Infectious disease surveillance and modelling across geographic frontiers and scientific specialties

Role of artificial intelligence in early diagnosis and treatment of infectious diseases

Infectious diseases remain a global health challenge, necessitating innovative approaches for their early diagnosis and effective treatment. Artificial Intelligence (AI) has emerged as a transformative force in healthcare, offering promising solutions to address this challenge. This review article provides a comprehensive overview of the pivotal role AI can play in the early diagnosis and treatment of infectious diseases. It explores how AI-driven diagnostic tools, including machine learning algorithms, deep learning, and image recognition systems, enhance the accuracy and efficiency of disease detection and surveillance. Furthermore, it delves into the potential of AI to predict disease outbreaks, optimise treatment strategies, and personalise interventions based on individual patient data and how AI can be used to gear up the drug discovery and development (D3) process.The ethical considerations, challenges, and limitations associated with the integration of AI in infectious disease management are also examined. By harnessing the capabilities of AI, healthcare systems can significantly improve their preparedness, responsiveness, and outcomes in the battle against infectious diseases.

Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine

Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.

A Novel Social Distancing Approach for Limiting the Number of Vehicles in Smart Buildings Using LiFi Hybrid-Network

The coronavirus (COVID-19) has arisen as one of the most severe problems due to its ongoing mutations as well as the absence of a suitable cure for this virus. The virus primarily spreads and replicates itself throughout huge groups of individuals through daily touch, which regretfully can happen in several unanticipated way. As a result, the sole viable attempts to constrain the spread of this new virus are to preserve social distance, perform contact tracing, utilize suitable safety gear, and enforce quarantine measures. In order to control the virus's proliferation, scientists and officials are considering using several social distancing models to detect possible diseased individuals as well as extremely risky areas to sustain separation and lockdown procedures. However, models and systems in the existing studies heavily depend on the human factor only and reveal serious privacy vulnerabilities. In addition, no social distancing model/technique was found for monitoring, tracking, and scheduling vehicles for smart buildings as a social distancing approach so far. In this study, a new system design that performs real-time monitoring, tracking, and scheduling of vehicles for smart buildings is proposed for the first time named the social distancing approach for limiting the number of vehicles (SDA-LNV). The proposed model employs LiFi technology as a wireless transmission medium for the first time in the social distance (SD) approach. The proposed work is considered as Vehicle-to-infrastructure (V2I) communication. It might aid authorities in counting the volume of likely affected people. In addition, the proposed system design is expected to help reduce the infection rate inside buildings in areas where traditional social distancing techniques are not used or applicable.

Disease transmission and mass gatherings: a case study on meningococcal infection during Hajj

Background

Mass gatherings can not only trigger major outbreaks on-site but also facilitate global spread of infectious pathogens. Hajj is one of the largest mass gathering events worldwide where over two million pilgrims from all over the world gather annually creating intense congestion.

Methods

We developed a meta-population model to represent the transmission dynamics of Neisseria meningitidis and the impact of Hajj pilgrimage on the risk of invasive meningococcal disease (IMD) for pilgrims population, local population at the Hajj site and country of origin of Hajj pilgrims. This model was calibrated using data on IMD over 17 years (1995–2011) and further used to simulate potential changes in vaccine policy and endemic conditions.

Results

The effect of increased density of contacts during Hajj was estimated to generate a 78-fold increase in disease transmission that impacts not only pilgrims but also the local population. Quadrivalent ACWY vaccination was found to be very effective in reducing the risk of outbreak during Hajj. Hajj has more limited impact on IMD transmission and exportation in the pilgrim countries of origin, although not negligible given the size of the population considered.

Conclusion

The analysis performed highlighted the amplifying effect of mass gathering on N. meningitidis transmission and confirm vaccination as a very effective preventive measure to mitigate outbreak risks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07234-4.

Wireless Technologies for Social Distancing in the Time of COVID-19: Literature Review, Open Issues, and Limitations

This research aims to provide a comprehensive background on social distancing as well as effective technologies that can be used to facilitate the social distancing practice. Scenarios of enabling wireless and emerging technologies are presented, which are especially effective in monitoring and keeping distance amongst people. In addition, detailed taxonomy is proposed summarizing the essential elements such as implementation type, scenarios, and technology being used. This research reviews and analyzes existing social distancing studies that focus on employing different kinds of technologies to fight the Coronavirus disease (COVID-19) pandemic. This study main goal is to identify and discuss the issues, challenges, weaknesses and limitations found in the existing models and/or systems to provide a clear understanding of the area. Articles were systematically collected and filtered based on certain criteria and within ten years span. The findings of this study will support future researchers and developers to solve specific issues and challenges, fill research gaps, and improve social distancing systems to fight pandemics similar to COVID-19.

The Role of Movement Patterns in Epidemic Models on Complex Networks

In this paper, we analyze the influence of the usual movement variables on the spread of an epidemic. Specifically, given two spatial topologies, we can deduce which topology produces less infected individuals. In particular, we determine the topology that minimizes the overall number of infected individuals. It is worth noting that we do not assume any of the common simplifying assumptions in network theory such as all the links have the same diffusion rate or the movement of the individuals is symmetric. Our main conclusion is that the degree of mobility of the population plays a critical role in the spread of a disease. Finally, we derive theoretical insights to management of epidemics.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach

Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic control interventions. The indoor place is considered to be a significant epidemic spreading risk origin, but existing widely-used epidemic spreading models are usually limited for indoor places since the dynamic physical distance changes between people are ignored, and the empirical features of the essential and non-essential travel are not differentiated. In this paper, we introduce a pedestrian-based epidemic spreading model that is capable of modeling indoor transmission risks of diseases during people's social activities. Taking advantage of the before-and-after mobility data from the University of Maryland COVID-19 Impact Analysis Platform, it's found that people tend to spend more time in grocery stores once their travel frequencies are restricted to a low level. In other words, an increase in dwell time could balance the decrease in travel frequencies and satisfy people's demands. Based on the pedestrian-based model and the empirical evidence, combined non-pharmaceutical interventions from different operational levels are evaluated. Numerical simulations show that restrictions on people's travel frequency and open hours of indoor places may not be universally effective in reducing average infection risks for each pedestrian who visit the place. Entry limitations can be a widely effective alternative, whereas the decision-maker needs to balance the decrease in risky contacts and the increase in queue length outside the place that may impede people from fulfilling their travel needs. The results show that a good coordination among the decision-makers can contribute to the improvement of the performance of combined non-pharmaceutical interventions, and it also benefits the short-term and long-term interventions in the future.

