Surveillance and Outbreak Response Management System (SORMAS) to support the control of the Ebola virus disease outbreak in West Africa

Cost-effectiveness of One Health intervention to reduce risk of human exposure and infection with non-typhoidal salmonellosis (NTS) in Nigeria

Background

Non-typhoidal Salmonella infection (NTS) is an important foodborne zoonosis with underappreciated health and economic burdens, and low case fatality. It has global prevalence, with more burdens in under-resourced countries with poor health infrastructures. Using a cohort study, we determined the cost-effectiveness of NTS in humans in Nigeria for the year 2020.

Methods

Using a customized Excel-based cost-effectiveness analysis tool, structured (One Health) and unstructured (episodic intervention against NTS) in Nigeria were evaluated. Input data on the disease burdens, costs surveillance, response and control of NTS were obtained from validated sources and the public health system.

Results

The non-complicated and complicated cases were 309,444 (95%) and 16,287 (5%) respectively, and the overall programme cost was US$ 31,375,434.38. The current non-systematic episodic intervention costed US$ 14,913,480.36, indicating an additional US$ 16,461,954 to introduce the proposed intervention. The intervention will avert 4036.98 NTS DALYs in a single year. The non-complicated NTS case was US$ 60/person with significant rise in complicated cases. The cumulative costs of NTS with and without complications far outweighed the program cost for One Health intervention with an incremental cost-effectiveness ratio (ICER) of -US$ 221.30).

Conclusions

Utilising structured One Health intervention is cost-effective against NTS in Nigeria, it carries additional mitigative benefits for other diseases and is less costly and more effective, indicative of a superior health system approach. Identified limitations must be improved to optimize benefits associated and facilitate policy discussions and resource allocation.

A snapshot on a journey from frustration to readiness-A qualitative pre-implementation exploration of readiness for technology adoption in Public Health Protection in Ireland

In an era of emergent infectious disease, the timely and efficient management of disease outbreaks is critical to public health protection. Integrated technologies for case and incident management (CIM) collect real-time health intelligence for decision making in Public Health. In Ireland, a Public Health reform program is preparing for implementation of a health information system for health protection. Project implementers seek to document and understand the readiness and willingness of future users to adopt the new system, prior to system procurement and implementation. Qualitative key informant interviews were conducted (n = 8) with Public Health personnel from a single regional department of Public Health representing medical, nursing, disease surveillance and administrative roles, at managerial and staff levels. A qualitative thematic analysis was performed. Participants were frustrated by weaknesses in the current practice of CIM and were ready and willing to adopt a digital CIM system if it met their needs. However, they were frustrated by lack of clear timelines. We identified 7 enablers and 3 barriers to readiness and willingness to adopt a CIM system. 'Newness of the workforce' was the main enabler of readiness and willingness, while 'lack of knowledge and familiarity with system' was the main barrier to readiness and willingness. Experiences during the COVID-19 pandemic gave a clear understanding of the problems and need for a digital CIM system and the reform program facilitated a culture of change, readying the workforce for the new health information system. New members of the Public Health departments are a likely ready and eager cohort for adoption of a modern, 'fit for purpose' CIM system and the execution of implementation will likely determine how ready and willing the wider network of departments will be to adopt a national CIMS.

Software Tools to Facilitate Community-Based Surveillance: A Scoping Review

INTRODUCTION

Public health surveillance traditionally occurs at a health facility; however, there is growing concern that this provides only partial and untimely health information. Community-based surveillance (CBS) enables early warning and the mobilization of early intervention and response to disease outbreaks. CBS is a method of surveillance that can monitor a wide range of information directly from community members. CBS can be done using short message service, phone calls, paper forms, or a specialized software tool. No scoping review of the available software tools with the capability for CBS exists in the literature. This review aims to map software tools that can be used for CBS in both community health programs and emergency settings and demonstrate their use cases.

METHODS

We conducted a scoping review of academic literature and supplemental resources and conducted qualitative interviews with stakeholders working with digital community health and surveillance tools.

RESULTS

All of the tools reviewed have features necessary to support the reporting process of CBS; only 3 (CommCare, Community Health Toolkit, and DHIS2 Tracker) provided all 10 attributes included in the mapping. AVADAR and Nyss were the only tools designed specifically for CBS and for use by volunteers, while the other tools were designed for community health workers and have a broader use case.

