Global capacity for emerging infectious disease detection. Proc Natl Acad Sci U S A. 2010;107:21701-21706. [PMID: 21115835 DOI: 10.1073/pnas.1006219107]

How feasible or useful are timeliness metrics as a tool to optimise One Health outbreak responses?

INTRODUCTION

As timeliness metrics gain traction to assess and optimise outbreak detection and response performance, implementation and scale-up require insight into the perspectives of stakeholders adopting these tools. This study sought to characterise the feasibility and utility of tracking One Health outbreak milestones across relevant human, animal, plant, and environmental sectors to systematically quantify timeliness metrics in Uganda, a country prone to outbreaks of WHO priority diseases.

METHODS

A database of outbreak events occurring in Uganda between 2018 and 2022 was compiled. Outbreak reports meeting our inclusion criteria were reviewed to quantify the frequency of milestone reporting. Key informant interviews were conducted with expert stakeholders to explore the feasibility and utility of tracking metrics using a framework analysis. Quantitative and qualitative data were collected and analysed concurrently.

RESULTS

Of the 282 public health emergencies occurring between 2018 and 2022, 129 events met our inclusion criteria, and complete data were available for 82 outbreaks. For our qualitative portion, 10 informants were interviewed from 7 institutions, representing the human, animal and environmental sectors. Informants agreed most One Health milestones are feasible to track, which was supported by the frequency of milestone reporting; however, there was a demonstrated need for increased reporting of after-action reviews, as well as outbreak start and end dates. Predictive alerts signalling potential outbreaks and preventive responses to alerts are seen as challenging to routinely capture, reflecting the lack of public health action for these domains.

CONCLUSION

Despite consensus among stakeholders that timeliness metrics are a beneficial tool to assess outbreak performance, not all One Health metrics are being tracked consistently, thereby missing opportunities to optimise epidemic intelligence, preparedness and prevention. The feasibility of tracking these metrics depends on the integration of reporting channels, enhanced documentation of milestones and development of guidance for early adopters, recognising country-specific on-the-ground realities and challenges to national scaling efforts.

Barriers and benefits of mHealth for community health workers in integrated community case management of childhood diseases in Banda Parish, Kampala, Uganda: a cross-sectional study

BACKGROUND

Low-quality data presents a significant challenge for community health workers (CHWs) in low and middle-income countries (LMICs). Mobile health (mHealth) applications offer a solution by enabling CHWs to record and submit data electronically. However, the barriers and benefits of mHealth usage among CHWs in informal urban settlements remain poorly understood. This study sought to determine the barriers and benefits of mHealth among CHWs in Banda parish, Kampala.

METHODS

This qualitative study involved 12 key informant interviews (KIIs) among focal persons from Kampala City Council Authority (KCCA) and NGOs involved in data collected by CHWs, and officials from the Ministry of Health (MOH) and two mixed-sex Focused Group Discussions (FGDs) of CHWs from Banda parish, Kampala district. Data analysis utilised Atlas Ti Version 7.5.7. Thematic analysis was conducted, and themes were aligned with the social-ecological model.

RESULTS

Three themes of institutional and policy, community and interpersonal, and individual aligning to the Social ecological model highlighted the factors contributing to barriers and the benefits of mHealth among CHWs for iCCM. The key barriers to usability, acceptability and sustainability included high training costs, CHW demotivation, infrastructure limitations, data security concerns, community awareness deficits, and skill deficiencies. Conversely, mHealth offers benefits such as timely data submission, enhanced data quality, geo-mapping capabilities, improved CHW performance monitoring, community health surveillance, cost-effective reporting, and CHW empowering with technology.

CONCLUSION

Despite limited mHealth experience, CHWs expressed enthusiasm for its potential. Implementation was viewed as a solution to multiple challenges, facilitating access to health information, efficient data reporting, and administrative processes, particularly in resource-constrained settings. Successful mHealth implementation requires addressing CHWs' demotivation, ensuring reliable power and network connectivity, and enhancing capacity for digital data ethics and management. By overcoming these barriers, mHealth can significantly enhance healthcare delivery at the community level, leveraging technology to optimize resource utilization and improve health outcomes. mHealth holds promise for transforming CHW practices, yet its effective integration necessitates targeted interventions to address systemic challenges and ensure sustainable implementation in LMIC contexts.

Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance

Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for "fever" and "cough" were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences.

Using EpiCore to Enable Rapid Verification of Potential Health Threats: Illustrated Use Cases and Summary Statistics

BACKGROUND

The proliferation of digital disease-detection systems has led to an increase in earlier warning signals, which subsequently have resulted in swifter responses to emerging threats. Such highly sensitive systems can also produce weak signals needing additional information for action. The delays in the response to a genuine health threat are often due to the time it takes to verify a health event. It was the delay in outbreak verification that was the main impetus for creating EpiCore.

OBJECTIVE

This paper describes the potential of crowdsourcing information through EpiCore, a network of voluntary human, animal, and environmental health professionals supporting the verification of early warning signals of potential outbreaks and informing risk assessments by monitoring ongoing threats.

METHODS

This paper uses summary statistics to assess whether EpiCore is meeting its goal to accelerate the time to verification of identified potential health events for epidemic and pandemic intelligence purposes from around the world. Data from the EpiCore platform from January 2018 to December 2022 were analyzed to capture request for information response rates and verification rates. Illustrated use cases are provided to describe how EpiCore members provide information to facilitate the verification of early warning signals of potential outbreaks and for the monitoring and risk assessment of ongoing threats through EpiCore and its utilities.

RESULTS

Since its launch in 2016, EpiCore network membership grew to over 3300 individuals during the first 2 years, consisting of professionals in human, animal, and environmental health, spanning 161 countries. The overall EpiCore response rate to requests for information increased by year between 2018 and 2022 from 65.4% to 68.8% with an initial response typically received within 24 hours (in 2022, 94% of responded requests received a first contribution within 24 h). Five illustrated use cases highlight the various uses of EpiCore.

CONCLUSIONS

As the global demand for data to facilitate disease prevention and control continues to grow, it will be crucial for traditional and nontraditional methods of disease surveillance to work together to ensure health threats are captured earlier. EpiCore is an innovative approach that can support health authorities in decision-making when used complementarily with official early detection and verification systems. EpiCore can shorten the time to verification by confirming early detection signals, informing risk-assessment activities, and monitoring ongoing events.

Disseminating information on acute public health events globally: experiences from the WHO's Disease Outbreak News

WHO works, on a daily basis, with countries globally to detect, prepare for and respond to acute public health events. A vital component of a health response is the dissemination of accurate, reliable and authoritative information. The Disease Outbreak News (DON) reports are a key mechanism through which WHO communicates on acute public health events to the public. The decision to produce a DON report is taken on a case-by-case basis after evaluating key criteria, and the subsequent process of producing a DON report is highly standardised to ensure the robustness of information. DON reports have been published since 1996, and up to 2022 over 3000 reports have been published. Between 2018 and 2022, the most frequently published DON reports relate to Ebola virus disease, Middle East respiratory syndrome, yellow fever, polio and cholera. The DON web page is highly visited with a readership of over 2.6 million visits per year, on average. The DON report structure has evolved over time, from a single paragraph in 1996 to a detailed report with seven sections currently. WHO regularly reviews the DON report process and structure for improvements. In the last 25 years, DON reports have played a unique role in rapidly disseminating information on acute public health events to health actors and the public globally. They have become a key information source for the global public health response to the benefit of individuals and communities.

