Public reaction to Chikungunya outbreaks in Italy-Insights from an extensive novel data streams-based structural equation modeling analysis

Use of Digital Tools in Arbovirus Surveillance: Scoping Review

BACKGROUND

The development of technology and information systems has led to important changes in public health surveillance.

OBJECTIVE

This scoping review aimed to assess the available evidence and gather information about the use of digital tools for arbovirus (dengue virus [DENV], zika virus [ZIKV], and chikungunya virus [CHIKV]) surveillance.

METHODS

The databases used were MEDLINE, SCIELO, LILACS, SCOPUS, Web of Science, and EMBASE. The inclusion criterion was defined as studies that described the use of digital tools in arbovirus surveillance. The exclusion criteria were defined as follows: letters, editorials, reviews, case reports, series of cases, descriptive epidemiological studies, laboratory and vaccine studies, economic evaluation studies, and studies that did not clearly describe the use of digital tools in surveillance. Results were evaluated in the following steps: monitoring of outbreaks or epidemics, tracking of cases, identification of rumors, decision-making by health agencies, communication (cases and bulletins), and dissemination of information to society).

RESULTS

Of the 2227 studies retrieved based on screening by title, abstract, and full-text reading, 68 (3%) studies were included. The most frequent digital tools used in arbovirus surveillance were apps (n=24, 35%) and Twitter, currently called X (n=22, 32%). These were mostly used to support the traditional surveillance system, strengthening aspects such as information timeliness, acceptability, flexibility, monitoring of outbreaks or epidemics, detection and tracking of cases, and simplicity. The use of apps to disseminate information to society (P=.02), communicate (cases and bulletins; P=.01), and simplicity (P=.03) and the use of Twitter to identify rumors (P=.008) were statistically relevant in evaluating scores. This scoping review had some limitations related to the choice of DENV, ZIKV, and CHIKV as arboviruses, due to their clinical and epidemiological importance.

CONCLUSIONS

In the contemporary scenario, it is no longer possible to ignore the use of web data or social media as a complementary strategy to health surveillance. However, it is important that efforts be combined to develop new methods that can ensure the quality of information and the adoption of systematic measures to maintain the integrity and reliability of digital tools' data, considering ethical aspects.

Impact of Sickle Cell Awareness Day on online health information seeking in Africa using Google Trends

The United Nations Council Assembly recognized sickle cell disease (SCD) as a global public health problem due to its increasing burden, particularly in sub-Saharan Africa. To raise awareness, a resolution was adopted, designating June 19th as SCD awareness day. However, the impact of this awareness day on online health information seeking behaviour (OHISB) in African countries is not well understood, especially in Nigeria, Ghana and Uganda where SCD prevalence is high. To assess the impact, the study used Google Trends data as a measure of OHISB for SCD. The analysis covered the 60 days before the awareness day, the awareness day itself, and the 60 days afterward. Time series analysis was conducted using joinpoint regression to identify significant changes in OHISB trends. The results indicated that the impact of the Sickle Cell Awareness Day on OHISB varied across African countries and did not consistently inspire significant changes in information seeking behaviour. This suggests the need for more targeted awareness campaigns to improve public knowledge of SCD in Africa. It also highlights the importance of revising the current awareness day or creating alternative health awareness initiatives that adopt a long-term approach and address the specific health needs of the African population. Furthermore, due to limitations in using Google Trends data in some African countries with insufficient data, future research should explore other sources of internet data or conduct surveys to gain a more comprehensive understanding of the impact of the Sickle Cell Awareness Day on OHISB in Africa.

Wikipedia page views for health research: a review

Wikipedia is an open-source online encyclopedia and one of the most-read sources of online health information. Likewise, Wikipedia page views have also been analyzed to inform public health services and policies. The present review analyzed 29 studies utilizing Wikipedia page views for health research. Most reviewed studies were published in recent years and emanated from high-income countries. Together with Wikipedia page views, most studies also used data from other internet sources, such as Google, Twitter, YouTube, and Reddit. The reviewed studies also explored various non-communicable diseases, infectious diseases, and health interventions to describe changes in the utilization of online health information from Wikipedia, to examine the effect of public events on public interest and information usage about health-related Wikipedia pages, to estimate and predict the incidence and prevalence of diseases, to predict data from other internet data sources, to evaluate the effectiveness of health education activities, and to explore the evolution of a health topic. Given some of the limitations in replicating some of the reviewed studies, future research can specify the specific Wikipedia page or pages analyzed, the language of the Wikipedia pages examined, dates of data collection, dates explored, type of data, and whether page views were limited to Internet users and whether web crawlers and redirects to the Wikipedia page were included. Future research can also explore public interest in other commonly read health topics available in Wikipedia, develop Wikipedia-based models that can be used to predict disease incidence and improve Wikipedia-based health education activities.

