New digital technologies for the surveillance of infectious diseases at mass gathering events

Artificial intelligence (AI) in Monkeypox infection prevention

Monkeypox is a possible public health concern that requires appropriate attention in order to prevent the spread of the disease. Currently, artificial intelligence (AI) is making a significant impact on precision medicine, reshaping and integrating the large amount of data derived from multiomics analyses and revolutionizing the deep-learning strategies. There has been a significant progress in the use of AI to detect, screen, diagnose, and classify diseases, characterize virus genomes, assess biomarkers for prognostic and predictive purposes, and develop follow-up strategies. Hence, it is possible to use AI for the identification of disease clusters, cases monitoring, forecasting the future outbreak, determining mortality risk, diagnosing, managing, and identifying patterns for studying disease trends. AI may also be utilized to assist gene therapy and other therapies that we are not currently able to use in healthcare. It is possible to combine pharmacology and gene therapy with regenerative medicine with the help of AI. It will directly benefit the public in overcoming fear and panic of health risks. Therefore, AI can be an effective weapon to fight against Monkeypox infection, and may prove to be an invaluable future tool in improving the clinical management of patients. Key Points: Emergence and spread of the Monkeypox virus is a new public health crisis; threatening the world. This opinion piece highlights the urgently required information for immediate delivery of solutions on controlling and monitoring the spread of Monkeypox infection through Artificial IntelligenceCommunicated by Ramaswamy H. Sarma.

The moderating role of Covid-19-related support on urban livelihood capitals: Evidence from suburban Accra

In the Global South, the COVID-19 crisis has compelled varied efforts to quickly address the pandemic's impact on urban livelihoods. Families, friends as well as public, private, and civil society organizations have mobilized various resources to avert the pandemic's onslaught on the survival of the urban vulnerable. Indeed, there is a burgeoning ‘pandemic urban scholarship’ that shed insights on COVID-19 risks, local responses, and impacts on everyday urban life. Yet, it is unclear how many of these responses are affecting urban livelihoods. This paper thus investigates the impact of COVID-19 on urban livelihood capitals (financial, human, social, and physical) and analyses the moderating role of COVID-19-related support (from families, friends, government agencies, faith-based and non-governmental organizations) to address the pandemic's impact on these capitals. Drawing on a quantitative study in Adenta Municipality of the Greater Accra Region, Ghana, the study finds a negative association between COVID-19 impacts and all urban livelihood capitals. Crucially, COVID-19-related support only reduced the negative impact of the pandemic on financial capital, and not on the other forms of capital. The study suggests that building post-pandemic community resilience warrants the need to transition from the usual reactive, fragmented support to integrated, holistic, and contextually embedded long-term strategies that consider the multi-dimensionality of everyday urban life.

Global event-based surveillance of chemical incidents

BACKGROUND

The number of chemicals in our society and in our daily lives continues to increase. Accompanying this is an increasing risk of human exposure to and injury from hazardous substances. Performing regular, structured surveillance of chemical incidents allows a greater awareness of the types of chemical hazards causing injury and the frequency of their occurrence, as well as providing a better understanding of exposures.

OBJECTIVE

The objective of performing event-based surveillance (EBS) and capturing chemical incidents is to use this information to increase the situational awareness of chemical incidents, improve the management of these incidents and to inform measures to protect public health.

METHODS

This paper describes a method for EBS for chemical incidents, including the sources used, storing the gathered information and subsequent analysis of potential trends in the data.

RESULTS

We describe trends in the type of incidents that have been detected, the chemicals involved in these incidents and the health effects caused, in different geographic regions of the world.

SIGNIFICANCE

The methodology presented here provides a rapid and simple means of identifying chemical incidents that can be set up rapidly and with minimal cost, the outputs of which can be used to identify emerging risks and inform preparedness planning, response and training for chemical incidents.

Governing digital health for infectious disease outbreaks

ABSTRACTHow can governing digital health for infectious disease outbreaks be enhanced? In many ways, the COVID-19 pandemic has simultaneously represented both the potential and marked limitations of digital health practices for infectious disease outbreaks. During the pandemic's initial stages, states along with Big Data and Big Tech actors unleashed a scope of both established and experimental digital technologies for tracking infections, hospitalisations, and deaths from COVID-19 - and sometimes exposure to the virus SARS-CoV-2. Despite the proliferation of these technologies at the global level, transnational and cross-border integration, and cooperation within digital health responses to COVID-19 often faltered, while digital health regulations were fragmented, contested, and uncoordinated. This article presents a critiquing reflection of approaches to conceptualising, understanding, and implementing digital health for infectious disease outbreaks, observed from COVID-19 and previous examples. In assessing the strengths and limitations of existing practices of governing digital health for infectious disease outbreaks, this article particularly examines 'informal' digital health to build upon and consider how digitised responses to addressing and governing infectious disease outbreaks may be reconceptualised, revisited, or revised.