Cracking the code of health security: unveiling the balanced indices through rank-ordered effect analysis

BACKGROUND

Health security is a critical issue which involves multiple dimensions. It has received increasing attention in recent years, especially in China. In order to improve the national health level, China has made many efforts, such as the "Healthy China 2030" plan proposed several years ago. However, due to the complexity of its national conditions and the difficulty of index design, the results of these efforts are not significant. Therefore, it is necessary to construct a new measurement index system.

METHODS

Based on the questionnaire of "Health China 2030", we have collected a total of 3,000 participants from all 31 provinces, autonomous regions, and municipalities in China. We used statistical methods such as multiple correspondence analysis and rank-ordered effect analysis to process the data. The balance index is constructed by a series of actions such as weight division, order calculation and ranking.

RESULTS

Through multiple correspondence analysis, we can find that there was a close relation in the correspondence space between the satisfaction degrees 1, 2, and 3, while a far distance from satisfaction degrees 4 and 5. There were four positive and four negative indices separately based on the average expected level and four clusters after ordinal rank cluster analysis. Generally speaking, there are no prominent discrepancies across gender and residential areas.

CONCLUSIONS

We created and examined balanced indicators for health security in China based on the "Health China 2030" questionnaire. The findings of this study give insight into the overall situation of health security in China and indicate opportunities for improvement.

Nycteribiid bat flies (Arthropoda, Insecta, Diptera, Nycteribiidae) of Kenya

Bat flies (Diptera: Nycteribiidae and Streblidae) are hematophagous ectoparasites of bats characterized by viviparous pupiparity and generally high host specificity. Nycteribiid bat flies are wingless, morphologically constrained, and are most diverse in the Eastern Hemisphere. Africa hosts approximately 22% of global bat biodiversity and nearly one-third of all African bat species occur in Kenya, one of Africa's most bat-rich countries. However, records of nycteribiid bat fly diversity in Kenya remain sparse and unconsolidated. This paper combines all past species records of nycteribiid bat flies with records from a survey of 4,255 Kenyan bats across 157 localities between 2006 and 2015. A total of seven nycteribiid genera and 17 species are recorded, with seven species from the recent 'Bats of Kenya' surveys representing previously undocumented country records. Host associations and geographic distributions based on all available records are also described. This comprehensive species catalog addresses and further emphasizes the need for similar investigations of nycteribiid biodiversity across Africa.

Implementation of the 7-1-7 target for detection, notification, and response to public health threats in five countries: a retrospective, observational study

BACKGROUND

Suboptimal detection and response to recent outbreaks, including COVID-19 and mpox (formerly known as monkeypox), have shown that the world is insufficiently prepared for public health threats. Routine monitoring of detection and response performance of health emergency systems through timeliness metrics has been proposed to evaluate and improve outbreak preparedness and contain health threats early. We implemented 7-1-7 to measure the timeliness of detection (target of ≤7 days from emergence), notification (target of ≤1 day from detection), and completion of seven early response actions (target of ≤7 days from notification), and we identified bottlenecks to and enablers of system performance.

METHODS

In this retrospective, observational study, we conducted reviews of public health events in Brazil, Ethiopia, Liberia, Nigeria, and Uganda with staff from ministries of health and national public health institutes. For selected public health events occurring from Jan 1, 2018, to Dec 31, 2022, we calculated timeliness intervals for detection, notification, and early response actions, and synthesised identified bottlenecks and enablers. We mapped bottlenecks and enablers to Joint External Evaluation (second edition) indicators.

FINDINGS

Of 41 public health events assessed, 22 (54%) met a target of 7 days to detect (median 6 days [range 0-157]), 29 (71%) met a target of 1 day to notify (0 days [0-24]), and 20 (49%) met a target of 7 days to complete all early response actions (8 days [0-72]). 11 (27%) events met the complete 7-1-7 target, with variation among event types. 25 (61%) of 41 bottlenecks to and 27 (51%) of 53 enablers of detection were at the health facility level, with delays to notification (14 [44%] of 32 bottlenecks) and response (22 [39%] of 56 bottlenecks) most often at an intermediate public health (ie, municipal, district, county, state, or province) level. Rapid resource mobilisation for responses (six [9%] of 65 enablers) from the national level enabled faster responses.

INTERPRETATION

The 7-1-7 target is feasible to measure and to achieve, and assessment with this framework can identify areas for performance improvement and help prioritise national planning. Increased investments must be made at the health facility and intermediate public health levels for improved systems to detect, notify, and rapidly respond to emerging public health threats.

FUNDING

Bill & Melinda Gates Foundation.

New-age vaccine adjuvants, their development, and future perspective

In the present scenario, immunization is of utmost importance as it keeps us safe and protects us from infectious agents. Despite the great success in the field of vaccinology, there is a need to not only develop safe and ideal vaccines to fight deadly infections but also improve the quality of existing vaccines in terms of partial or inconsistent protection. Generally, subunit vaccines are known to be safe in nature, but they are mostly found to be incapable of generating the optimum immune response. Hence, there is a great possibility of improving the potential of a vaccine in formulation with novel adjuvants, which can effectively impart superior immunity. The vaccine(s) in formulation with novel adjuvants may also be helpful in fighting pathogens of high antigenic diversity. However, due to the limitations of safety and toxicity, very few human-compatible adjuvants have been approved. In this review, we mainly focus on the need for new and improved vaccines; the definition of and the need for adjuvants; the characteristics and mechanisms of human-compatible adjuvants; the current status of vaccine adjuvants, mucosal vaccine adjuvants, and adjuvants in clinical development; and future directions.

The World Health Organization's Disease Outbreak News: A retrospective database

The World Health Organization (WHO) notifies the global community about disease outbreaks through the Disease Outbreak News (DON). These online reports tell important stories about both outbreaks themselves and the high-level decision making that governs information sharing during public health emergencies. However, they have been used only minimally in global health scholarship to date. Here, we collate all 2,789 of these reports from their first use through the start of the Covid-19 pandemic (January 1996 to December 2019), and develop an annotated database of the subjective and often inconsistent information they contain. We find that these reports are dominated by a mix of persistent worldwide threats (particularly influenza and cholera) and persistent epidemics (like Ebola virus disease in Africa or MERS-CoV in the Middle East), but also document important periods in history like the anthrax bioterrorist attacks at the turn of the century, the spread of chikungunya and Zika virus to the Americas, or even recent lapses in progress towards polio elimination. We present three simple vignettes that show how researchers can use these data to answer both qualitative and quantitative questions about global outbreak dynamics and public health response. However, we also find that the retrospective value of these reports is visibly limited by inconsistent reporting (e.g., of disease names, case totals, mortality, and actions taken to curtail spread). We conclude that sharing a transparent rubric for which outbreaks are considered reportable, and adopting more standardized formats for sharing epidemiological metadata, might help make the DON more useful to researchers and policymakers.