CONCLUSION

The findings demonstrate that several software tools are available to facilitate public health surveillance at the community level. In the future, emphasis should be put on contextualizing these tools to meet a country's public health needs and promoting institutionalization and ownership by the national health system. There is also an opportunity to explore improvements in event-based surveillance at the community level.

Facilitators and Barriers in the Implementation of a Digital Surveillance and Outbreak Response System in Ghana Before and During the COVID-19 Pandemic: Qualitative Analysis of Stakeholder Interviews

BACKGROUND

In the past 2 decades, many countries have recognized the use of electronic systems for disease surveillance and outbreak response as an important strategy for disease control and prevention. In low- and middle-income countries, the adoption of these electronic systems remains a priority and has attracted the support of global health players. However, the successful implementation and institutionalization of electronic systems in low- and middle-income countries have been challenged by the local capacity to absorb technologies, decisiveness and strength of leadership, implementation costs, workforce attitudes toward innovation, and organizational factors. In November 2019, Ghana piloted the Surveillance Outbreak Response Management and Analysis System (SORMAS) for routine surveillance and subsequently used it for the national COVID-19 response.

OBJECTIVE

This study aims to identify the facilitators of and barriers to the sustainable implementation and operation of SORMAS in Ghana.

METHODS

Between November 2021 and March 2022, we conducted a qualitative study among 22 resource persons representing different stakeholders involved in the implementation of SORMAS in Ghana. We interviewed study participants via telephone using in-depth interview guides developed consistent with the model of diffusion of innovations in health service organizations. We transcribed the interviews verbatim and performed independent validation of transcripts and pseudonymization. We performed deductive coding using 7 a priori categories: innovation, adopting health system, adoption and assimilation, diffusion and dissemination, outer context, institutionalization, and linkages among the aspects of implementation. We used MAXQDA Analytics Pro for transcription, coding, and analysis.

RESULTS

The facilitators of SORMAS implementation included its coherent design consistent with the Integrated Disease Surveillance and Response system, adaptability to evolving local needs, relative advantages for task performance (eg, real-time reporting, generation of case-base data, improved data quality, mobile offline capability, and integration of laboratory procedures), intrinsic motivation of users, and a smartphone-savvy workforce. Other facilitators were its alignment with health system goals, dedicated national leadership, political endorsement, availability of in-country IT capacities, and financial and technical support from inventors and international development partners. The main barriers were unstable technical interoperability between SORMAS and existing health information systems, reliance on a private IT company for data hosting, unreliable internet connectivity, unstable national power supply, inadequate numbers and poor quality of data collection devices, and substantial dependence on external funding.

CONCLUSIONS

The facilitators of and barriers to SORMAS implementation are multiple and interdependent. Important success conditions for implementation include enhanced scope and efficiency of task performance, strong technical and political stewardship, and a self-motivated workforce. Inadequate funding, limited IT infrastructure, and lack of software development expertise are mutually reinforcing barriers to implementation and progress to country ownership. Some barriers are external, relate to the overall national infrastructural development, and are not amenable even to unlimited project funding.

Go.Data as a digital tool for case investigation and contact tracing in the context of COVID-19: a mixed-methods study

BACKGROUND

A manual approach to case investigation and contact tracing can introduce delays in response and challenges for field teams. Go.Data, an outbreak response tool developed by the World Health Organization (WHO) in collaboration with the Global Outbreak Alert and Response Network, streamlines data collection and analysis during outbreaks. This study aimed to characterize Go.Data use during COVID-19, elicit shared benefits and challenges, and highlight key opportunities for enhancement.

METHODS

This study utilized mixed methods through qualitative interviews and a quantitative survey with Go.Data implementors on their experiences during COVID-19. Survey data was analyzed for basic univariate statistics. Interview data were coded using deductive and inductive reasoning and thematic analysis of categories. Overarching themes were triangulated with survey data to clarify key findings.

RESULTS

From April to June 2022, the research team conducted 33 interviews and collected 41 survey responses. Participants were distributed across all six WHO regions and 28 countries. While most implementations represented government actors at national or subnational levels, additional inputs were collected from United Nations agencies and universities. Results highlighted WHO endorsement, accessibility, adaptability, and flexible support modalities as main enabling factors. Formalization and standardization of data systems and people processes to prepare for future outbreaks were a welcomed byproduct of implementation, as 76% used paper-based reporting prior and benefited from increased coordination around a shared platform. Several challenges surfaced, including shortage of the appropriate personnel and skill-mix within teams to ensure smooth implementation. Among opportunities for enhancements were improved product documentation and features to improve usability with large data volumes.