The impact of crowd gatherings on the spread of COVID-19

Crowd gatherings are an important cause of COVID-19 outbreaks. However, how the scale, scene and other factors of gatherings affect the spread of the epidemic remains unclear. A total of 184 gathering events worldwide were collected to construct a database, and 99 of them with a clear gathering scale were used for statistical analysis of the impact of these factors on the disease incidence among the crowd in the study. The results showed that the impact of small-scale (less than 100 people) gathering events on the spread of COVID-19 in the city is also not to be underestimated due to their characteristics of more frequent occurrence and less detection and control. In our dataset, 22.22% of small-scale events have an incidence of more than 0.8. In contrast, the incidence of most large-scale events is less than 0.4. Gathering scenes such as "Meal" and "Family" occur in densely populated private or small public places have the highest incidence. We further designed a model of epidemic transmission triggered by crowd gathering events and simulated the impact of crowd gathering events on the overall epidemic situation in the city. The simulation results showed that the number of patients will be drastically reduced if the scale and the density of crowds gathering are halved. It indicated that crowd gatherings should be strictly controlled on a small scale. In addition, it showed that the model well reproduce the epidemic spread after crowd gathering events better than does the original SIER model and could be applied to epidemic prediction after sudden gathering events.

Measles Epidemiology and Viral Nucleoprotein Gene Evolution in Shandong Province, China

Measles, caused by measles virus (MeV), has not been eradicated in many regions and countries, threatening human health. Thus, it is beneficial for measles elimination to understand measles epidemiology and molecular evolution of key viral genes, such as nucleoprotein (N) gene. Based on public data, measles epidemiological information and MeV N gene sequences reported in Shandong Province, China were comprehensively collected and systematically analyzed. The results showed a positive correlation between population density and measles incidence (r = + 0.31), while negative correlations were found between measles incidence and healthcare condition (r = - 0.21) as well as average routine vaccination rate (r = - 0.11). Additionally, the predominant lineage of MeV in Shandong was formed by genotype H1 strains, and the time of the most recent common ancestor of the N gene of MeV genotype H1 in Shandong traced back to 1987 (95% highest posterior density, 1984-1990) with relatively rapid evolution (mean rate, 1.267×10^-3 substitutions/site/year). The genetic diversity of MeV N gene increased with the substantial emergence of major divergent clades of genotype H1 before 2005 and then remained relatively stable. In summary, these findings provided a significant insight into the measles elimination. This article is protected by copyright. All rights reserved.

The Korean 3T Practice: New Biosurveillance Model Utilizing New Information Technology and Digital Tools

In South Korea, COVID-19 pandemic responses, namely the 3T (testing, tracing, and treating) strategy, emerged as a new biosurveillance regime actively using new information technology (IT) and digital tools. The foundation of the Korean 3T system is epidemiological investigation efforts and clinical practices exploiting the use of new digital and IT tools. Due to these unique features, the Korean 3T system can be referred to as a “contact-based biosurveillance system,” which is an advanced version of the traditional biosurveillance models (indicator-based or event-based models). This article illustrates how the contact-based biosurveillance system originated from the experience with the 2015 Middle East Respiratory Syndrome (MERS) outbreak. The post-MERS Korean biosurveillance regime actively adopted the utility of new digital and IT tools to strengthen not only the ex-ante epidemic intelligence capabilities (by traditional models) but also the ex-post response and recovery capabilities (digital contact tracing and digital health intervention). However, critics claim that the Korean 3T system may violate individuals’ privacy and human rights by addressing the fact that the Korean biosurveillance system would strengthen social surveillance and population control by the government as a “digital big brother” in the cyber age. Nevertheless, 3T biosurveillance promises a positive future direction for digital health practice in the current biosurveillance regimes.

Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis

Background

Association between human mobility and disease transmission has been established for COVID-19, but quantifying the levels of mobility over large geographical areas is difficult. Google has released Community Mobility Reports (CMRs) containing data about the movement of people, collated from mobile devices.