A framework for measuring timeliness in the outbreak response path: lessons learned from the Middle East respiratory syndrome (MERS) epidemic, September 2012 to January 2019

BackgroundEpidemics are a constant threat in the 21st century, particularly disease outbreaks following spillover of an animal virus to humans. Timeliness, a key metric in epidemic response, can be examined to identify critical steps and delays in public health action.AimTo examine timeliness, we analysed the response to the Middle East respiratory syndrome (MERS) epidemic, with a focus on the international and One Health response efforts.MethodsWe performed a historical review of the MERS epidemic between September 2012 and January 2019 in three steps: (i) the construction of a timeline identifying critical events in the global response, (ii) the performance of a critical path analysis to define outbreak milestones and (iii) a time gap analysis to measure timeliness in the execution of these milestones.ResultsWe proposed 14 MERS-specific milestones at different phases of the epidemic, assessing timeliness of the public health response as well as at the animal-human interface, where we identified the most significant delays.ConclusionsWhen comparing timeliness across three coronavirus epidemics, i.e. MERS (2012), SARS (2002) and COVID-19 (2019), we identified clear improvements over time for certain milestones including laboratory confirmation and diagnostics development, while this was not as apparent for others, as the identification of zoonotic hosts. To more efficiently respond to emerging threats, the global health community should widely assess and tackle specific delays in implementing response interventions by addressing challenges in the sharing of information, data and resources, as well as efficiency, quality, transparency and reliability of reporting events.

One Health timeliness metrics to track and evaluate outbreak response reporting: A scoping review

Background

As the global population soars, human behaviours are increasing the risk of epidemics. Objective performance evaluation of outbreak responses requires that metrics of timeliness, or speed in response time, be recorded and reported. We sought to evaluate how timeliness data are being conveyed for multisectoral outbreaks and make recommendations on how One Health metrics can be used to improve response success.

Methods

We conducted a scoping review of outbreaks reported January 1, 2010- March 15, 2020, in organizational reports and peer-reviewed literature on PubMed and Embase databases. We tracked 11 outbreak milestones and calculated timeliness metrics, the median time in days, between the following: 1) Predict; 2) Prevent; 3) Start; 4) Detect; 5) Notify; 6) Verify; 7) Diagnostic; 8) Respond; 9) Communication; 10) End; and 11) After-Action Review.

Findings

We identified 26783 outbreak reports, 1014 of which involved more than just the human health sector. Only six of the eleven milestones were mentioned in >50% of reports. The time between most milestones was on average shorter for outbreaks reporting both Predict (alert of a potential outbreak) and Prevent (response to predictive alert) events.

Interpretation

Tracking progress in timeliness during outbreaks can focus efforts to prevent outbreaks from evolving into epidemics or pandemics. Response to predictive alerts demonstrated improved expediency in time to most milestones. We recommend the adoption of universally defined One Health outbreak milestones, including After Action Review, such that timeliness metrics can be used to assess outbreak response improvements over time.

Funding

This study was made possible by the United States Agency for International Development's One Health Workforce-Next Generation Project (Cooperative Agreement 7200AA19CA00018).

Infectious Disease Underreporting Is Predicted by Country-Level Preparedness, Politics, and Pathogen Severity

Underreporting of infectious diseases is a pervasive challenge in public health that has emerged as a central issue in characterizing the dynamics of the COVID-19 pandemic. Infectious diseases are underreported for a range of reasons, including mild or asymptomatic infections, weak public health infrastructure, and government censorship. In this study, we investigated factors associated with cross-country and cross-pathogen variation in reporting. We performed a literature search to collect estimates of empirical reporting rates, calculated as the number of cases reported divided by the estimated number of true cases. This literature search yielded a dataset of reporting rates for 32 pathogens, representing 52 countries. We combined epidemiological and social science theory to identify factors specific to pathogens, country health systems, and politics that could influence empirical reporting rates. We performed generalized linear regression to test the relationship between the pathogen- and country-specific factors that we hypothesized could influence reporting rates, and the reporting rate estimates that we collected in our literature search. Pathogen- and country-specific factors were predictive of reporting rates. Deadlier pathogens and sexually transmitted diseases were more likely to be reported. Country epidemic preparedness was positively associated with reporting completeness, while countries with high levels of media bias in favor of incumbent governments were less likely to report infectious disease cases. Underreporting is a complex phenomenon that is driven by factors specific to pathogens, country health systems, and politics. In this study, we identified specific and measurable components of these broader factors that influence pathogen- and country-specific reporting rates and used model selection techniques to build a model that can guide efforts to diagnose, characterize, and reduce underreporting. Furthermore, this model can characterize uncertainty and correct for bias in reported infectious disease statistics, particularly when outbreak-specific empirical estimates of underreporting are unavailable. More precise estimates can inform control policies and improve the accuracy of infectious disease models.

Using internet-based query and climate data to predict climate-sensitive infectious disease risks: a systematic review of epidemiological evidence

The use of internet-based query data offers a novel approach to improve disease surveillance and provides timely disease information. This paper systematically reviewed the literature on infectious disease predictions using internet-based query data and climate factors, discussed the current research progress and challenges, and provided some recommendations for future studies. We searched the relevant articles in the PubMed, Scopus, and Web of Science databases between January 2000 and December 2019. We initially included studies that used internet-based query data to predict infectious disease epidemics, then we further filtered and appraised the studies that used both internet-based query data and climate factors. In total, 129 relevant papers were included in the review. The results showed that most studies used a simple descriptive approach (n=80; 62%) to detect epidemics of influenza (including influenza-like illness (ILI)) (n=88; 68%) and dengue (n=9; 7%). Most studies (n=61; 47%) purely used internet search metrics to predict the epidemics of infectious diseases, while only 3 out of the 129 papers included both climate variables and internet-based query data. Our research shows that including internet-based query data and climate variables could better predict climate-sensitive infectious disease epidemics; however, this method has not been widely used to date. Moreover, previous studies did not sufficiently consider the spatiotemporal uncertainty of infectious diseases. Our review suggests that further research should use both internet-based query and climate data to develop predictive models for climate-sensitive infectious diseases based on spatiotemporal models.

Characteristics of the 100 largest modern zoonotic disease outbreaks

Zoonotic disease outbreaks are an important threat to human health and numerous drivers have been recognized as contributing to their increasing frequency. Identifying and quantifying relationships between drivers of zoonotic disease outbreaks and outbreak severity is critical to developing targeted zoonotic disease surveillance and outbreak prevention strategies. However, quantitative studies of outbreak drivers on a global scale are lacking. Attributes of countries such as press freedom, surveillance capabilities and latitude also bias global outbreak data. To illustrate these issues, we review the characteristics of the 100 largest outbreaks in a global dataset (n = 4463 bacterial and viral zoonotic outbreaks), and compare them with 200 randomly chosen background controls. Large outbreaks tended to have more drivers than background outbreaks and were related to large-scale environmental and demographic factors such as changes in vector abundance, human population density, unusual weather conditions and water contamination. Pathogens of large outbreaks were more likely to be viral and vector-borne than background outbreaks. Overall, our case study shows that the characteristics of large zoonotic outbreaks with thousands to millions of cases differ consistently from those of more typical outbreaks. We also discuss the limitations of our work, hoping to pave the way for more comprehensive future studies. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Surveillance in Next-Generation Personalized Healthcare: Science and Ethics of Data Analytics in Healthcare

Advances in science and technology have allowed for incredible improvements in healthcare. Additionally, the digital revolution in healthcare provides new ways of collecting and storing large volumes of patient data, referred to as big healthcare data. As a result, healthcare providers are now able to use data to gain a deeper understanding of how to treat an individual in what is referred to as personalized healthcare. Regardless, there are several ethical challenges associated with big healthcare data that affect how personalized healthcare is delivered. To highlight these issues, this article will review the role of big data in personalized healthcare while also discussing the ethical challenges associated with it. The article will also discuss public health surveillance, its implications, and the challenges associated with collecting participants' information. The article will proceed by highlighting next generation technologies, including robotics and 3D printing. The article will conclude by providing recommendations on how patient privacy can be protected in next-generation personalized healthcare.