CONCLUSIONS

This study was the first to provide a comprehensive picture of Go.Data implementations during COVID-19 and what joint lessons could be learned. It ultimately demonstrated that Go.Data was a useful complement to responses across diverse contexts, and helped set a reproducible foundation for future outbreaks. Concerted preparedness efforts across the domains of workforce composition, data architecture and political sensitization should be prioritized as key ingredients for future Go.Data implementations. While major developments in Go.Data functionality have addressed some key gaps highlighted during the pandemic, continued dialogue between WHO and implementors, including cross-country experience sharing, is needed ensure the tool is reactive to evolving user needs.

Ebola outbreak in DRC and Uganda; an East African public health concern

On August 21, 2022, healthcare authorities in the Democratic Republic of the Congo (DRC) announced an outbreak of Ebola virus disease in North Kivu Province, bringing the total to 15 outbreaks nationwide. On September 20, 2022, Uganda's authorities declared an outbreak of the Sudan strain of the Ebola virus following a confirmed a case in Mubende district. As of October 6, 2022, the reported numbers of cases were 63, with 29 deaths in Uganda and 1 case with 1 death in DRC, respectively. Ebola virus causes an acute and severely fatal illness, resulting in death within a very short time if left untreated. In addition, these outbreaks in DRC and Uganda pose a major threat to the health and socioeconomic well-being of the people of East Africa due to multiple cross-border activities. Adequate preparations need to be made by the healthcare authorities of the nations concerned; the government, healthcare workers, and the East-African community as a whole have important roles to play in the effective prevention and control of the spread of Ebola virus within and across their borders.

Inadequate receipt of ANC components and associated factors among pregnant women in Northwest Ethiopia, 2020-2021: a community-based cross-sectional study

BACKGROUND

Women's health and pregnancy outcomes are directly depends on the extent of ANC components received during their ANC visits. There are limited information about the components of ANC and associated factors. Therefore, the aim of this study was to assess the magnitude of inadequate recipient of ANC components and associated factors in northwest Ethiopia.

METHODS

This is a community based cross sectional survey conducted in Dabat Demographic and health survey from December 10/2020 to January 10/2021 among women who gave birth within two years before the survey. This study applied a census method to identify and select eligible pregnant women. A structured and pretested questionnaire was used to collect the data. The data was entered into Epi-data version 3.1 and exported to STATA version 14 for analysis purpose. Adjusted Odds Ratio at 95% confidence interval was used to show the association between dependent and independent variables. Statistical significance was declared at a P value less than 0.05.

RESULTS

A total of 871 pregnant women were identified from the survey and included in this study. Overall, 96.67% (95% CI: 95.24, 97.67) had not get adequate (all components) ANC. The components of ANC services were increased from 3.35 to 32.34%, 2.52 to 46.33% 1.96 to 55.8%, 2.31 to 46.53%, 3.54 to 55.75%, 2.46 to 44.62%, 1.18 to 45.96%, and 2.45 to 54.6% for tetanus toxoid Vaccine, HIV/AIDS testing and counseling, danger sign, place of delivery, deworming, iron folic acid, family planning, and breast feeding counseling, from first ANC visit to fourth ANC visit, respectively. Rural residence (AOR = 4.89, 95% CI: 1.21, 19.86), and less than four number of ANC visit (AOR = 5.15, 95% CI: 2.06, 12.86) were significantly associated with inadequate uptake of ANC components.

CONCLUSION

Only three in hundred pregnant women were received adequate ANC components in the study area. Rural residence and less than four number of ANC visit were factors significantly associated with inadequate ANC uptake. Therefore, the district health department managers and program implementers need to train the health care providers about the components of ANC. As well, increasing community and facility awareness of WHO recommendations on ANC visits focusing on rural women is needed.

The Global Health Informatics landscape and JAMIA

Global Health Informatics (GHI) as a branch of health informatics has been established for 2 decades now. During that time, great strides have been made in the creation and implementation of informatics tools to improve healthcare delivery and outcomes in the most vulnerable and remote communities worldwide. In many of the most successful projects, innovation has been shared between teams in high- and low- or middle-income countries (LMICs). In this perspective, we review the state of the academic field of GHI and the work published in JAMIA in the last 6 1/2 years. We apply criteria for articles about LMICs, those on international health, and on indigenous and refugee population, and subtypes of research. For comparison, we apply those criteria to JAMIA Open and 3 other health informatics journals which publish articles on GHI. We make recommendations for future directions and the role that journals like JAMIA can play in strengthening this work worldwide.