Macro level factors influencing strategic responses to emergent pandemics: A scoping review

BACKGROUND

Strategic planning is critical for successful pandemic management. This study aimed to identify and review the scope and analytic depth of situation analyses conducted to understand their utility, and capture the documented macro-level factors impacting pandemic management.

METHODS

To synthesise this disparate body of literature, we adopted a two-step search and review process. A systematic search of the literature was conducted to identify all studies since 2000, that have 1) employed a situation analysis; and 2) examined contextual factors influencing pandemic management. The included studies are analysed using a seven-domain systems approach from the discipline of strategic management.

RESULTS

Nineteen studies were included in the final review ranging from single country (6) to regional, multi-country studies (13). Fourteen studies had a single disease focus, with 5 studies evaluating responses to one or more of COVID-19, Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), Influenza A (H1N1), Ebola virus disease, and Zika virus disease pandemics. Six studies examined a single domain from political, economic, sociological, technological, ecological or wider industry (PESTELI), 5 studies examined two to four domains, and 8 studies examined five or more domains. Methods employed were predominantly literature reviews. The recommendations focus predominantly on addressing inhibitors in the sociological and technological domains with few recommendations articulated in the political domain. Overall, the legislative domain is least represented.

CONCLUSIONS

Ex-post analysis using the seven-domain strategic management framework provides further opportunities for a planned systematic response to pandemics which remains critical as the current COVID-19 pandemic evolves.

The effect of the definition of 'pandemic' on quantitative assessments of infectious disease outbreak risk

In the early stages of an outbreak, the term 'pandemic' can be used to communicate about infectious disease risk, particularly by those who wish to encourage a large-scale public health response. However, the term lacks a widely accepted quantitative definition. We show that, under alternate quantitative definitions of 'pandemic', an epidemiological metapopulation model produces different estimates of the probability of a pandemic. Critically, we show that using different definitions alters the projected effects of key parameters-such as inter-regional travel rates, degree of pre-existing immunity, and heterogeneity in transmission rates between regions-on the risk of a pandemic. Our analysis provides a foundation for understanding the scientific importance of precise language when discussing pandemic risk, illustrating how alternative definitions affect the conclusions of modelling studies. This serves to highlight that those working on pandemic preparedness must remain alert to the variability in the use of the term 'pandemic', and provide specific quantitative definitions when undertaking one of the types of analysis that we show to be sensitive to the pandemic definition.

Shared social identification in mass gatherings lowers health risk perceptions via lowered disgust

Previous research concerning mass gathering‐associated health risks has focused on physical factors while largely neglecting the role of psychological factors. The present research examined the effect of experiencing shared social identification on perceptions of susceptibility to health risks in mass gatherings. Participants in Study 1 were asked to either recall a crowd in which they shared a social identity with other crowd members or a crowd in which they did not. Participants subsequently completed measures assessing shared social identity, disgust, and health risk perceptions. Study 2 involved administering the same measures as part of a survey to participants who had recently attended a music festival. The results from both studies indicated that sharing a social identity lowered health risk perceptions; this effect was indirect and mediated via disgust. This highlights the importance of considering social identity processes in the design of health communication aimed at reducing mass gathering‐associated health risks.

Identifying potential emerging threats through epidemic intelligence activities-looking for the needle in the haystack?

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The manual epidemic intelligence system was quick and accurate.

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All significant alerts were identified first through unofficial sources.

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The system was adaptable and allowed for monitoring of events as they evolved.

Background

Epidemic intelligence (EI) for emerging infections is the process of identifying key information on emerging infectious diseases and specific incidents. Automated web-based infectious disease surveillance technologies are available; however, human input is still needed to review, validate, and interpret these sources. In this study, entries captured by Public Health England’s (PHE) manual event-based EI system were examined to inform future intelligence gathering activities.

Methods

A descriptive analysis of unique events captured in a database between 2013 and 2017 was conducted. The top five diseases in terms of the number of entries were described in depth to determine the effectiveness of PHE’s EI surveillance system compared to other sources.

Results

Between 2013 and 2017, a total of 22 847 unique entries were added to the database. The top three initial and definitive information sources varied considerably by disease. Ebola entries dominated the database, making up 23.7% of the total, followed by Zika (11.8%), Middle East respiratory syndrome (6.7%), cholera (5.5%), and yellow fever and undiagnosed morbidity (both 3.3%). Initial reports of major outbreaks due to the top five disease agents were picked up through the manual system prior to being publicly reported by official sources.

Conclusions

PHE’s manual EI process quickly and accurately detected global public health threats at the earliest stages and allowed for monitoring of events as they evolved.

The use and reporting of airline passenger data for infectious disease modelling: a systematic review

BackgroundA variety of airline passenger data sources are used for modelling the international spread of infectious diseases. Questions exist regarding the suitability and validity of these sources.AimWe conducted a systematic review to identify the sources of airline passenger data used for these purposes and to assess validation of the data and reproducibility of the methodology.MethodsArticles matching our search criteria and describing a model of the international spread of human infectious disease, parameterised with airline passenger data, were identified. Information regarding type and source of airline passenger data used was collated and the studies' reproducibility assessed.ResultsWe identified 136 articles. The majority (n = 96) sourced data primarily used by the airline industry. Governmental data sources were used in 30 studies and data published by individual airports in four studies. Validation of passenger data was conducted in only seven studies. No study was found to be fully reproducible, although eight were partially reproducible.LimitationsBy limiting the articles to international spread, articles focussed on within-country transmission even if they used relevant data sources were excluded. Authors were not contacted to clarify their methods. Searches were limited to articles in PubMed, Web of Science and Scopus.ConclusionWe recommend greater efforts to assess validity and biases of airline passenger data used for modelling studies, particularly when model outputs are to inform national and international public health policies. We also recommend improving reporting standards and more detailed studies on biases in commercial and open-access data to assess their reproducibility.