Objective

The aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India.

Methods

In this ecological study, we analyzed CMRs to determine human mobility between March and October 2020. The data were compared for the phases before the lockdown (between March 14 and 25, 2020), during lockdown (March 25-June 7, 2020), and after the lockdown (June 8-October 15, 2020) with the reference periods (ie, January 3-February 6, 2020). Another data set depicting the burden of COVID-19 as per various disease severity indicators was derived from a crowdsourced API. The relationship between the two data sets was investigated using the Kendall tau correlation to depict the correlation between mobility and disease severity.

Results

At the national level, mobility decreased from –38% to –77% for all areas but residential (which showed an increase of 24.6%) during the lockdown compared to the reference period. At the beginning of the unlock phase, the state of Sikkim (minimum cases: 7) with a –60% reduction in mobility depicted more mobility compared to –82% in Maharashtra (maximum cases: 1.59 million). Residential mobility was negatively correlated (–0.05 to –0.91) with all other measures of mobility. The magnitude of the correlations for intramobility indicators was comparatively low for the lockdown phase (correlation ≥0.5 for 12 indicators) compared to the other phases (correlation ≥0.5 for 45 and 18 indicators in the prelockdown and unlock phases, respectively). A high correlation coefficient between epidemiological and mobility indicators was observed for the lockdown and unlock phases compared to the prelockdown phase.

Conclusions

Mobile-based open-source mobility data can be used to assess the effectiveness of social distancing in mitigating disease spread. CMR data depicted an association between mobility and disease severity, and we suggest using this technique to supplement future COVID-19 surveillance.

Assessment of core and support functions of the communicable disease surveillance system in the Kurdistan Region of Iraq

Early detection and prompt response are crucial measures to prevent and control outbreaks. Public health agencies, therefore, designed the Communicable Disease Surveillance System (CDSS) to obtain essential data instantaneously to be used for appropriate action. However, a periodic evaluation of CDSS is indispensable to ensure the functionality of the system. For this reason, this study aims to assess the performance of the core and support functions of the CDSS in the Kurdistan Region of Iraq. A descriptive cross‐sectional study was used. From a total of 291 health facilities HFs (Primary health care centers and Hospitals) in the Kurdistan region of Iraq that have surveillance activities, 74 HFs were selected using a random stratified sampling approach. The World Health Organization (WHO) generic questionnaire has been used to interview the surveillance staff, together with direct collection of the data. Our analysis shows a lack of surveillance guiding manual in the HFs. Even at the district level, where a surveillance manual existed, case definitions, thresholds, and control measures were still missing. To note, more than 93% of HFs had organized and comprehensive patients registers for the collection of their clinical and secondary data. Also, all HFs had functioning laboratories. The majority of them (almost 93%) were equipped to collect, process, and store blood, stool, and urine specimens. About 72% of these laboratories were also able to transport timely the specimens to more specialized laboratories. At all levels, data reporting to the higher level exceeded the recommended minimum rate of 80%. The reporting system at the district level was based on emails, while in the periphery on hand‐delivered in paper‐based formats (50%), telephone (22%), and social media (22%). Furthermore, our analysis highlights the lack of data analysis: only 3.8% of Primary Health Care Centers conduct simple data analysis regularly, while hospitals do not do any sort of analysis. Also, only a few HFs investigated an outbreak, though using system routine sources to capture these public health events. Our findings show a lack in epidemic preparedness (3%), in feedback (53%), in standard guidelines, training, supervision, and resource allocations in HFs (0%). Taken together, our data show the importance of strengthening the CDSS in the Kurdistan region of Iraq, by reinforcing the surveillance system with continuous feedback, supervision, well‐trained and motivated staff, technical support, and coordination between researchers and physicians.

Public health risks threatening health of people participating in mass gatherings: A qualitative study

Background

Mass gatherings (MGs) have made many problems for the health system and potentially threaten the health of those participated in these gatherings. MGs account for a range of public health risks including communicable diseases, waterborne diseases, water treatment, and outbreaks.

Objectives

The present study aimed to identify public health risks threatening the health of people participating in MGs in Iran.