The resurgence of yellow fever outbreaks in Nigeria: a 2-year review 2017-2019

Background

Yellow fever outbreaks are documented to have a considerable impact not only on the individuals but on the health system with significant economic implications. Efforts to eliminate yellow fever outbreaks globally through the EYE strategy remains important following outbreaks in Africa, Nigeria included. The outbreaks reported in Nigeria, since 2017 and the response efforts provide an opportunity to document and guide interventions for improving future outbreaks in Nigeria and other countries in Africa.

Methods

We reviewed the available yellow fever surveillance and vaccination response data between September 2017 and September 2019 across the 36 states across Nigeria. We described the epidemiology of the difference outbreaks and the periods for all interventions. We also documented the emergency vaccination responses as well as preventive mass vaccinations implemented towards improving population immunity and limiting epidemic potentials in Nigeria.

Results

A total of 7894 suspected cases with 287 laboratory-confirmed cases were reported in Nigeria between September 2017 and September 2019 with a mean age of 19 years and a case fatality of 2.7% amongst all reported cases. Outbreaks were confirmed in 55 LGAs with most of the outbreaks across four major epicentres in Kwara/Kogi, Edo, Ebonyi and Bauchi states. In response to these outbreaks, eight reactive vaccination campaigns, supported through ICG applications, were implemented. The duration for responding to the outbreaks ranged from 15 to 132 days (average 68 days) and a total of 45,648,243 persons aged < 45 years vaccinated through reactive and preventive mass campaigns between September 2017 and September 2019.

Conclusions

Nigeria experienced intermediate outbreaks of yellow fever between September 2017 and 2019 with vaccination responses conducted to control these outbreaks. However, there are delays in the timeliness of responses and more efforts required in improving reporting, response times and preparedness to further prevent morbidity and mortality from the yellow fever disease outbreaks. These efforts, including improving routine yellow fever coverage, contribute towards improving population immunity and other activities related to achieving the goals of the EYE strategy.

The Establishment and Application of Mobile Electronic Surveillance System for Infectious Diseases with the Help of China - Sierra Leone, 2016-Present

Introduction

Infectious disease surveillance has long been a challenge for low-income countries like Sierra Leone. Traditional approaches based on paper and Short Message Service (SMS) were subject to severe delays in obtaining, transmitting, and analyzing information.

Methods

During the China aid operation for fighting Ebola since the end of 2014, a mobile electronic surveillance system for infectious diseases (MESSID) was developed in collaboration with the Republic of Sierra Leone Armed Forces (RSLAF), which comprised an Android-based reporting system and a complementary web-based program designed by Active Server Page.NET (ASP.NET) with the main functions including surveillance, real-time reporting, and risk assessment of infectious diseases.

Results

MESSID was successfully registered in June 2016 and had been used by all medical and health institutions in RSLAF. From June 1, 2016 to July 5, 2021, 34,419 cases were diagnosed with 47 infectious diseases of 5 categories, with a total of 42 clinical symptoms. Compared to traditional approaches based on paper and SMS, the MESSID showed flexibility, high efficiency, convenience, and acceptability.

Discussion

MESSID is an accessible tool for surveillance of infectious diseases in Sierra Leone and possibly in other African countries with similar needs, capable of improving timeliness of disease reporting, thus rendering a timely outbreak detection and response.

Measuring Timeliness of Outbreak Response in the World Health Organization African Region, 2017-2019

Large-scale protracted outbreaks can be prevented through early detection, notification, and rapid control. We assessed trends in timeliness of detecting and responding to outbreaks in the African Region reported to the World Health Organization during 2017–2019. We computed the median time to each outbreak milestone and assessed the rates of change over time using univariable and multivariable Cox proportional hazard regression analyses. We selected 296 outbreaks from 348 public reported health events and evaluated 184 for time to detection, 232 for time to notification, and 201 for time to end. Time to detection and end decreased over time, whereas time to notification increased. Multiple factors can account for these findings, including scaling up support to member states after the World Health Organization established its Health Emergencies Programme and support given to countries from donors and partners to strengthen their core capacities for meeting International Health Regulations.

Sustainable Laboratory Capacity Building After the 2014 Ebola Outbreak in the Republic of Guinea

Background: The 2014–2016 West Africa Ebola virus disease outbreak heavily impacted the Republics of Guinea, Sierra Leone, and Liberia. The outbreak uncovered the weaknesses of the public health systems, including inadequately trained and insufficient health personnel as well as limited and poorly equipped health infrastructures. These weaknesses represent significant threats to global health security. In the wake of the outbreak, affected countries made urgent requests for international engagement to help strengthening the public health systems.

Methods: This work describes the successful multi-year implementation of a laboratory capacity building program in the Republic of Guinea. The program integrated biorisk and quality management systems training, infectious diseases diagnostic training, facility engineering and maintenance training, and mentorship to strengthen Guinea's bio-surveillance capacity.

Results: The major outcome of these efforts was an established and local staff-operated public health laboratory that performs disease surveillance and reporting and diagnostic of priority diseases and pathogens of security concerns.

Conclusions: This work has improved the Guinea country's capabilities to address country public health issues and preparedness to respond to future infectious disease threats.

Leveraging natural history biorepositories as a global, decentralized, pathogen surveillance network

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic reveals a major gap in global biosecurity infrastructure: a lack of publicly available biological samples representative across space, time, and taxonomic diversity. The shortfall, in this case for vertebrates, prevents accurate and rapid identification and monitoring of emerging pathogens and their reservoir host(s) and precludes extended investigation of ecological, evolutionary, and environmental associations that lead to human infection or spillover. Natural history museum biorepositories form the backbone of a critically needed, decentralized, global network for zoonotic pathogen surveillance, yet this infrastructure remains marginally developed, underutilized, underfunded, and disconnected from public health initiatives. Proactive detection and mitigation for emerging infectious diseases (EIDs) requires expanded biodiversity infrastructure and training (particularly in biodiverse and lower income countries) and new communication pipelines that connect biorepositories and biomedical communities. To this end, we highlight a novel adaptation of Project ECHO’s virtual community of practice model: Museums and Emerging Pathogens in the Americas (MEPA). MEPA is a virtual network aimed at fostering communication, coordination, and collaborative problem-solving among pathogen researchers, public health officials, and biorepositories in the Americas. MEPA now acts as a model of effective international, interdisciplinary collaboration that can and should be replicated in other biodiversity hotspots. We encourage deposition of wildlife specimens and associated data with public biorepositories, regardless of original collection purpose, and urge biorepositories to embrace new specimen sources, types, and uses to maximize strategic growth and utility for EID research. Taxonomically, geographically, and temporally deep biorepository archives serve as the foundation of a proactive and increasingly predictive approach to zoonotic spillover, risk assessment, and threat mitigation.

Health impacts of climate change on smallholder farmers

The health of smallholder farmers is crucial for ensuring food and nutritional security for two billion people. However, their health is in jeopardy for several reasons including challenges from climate change impacts. Using a narrative literature review supported by field observations and informal interviews with key informants in India, Bangladesh and Malawi, this paper identifies and discusses the health impacts of climate change under four categories: (i) communicable diseases, (ii) non-communicable diseases, (iii) mental health, and (iv) occupational health, safety and other health issues. The health impacts of climate change on smallholder farmers will hamper the realization of many of the United Nations' Sustainable Development Goals, and a series of recommendations are made to regional and country governments to address the increasing health impacts of accelerating climate change among smallholder farmers.