Correlating global trends in COVID-19 cases with online symptom checker self-assessments

BACKGROUND

Online symptom checkers are digital health solutions that provide a differential diagnosis based on a user's symptoms. During the coronavirus disease 2019 (COVID-19) pandemic, symptom checkers have become increasingly important due to physical distance constraints and reduced access to in-person medical consultations. Furthermore, various symptom checkers specialised in the assessment of COVID-19 infection have been produced.

OBJECTIVES

Assess the correlation between COVID-19 risk assessments from an online symptom checker and current trends in COVID-19 infections. Analyse whether those correlations are reflective of various country-wise quality of life measures. Lastly, determine whether the trends found in symptom checker assessments predict or lag relative to those of the COVID-19 infections.

MATERIALS AND METHODS

In this study, we compile the outcomes of COVID-19 risk assessments provided by the symptom checker Symptoma (www.symptoma.com) in 18 countries with suitably large user bases. We analyse this dataset's spatial and temporal features compared to the number of newly confirmed COVID-19 cases published by the respective countries.

RESULTS

We find an average correlation of 0.342 between the number of Symptoma users assessed to have a high risk of a COVID-19 infection and the official COVID-19 infection numbers. Further, we show a significant relationship between that correlation and the self-reported health of a country. Lastly, we find that the symptom checker is, on average, ahead (median +3 days) of the official infection numbers for most countries.

CONCLUSION

We show that online symptom checkers can capture the national-level trends in coronavirus infections. As such, they provide a valuable and unique information source in policymaking against pandemics, unrestricted by conventional resources.

Magnitude of optimal access to ANC and its predictors in Ethiopia: Multilevel mixed effect analysis of nationally representative cross-sectional survey

BACKGROUND

Optimal access to ANC, such as the first ANC visit at first trimester, four or more ANC visits, and skilled health care provider can significantly reduce maternal mortality in an inclusive way. Previous studies conducted in Ethiopia on optimal ANC are restricted to frequencies of ANC visit. Therefore, the aim of this study was to assess the magnitude of optimal ANC access as a comprehensive way and its predictors among pregnant women in Ethiopia.

METHODS

Secondary data source from a recent demographic and health survey was used for analysis. This study includes a weighted sample of 4771 pregnant women. A multilevel mixed-effect binary logistic regression analyses was done to identify both the individual and community level factors. Odds ratio along with the 95% confidence interval was generated to identify the predictors of optimal access to ANC. A p-value less than 0.05 was declared as statistical significant.

RESULTS

In Ethiopia, one in five (20%) pregnant women had optimal access to antenatal care. Regarding the factors at individual level, pregnant women aged 25-34 years [aOR = 1.58, 95% CI = 1.23-2.03] and 35-49 years [aOR = 2.04, 95% CI = 1.43-2.89], those who had educated primary [aOR = 1.67, 95% CI = 1.33-2.09], secondary and higher [aOR = 1.81, 95% CI = 1.15-2.85], Primipara [aOR = 2.45, 95% CI = 1.68-3.59] and multipara [aOR = 1.48, 95% CI = 1.11-1.98] had higher odds of accessing optimal ANC. With the community level factors, the odds of optimal access to ANC was higher among pregnant women who lived in urban area [aOR = 2.08, 95% CI = 1.33-3.27], whereas, lower odds of optimal ANC access among those pregnant women who reported distance to the health facility as a big problem [aOR = 0.78, 95% CI = 0.63-0.96].

CONCLUSION AND RECOMMENDATION

The study concludes that in Ethiopia, optimal access to ANC was low. The study identified that both individual and community level factors were predictors for optimal ANC access. Therefore, the Ethiopian government should intensify extensive education on ANC in a comprehensive way. Moreover, especial attention from the Ethiopian ministry of health for those women who reported distance as a big problem and for rural resident women is mandatory.