Probability of dengue transmission and propagation in a non-endemic temperate area: conceptual model and decision risk levels for early alert, prevention and control

BACKGROUND

Dengue viruses have spread rapidly across tropical regions of the world in recent decades. Today, dengue transmission is observed in the Americas, Southeast Asia, Western Pacific, Africa and in non-endemic areas of the USA and Europe. Dengue is responsible for 16% of travel-related febrile illnesses. Although most prevalent in tropical areas, risk maps indicate that subtropical regions are suitable for transmission. Dengue-control programs in these regions should focus on minimizing virus importation, community engagement, improved vector surveillance and control.

RESULTS

We developed a conceptual model for the probability of local introduction and propagation of dengue, comprising disease vulnerability and receptivity, in a temperate area, considering risk factors and social media indicators. Using a rich data set from a temperate area in the south of Brazil (where there is active surveillance of mosquitoes, viruses and human cases), we used a conceptual model as a framework to build two probabilistic models to estimate the probability of initiation and propagation of local dengue transmission. The final models estimated with good accuracy the probabilities of local transmission and propagation, with three and four weeks in advance, respectively. Vulnerability indicators (number of imported cases and dengue virus circulation in mosquitoes) and a receptivity indicator (vector abundance) could be optimally integrated with tweets and temperature data to estimate probability of early local dengue transmission.

CONCLUSIONS

We demonstrated how vulnerability and receptivity indicators can be integrated into probabilistic models to estimate initiation and propagation of dengue transmission. The models successfully estimate disease risk in different scenarios and periods of the year. We propose a decision model with three different risk levels to assist in the planning of prevention and control measures in temperate regions at risk of dengue introduction.

Mass Gatherings

A mass gathering is “any occasion, either organized or spontaneous, that attracts sufficient numbers of people to strain the planning and response resources of the community, city, or nation hosting the event.”² Often the existing infrastructure (e.g., public health, health care, or emergency services) is inadequate for the disproportionate sudden surge in demand, hence careful planning is required. Although infectious diseases are most feared due to the potential for rapid international spread, noncommunicable diseases, accidents, and stampedes are more frequent. Mass gatherings may lead to three potential infectious disease public health threats: the risk of importation of infectious diseases usually not seen in the country of the gathering; the amplification of transmission during the event; and the international spread of infectious disease through global mobility networks.

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Mass gatherings are the temporary collection of large numbers of people at one site or location for a common purpose.

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Mass gatherings present some of the most complex management challenges faced by governments and organizers.

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Mass gatherings may lead to three potential infectious disease public health threats: the risk of importation of infectious diseases usually not seen in the country of the gathering; the amplification of transmission during the event; and the international spread of infectious disease through global mobility networks.

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While infectious diseases may be of greater global public health relevance, noncommunicable diseases, accidents, and stampede usually have a higher local impact with respect to morbidity and mortality during mass gatherings.

Methicillin-resistant Staphylococcus aureus nasal carriage in international medical conference attendees

BACKGROUND

Carriage of methicillin-resistant Staphylococcus aureus (MRSA) is associated with its transmission. International travels and massive gatherings may accelerate such transmission. MRSA carriage was surveyed among the attendees of two international medical conferences held in Taipei in 2010.

METHODS

A total of 209 attendees from 23 countries were recruited. Nasal specimens were collected from each volunteer and subjected to polymerase chain reaction (PCR) detection for MRSA. Molecular analysis, including pulsed-field gel electrophoresis, multilocus sequence typing (MLST), typing of staphylococcal cassette chromosome mec (SCCmec) and staphylococcal protein A (spa) genes, and detection of Panton-Valentine leukocidin (PVL) and sasX genes, was performed.

RESULTS

MRSA carriage was detected in 10 (4.8%) attendees from Vietnam (3/8, 37.5%), Korea (2/6, 33.3%), Japan (2/41, 4.9%), Philippines (2/52, 3.8%), and Bangladesh (1/4, 25.0%). The proportion of MRSA colonizers was significantly higher in the local hospital group compared to those from the other groups (3/17 vs. 7/192, p < 0.05). Six MRSA isolates were available for molecular analysis. They all carried a type IV SCCmec gene. Five pulsotypes were identified; four genotypes, respectively, were identified by MLST and spa typing. None of the isolates carried either PVL or sasX genes. None of common molecular characteristics was shared by isolates from different countries. Most of these isolates were local endemic community clone in each country.

CONCLUSIONS

As healthcare workers, a certain proportion of international medical conference attendees harbored MRSA in their nares, mostly local endemic community clones in each country, which has the potential of spread among attendees.

ILLEGAL ANIMAL-ORIGIN PRODUCTS SEIZED IN BAGGAGE FROM INTERNATIONAL FLIGHTS AT SAO PAULO GUARULHOS AIRPORT (GRU / SBGR), BRAZIL

Abstract Air transportation is one of the most important means to introduce illegally imported animal-origin products into a country. Studies have demonstrated that these items pose a risk of disseminating diseases. São Paulo Guarulhos International Airport (GRU / SBGR) is the main international airport in Brazil in terms of people movement and it has the largest number of seizures of animal-origin products. The aim of the present work was to describe the dynamics of the seizure of illegally imported animal-origin products in baggage from international flight passengers at GRU / SBGR Airport in Brazil. Five hundred and eighty-nine different flights from 43 airlines, arriving from 117 countries were analyzed between 2006 and 2009. The total number of seized items increased from 2006 to 2009 and a single flight from France had the highest number of seizures, followed by flights from South Africa and Germany. Countries were grouped into regions or continents to facilitate the analysis. This grouping was based on historical and cultural ties rather than geographical aspects. Seafood was the most frequently seized product, followed by dairy products, as well as processed and raw meat.

Clinical Age-Specific Seasonal Conjunctivitis Patterns and Their Online Detection in Twitter, Blog, Forum, and Comment Social Media Posts

Purpose

We sought to determine whether big data from social media might reveal seasonal trends of conjunctivitis, most forms of which are nonreportable.