Using Timeliness Metrics to Track Progress and Identify Gaps in Disease Surveillance

Timely outbreak detection and response can translate into illnesses averted and lives saved. As such, timeliness is an important criterion for evaluating performance of infectious disease surveillance systems. Through the use of clearly defined outbreak milestones, timeliness metrics can capture the speed of outbreak detection, verification, response, and other key actions across the timeline of an outbreak and evaluate progress over time. In this article, we describe a series of country-level pilot studies designed to assess the feasibility and utility of tracking timeliness metrics and highlight key findings. We then discuss subsequent efforts to develop a timeliness metrics measurement framework through expert consultation and provide recommendations for implementation. National surveillance programs, international agencies, and donor organizations can use timeliness metrics to identify gaps in surveillance performance and track progress toward improved global health security.

An exploration of the protective effect of rodent species richness on the geographical expansion of Lassa fever in West Africa

BACKGROUND

Lassa fever (LF) is one of the most devastating rodent-borne diseases in West Africa, causing thousands of deaths annually. The geographical expansion of LF is also a concern; cases were recently identified in Ghana and Benin. Previous ecological studies have suggested that high natural-host biodiversity reduces the likelihood of spillover transmission of rodent-borne diseases, by suppressing the activities of reservoir species. However, the association of biodiversity with the geographical expansion of LF has not been the subject of epidemiological studies.

METHODOLOGY/PRINCIPAL FINDINGS

We conducted a spatial analysis based on sociodemographic, geographical, and ecological data, and found that higher rodent species richness was significantly associated with a lower risk of LF emergence in West Africa from 2008 to 2017 (Odds Ratio = 0.852, 95% Credible Interval = 0.745-0.971).

CONCLUSIONS/SIGNIFICANCE

The results reinforce the importance of the 'One Health' approach by demonstrating that a high level of biodiversity could benefit human health.

Preparing for Emerging Zoonotic Viruses

The risk of emergence and spread of novel human pathogens originating from an animal reservoir has increased in the past decades. However, the unpredictable nature of disease emergence makes surveillance and preparedness challenging. Knowledge of general risk factors for emergence and spread, combined with local level data is needed to develop a risk-based methodology for early detection. This involves the implementation of the One Health approach, integrating human, animal and environmental health sectors, as well as social sciences, bioinformatics and more. Recent technical advances, such as metagenomic sequencing, will aid the rapid detection of novel pathogens on the human-animal interface.

Community Health Workers and Disease Surveillance in Tanzania: Promoting the Use of Mobile Technologies in Detecting and Reporting Health Events

This cross-sectional study was conducted in the Kilosa, Morogoro Urban, Ngorongoro, and Ulanga districts of Tanzania to investigate the practices of community health workers (CHWs) related to disease surveillance functions and to establish their needs and technology capacities. We also established the strength of mobile phone networks and internet connections in the study areas to inform the feasibility of using mobile-based applications in community-based disease surveillance. A total of 135 CHWs from 85 villages participated in the study. Health events captured at the community level were entirely paper-based. CHWs submitted reports to higher-level health authorities mainly on foot (100%), but they also used public transport (65%) and telephone calls (56%). The median number of days between the onset of a suspected disease outbreak at the community level and reporting to a primary healthcare facility was 10 days (interquartile range [IQR] 2-30). The median number of days between submitting a report and receiving a response was 7 days (IQR 2-30). Of the 53 CHWs who reported the most recent health events to a higher-level health authority, 39 (74%) never received feedback. All 85 villages had a reliable mobile phone network and 74 (87%) had a mobile phone internet connection that was strong enough to support data transmission using digital technology. Almost all (n = 132, 98%) of the CHWs owned mobile phones. The practices related to detection and reporting of health events could be improved to enhance early warning disease surveillance. Reliable mobile networks and internet connections and the ownership of mobile phones among CHWs in the study areas present opportunities to strengthen community event-based surveillance using mobile-based solutions.

Spatial and Temporal Patterns of Typhoid and Paratyphoid Fever Outbreaks: A Worldwide Review, 1990-2018

BACKGROUND

Analyses of the global spatial and temporal distribution of enteric fever outbreaks worldwide are important factors to consider in estimating the disease burden of enteric fever disease burden.

METHODS

We conducted a global literature review of enteric fever outbreak data by systematically using multiple databases from 1 January 1990 to 31 December 2018 and classified them by time, place, diagnostic methods, and drug susceptibility, to illustrate outbreak characteristics including spatial and temporal patterns.

RESULTS

There were 180 940 cases in 303 identified outbreaks caused by infection with Salmonella enterica serovar Typhi (S. Typhi) and Salmonella enterica serovar Paratyphi A or B (S. Paratyphi). The size of outbreak ranged from 1 to 42 564. Fifty-one percent of outbreaks occurred in Asia, 15% in Africa, 14% in Oceania, and the rest in other regions. Forty-six percent of outbreaks specified confirmation by blood culture, and 82 outbreaks reported drug susceptibility, of which 54% had multidrug-resistant pathogens. Paratyphoid outbreaks were less common compared to typhoid (22 vs 281) and more prevalent in Asia than Africa. Risk factors were multifactorial, with contaminated water being the main factor.

CONCLUSIONS

Enteric fever outbreak burden remains high in endemic low- and middle-income countries and, despite its limitations, outbreak data provide valuable contemporary evidence in prioritizing resources, public health policies, and actions. This review highlights geographical locations where urgent attention is needed for enteric fever control and calls for global action to prevent and contain outbreaks.

A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China

A novel pneumonia-like coronavirus disease (COVID-19) caused by a novel coronavirus named SARS-CoV-2 has swept across China and the world. Public health measures that were effective in previous infection outbreaks (eg, wearing a face mask, quarantining) were implemented in this outbreak. Available multidimensional social network data that take advantage of the recent rapid development of information and communication technologies allow for an exploration of disease spread and control via a modernized epidemiological approach. By using spatiotemporal data and real-time information, we can provide more accurate estimates of disease spread patterns related to human activities and enable more efficient responses to the outbreak. Two real cases during the COVID-19 outbreak demonstrated the application of emerging technologies and digital data in monitoring human movements related to disease spread. Although the ethical issues related to using digital epidemiology are still under debate, the cases reported in this article may enable the identification of more effective public health measures, as well as future applications of such digitally directed epidemiological approaches in controlling infectious disease outbreaks, which offer an alternative and modern outlook on addressing the long-standing challenges in population health.

Earlier Outbreak Detection-A Generic Model and Novel Methodology to Guide Earlier Detection Supported by Data From Low- and Mid-Income Countries

Infectious disease outbreaks can have significant impact on individual health, national economies, and social well-being. Through early detection of an infectious disease, the outbreak can be contained at the local level, thereby reducing adverse effects on populations. Significant time and funding have been invested to improve disease detection timeliness. However, current evaluation methods do not provide evidence-based suggestions or measurements on how to detect outbreaks earlier. Key conditions for earlier detection and their influencing factors remain unclear and unmeasured. Without clarity about conditions and influencing factors, attempts to improve disease detection remain ad hoc and unsystematic.

Methods: We developed a generic five-step disease detection model and a novel methodology to use for data collection, analysis, and interpretation. Data was collected in two workshops in Southeast Europe (n = 33 participants) and Southern and East Africa (n = 19 participants), representing mid- and low-income countries. Through systematic, qualitative, and quantitative data analyses, we identified key conditions for earlier detection and prioritized factors that influence them. As participants joined a workshop format and not an experimental setting, no ethics approval was required.