Community-Based Surveillance and Geographic Information System‒Linked Contact Tracing in COVID-19 Case Identification, Ghana, March‒June 2020

In response to the COVID-19 pandemic, Ghana implemented various mitigation strategies. We describe use of geographic information system (GIS)‒linked contact tracing and increased community-based surveillance (CBS) to help control spread of COVID-19 in Ghana. GIS-linked contact tracing was conducted during March 31-June 16, 2020, in 43 urban districts across 6 regions, and 1-time reverse transcription PCR testing of all persons within a 2-km radius of a confirmed case was performed. CBS was intensified in 6 rural districts during the same period. We extracted and analyzed data from Surveillance Outbreak Response Management and Analysis System and CBS registers. A total of 3,202 COVID-19 cases reported through GIS-linked contact tracing were associated with a 4-fold increase in the weekly number of reported SARS-CoV-2 infected cases. CBS identified 5.1% (8/157) of confirmed cases in 6 districts assessed. Adaptation of new methods, such as GIS-linked contact tracing and intensified CBS, improved COVID-19 case detection in Ghana.

Indirect Effects of Ebola Virus Disease Epidemics on Health Systems in the Democratic Republic of the Congo, Guinea, Sierra Leone and Liberia: A Scoping Review Supplemented with Expert Interviews

Ebola Virus Disease (EVD) epidemics have been extensively documented and have received large scientific and public attention since 1976. Until July 2022, 16 countries worldwide had reported at least one case of EVD, resulting in 43 epidemics. Most of the epidemics occurred in the Democratic Republic of Congo (DRC) but the largest epidemic occurred from 2014-2016 in Guinea, Sierra Leone and Liberia in West Africa. The indirect effects of EVD epidemics on these countries' health systems, i.e., the consequences beyond infected patients and deaths immediately related to EVD, can be significant. The objective of this review was to map and measure the indirect effects of the EVD epidemics on the health systems of DRC, Guinea, Sierra Leone and Liberia and, from thereon, draw lessons for strengthening their resilience vis-à-vis future EVD outbreaks and other similar health emergencies. A scoping review of published articles from the PubMed database and gray literature was conducted. It was supplemented by interviews with experts. Eighty-six articles were included in this review. The results were structured based on WHO's six building blocks of a health system. During the EVD outbreaks, several healthcare services and activities were disrupted. A significant decline in indicators of curative care utilization, immunization levels and disease control activities was noticeable. Shortages of health personnel, poor health data management, insufficient funding and shortages of essential drugs characterized the epidemics that occurred in the above-mentioned countries. The public health authorities had virtually lost their leadership in the management of an EVD response. Governance was characterized by the development of a range of new initiatives to ensure adequate response. The results of this review highlight the need for countries to invest in and strengthen their health systems, through the continuous reinforcement of the building blocks, even if there is no imminent risk of an epidemic.

Uncovering chains of infections through spatio-temporal and visual analysis of COVID-19 contact traces

A major challenge for departments of public health (DPHs) in dealing with the ongoing COVID-19 pandemic is tracing contacts in exponentially growing SARS-CoV-2 infection clusters. Prevention of further disease spread requires a comprehensive registration of the connections between individuals and clusters. Due to the high number of infections with unknown origin, the healthcare analysts need to identify connected cases and clusters through accumulated epidemiological knowledge and the metadata of the infections in their database. Here we contribute a visual analytics dashboard to identify, assess and visualize clusters in COVID-19 contact tracing networks. Additionally, we demonstrate how graph-based machine learning methods can be used to find missing links between infection clusters and thus support the mission to get a comprehensive view on infection events. This work was developed through close collaboration with DPHs in Germany. We argue how our dashboard supports the identification of clusters by public health experts, discuss ongoing developments and possible extensions.

Analysis of mHealth research: mapping the relationship between mobile apps technology and healthcare during COVID-19 outbreak

BACKGROUND

Mobile health applications (mHealth apps) offer enormous promise for illness monitoring and treatment to improve the provided medical care and promote health and wellbeing.

OBJECTIVE

We applied bibliometric quantitative analysis and network visualization to highlight research trends and areas of particular interest. We expect by summarizing the trends in mHealth app research, our work will serve as a roadmap for future investigations.

METHODS

Relevant English publications were extracted from the Scopus database. VOSviewer (version 1.6.17) was used to build coauthorship networks of authors, countries, and the co-occurrence networks of author keywords.