Methods

A qualitative study was designed using a conventional qualitative content analysis approach. Participants were selected using a purposive sampling method among the managers and staff who have experiences in the field of health in MGs in Iran. Subjects were interviewed through individual in-depth interviews after obtaining informed consent. Semi-structured interviews were used to collect the data from December 2018 to February 2019. To analyze the data, the content of recorded interviews was written verbatim. Preliminary data were repeatedly and simultaneously retrieved to give a general understanding of them.

Results

The main theme emerged from data analysis on 16 interviews was related to public health risks involving the following categories: food hygiene deficiencies, communicable diseases risks, defects in health supervision, and the risk of environmental contamination.

Conclusion

It is of paramount importance to identify the risks threatening the health of people participating in MGs. In the present study, the main public health risks were detected.

Future developments in training

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the value of highly skilled and extensively trained specialists in clinical microbiology (CM) and infectious diseases (ID). Training curricula in CM and ID must constantly evolve to prepare trainees for future pandemics and to allow trainees to reach their full clinical and academic potential.

OBJECTIVES

In this narrative review, we aim to outline necessary future adaptations in CM and ID training curricula and identify current structural barriers in training with the aim of discussing possibilities to address these shortcomings.

SOURCES

We reviewed literature from PubMed and included selected books and online publications as appropriate. There was no time constraint on the included publications.

CONTENT

Drawing from the lessons learnt during the pandemic, we summarize novel digital technologies relevant to CM and ID trainees and highlight interdisciplinary teamwork and networking skills as important competencies. We centre CM and ID training within the One Health framework and discuss gender inequalities and structural racism as barriers in both CM and ID training and patient care.

IMPLICATIONS

CM and ID trainees should receive training and support developing skills in novel digital technologies, leadership, interdisciplinary teamwork and networking. Equally important is the need for equity of opportunity, with firm commitments to end gender inequality and structural racism in CM and ID. Policy-makers and CM and ID societies should ensure that trainees are better equipped to achieve their professional goals and are better prepared for the challenges awaiting in their fields.

Learning Transferable Driven and Drone Assisted Sustainable and Robust Regional Disease Surveillance for Smart Healthcare

Smart healthcare has been applied in many fields such as disease surveillance and telemedicine, etc. However, there are some challenges for device deployment, data collection and guarantee of stainability in regional disease surveillance. First, it is difficult to deploy sensors and adjust the sensor network in unknown region for dynamic disease surveillance. Second, the limited life-cycle of sensor network may cause the loss of surveillance data. Thus, it is important to provide a sustainable and robust regional disease surveillance system. Given a set of Disease surveillance Area (DsA)s and Point of disease Surveillance (PoS)s, some sensors are deployed to monitor these PoSs, and a drone collect data from the sensors as well as charge the sensors to extend their life-cycles. The drone replenish its energy by relying on the bus network. We first formulate the drone assisted regional disease surveillance problem under the constraints of life-cycle of sensors and energy of drone, and propose an approximation algorithm to find a feasible cycle of drone to minimize the traveling time cost of drone. To satisfy the diversity requirements and dynamic scalability of regional disease surveillance, we deploy one robot in each DsA instead of sensors. We further formulate the learning transferable driven regional disease surveillance problem, and propose a joint schedule algorithm of drone and robots. The results of both theoretical analysis and extensive simulations show that the proposed algorithms can reduce the total time cost by 39.71 and 48.74 percent, average waiting time by 42.00 and 50.14 percent, and increase the average accessing ratio of PoSs by 15.53 and 22.30 percent, through the assistance of bus network and learning transferable features.

Disease surveillance during a large religious mass gathering in India: The Prayagraj Kumbh 2019 experience

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At Kumbh Mela 2019, disease surveillance was established for 22 acute diseases and syndromes.

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Among the reported illnesses, 95% were communicable diseases such as acute respiratory illness (35%), acute fever (28%), and skin infections (18%).

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The incident command centre generated 12 early warning signals from indicator-based and event-based surveillance: acute diarrheal diseases (n = 8, 66%), vector-borne diseases (n = 2, 16%), vaccine-preventable disease (n = 1, 8%), and thermal event (n = 1, 8%).

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There were two outbreaks (acute gastroenteritis and chickenpox) that were investigated and controlled.

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Implementation of disease surveillance facilitated early outbreak detection and response.

Background

Mass gathering (MG) events are associated with public health risks. During the period January 14 to March 4, 2019, Kumbh Mela in Prayagraj, India was attended by an estimated 120 million visitors. An onsite disease surveillance was established to identify and respond to disease outbreaks.