Findings: Our analyses suggest that governance is the most important condition for earlier detection in both regions. Facilitating factors for earlier detection are risk communication activities such as information sharing, communication, and collaboration activities. Impeding factors are lack of communication, coordination, and leadership.

Interpretation: Governance and risk communication are key influencers for earlier detection in both regions. However, inadequate technical capacity, commonly assumed to be a leading factor impeding early outbreak detection, was not found a leading factor. This insight may be used to pinpoint further improvement strategies.

Setting a baseline for global urban virome surveillance in sewage

The rapid development of megacities, and their growing connectedness across the world is becoming a distinct driver for emerging disease outbreaks. Early detection of unusual disease emergence and spread should therefore include such cities as part of risk-based surveillance. A catch-all metagenomic sequencing approach of urban sewage could potentially provide an unbiased insight into the dynamics of viral pathogens circulating in a community irrespective of access to care, a potential which already has been proven for the surveillance of poliovirus. Here, we present a detailed characterization of sewage viromes from a snapshot of 81 high density urban areas across the globe, including in-depth assessment of potential biases, as a proof of concept for catch-all viral pathogen surveillance. We show the ability to detect a wide range of viruses and geographical and seasonal differences for specific viral groups. Our findings offer a cross-sectional baseline for further research in viral surveillance from urban sewage samples and place previous studies in a global perspective.

nosoi: A stochastic agent-based transmission chain simulation framework in r

The transmission process of an infectious agent creates a connected chain of hosts linked by transmission events, known as a transmission chain. Reconstructing transmission chains remains a challenging endeavour, except in rare cases characterized by intense surveillance and epidemiological inquiry. Inference frameworks attempt to estimate or approximate these transmission chains but the accuracy and validity of such methods generally lack formal assessment on datasets for which the actual transmission chain was observed.We here introduce nosoi, an open-source r package that offers a complete, tunable and expandable agent-based framework to simulate transmission chains under a wide range of epidemiological scenarios for single-host and dual-host epidemics. nosoi is accessible through GitHub and CRAN, and is accompanied by extensive documentation, providing help and practical examples to assist users in setting up their own simulations.Once infected, each host or agent can undergo a series of events during each time step, such as moving (between locations) or transmitting the infection, all of these being driven by user-specified rules or data, such as travel patterns between locations. nosoi is able to generate a multitude of epidemic scenarios, that can-for example-be used to validate a wide range of reconstruction methods, including epidemic modelling and phylodynamic analyses. nosoi also offers a comprehensive framework to leverage empirically acquired data, allowing the user to explore how variations in parameters can affect epidemic potential. Aside from research questions, nosoi can provide lecturers with a complete teaching tool to offer students a hands-on exploration of the dynamics of epidemiological processes and the factors that impact it. Because the package does not rely on mathematical formalism but uses a more intuitive algorithmic approach, even extensive changes of the entire model can be easily and quickly implemented.

Regional event-based surveillance in WHO's Western Pacific Region

In the World Health Organization's Western Pacific Region, event-based surveillance has been conducted for more than a decade to rapidly detect and assess public health events. This report describes the establishment and evolution of the Western Pacific Region's event-based surveillance system and presents an analysis of public health events in the Region. Between July 2008 and June 2017, a total of 2396 events were reported in the Western Pacific Region, an average of 266 events per year. Infectious diseases in humans and animals accounted for the largest proportion of events recorded during this period (73%, 1743 events). Maintaining and strengthening this well established system is critical to support the rapid detection, assessment and response to public health events to sustain regional health security.

Using big data to predict pertussis infections in Jinan city, China: a time series analysis

This study aims to use big data (climate data, internet query data and school calendar patterns (SCP)) to improve pertussis surveillance and prediction, and develop an early warning model for pertussis epidemics. We collected weekly pertussis notifications, SCP, climate and internet search query data (Baidu index (BI)) in Jinan, China between 2013 and 2017. Time series decomposition and temporal risk assessment were used for examining the epidemic features in pertussis infections. A seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to predict pertussis occurrence using identified predictors. Our study demonstrates clear seasonal patterns in pertussis epidemics, and pertussis activity was most significantly associated with BI at 2-week lag (r_BI = 0.73, p < 0.05), temperature at 1-week lag (r_temp = 0.19, p < 0.05) and rainfall at 2-week lag (r_rainfall = 0.27, p < 0.05). No obvious relationship between pertussis peaks and school attendance was found in the study. Pertussis cases were more likely to be temporally concentrated throughout the epidemics during the study period. SARIMA models with 2-week-lagged BI and 1-week-lagged temperature had better predictive performance (β_{search query} = 0.06, p = 0.02; β_temp = 0.16, p = 0.03) with large correlation coefficients (r = 0.67, p < 0.01) and low root mean squared error (RMSE) value (r = 3.59). The regression tree model identified threshold values of potential predictors (search query, climate and SCP) for pertussis epidemics. Our results showed that internet query in conjunction with social and climatic data can predict pertussis epidemics, which is a foundation of using such data to develop early warning systems.

Association of sociodemographic factors and internet query data with pertussis infections in Shandong, China

This study explored how internet queries vary in facilitating monitoring of pertussis, and the effects of sociodemographic characteristics on such variation by city in Shandong province, China. We collected weekly pertussis notifications, Baidu Index (BI) data and yearly sociodemographic data at the city level between 1 January 2009 and 31 December 2017. Spearman's correlation was performed for temporal risk indices, generalised linear models and regression tree models were developed to identify the hierarchical effects and the threshold between sociodemographic factors and internet query data with pertussis surveillance. The BI was correlated with pertussis notifications, with a strongly spatial variation among cities in temporal risk indices (composite temporal risk metric (CTRM) range: 0.59–1.24). The percentage of urban population (relative risk (RR): 1.05, 95% confidence interval (CI) 1.03–1.07), the proportion of highly educated population (RR: 1.27, 95% CI 1.16–1.39) and the internet access rate (RR: 1.04, 95% CI 1.02–1.05) were correlated with CTRM. Higher RRs in the three identified sociodemographic factors were associated with higher stratified CTRM. The percentage of highly educated population was the most important determinant in the BI with pertussis surveillance. The findings may lead to spatially-specific criteria to inform development of an early warning system of pertussis infections using internet query data.

Concealing Disease: Trade and Travel Barriers and the Timeliness of Outbreak Reporting

Slow outbreak reporting by states is a key challenge to effectively responding to global health emergencies like Zika, Ebola, and H1N1. Current policy focuses on improving domestic outbreak surveillance capacity globally in order to reduce reporting lags. However, governments also face economic and political incentives to conceal outbreaks, and these incentives largely are ignored in policy discussions. In spite of the policy implications for outbreak response, the "capacity" and "will" explanations have not been systematically examined. Analysis of a dataset coding the timeliness of outbreak reporting from 1996-2014 finds evidence that states' unwillingness to report-rather than just their inability-leads to delayed reporting. The findings suggest that though building surveillance capacity is critical, doing so may not be sufficient to reduce reporting lags. Policy aimed at encouraging rapid reporting must also mitigate the associated economic and political costs.