Methods

Social media posts (from Twitter, and from online forums and blogs) were classified by age and by conjunctivitis type (allergic or infectious) using Boolean and machine learning methods. Based on spline smoothing, we estimated the circular mean occurrence time (a measure of central tendency for occurrence) and the circular variance (a measure of uniformity of occurrence throughout the year, providing an index of seasonality). Clinical records from a large tertiary care provider were analyzed in a similar way for comparison.

Results

Social media posts machine-coded as being related to infectious conjunctivitis showed similar times of occurrence and degree of seasonality to clinical infectious cases, and likewise for machine-coded allergic conjunctivitis posts compared to clinical allergic cases. Allergic conjunctivitis showed a distinctively different seasonal pattern than infectious conjunctivitis, with a mean occurrence time later in the spring. Infectious conjunctivitis for children showed markedly greater seasonality than for adults, though the occurrence times were similar; no such difference for allergic conjunctivitis was seen.

Conclusions

Social media posts broadly track the seasonal occurrence of allergic and infectious conjunctivitis, and may be a useful supplement for epidemiologic monitoring.

Estimating the frequency and characteristics of respiratory disease outbreaks at mass gatherings in the United States: Findings from a state and local health department assessment

Mass gatherings create environments conducive to the transmission of infectious diseases. Thousands of mass gatherings are held annually in the United States; however, information on the frequency and characteristics of respiratory disease outbreaks and on the use of nonpharmaceutical interventions at these gatherings is scarce. We administered an online assessment to the 50 state health departments and 31 large local health departments in the United States to gather information about mass gathering-related respiratory disease outbreaks occurring between 2009 and 2014. The assessment also captured information on the use of nonpharmaceutical interventions to slow disease transmission in these settings. We downloaded respondent data into a SAS dataset for descriptive analyses. We received responses from 43 (53%) of the 81 health jurisdictions. Among these, 8 reported 18 mass gathering outbreaks. More than half (n = 11) of the outbreaks involved zoonotic transmission of influenza A (H3N2v) at county and state fairs. Other outbreaks occurred at camps (influenza A (H1N1)pdm09 [n = 2] and A (H3) [n = 1]), religious gatherings (influenza A (H1N1)pdm09 [n = 1] and unspecified respiratory virus [n = 1]), at a conference (influenza A (H1N1)pdm09), and a sporting event (influenza A). Outbreaks ranged from 5 to 150 reported cases. Of the 43 respondents, 9 jurisdictions used nonpharmaceutical interventions to slow or prevent disease transmission. Although respiratory disease outbreaks with a large number of cases occur at many types of mass gatherings, our assessment suggests that such outbreaks may be uncommon, even during the 2009 influenza A (H1N1) pandemic, which partially explains the reported, but limited, use of nonpharmaceutical interventions. More research on the characteristics of mass gatherings with respiratory disease outbreaks and effectiveness of nonpharmaceutical interventions would likely be beneficial for decision makers at state and local health departments when responding to future outbreaks and pandemics.

Quantifying Network Dynamics and Information Flow Across Chinese Social Media During the African Ebola Outbreak

OBJECTIVE

Social media provides us with a new platform on which to explore how the public responds to disasters and, of particular importance, how they respond to the emergence of infectious diseases such as Ebola. Provided it is appropriately informed, social media offers a potentially powerful means of supporting both early detection and effective containment of communicable diseases, which is essential for improving disaster medicine and public health preparedness.

METHODS

The 2014 West African Ebola outbreak is a particularly relevant contemporary case study on account of the large number of annual arrivals from Africa, including Chinese employees engaged in projects in Africa. Weibo (Weibo Corp, Beijing, China) is China's most popular social media platform, with more than 2 billion users and over 300 million daily posts, and offers great opportunity to monitor early detection and promotion of public health awareness.

RESULTS

We present a proof-of-concept study of a subset of Weibo posts during the outbreak demonstrating potential and identifying priorities for improving the efficacy and accuracy of information dissemination. We quantify the evolution of the social network topology within Weibo relating to the efficacy of information sharing.

CONCLUSIONS

We show how relatively few nodes in the network can have a dominant influence over both the quality and quantity of the information shared. These findings make an important contribution to disaster medicine and public health preparedness from theoretical and methodological perspectives for dealing with epidemics. (Disaster Med Public Health Preparedness. 2018;12:26-37).

Surveillance Tools Emerging From Search Engines and Social Media Data for Determining Eye Disease Patterns

IMPORTANCE

Internet-based search engine and social media data may provide a novel complementary source for better understanding the epidemiologic factors of infectious eye diseases, which could better inform eye health care and disease prevention.

OBJECTIVE

To assess whether data from internet-based social media and search engines are associated with objective clinic-based diagnoses of conjunctivitis.

DESIGN, SETTING, AND PARTICIPANTS

Data from encounters of 4143 patients diagnosed with conjunctivitis from June 3, 2012, to April 26, 2014, at the University of California San Francisco (UCSF) Medical Center, were analyzed using Spearman rank correlation of each weekly observation to compare demographics and seasonality of nonallergic conjunctivitis with allergic conjunctivitis. Data for patient encounters with diagnoses for glaucoma and influenza were also obtained for the same period and compared with conjunctivitis. Temporal patterns of Twitter and Google web search data, geolocated to the United States and associated with these clinical diagnoses, were compared with the clinical encounters. The a priori hypothesis was that weekly internet-based searches and social media posts about conjunctivitis may reflect the true weekly clinical occurrence of conjunctivitis.

MAIN OUTCOMES AND MEASURES

Weekly total clinical diagnoses at UCSF of nonallergic conjunctivitis, allergic conjunctivitis, glaucoma, and influenza were compared using Spearman rank correlation with equivalent weekly data on Tweets related to disease or disease-related keyword searches obtained from Google Trends.