RESULTS

We analyzed 550 published articles on mHealth apps from 2020 to February 1, 2021. The yearly publications increased from 130 to 390 in 2021. JMIR mHealth and uHealth (33/550, 6.0%), J. Med. Internet Res. (27/550, 4.9%), JMIR Res. Protoc. (22/550, 4.0%) were the widest journals for these publications. The United States has the largest number of publications (143/550, 26.0%), and England ranks second (96/550, 17.5%). The top three productive authors were: Giansanti D., Samuel G., Lucivero F., and Zhang L. Frequent authors' keywords have formed major 4 clusters representing the hot topics in the field: (1) artificial intelligence and telehealthcare; (2) digital contact tracing apps, privacy and security concerns; (3) mHealth apps and mental health; (4) mHealth apps in public health and health promotion.

CONCLUSIONS

mHealth apps undergo current developments, and they remain hot topics in COVID-19. These findings might be useful in determining future perspectives to improve infectious disease control and present innovative solutions for healthcare.

A Novel Tool for Real-Time Estimation of Epidemiological Parameters of Communicable Diseases Using Contact-Tracing Data: Development and Deployment

Background

The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in their epidemic response. It consists of the documentation, linkage, and follow-up of cases, contacts, and events. To allow SORMAS users to visualize data, compute essential surveillance indicators, and estimate epidemiological parameters from such network data in real-time, we developed the SORMAS Statistics (SORMAS-Stats) application.

Objective

This study aims to describe the essential visualizations, surveillance indicators, and epidemiological parameters implemented in the SORMAS-Stats application and illustrate the application of SORMAS-Stats in response to the COVID-19 outbreak.

Methods

Based on findings from a rapid review and SORMAS user requests, we included the following visualization and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number R(t), dispersion parameter k, and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptom onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. Furthermore, we applied the Markov Chain Monte Carlo approach and estimated R(t) using the incidence data and the observed SI computed from the transmission network data.

Results

Using COVID-19 contact-tracing data of confirmed cases reported between July 31 and October 29, 2021, in the Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63,570 nodes. The network comprises 1.75% (1115/63,570) events, 19.59% (12,452/63,570) case persons, and 78.66% (50,003/63,570) exposed persons, including 1238 infector-infectee pairs and 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with the best fit to the observed SI data was a lognormal distribution with a mean of 4.30 (95% CI 4.09-4.51) days. We estimated a dispersion parameter k of 21.11 (95% CI 7.57-34.66) and an effective reproduction number R of 0.9 (95% CI 0.58-0.60). The weekly estimated R(t) values ranged from 0.80 to 1.61.

Conclusions

We provide an application for real-time estimation of epidemiological parameters, which is essential for informing outbreak response strategies. The estimates are commensurate with findings from previous studies. The SORMAS-Stats application could greatly assist public health authorities in the regions using SORMAS or similar tools by providing extensive visualizations and computation of surveillance indicators.

Availability and Suitability of Digital Health Tools in Africa for Pandemic Control: Scoping Review and Cluster Analysis

Background

Gaining oversight into the rapidly growing number of mobile health tools for surveillance or outbreak management in Africa has become a challenge.

Objective

The aim of this study is to map the functional portfolio of mobile health tools used for surveillance or outbreak management of communicable diseases in Africa.

Methods

We conducted a scoping review by combining data from a systematic review of the literature and a telephone survey of experts. We applied the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines by searching for articles published between January 2010 and December 2020. In addition, we used the respondent-driven sampling method and conducted a telephone survey from October 2019 to February 2020 among representatives from national public health institutes from all African countries. We combined the findings and used a hierarchical clustering method to group the tools based on their functionalities (attributes).

Results

We identified 30 tools from 1914 publications and 45 responses from 52% (28/54) of African countries. Approximately 13% of the tools (4/30; Surveillance Outbreak Response Management and Analysis System, Go.Data, CommCare, and District Health Information Software 2) covered 93% (14/15) of the identified attributes. Of the 30 tools, 17 (59%) tools managed health event data, 20 (67%) managed case-based data, and 28 (97%) offered a dashboard. Clustering identified 2 exceptional attributes for outbreak management, namely contact follow-up (offered by 8/30, 27%, of the tools) and transmission network visualization (offered by Surveillance Outbreak Response Management and Analysis System and Go.Data).