The second information revolution: digitalization brings opportunities and concerns for public health

The spread of the written word, facilitated by the introduction of the printing press, was an information revolution with profound implications for European society. Now, a second information revolution is underway, a digital transformation that is shaping the way Europeans live and interact with each other and the world around them. We are confronted with an unprecedented expansion in ways to share and access information and experiences, to express ourselves and communicate. Yet while these changes have undoubtedly provided many benefits for health, from information sharing to improved surveillance and diagnostics, they also open up many potential threats. These come in many forms. Here we review some the pressing issues of concern; discrimination; breaches of privacy; iatrogenesis; disinformation and misinformation or 'fake news' and cyber-attacks. These have the potential to impact negatively on the health and wellbeing of individuals as well as entire communities and nations. We call for a concerted European response to maximize the benefits of the digital revolution while minimizing the harms, arguably one of the greatest challenges facing the public health community today.

Mobile phone-based surveillance for animal disease in rural communities: implications for detection of zoonoses spillover

Improving the speed of outbreak detection and reporting at the community level are critical in managing the threat of emerging infectious diseases, many of which are zoonotic. The widespread use of mobile phones, including in rural areas, constitutes a potentially effective tool for real-time surveillance of infectious diseases. Using longitudinal data from a disease surveillance system implemented in 1500 households in rural Kenya, we test the effectiveness of mobile phone animal syndromic surveillance by comparing it with routine household animal health surveys, determine the individual and household correlates of its use and examine the broader implications for surveillance of zoonotic diseases. A total of 20 340 animal and death events were reported from the community through the two surveillance systems, half of which were confirmed as valid disease events. The probability of an event being valid was 2.1 times greater for the phone-based system, compared with the household visits. Illness events were 15 times (95% CI 12.8, 17.1) more likely to be reported through the phone system compared to routine household visits, but not death events (OR 0.1 (95% CI 0.09, 0.11)). Disease syndromes with severe presentations were more likely to be reported through the phone system. While controlling for herd and flock sizes owned, phone ownership was not a determinant of using the phone-based surveillance system, but the lack of a formal education, and having additional sources of income besides farming were associated with decreased likelihood of reporting through the phone system. Our study suggests that a phone-based surveillance system will be effective at detecting outbreaks of diseases such as Rift Valley fever that present with severe clinical signs in animal populations, but in the absence of additional reporting incentives, it may miss early outbreaks of diseases such as avian influenza that present primarily with mortality.

This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

Effect of MyMAFI-A Newly Developed Mobile App for Field Investigation of Food Poisoning Outbreak on the Timeliness in Reporting: A Randomized Crossover Trial

Prompt investigation of food poisoning outbreak are essential, as it usually involves a short incubation period. Utilizing the advancement in mobile technology, a mobile application named MyMAFI (My Mobile Apps for Field Investigation) was developed with the aim to be an alternative and better tool for current practices of field investigation of food poisoning outbreak. A randomized cross-over trial with two arms and two treatment periods was conducted to assess the effectiveness of the newly developed mobile application as compared to the standard paper-based format approach. Thirty-six public health inspectors from all districts in Kelantan participated in this study and they were randomized into two equal sized groups. Group A started the trial as control group using the paper-format investigation form via simulated outbreaks and group B used the mobile application. After a one-month ‘washout period’, the group was crossed over. The primary outcome measured was the time taken to complete the outbreak investigation. The treatment effects, the period effects and the period-by-treatment interaction were analyzed using Pkcross command in Stata software. There was a significant treatment effect with mean square 21840.5 and its corresponding F statistic 4.47 (p-value = 0.038), which indicated that the mobile application had significantly improve the reporting timeliness. The results also showed that there was a significant period effect (p-value = 0.025); however, the treatment by period interaction was not significant (p-value = 0.830). The newly developed mobile application—MyMAFI—can improve the timeliness in reporting for investigation of food poisoning outbreak.

Paramyxo- and Coronaviruses in Rwandan Bats

A high diversity of corona- and paramyxoviruses have been detected in different bat species at study sites worldwide, including Africa, however no biosurveillance studies from Rwanda have been reported. In this study, samples from bats collected from caves in Ruhengeri, Rwanda, were tested for the presence of corona- and paramyxoviral RNA using reverse transcription PCR assays. Positive results were further characterized by DNA sequencing and phylogenetic analysis. In addition to morphological identification of bat species, we also did molecular confirmation of species identities, contributing to the known genetic database available for African bat species. We detected a novel Betacoronavirus in two Geoffroy’s horseshoe bats (Rhinolophus clivosus) bats. We also detected several different paramyxoviral species from various insectivorous bats. One of these viral species was found to be homologous to the genomes of viruses belonging to the Jeilongvirus genus. Additionally, a Henipavirus-related sequence was detected in an Egyptian rousette fruit bat (Rousettus aegyptiacus). These results expand on the known diversity of corona- and paramyxoviruses and their geographical distribution in Africa.

The Disease Outbreak-Human Trafficking Connection: A Missed Opportunity

This article examines the connection between disease outbreaks and human trafficking. A central challenge in combating trafficking is poor data on its nature and scope. One way to deal with these gaps in knowledge and still target resources effectively is to identify key "push and pull" factors that increase the likelihood of trafficking from origin countries and to destination countries. One under-examined push factor is the outbreak of disease. Outbreaks are associated with several well-documented trafficking risk factors, from the breakdown of rule of law and increase in criminal activity to competition for resources and diminished economic opportunity. Disease outbreaks can also disrupt family ties. For example, the 2014 Ebola outbreak in West Africa left thousands of orphans at increased risk of exploitation. The article outlines possible mechanisms through which outbreaks could increase trafficking risk and, using data on disease outbreaks and trafficking across states over the past 2 decades, provides evidence that countries that have recently experienced a disease outbreak are more likely to have trafficking outflows. The findings point to the importance of integrating trafficking prevention into outbreak response and call for a research agenda more fully examining the connection between trafficking and outbreaks (and potentially other types of natural disasters as well).

Participatory Disease Surveillance Systems: Ethical Framework

Advances in information technology are changing public health at an unprecedented rate. Participatory surveillance systems are contributing to public health by actively engaging digital (eg, Web-based) communities of volunteer citizens to report symptoms and other pertinent information on public health threats and also by empowering individuals to promptly respond to them. However, this digital model raises ethical issues on top of those inherent in traditional forms of public health surveillance. Research ethics are undergoing significant changes in the digital era where not only participants' physical and psychological well-being but also the protection of their sensitive data have to be considered. In this paper, the digital platform of Influenzanet is used as a case study to illustrate those ethical challenges posed to participatory surveillance systems using digital platforms and mobile apps. These ethical challenges include the implementation of electronic consent, the protection of participants' privacy, the promotion of justice, and the need for interdisciplinary capacity building of research ethics committees. On the basis of our analysis, we propose a framework to regulate and strengthen ethical approaches in the field of digital public health surveillance.

Predicting seasonal influenza epidemics using cross-hemisphere influenza surveillance data and local internet query data

Can early warning systems be developed to predict influenza epidemics? Using Australian influenza surveillance and local internet search query data, this study investigated whether seasonal influenza epidemics in China, the US and the UK can be predicted using empirical time series analysis. Weekly national number of respiratory cases positive for influenza virus infection that were reported to the FluNet surveillance system in Australia, China, the US and the UK were obtained from World Health Organization FluNet surveillance between week 1, 2010, and week 9, 2018. We collected combined search query data for the US and the UK from Google Trends, and for China from Baidu Index. A multivariate seasonal autoregressive integrated moving average model was developed to track influenza epidemics using Australian influenza and local search data. Parameter estimates for this model were generally consistent with the observed values. The inclusion of search metrics improved the performance of the model with high correlation coefficients (China = 0.96, the US = 0.97, the UK = 0.96, p < 0.01) and low Maximum Absolute Percent Error (MAPE) values (China = 16.76, the US = 96.97, the UK = 125.42). This study demonstrates the feasibility of combining (Australia) influenza and local search query data to predict influenza epidemics a different (northern hemisphere) scales.