RESULTS

Seasonality of clinical diagnoses of nonallergic conjunctivitis among the 4143 patients (2364 females [57.1%] and 1776 males [42.9%]) with 5816 conjunctivitis encounters at UCSF correlated strongly with results of Google searches in the United States for the term pink eye (ρ, 0.68 [95% CI, 0.52 to 0.78]; P < .001) and correlated moderately with Twitter results about pink eye (ρ, 0.38 [95% CI, 0.16 to 0.56]; P < .001) and with clinical diagnosis of influenza (ρ, 0.33 [95% CI, 0.12 to 0.49]; P < .001), but did not significantly correlate with seasonality of clinical diagnoses of allergic conjunctivitis diagnosis at UCSF (ρ, 0.21 [95% CI, -0.02 to 0.42]; P = .06) or with results of Google searches in the United States for the term eye allergy (ρ, 0.13 [95% CI, -0.06 to 0.32]; P = .19). Seasonality of clinical diagnoses of allergic conjunctivitis at UCSF correlated strongly with results of Google searches in the United States for the term eye allergy (ρ, 0.44 [95% CI, 0.24 to 0.60]; P < .001) and eye drops (ρ, 0.47 [95% CI, 0.27 to 0.62]; P < .001).

CONCLUSIONS AND RELEVANCE

Internet-based search engine and social media data may reflect the occurrence of clinically diagnosed conjunctivitis, suggesting that these data sources can be leveraged to better understand the epidemiologic factors of conjunctivitis.

Spread rate of lumpy skin disease in the Balkans, 2015-2016

After its introduction in Turkey in November 2013 and subsequent spread in this country, lumpy skin disease (LSD) was first reported in the western Turkey in May 2015. It was observed in cattle in Greece and reported to the World Organization for Animal Health (OIE) in August 2015. From May 2015 to August 2016, 1,092 outbreaks of lumpy skin disease were reported in cattle from western Turkey and eight Balkan countries: Greece, Bulgaria, The Former Yugoslav Republic of Macedonia, Serbia, Kosovo, and Albania. During this period, the median LSD spread rate was 7.3 km/week. The frequency of outbreaks was highly seasonal, with little or no transmission reported during the winter. Also, the skewed distribution of spread rates suggested two distinct underlying epidemiological processes, associating local and distant spread possibly related to vectors and cattle trade movements, respectively.

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.

SCM: a practical tool to implement hospital-based syndromic surveillance

BACKGROUND

Syndromic surveillance has been widely used for the early warning of infectious disease outbreaks, especially in mass gatherings, but the collection of electronic data on symptoms in hospitals is one of the fundamental challenges that must be overcome during operating a syndromic surveillance system. The objective of our study is to describe and evaluate the implementation of a symptom-clicking-module (SCM) as a part of the enhanced hospital-based syndromic surveillance during the 41st World Exposition in Shanghai, China, 2010.

METHODS

The SCM, including 25 targeted symptoms, was embedded in the sentinels' Hospital Information Systems (HIS). The clinicians used SCM to record these information of all the visiting patients, and data were collated and transmitted automatically in daily batches. The symptoms were categorized into seven targeted syndromes using pre-defined criteria, and statistical algorithms were applied to detect temporal aberrations in the data series.

RESULTS

SCM was deployed successfully in each sentinel hospital and was operated during the 184-day surveillance period. A total of 1,730,797 patient encounters were recorded by SCM, and 6.1 % (105,352 visits) met the criteria of the seven targeted syndromes. Acute respiratory and gastrointestinal syndromes were reported most frequently, accounted for 92.1 % of reports in all syndromes, and the aggregated time-series presented an obvious day-of-week variation over the study period. In total, 191 aberration signals were triggered, and none of them were identified as outbreaks after verification and field investigation.

CONCLUSIONS

SCM has acted as a practical tool for recording symptoms in the hospital-based enhanced syndromic surveillance system during the 41st World Exposition in Shanghai, in the context of without a preexisting electronic tool to collect syndromic data in the HIS of the sentinel hospitals.

Social media in Ebola outbreak

The West African 2014 Ebola outbreak has highlighted the need for a better information network. Hybrid information networks, an integration of both hierarchical and formalized command control-driven and community-based, or ad hoc emerging networks, could assist in improving public health responses. By filling the missing gaps with social media use, the public health response could be more proactive rather than reactive in responding to such an outbreak of global concern. This article provides a review of the current social media use specifically in this outbreak by systematically collecting data from ProQuest Newsstand, Dow Jones Factiva, Program for Monitoring Emerging Diseases (ProMED) as well as Google Trends. The period studied is from 19 March 2014 (first request for information on ProMED) to 15 October 2014, a total of 31 weeks. The term 'Ebola' was used in the search for media reports. The outcome of the review shows positive results for social media use in effective surveillance response mechanisms - for improving the detection, preparedness and response of the outbreak - as a complement to traditional, filed, work-based surveillance approach.

International Consensus on Key Concepts and Data Definitions for Mass-gathering Health: Process and Progress

Mass gatherings (MGs) occur worldwide on any given day, yet mass-gathering health (MGH) is a relatively new field of scientific inquiry. As the science underpinning the study of MGH continues to develop, there will be increasing opportunities to improve health and safety of those attending events. The emerging body of MG literature demonstrates considerable variation in the collection and reporting of data. This complicates comparison across settings and limits the value and utility of these reported data. Standardization of data points and/or reporting in relation to events would aid in creating a robust evidence base from which governments, researchers, clinicians, and event planners could benefit. Moving towards international consensus on any topic is a complex undertaking. This report describes a collaborative initiative to develop consensus on key concepts and data definitions for a MGH “Minimum Data Set.” This report makes transparent the process undertaken, demonstrates a pragmatic way of managing international collaboration, and proposes a number of steps for progressing international consensus. The process included correspondence through a journal, face-to-face meetings at a conference, then a four-day working meeting; virtual meetings over a two-year period supported by online project management tools; consultation with an international group of MGH researchers via an online Delphi process; and a workshop delivered at the 19thWorld Congress on Disaster and Emergency Medicine held in Cape Town, South Africa in April 2015. This resulted in an agreement by workshop participants that there is a need for international consensus on key concepts and data definitions.

TurrisSA,SteenkampM,LundA,HuttonA,RanseJ,BowlesR,ArbuthnottK,AnikeevaO,ArbonP.International consensus on key concepts and data definitions for mass-gathering health: process and progress.Prehosp Disaster Med.2016;31(2):220–223.