Methods

A health coordination committee was established for planning. Disease surveillance was prioritized and risk assessment was done to identify diseases/conditions based on epidemic potential, severity of illness, and reporting requirement under the International Health Regulations (IHR) of 2005. A daily indicator and event-based disease surveillance was planned. The indicator-based surveillance (IBS) manually and electronically recorded data from patient hospital visits and collected MG area water testing data to assess trends. The event-based surveillance (EBS) helped identify outbreak signals based on pre-identified event triggers from the media, private health facilities, and the food safety department. Epidemic intelligence was used to analyse the data and events to detect signals, verify alerts, and initiate the response.

Results

At Kumbh Mela, disease surveillance was established for 22 acute diseases/syndromes. Sixty-five health facilities reported 156 154 illnesses (21% of a total 738 526 hospital encounters). Among the reported illnesses, 95% (n = 148 834) were communicable diseases such as acute respiratory illness (n = 52 504, 5%), acute fever (n = 41 957, 28%), and skin infections (n = 27 094, 18%). The remaining 5% (n = 7300) were non-communicable diseases (injuries n = 6601, 90%; hypothermia n = 224, 3%; burns n = 210, 3%). Water samples tested inadequate for residual chlorine in 20% of samples (102/521). The incident command centre generated 12 early warning signals from IBS and EBS: acute diarrheal disease (n = 8, 66%), vector-borne disease (n = 2, 16%), vaccine-preventable disease (n = 1, 8%), and thermal event (n = 1, 8%). There were two outbreaks (acute gastroenteritis and chickenpox) that were investigated and controlled.

Conclusions

This onsite disease surveillance imparted a public health legacy by successfully implementing an epidemic intelligence enabled system for early disease detection and response to monitor public health risks. Acute respiratory illnesses emerged as a leading cause of morbidity among visitors. Future MG events should include disease surveillance as part of planning and augment capacity for acute respiratory illness diagnosis and management.

Towards health monitoring using remote heart rate measurement using digital camera: A feasibility study

The paper presents a feasibility study for heart rate measurement using a digital camera to perform health monitoring. The feasibility study investigates the reliability of the state of the art heart rate measuring methods in realistic situations. Therefore, an experiment was designed and carried out on 45 subjects to investigate the effects caused by illumination, motion, skin tone, and distance variance. The experiment was conducted for two main scenarios; human-computer interaction scenario and health monitoring scenario. The human-computer scenario investigated the effects caused by illumination variance, motion variance, and skin tone variance. The health monitoring scenario investigates the feasibility of health monitoring at public spaces (i.e. airports, subways, malls). Five state of the art heart rate measuring methods were re-implemented and tested with the feasibility study database. The results were compared with ground truth to estimate the heart rate measurement error. The heart rate measurement error was analyzed using mean error, standard deviation; root means square error and Pearson correlation coefficient. The findings of this experiment inferred promising results for health monitoring of subjects standing at a distance of 500 cm.

World's Largest Mass Bathing Event Influences the Bacterial Communities of Godavari, a Holy River of India

Kumbh Mela is one of the largest religious mass gathering events (MGE) involving bathing in rivers. The exponential rise in the number of devotees, from around 0.4 million in 1903 to 120 million in 2013, bathing in small specified sites can have a dramatic impact on the river ecosystem. Here, we present the spatiotemporal profiling of bacterial communities in Godavari River, Nashik, India, comprising five sites during the Kumbh Mela, held in 2015. Assessment of environmental parameters indicated deterioration of water quality. Targeted amplicon sequencing demonstrates approximately 37.5% loss in microbial diversity because of anthropogenic activity during MGE. A significant decrease in phyla viz. Actinobacteria, Chloroflexi, Proteobacteria, and Bacteroidetes was observed, while we noted substantial increase in prevalence of the phylum Firmicutes (94.6%) during MGE. qPCR estimations suggested nearly 130-fold increase in bacterial load during the event. Bayesian mixing model accounted the source of enormous incorporation of bacterial load of human origin. Further, metagenomic imputations depicted increase in virulence and antibiotic resistance genes during the MGE. These observations suggest the striking impact of the mass bathing on river ecosystem. The subsequent increase in infectious diseases and drug-resistant microbes pose a critical public health concern.