Conclusions

There is a large range of tools in use; however, most of them do not offer a comprehensive set of attributes, resulting in the need for public health workers having to use multiple tools in parallel. Only 13% (4/30) of the tools cover most of the attributes, including those most relevant for response to the COVID-19 pandemic, such as laboratory interface, contact follow-up, and transmission network visualization.

A systematic review on mobile health applications for foodborne disease outbreak management

Background

Foodborne disease outbreaks are common and notifiable in South Africa; however, they are rarely reported and poorly investigated. Surveillance data from the notification system is suboptimal and limited, and does not provide adequate information to guide public health action and inform policy. We performed a systematic review of published literature to identify mobile application-based outbreak response systems for managing foodborne disease outbreaks and to determine the elements that the system requires to generate foodborne disease data needed for public action.

Methods

Studies were identified through literature searches using online databases on PubMed/Medline, CINAHL, Academic Search Complete, Greenfile, Library, Information Science & Technology. Search was limited to studies published in English during the period January 1990 to November 2020. Search strategy included various terms in varying combinations with Boolean phrases “OR” and “AND”. Data were collected following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement. A standardised data collection tool was used to extract and summarise information from identified studies. We assessed qualities of mobile applications by looking at the operating system, system type, basic features and functionalities they offer for foodborne disease outbreak management.

Results

Five hundred and twenty-eight (528) publications were identified, of which 48 were duplicates. Of the remaining 480 studies, 2.9% (14/480) were assessed for eligibility. Only one of the 14 studies met the inclusion criteria and reported on one mobile health application named MyMAFI (My Mobile Apps for Field Investigation). There was lack of detailed information on the application characteristics. However, based on minimal information available, MyMAFI demonstrated the ability to generate line lists, reports and offered functionalities for outbreak verification and epidemiological investigation. Availability of other key components such as environmental and laboratory investigations were unknown.

Conclusions

There is limited use of mobile applications on management of foodborne disease outbreaks. Efforts should be made to set up systems and develop applications that can improve data collection and quality of foodborne disease outbreak investigations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12283-6.

Twenty years of integrated disease surveillance and response in Sub-Saharan Africa: challenges and opportunities for effective management of infectious disease epidemics

INTRODUCTION

This systematic review aimed to analyse the performance of the Integrated Disease Surveillance and Response (IDSR) strategy in Sub-Saharan Africa (SSA) and how its implementation has embraced advancement in information technology, big data analytics techniques and wealth of data sources.

METHODS

HINARI, PubMed, and advanced Google Scholar databases were searched for eligible articles. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols.

RESULTS

A total of 1,809 articles were identified and screened at two stages. Forty-five studies met the inclusion criteria, of which 35 were country-specific, seven covered the SSA region, and three covered 3-4 countries. Twenty-six studies assessed the IDSR core functions, 43 the support functions, while 24 addressed both functions. Most of the studies involved Tanzania (9), Ghana (6) and Uganda (5). The routine Health Management Information System (HMIS), which collects data from health care facilities, has remained the primary source of IDSR data. However, the system is characterised by inadequate data completeness, timeliness, quality, analysis and utilisation, and lack of integration of data from other sources. Under-use of advanced and big data analytical technologies in performing disease surveillance and relating multiple indicators minimises the optimisation of clinical and practice evidence-based decision-making.

CONCLUSIONS

This review indicates that most countries in SSA rely mainly on traditional indicator-based disease surveillance utilising data from healthcare facilities with limited use of data from other sources. It is high time that SSA countries consider and adopt multi-sectoral, multi-disease and multi-indicator platforms that integrate other sources of health information to provide support to effective detection and prompt response to public health threats.

Considerations for Establishing Successful Coronavirus Disease Vaccination Programs in Africa

The accelerated development of coronavirus disease (COVID-19) candidate vaccines is intended to achieve worldwide immunity. Ensuring COVID-19 vaccination is crucial to stemming the pandemic, reclaiming everyday life, and helping restore economies. However, challenges exist to deploying these vaccines, especially in resource-limited sub-Saharan Africa. In this article, we highlight lessons learned from previous efforts to scale up vaccine distribution and offer considerations for policymakers and key stakeholders to use for successful COVID-19 vaccination rollout in Africa. These considerations range from improving weak infrastructure for managing data and identifying adverse events after immunization to considering financing options for overcoming the logistical challenges of vaccination campaigns and generating demand for vaccine uptake. In addition, providing COVID-19 vaccination can be used to promote the adoption of universal healthcare, especially in sub-Saharan Africa countries.