Health response to Hajj mass gathering from emergency perspective, narrative review

Hajj is a unique gathering with Mecca and Kaaba being spiritually important to many faiths across the globe, especially Muslims. This is because of the proclamation of the prophet's father, Ibrahaam, when he called all mankind to perform Hajj. That is why all Muslims on Earth feel that they have to visit Mecca and Kaaba on a specific date and time, and that is the reason this small location hosts one of the largest human gatherings in the world. Hajj is one of the five pillars of Islam that every financially and physically able Muslim must perform once in his/her lifetime. For 14 centuries countless millions of Muslim men and women from the four corners of the earth have undertaken pilgrimage to Mecca. In conclusion this review article confirm that Hajj is oldest and largest mass gathering in all mankind and there is some issues influence the health response such as size of gathering. diversity of population, climate and health facilities around hajj site, also we discuss the infectious and non infectious related illness in hajj and their prevention methods.

Controlling Ebola: what we can learn from China's 1911 battle against the pneumonic plague in Manchuria

The pneumonic plague, which spread across Northeast China during the winter of 1910 and spring of 1911, caused numerous deaths and brought about severe social turmoil. After compulsory quarantine and other epidemic prevention measures were enforced by Dr Wu Lien-teh, the epidemic was brought to an end within 4 months. This article reviews the ways in which the plague was dealt with from a historical perspective, based on factors such as clinical manifestations, duration of illness, case fatality rate, degree of transmissibility, poverty, inadequate healthcare infrastructure, and the region's recent strife-filled history. Similarities were sought between the pneumonic plague in Northeast China in the twentieth century and the Ebola virus outbreak that is currently ravaging Africa, and an effort made to summarize the ways in which specific measures were applied successfully to fight the earlier epidemic. Our efforts highlight valuable experiences that are of potential benefit in helping to fight the current rampant Ebola epidemic in West Africa.

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

Outbreaks of infectious diseases at mass gatherings can strain the health system of the host region and pose a threat to local and global health. In addition to strengthening existing surveillance systems, most host nations also use novel technologies to assess disease risk and augment traditional surveillance approaches. We review novel approaches to disease surveillance using the Internet, mobile phone applications, and wireless sensor networks. These novel approaches to disease surveillance can result in prompt detection.

Using Geographic Information for Disease Surveillance at Mass Gatherings

Mass gatherings present the medical community with an excellent window of opportunity to study infectious diseases that can be transmitted over long distances. This is because the venue of a mass gathering usually does not change year-to-year. As a result, special attention can be given to the public health risks that are introduced by travelers from around the world into these mass gatherings. Travelers can also be infected with diseases that are endemic in the host country and transport the locally acquired infectious diseases to their home environments. Therefore, mass gatherings can be thought of as global-to-local-to-global events because of the initial convergence of global populations and the subsequent divergence of populations throughout the world. This chapter discusses three active areas of geographic research that have emerged from our understanding of disease surveillance at mass gatherings: the role of transportation and population geographies in disease surveillance; the spatial and temporal dimensions of environmental geography in the spread of disease; and the advances in GIScience that provide real-world surveillance and monitoring of disease and injuries at mass gatherings.

Mass-gathering health research foundational theory: part 2 - event modeling for mass gatherings

BACKGROUND

Current knowledge about mass-gathering health (MGH) fails to adequately inform the understanding of mass gatherings (MGs) because of a relative lack of theory development and adequate conceptual analysis. This report describes the development of a series of event lenses that serve as a beginning "MG event model," complimenting the "MG population model" reported elsewhere.

METHODS

Existing descriptions of "MGs" were considered. Analyzing gaps in current knowledge, the authors sought to delineate the population of events being reported. Employing a consensus approach, the authors strove to capture the diversity, range, and scope of MG events, identifying common variables that might assist researchers in determining when events are similar and might be compared. Through face-to-face group meetings, structured breakout sessions, asynchronous collaboration, and virtual international meetings, a conceptual approach to classifying and describing events evolved in an iterative fashion. Findings Embedded within existing literature are a variety of approaches to event classification and description. Arising from these approaches, the authors discuss the interplay between event demographics, event dynamics, and event design. Specifically, the report details current understandings about event types, geography, scale, temporality, crowd dynamics, medical support, protective factors, and special hazards. A series of tables are presented to model the different analytic lenses that might be employed in understanding the context of MG events. Interpretation The development of an event model addresses a gap in the current body of knowledge vis a vis understanding and reporting the full scope of the health effects related to MGs. Consistent use of a consensus-based event model will support more rigorous data collection. This in turn will support meta-analysis, create a foundation for risk assessment, allow for the pooling of data for illness and injury prediction, and support methodology for evaluating health promotion, harm reduction, and clinical response interventions at MGs.

Estimating contact rates at a mass gathering by using video analysis: a proof-of-concept project

Background

Current approaches for estimating social mixing patterns and infectious disease transmission at mass gatherings have been limited by various constraints, including low participation rates for volunteer-based research projects and challenges in quantifying spatially and temporally accurate person-to-person interactions. We developed a proof-of-concept project to assess the use of automated video analysis for estimating contact rates of attendees of the GameFest 2013 event at Rensselaer Polytechnic Institute (RPI) in Troy, New York.

Methods

Video tracking and analysis algorithms were used to estimate the number and duration of contacts for 5 attendees during a 3-minute clip from the RPI video. Attendees were considered to have a contact event if the distance between them and another person was ≤1 meter. Contact duration was estimated in seconds. We also simulated 50 attendees assuming random mixing using a geo-spatially accurate representation of the same GameFest location.

Results

The 5 attendees had an overall median of 2 contact events during the 3-minute video clip (range: 0–6). Contact events varied from less than 5 seconds to the full duration of the 3-minute clip. The random mixing simulation was visualized and presented as a contrasting example.

Conclusion

We were able to estimate the number and duration of contacts for 5 GameFest attendees from a 3-minute video clip that can be compared to a random mixing simulation model at the same location. The next phase will involve scaling the system for simultaneous analysis of mixing patterns from hours-long videos and comparing our results with other approaches for collecting contact data from mass gathering attendees.