Assessing global preparedness for the next pandemic: development and application of an Epidemic Preparedness Index

Introduction

Robust metrics for national-level preparedness are critical for assessing global resilience to epidemic and pandemic outbreaks. However, existing preparedness assessments focus primarily on public health systems or specific legislative frameworks, and do not measure other essential capacities that enable and support public health preparedness and response.

Methods

We developed an Epidemic Preparedness Index (EPI) to assess national-level preparedness. The EPI is global, covering 188 countries. It consists of five subindices measuring each country’s economic resources, public health communications, infrastructure, public health systems and institutional capacity. To evaluate the construct validity of the EPI, we tested its correlation with proxy measures for preparedness and response capacity, including the timeliness of outbreak detection and reporting, as well as vaccination rates during the 2009 H1N1 influenza pandemic.

Results

The most prepared countries were concentrated in Europe and North America, while the least prepared countries clustered in Central and West Africa and Southeast Asia. Better prepared countries were found to report infectious disease outbreaks more quickly and to have vaccinated a larger proportion of their population during the 2009 pandemic.

Conclusion

The EPI measures a country’s capacity to detect and respond to infectious disease events. Existing tools, such as the Joint External Evaluation (JEE), have been designed to measure preparedness within a country over time. The EPI complements the JEE by providing a holistic view of preparedness and is constructed to support comparative risk assessment between countries. The index can be updated rapidly to generate global estimates of pandemic preparedness that can inform strategy and resource allocation.

Epidemic intelligence needs of stakeholders in the Asia-Pacific region

Objective

To understand the global outbreak surveillance needs of stakeholders involved in epidemic response in selected countries and areas in the Asia-Pacific region to inform development of an epidemic observatory, Epi-watch.

Methods

We designed an online, semi-structured stakeholder questionnaire to collect information on global outbreak surveillance sources and limitations from participants who use epidemic intelligence and outbreak alert services in their work in government and nongovernment organizations in the Asia-Pacific region.

Results

All respondents agreed that it was important to remain up to date with global outbreaks. The main reason cited for following global outbreak news was as an early warning for serious epidemics. Mainstream media and specialist Internet sources such as the World Health Organization (n = 54/91; 59%), the Program for Monitoring Emerging Diseases (ProMED)-mail (n = 45/91; 49%) and the United States Centers for Disease Control and Prevention (n = 31/91; 34%) were the most common sources for global outbreak news; rapid intelligence services such as HealthMap were less common (n = 9/91; 10%). Only 51% (n = 46/91) of respondents thought that their sources of outbreak news were timely and sufficient for their needs.

Conclusion

For those who work in epidemic response, epidemic intelligence is important and widely used. Stakeholders are less aware of and less frequently use rapid sources such as HealthMap and rely more on validated but less timely traditional sources of disease surveillance. Users identified a need for more timely and reliable epidemic intelligence.

Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System

Since 2013, the French Animal Health Epidemic Intelligence System (in French: Veille Sanitaire Internationale, VSI) has been monitoring signals of the emergence of new and exotic animal infectious diseases worldwide. Once detected, the VSI team verifies the signals and issues early warning reports to French animal health authorities when potential threats to France are detected. To improve detection of signals from online news sources, we designed the Platform for Automated extraction of Disease Information from the web (PADI-web). PADI-web automatically collects, processes and extracts English-language epidemiological information from Google News. The core component of PADI-web is a combined information extraction (IE) method founded on rule-based systems and data mining techniques. The IE approach allows extraction of key information on diseases, locations, dates, hosts and the number of cases mentioned in the news. We evaluated the combined method for IE on a dataset of 352 disease-related news reports mentioning the diseases involved, locations, dates, hosts and the number of cases. The combined method for IE accurately identified (F-score) 95% of the diseases and hosts, respectively, 85% of the number of cases, 83% of dates and 80% of locations from the disease-related news. We assessed the sensitivity of PADI-web to detect primary outbreaks of four emerging animal infectious diseases notifiable to the World Organisation for Animal Health (OIE). From January to June 2016, PADI-web detected signals for 64% of all primary outbreaks of African swine fever, 53% of avian influenza, 25% of bluetongue and 19% of foot-and-mouth disease. PADI-web timely detected primary outbreaks of avian influenza and foot-and-mouth disease in Asia, i.e. they were detected 8 and 3 days before immediate notification to OIE, respectively.

Using Google Trends and ambient temperature to predict seasonal influenza outbreaks

BACKGROUND

The discovery of the dynamics of seasonal and non-seasonal influenza outbreaks remains a great challenge. Previous internet-based surveillance studies built purely on internet or climate data do have potential error.

METHODS

We collected influenza notifications, temperature and Google Trends (GT) data between January 1st, 2011 and December 31st, 2016. We performed time-series cross correlation analysis and temporal risk analysis to discover the characteristics of influenza epidemics in the period. Then, the seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to track influenza epidemics using GT and climate data.

RESULTS

Influenza infection was significantly corrected with GT at lag of 1-7 weeks in Brisbane and Gold Coast, and temperature at lag of 1-10 weeks for the two study settings. SARIMA models with GT and temperature data had better predictive performance. We identified autoregression (AR) for influenza was the most important determinant for influenza occurrence in both Brisbane and Gold Coast.

CONCLUSIONS

Our results suggested internet search metrics in conjunction with temperature can be used to predict influenza outbreaks, which can be considered as a pre-requisite for constructing early warning systems using search and temperature data.

Smolinski M, W. Crawley A, Ko Oo M. Data quality and timeliness of outbreak reporting system among countries in Greater Mekong subregion: Challenges for international data sharing

Cross-border disease transmission is a key challenge for prevention and control of outbreaks. Variation in surveillance structure and national guidelines used in different countries can affect their data quality and the timeliness of outbreak reports. This study aimed to evaluate timeliness and data quality of national outbreak reporting for four countries in the Mekong Basin Disease Surveillance network (MBDS). Data on disease outbreaks occurring from 2010 to 2015 were obtained from the national disease surveillance reports of Cambodia, Lao PDR, Myanmar, and Vietnam. Data included total cases, geographical information, and dates at different timeline milestones in the outbreak detection process. Nine diseases or syndromes with public health importance were selected for the analysis including: dengue, food poisoning & diarrhea, severe diarrhea, diphtheria, measles, H5N1 influenza, H1N1 influenza, rabies, and pertussis. Overall, 2,087 outbreaks were reported from the four countries. The number of outbreaks and number of cases per outbreak varied across countries and diseases, depending in part on the outbreak definition used in each country. Dates on index onset, report, and response were >95% complete in all countries, while laboratory confirmation dates were 10%-100% incomplete in most countries. Inconsistent and out of range date data were observed in 1%-5% of records. The overall timeliness of outbreak report, response, and public communication was within 1-15 days, depending on countries and diseases. Diarrhea and severe diarrhea outbreaks showed the most rapid time to report and response, whereas diseases such as rabies, pertussis and diphtheria required a longer time to report and respond. The hierarchical structure of the reporting system, data collection method, and country's resources could affect the data quality and timeliness of the national outbreak reporting system. Differences in data quality and timeliness of outbreak reporting system among member countries should be considered when planning data sharing strategies within a regional network.