Contemporary challenges and opportunities in the diagnosis and outbreak detection of multidrug-resistant infectious disease

INTRODUCTION

The dissemination of multi-drug resistant bacteria (MDRB) has become a major public health concern worldwide because of the increase in infections caused by MDRB, the difficulty in treating them, and expenditures in patient care. Areas covered: We have reviewed challenges and contemporary opportunities for rapidly confronting infections caused by MDRB in the 21st century, including surveillance, detection, identification of resistance mechanisms, and action steps. Expert commentary: In this context, the first critical point for clinical microbiologists is to be able to rapidly detect an abnormal event, an outbreak and/or the spread of a MDRB with surveillance tools so that healthcare policies and therapies adapted to a new stochastic event that will certainly occur again in the future can be implemented.

Mass Gatherings and Respiratory Disease Outbreaks in the United States - Should We Be Worried? Results from a Systematic Literature Review and Analysis of the National Outbreak Reporting System

BACKGROUND

Because mass gatherings create environments conducive for infectious disease transmission, public health officials may recommend postponing or canceling large gatherings during a moderate or severe pandemic. Despite these recommendations, limited empirical information exists on the frequency and characteristics of mass gathering-related respiratory disease outbreaks occurring in the United States.

METHODS

We conducted a systematic literature review to identify articles about mass gathering-related respiratory disease outbreaks occurring in the United States from 2005 to 2014. A standard form was used to abstract information from relevant articles identified from six medical, behavioral and social science literature databases. We also analyzed data from the National Outbreaks Reporting System (NORS), maintained by the Centers for Disease Control and Prevention since 2009, to estimate the frequency of mass gathering-related respiratory disease outbreaks reported to the system.

RESULTS

We identified 21 published articles describing 72 mass gathering-related respiratory disease outbreaks. Of these 72, 40 (56%) were associated with agriculture fairs and Influenza A H3N2v following probable swine exposure, and 25 (35%) with youth summer camps and pandemic Influenza A H1N1. Outbreaks of measles (n = 1) and mumps (n = 2) were linked to the international importation of disease. Between 2009 and 2013, 1,114 outbreaks were reported to NORS, including 96 respiratory disease outbreaks due to Legionella. None of these legionellosis outbreaks was linked to a mass gathering according to available data.

CONCLUSION

Mass gathering-related respiratory disease outbreaks may be uncommon in the United States, but have been reported from fairs (zoonotic transmission) as well as at camps where participants have close social contact in communal housing. International importation can also be a contributing factor. NORS collects information on certain respiratory diseases and could serve as a platform to monitor mass gathering-related respiratory outbreaks in the future.

Social Media, Big Data, and Mental Health: Current Advances and Ethical Implications

Mental health (including substance abuse) is the fifth greatest contributor to the global burden of disease, with an economic cost estimated to be US $2.5 trillion in 2010, and expected to double by 2030. Developing information systems to support and strengthen population-level mental health monitoring forms a core part of the World Health Organization's Comprehensive Action Plan 2013-2020. In this paper, we review recent work that utilizes social media "big data" in conjunction with associated technologies like natural language processing and machine learning to address pressing problems in population-level mental health surveillance and research, focusing both on technological advances and core ethical challenges.

Pilot use of a novel smartphone application to track traveller health behaviour and collect infectious disease data during a mass gathering: Hajj pilgrimage 2014

This study examines the feasibility of using a smartphone application (app) to conduct surveys among travellers during the Hajj pilgrimage, where the use of apps has not been evaluated for infectious disease surveillance. A longitudinal study was conducted among pilgrims at the Hajj 2014 using an iPhone app with separate questionnaires for three study phases covering before, during, and after Hajj. Forty-eight pilgrims from 13 countries downloaded the app. Respondents were aged between 21 and 61 (median 36) years and 58.5% (24/41) were male. Of these, 85% (41/48) completed the first phase, 52% (25/41) completed both the second and third phases, and 25 of these reported meningococcal vaccination, with 36% (9/25) receiving other vaccines. All (25) reported hand hygiene use and 64% (16/25) wore a facemask at some point during the pilgrimage. Four (6%) reported close contact with camels. Respiratory symptoms commenced from the 4th day of Hajj, with sore throat (20%) and cough (12%) being the most common. Three participants (12%) reported respiratory symptoms after returning home. Conducting a prospective survey using a smartphone app to collect data on travel-associated infections and traveller compliance to prevention is feasible at mass gatherings and can provide useful data associated with health-related behaviour.