Euro 2012 European Football Championship Finals: planning for a health legacy

The revised international health regulations offer a framework that can be used by host countries to organise public health activities for mass gatherings. From June 8, to July 1, 2012, Poland and Ukraine jointly hosted the Union of European Football Associations European Football Championship Finals (Euro 2012). More than 8 million people from around the world congregated to watch the games. Host countries and international public health agencies planned extensively to assess and build capacity in the host countries and to develop effective strategies for dissemination of public health messages. The effectiveness of public health services was maximised through rapid sharing of information between parties, early use of networks of experienced individuals, and the momentum of existing national health programmes. Organisers of future mass gatherings for sporting events should share best practice and their experiences through the WHO International Observer Program. Research about behaviour of large crowds is needed for crowd management and the evidence base translated into practice. A framework to measure and evaluate the legacy of Euro 2012 is needed based on the experiences and the medium-term and long-term benefits of the tournament.

Hajj: infectious disease surveillance and control

Religious festivals attract a large number of pilgrims from worldwide and are a potential risk for the transmission of infectious diseases between pilgrims, and to the indigenous population. The gathering of a large number of pilgrims could compromise the health system of the host country. The threat to global health security posed by infectious diseases with epidemic potential shows the importance of advanced planning of public health surveillance and response at these religious events. Saudi Arabia has extensive experience of providing health care at mass gatherings acquired through decades of managing millions of pilgrims at the Hajj. In this report, we describe the extensive public health planning, surveillance systems used to monitor public health risks, and health services provided and accessed during Hajj 2012 and Hajj 2013 that together attracted more than 5 million pilgrims from 184 countries. We also describe the recent establishment of the Global Center for Mass Gathering Medicine, a Saudi Government partnership with the WHO Collaborating Centre for Mass Gatherings Medicine, Gulf Co-operation Council states, UK universities, and public health institutions globally.

New Delhi Metallo-beta-lactamase around the world: An eReview using Google Maps

Gram-negative carbapenem-resistant bacteria, in particular those producing New Delhi Metallo-beta-lactamase-1 (NDM-1), are a major global health problem. To inform the scientific and medical community in real time about worldwide dissemination of isolates of NDM-1-producing bacteria, we used the PubMed database to review all available publications from the first description in 2009 up to 31 December 2012, and created a regularly updated worldwide dissemination map using a web-based mapping application. We retrieved 33 reviews, and 136 case reports describing 950 isolates of NDM-1-producing bacteria. Klebsiella pneumoniae (n= 359) and Escherichia coli (n=268) were the most commonly reported bacteria producing NDM-1 enzyme. Several case reports of infections due to imported NDM-1 producing bacteria have been reported in a number of countries, including the United Kingdom, Italy, and Oman. In most cases (132/153, 86.3%), patients had connections with the Indian subcontinent or Balkan countries. Those infected were originally from these areas, had either spent time and/or been hospitalised there, or were potentially linked to other patients who had been hospitalised in these regions. By using Google Maps, we were able to trace spread of NDM-1-producing bacteria. We strongly encourage epidemiologists to use these types of interactive tools for surveillance purposes and use the information to prevent the spread and outbreaks of such bacteria.

Modeling cholera outbreaks

Mathematical modeling can be a valuable tool for studying infectious disease outbreak dynamics and simulating the effects of possible interventions. Here, we describe approaches to modeling cholera outbreaks and how models have been applied to explore intervention strategies, particularly in Haiti. Mathematical models can play an important role in formulating and evaluating complex cholera outbreak response options. Major challenges to cholera modeling are insufficient data for calibrating models and the need to tailor models for different outbreak scenarios.

Internet search patterns of human immunodeficiency virus and the digital divide in the Russian Federation: infoveillance study

Background

Human immunodeficiency virus (HIV) is a serious health problem in the Russian Federation. However, the true scale of HIV in Russia has long been the subject of considerable debate. Using digital surveillance to monitor diseases has become increasingly popular in high income countries. But Internet users may not be representative of overall populations, and the characteristics of the Internet-using population cannot be directly ascertained from search pattern data. This exploratory infoveillance study examined if Internet search patterns can be used for disease surveillance in a large middle-income country with a dispersed population.

Objective

This study had two main objectives: (1) to validate Internet search patterns against national HIV prevalence data, and (2) to investigate the relationship between search patterns and the determinants of Internet access.

Methods

We first assessed whether online surveillance is a valid and reliable method for monitoring HIV in the Russian Federation. Yandex and Google both provided tools to study search patterns in the Russian Federation. We evaluated the relationship between both Yandex and Google aggregated search patterns and HIV prevalence in 2011 at national and regional tiers. Second, we analyzed the determinants of Internet access to determine the extent to which they explained regional variations in searches for the Russian terms for “HIV” and “AIDS”. We sought to extend understanding of the characteristics of Internet searching populations by data matching the determinants of Internet access (age, education, income, broadband access price, and urbanization ratios) and searches for the term “HIV” using principal component analysis (PCA).

Results

We found generally strong correlations between HIV prevalence and searches for the terms “HIV” and “AIDS”. National correlations for Yandex searches for “HIV” were very strongly correlated with HIV prevalence (Spearman rank-order coefficient [rs]=.881, P≤.001) and strongly correlated for “AIDS” (rs=.714, P≤.001). The strength of correlations varied across Russian regions. National correlations in Google for the term “HIV” (rs=.672, P=.004) and “AIDS” (rs=.584, P≤.001) were weaker than for Yandex. Second, we examined the relationship between the determinants of Internet access and search patterns for the term “HIV” across Russia using PCA. At the national level, we found Principal Component 1 loadings, including age (-0.56), HIV search (-0.533), and education (-0.479) contributed 32% of the variance. Principal Component 2 contributed 22% of national variance (income, -0.652 and broadband price, -0.460).

Conclusions

This study contributes to the methodological literature on search patterns in public health. Based on our preliminary research, we suggest that PCA may be used to evaluate the relationship between the determinants of Internet access and searches for health problems beyond high-income countries. We believe it is in middle-income countries that search methods can make the greatest contribution to public health.