Online health information seeking for mpox in endemic and non-endemic countries: A Google Trends study. (Preprint)

BACKGROUND

The recent global outbreak of mpox (monkeypox) has already been declared a public health emergency of international concern by the World Health Organization. Given the health, social, and economic impacts of the COVID-19 pandemic, there is understandable concern and anxiety around the emergence of another infectious disease-especially one about which little is known.

OBJECTIVE

We used Google Trends to explore online health information seeking patterns for mpox in endemic and nonendemic countries and investigated the impact of the publication of the first in-country case on internet search volume.

METHODS

Google Trends is a publicly accessible and free data source that aggregates worldwide Google search data. Google search data were used as a surrogate measure of online health information seeking for 178 days between February 18 and August 18, 2022. Searching data were downloaded across this time period for nonendemic countries with the highest case count (United States, Spain, Germany, United Kingdom, and France) and 5 endemic countries (Democratic Republic of Congo, Nigeria, Ghana, Central African Republic, and Cameroon). Joinpoint regression analysis was used to measure changes in searching trends for mpox preceding and following the announcement of the first human case.

RESULTS

Online health information seeking significantly increased after the publication of the first case in all the nonendemic countries-United States, Spain, Germany, United Kingdom, and France, as illustrated by significant joinpoint regression models. Joinpoint analysis revealed that models with 3 significant joinpoints were the most appropriate fit for these data, where the first joinpoint represents the initial rise in mpox searching trend, the second joinpoint reflects the start of the decrease in the mpox searching trend, and the third joinpoint represents searching trends' return to searching levels prior to the first case announcement. Although this model was also found in 2 endemic countries (ie, Ghana and Nigeria), it was not found in Central African Republic, Democratic Republic of Congo, or Cameroon.

CONCLUSIONS

Findings demonstrate a surge in online heath information seeking relating to mpox after the first in-country case was publicized in all the nonendemic countries and in Ghana and Nigeria among the endemic counties. The observed increases in mpox searching levels are characterized by sharp but short-lived periods of searching before steep declines back to levels observed prior to the publication of the first case. These findings emphasize the importance of the provision of accurate, relevant online public health information during disease outbreaks. However, online health information seeking behaviors only occur for a short time period, and the provision of accurate information needs to be timely in relation to the publication of new case-related information.

Impact of a health campaign on Chinese public awareness of stroke: evidence from internet search data

INTRODUCTION

Health campaigns have the potential to improve public awareness, but their impact can be difficult to assess. Internet search data provide information concerning online health information-seeking behaviour in the population and may serve as a proxy for public awareness to evaluate health campaigns. This study aimed to measure the impact of World Stroke Day (WSD) in China using Baidu search data.

METHODS

Daily search index values (SIV) for the term 'stroke' were collected from January 2011 to December 2019 using the Baidu Index platform. We examined the mean difference in SIV between the 4 weeks surrounding WSD (period of interest) and the rest of the year (control period) for each year by t-test analysis. The mean difference between the period of interest and the control period was also calculated. The joinpoint regression model was used to analyse the trends of internet search activity 30 days before and after WSD for each year (2011-2019). Finally, the top and rising queries related to stroke during the week of the campaign in 2020 were summarised.

RESULTS

A significant mean increase in SIV of 418.5 (95% CI: 298.8 to 538.2) for the period of interest surrounding WSD was observed, 36.2% greater than the SIV during the control period (2011-2019). Short-term joinpoint analysis showed a significant increase in SIV 3 days before WSD, a peak on WSD and a decrease to the precampaign level 3 days after WSD. The rising related queries suggested that the public had increasing concerns about stroke warning signs, stroke prevention and stroke recovery during the campaign.

CONCLUSIONS

The WSD campaign increased internet search activity. These research techniques can be applied to evaluation of other health campaigns. Advancing understanding of public demand will enable tailoring of the campaign and strengthen health management.

Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective

SARS-CoV-2 has infected over ∼165 million people worldwide causing Acute Respiratory Distress Syndrome (ARDS) and has killed ∼3.4 million people. Artificial Intelligence (AI) has shown to benefit in the biomedical image such as X-ray/Computed Tomography in diagnosis of ARDS, but there are limited AI-based systematic reviews (aiSR). The purpose of this study is to understand the Risk-of-Bias (RoB) in a non-randomized AI trial for handling ARDS using novel AtheroPoint-AI-Bias (AP(ai)Bias). Our hypothesis for acceptance of a study to be in low RoB must have a mean score of 80% in a study. Using the PRISMA model, 42 best AI studies were analyzed to understand the RoB. Using the AP(ai)Bias paradigm, the top 19 studies were then chosen using the raw-cutoff of 1.9. This was obtained using the intersection of the cumulative plot of "mean score vs. study" and score distribution. Finally, these studies were benchmarked against ROBINS-I and PROBAST paradigm. Our observation showed that AP(ai)Bias, ROBINS-I, and PROBAST had only 32%, 16%, and 26% studies, respectively in low-moderate RoB (cutoff>2.5), however none of them met the RoB hypothesis. Further, the aiSR analysis recommends six primary and six secondary recommendations for the non-randomized AI for ARDS. The primary recommendations for improvement in AI-based ARDS design inclusive of (i) comorbidity, (ii) inter-and intra-observer variability studies, (iii) large data size, (iv) clinical validation, (v) granularity of COVID-19 risk, and (vi) cross-modality scientific validation. The AI is an important component for diagnosis of ARDS and the recommendations must be followed to lower the RoB.

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Using Google Trends to assess the impact of global public health days on online health information seeking behaviour in Central and South America

Background

Public health awareness can help prevent illness and result in earlier intervention when it does occur. For this reason, health promotion and disease awareness campaigns have great potential to alleviate the global burden of disease. Global Public Health Days (GPHD) are frequently implemented with this intent, but research evaluating their effectiveness, especially in the developing world setting, is scant.

Objectives

We aimed to evaluate the impact of four GPHDs (World Cancer Day, World Diabetes Day, World Mental Health Day, World AIDS Day) on online health information seeking behaviour (OHISB) in five Central and South American (CSA) countries which differ in their stage of economic development and epidemiological transition (Uruguay, Chile, Brazil, Colombia, Nicaragua).

Methods

Google Trends data was used as a ‘surrogate’ of OHISB. This was measured on the 28 days leading up to the GPHD, on the date of the GPHD, and on the seven days following it. The Joinpoint regression programme was used to perform a time trend analysis on the Google Trends data. This allowed us to identify statistically significant time points of a change in trend, which reflect significant ‘changes’ to OHISB.

Results

GPHDs were inconsistently effective at influencing internet search query activity in the studied countries. In situations where an effect was significant, this impact was consistently short-term, with Relative Search Volume level returning to precampaign levels within 7 days of the GPHD.

Conclusions

Our findings imply the need to revise GPHDs or create alternative health awareness campaigns, perhaps with a more long-term approach and tailored to the specific health needs of the CSA population. Developing effective preventive strategies is vital in helping combat the rising threat of NCDs in this region.

Coronavirus disease 2019 (COVID-19): an evidence map of medical literature

BACKGROUND

Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of June 2020, gaps and longitudinal trends in the COVID-19 medical literature remain unidentified, despite potential benefits for research prioritisation and policy setting in both the COVID-19 pandemic and future large-scale public health crises.

METHODS

In this paper, we searched PubMed and Embase for medical literature on COVID-19 between 1 January and 24 March 2020. We characterised the growth of the early COVID-19 medical literature using evidence maps and bibliometric analyses to elicit cross-sectional and longitudinal trends and systematically identify gaps.

RESULTS

The early COVID-19 medical literature originated primarily from Asia and focused mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health, the use of novel technologies and artificial intelligence, pathophysiology of COVID-19 within different body systems, and indirect effects of COVID-19 on the care of non-COVID-19 patients. Few articles involved research collaboration at the international level (24.7%). The median submission-to-publication duration was 8 days (interquartile range: 4-16).

CONCLUSIONS

Although in its early phase, COVID-19 research has generated a large volume of publications. However, there are still knowledge gaps yet to be filled and areas for improvement for the global research community. Our analysis of early COVID-19 research may be valuable in informing research prioritisation and policy planning both in the current COVID-19 pandemic and similar global health crises.

Artificial Intelligence Applications in Tracking Health Behaviors During Disease Epidemics

The threat of emerging and re-emerging infectious diseases to global population health remains significantly enormous, and the pandemic preparedness capabilities necessary to confront such threats must be of greater potency. Artificial Intelligence (AI) offers new hope in not only effectively pre-empting, preventing and combating the threats of infectious disease epidemics, but also facilitating the understanding of health-seeking behaviors and public emotions during epidemics. From a systems-thinking perspective, and in today’s world of seamless boundaries and global interconnectivity, AI offers enormous potential for public health practitioners and policy makers to revolutionize healthcare and population health through focussed, context-specific interventions that promote cost-savings on therapeutic care, expand access to health information and services, and enhance individual responsibility for their health and well-being. This chapter systematically appraises the dawn of AI technology towards empowering population health to combat the rise of infectious disease epidemics.

Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study

BACKGROUND

Plague is a highly infectious zoonotic disease caused by the bacillus Yersinia pestis. Three major forms of the disease are known: bubonic, septicemic, and pneumonic plague. Though highly related to the past, plague still represents a global public health concern. Cases of plague continue to be reported worldwide. In recent months, pneumonic plague cases have been reported in Madagascar. However, despite such a long-standing and rich history, it is rather difficult to get a comprehensive overview of the general situation. Within the framework of electronic health (eHealth), in which people increasingly search the internet looking for health-related material, new information and communication technologies could enable researchers to get a wealth of data, which could complement traditional surveillance of infectious diseases.

OBJECTIVE

In this study, we aimed to assess public reaction regarding the recent plague outbreak in Madagascar by quantitatively characterizing the public's interest.

METHODS

We captured public interest using Google Trends (GT) and correlated it to epidemiological real-world data in terms of incidence rate and spread pattern.

RESULTS

Statistically significant positive correlations were found between GT search data and confirmed (R2=0.549), suspected (R2=0.265), and probable (R2=0.518) cases. From a geospatial standpoint, plague-related GT queries were concentrated in Toamasina (100%), Toliara (68%), and Antananarivo (65%). Concerning the forecasting models, the 1-day lag model was selected as the best regression model.

CONCLUSIONS

An earlier digital Web search reaction could potentially contribute to better management of outbreaks, for example, by designing ad hoc interventions that could contain the infection both locally and at the international level, reducing its spread.

Reducing Aedes albopictus breeding sites through education: A study in urban area

Aedes albopictus tends to proliferate in small, often man-made bodies of water, largely present in urban private areas. For this reason, education and community participation are considered crucial for source reduction and mosquito control. In the current study, we identify mosquito breeding habitat and evaluate the effectiveness of resident education. Since 2010 several outbreaks of West Nile virus infection occurred in Greece however urban population has no previous experience with mosquito–borne disease related to Aedes species, such as Dengue, Zika and Chikungunya. After the introduction of Ae. albopictus in Greece, urban areas have been considered to be at risk of epidemic arboviral outbreaks and identifying effective control strategies is imperative. Our study examines the relationship between mosquito breeding sources and socioeconomic or demographic characteristics of different households in a Greek municipality and evaluates efficacy of resident education. The results revealed that only a minority of residents knew where mosquitoes breed (18.6%) and only 46% felt that residents had any responsibility for managing breeding habitat. Our findings strongly suggest that only the presence of scientific staff inspecting possible habitats in their properties, could be enough to stimulate practices towards source reduction. However, educational interventions alone with printed education material cannot enhance significant community participation and source reduction.

Artificial Intelligence for infectious disease Big Data Analytics

BACKGROUND

Since the beginning of the 21st century, the amount of data obtained from public health surveillance has increased dramatically due to the advancement of information and communications technology and the data collection systems now in place.

METHODS

This paper aims to highlight the opportunities gained through the use of Artificial Intelligence (AI) methods to enable reliable disease-oriented monitoring and projection in this information age.

RESULTS AND CONCLUSION

It is foreseeable that together with reliable data management platforms AI methods will enable analysis of massive infectious disease and surveillance data effectively to support government agencies, healthcare service providers, and medical professionals to response to disease in the future.

The Surveillance of Chikungunya Virus in a Temperate Climate: Challenges and Possible Solutions from the Experience of Lazio Region, Italy

CHIKV has become an emerging public health concern in the temperate regions of the Northern Hemisphere as a consequenceof the expansion of the endemic areas of its vectors (mainly Aedes aegypti and Aedesalbopictus). In 2017, a new outbreak of CHIKV was detected in Italy with three clusters of autochthonous transmission in the Lazio Region (central Italy), in the cities of Anzio, Rome, and Latina and a secondary cluster in the Calabria Region (south Italy). Given the climate characteristics of Italy, sporadic outbreaks mostly driven by imported cases followed by autochthonous transmission could occur during the summer season. This highlights the importance of a well-designed surveillance system, which should promptly identify autochthonous transmission. The use of a surveillance system integrating different surveillance tools, including entomological surveillance in a one health approach, together with education of the health care professionals should facilitate the detection, response, and control of arboviruses spreading.