya [26] , and others of global interest. The current COVID-19 pandemic is a scenario for which such models are of obvious importance [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] .…”