Respiratory tract infections during the annual Hajj: potential risks and mitigation strategies

PURPOSE OF REVIEW

Mass gatherings such as religious pilgrimages, sporting events and music concerts are becoming larger and more frequent. The scale and frequency of large-scale international events pose substantial risks to the spread of infectious diseases. The available literature on respiratory tract infections at the Hajj pilgrimage - annually attended by 3 million pilgrims from all over the globe - are reviewed.

RECENT FINDINGS

The most common respiratory tract infection viruses are influenza and rhinovirus. Despite the occurrence of the Hajj during the 2009 H1N1 pandemic the available literature did not show an increased rate of infection. In hospitalized patients, pneumonia is a significant cause of admission accounting for 20-50% of such admissions.

SUMMARY

The use of masks may reduce exposure to droplet nuclei, the main mode of transmission of most respiratory tract infections. The practice of social distancing, hand hygiene, and contact avoidance was associated with reduced risk of respiratory illness. In addition, utilizing the recommended vaccines would decrease the risk of acquiring respiratory tract pathogens.

Global malaria connectivity through air travel

Background

Air travel has expanded at an unprecedented rate and continues to do so. Its effects have been seen on malaria in rates of imported cases, local outbreaks in non-endemic areas and the global spread of drug resistance. With elimination and global eradication back on the agenda, changing levels and compositions of imported malaria in malaria-free countries, and the threat of artemisinin resistance spreading from Southeast Asia, there is a need to better understand how the modern flow of air passengers connects each Plasmodium falciparum- and Plasmodium vivax-endemic region to the rest of the world.

Methods

Recently constructed global P. falciparum and P.vivax malaria risk maps, along with data on flight schedules and modelled passenger flows across the air network, were combined to describe and quantify global malaria connectivity through air travel. Network analysis approaches were then utilized to describe and quantify the patterns that exist in passenger flows weighted by malaria prevalence. Finally, the connectivity within and to the Southeast Asia region where the threat of imported artemisinin resistance arising is highest, was examined to highlight risk routes for its spread.

Results

The analyses demonstrate the substantial connectivity that now exists between and from malaria-endemic regions through air travel. While the air network provides connections to previously isolated malarious regions, it is clear that great variations exist, with significant regional communities of airports connected by higher rates of flow standing out. The structures of these communities are often not geographically coherent, with historical, economic and cultural ties evident, and variations between P. falciparum and P. vivax clear. Moreover, results highlight how well connected the malaria-endemic areas of Africa are now to Southeast Asia, illustrating the many possible routes that artemisinin-resistant strains could take.

Discussion

The continuing growth in air travel is playing an important role in the global epidemiology of malaria, with the endemic world becoming increasingly connected to both malaria-free areas and other endemic regions. The research presented here provides an initial effort to quantify and analyse the connectivity that exists across the malaria-endemic world through air travel, and provide a basic assessment of the risks it results in for movement of infections.

Global mapping of infectious disease

The primary aim of this review was to evaluate the state of knowledge of the geographical distribution of all infectious diseases of clinical significance to humans. A systematic review was conducted to enumerate cartographic progress, with respect to the data available for mapping and the methods currently applied. The results helped define the minimum information requirements for mapping infectious disease occurrence, and a quantitative framework for assessing the mapping opportunities for all infectious diseases. This revealed that of 355 infectious diseases identified, 174 (49%) have a strong rationale for mapping and of these only 7 (4%) had been comprehensively mapped. A variety of ambitions, such as the quantification of the global burden of infectious disease, international biosurveillance, assessing the likelihood of infectious disease outbreaks and exploring the propensity for infectious disease evolution and emergence, are limited by these omissions. An overview of the factors hindering progress in disease cartography is provided. It is argued that rapid improvement in the landscape of infectious diseases mapping can be made by embracing non-conventional data sources, automation of geo-positioning and mapping procedures enabled by machine learning and information technology, respectively, in addition to harnessing labour of the volunteer ‘cognitive surplus’ through crowdsourcing.

Contrasting the epidemiological and evolutionary dynamics of influenza spatial transmission

In the past decade, rapid increases in the availability of high-resolution molecular and epidemiological data, combined with developments in statistical and computational methods to simulate and infer migration patterns, have provided key insights into the spatial dynamics of influenza A viruses in humans. In this review, we contrast findings from epidemiological and molecular studies of influenza virus transmission at different spatial scales. We show that findings are broadly consistent in large-scale studies of inter-regional or inter-hemispheric spread in temperate regions, revealing intense epidemics associated with multiple viral introductions, followed by deep troughs driven by seasonal bottlenecks. However, aspects of the global transmission dynamics of influenza viruses are still debated, especially with respect to the existence of tropical source populations experiencing high levels of genetic diversity and the extent of prolonged viral persistence between epidemics. At the scale of a country or community, epidemiological studies have revealed spatially structured diffusion patterns in seasonal and pandemic outbreaks, which were not identified in molecular studies. We discuss the role of sampling issues in generating these conflicting results, and suggest strategies for future research that may help to fully integrate the epidemiological and evolutionary dynamics of influenza virus over space and time.

Modeling rapidly disseminating infectious disease during mass gatherings

We discuss models for rapidly disseminating infectious diseases during mass gatherings (MGs), using influenza as a case study. Recent innovations in modeling and forecasting influenza transmission dynamics at local, regional, and global scales have made influenza a particularly attractive model scenario for MG. We discuss the behavioral, medical, and population factors for modeling MG disease transmission, review existing model formulations, and highlight key data and modeling gaps related to modeling MG disease transmission. We argue that the proposed improvements will help integrate infectious-disease models in MG health contingency plans in the near future, echoing modeling efforts that have helped shape influenza pandemic preparedness plans in recent years.

Research agenda for mass gatherings: a call to action

Public health research is essential for the development of effective policies and planning to address health security and risks associated with mass gatherings (MGs). Crucial research topics related to MGs and their effects on global health security are discussed in this review. The research agenda for MGs consists of a framework of five major public health research directions that address issues related to reducing the risk of public health emergencies during MGs; restricting the occurrence of non-communicable and communicable diseases; minimisation of the effect of public health events associated with MGs; optimisation of the medical services and treatment of diseases during MGs; and development and application of modern public health measures. Implementation of the proposed research topics would be expected to provide benefits over the medium to long term in planning for MGs.