Assessing the application of human-centered design to translational research

Introduction:

Human-centered design (HCD) training offers the potential to improve both team processes and products. However, the use of HCD to improve the quality of team science is a relatively recent application, and its benefits and challenges have not been rigorously evaluated. We conducted a qualitative study with health sciences researchers trained in HCD methods. We aimed to determine how researchers applied HCD methods and perceived the benefits and barriers to using HCD on research teams.

Methods:

We conducted 1-hour, semi-structured interviews with trainees from three training cohorts. Interviews focused on perceptions of the training, subsequent uses of HCD, barriers and facilitators, and perceptions of the utility of HCD to science teams. Data analysis was conducted using Braun and Clarke’s process for thematic analysis.

Results:

We interviewed nine faculty and nine staff trained in HCD methods and identified four themes encompassing HCD use, benefits, challenges, and tensions between HCD approaches and academic culture.

Conclusions:

Trainees found HCD relevant to research teams for stakeholder engagement, research design, project planning, meeting facilitation, and team management. They also described benefits of HCD in five distinct areas: creativity, egalitarianism, structure, efficiency, and visibility. Our data suggest that HCD has the potential to help researchers work more inclusively and collaboratively on interdisciplinary teams and generate more innovative and impactful science. The application of HCD methods is not without challenges; however, we believe these challenges can be overcome with institutional investment.

A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study

BACKGROUND

Outbreaks of infectious diseases pose great risks, including hospitalization and death, to public health. Therefore, improving the management of outbreaks is important for preventing widespread infection and mitigating associated risks. Mobile health technology provides new capabilities that can help better capture, monitor, and manage infectious diseases, including the ability to quickly identify potential outbreaks.

OBJECTIVE

This study aims to develop a new infectious disease surveillance (IDS) system comprising a mobile app for accurate data capturing and dashboard for better health care planning and decision making.

METHODS

We developed the IDS system using a 2-pronged approach: a literature review on available and similar disease surveillance systems to understand the fundamental requirements and face-to-face interviews to collect specific user requirements from the local public health unit team at the Nepean Hospital, Nepean Blue Mountains Local Health District, New South Wales, Australia.

RESULTS

We identified 3 fundamental requirements when designing an electronic IDS system, which are the ability to capture and report outbreak data accurately, completely, and in a timely fashion. We then developed our IDS system based on the workflow, scope, and specific requirements of the public health unit team. We also produced detailed design and requirement guidelines. In our system, the outbreak data are captured and sent from anywhere using a mobile device or a desktop PC (web interface). The data are processed using a client-server architecture and, therefore, can be analyzed in real time. Our dashboard is designed to provide a daily, weekly, monthly, and historical summary of outbreak information, which can be potentially used to develop a future intervention plan. Specific information about certain outbreaks can also be visualized interactively to understand the unique characteristics of emerging infectious diseases.

CONCLUSIONS

We demonstrated the design and development of our IDS system. We suggest that the use of a mobile app and dashboard will simplify the overall data collection, reporting, and analysis processes, thereby improving the public health responses and providing accurate registration of outbreak information. Accurate data reporting and collection are a major step forward in creating a better intervention plan for future outbreaks of infectious diseases.

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

In many countries, the Internet of Medical Things (IoMT) has been deployed in tandem with other strategies to curb the spread of COVID-19, improve the safety of front-line personnel, increase efficacy by lessening the severity of the disease on human lives, and decrease mortality rates. Significant inroads have been achieved in terms of applications and technology, as well as security which have also been magnified through the rapid and widespread adoption of IoMT across the globe. A number of on-going researches show the adoption of secure IoMT applications is possible by incorporating security measures with the technology. Furthermore, the development of new IoMT technologies merge with Artificial Intelligence, Big Data and Blockchain offers more viable solutions. Hence, this paper highlights the IoMT architecture, applications, technologies, and security developments that have been made with respect to IoMT in combating COVID-19. Additionally, this paper provides useful insights into specific IoMT architecture models, emerging IoMT applications, IoMT security measurements, and technology direction that apply to many IoMT systems within the medical environment to combat COVID-19.

An artificial intelligence–based decision support and resource management system for COVID-19 pandemic

COVID-19 crisis has shown that the World is not ready for such a rapid spread of a virus resulting in a catastrophic pandemic. Effective use of information technologies is one of the key aspects in reducing the adverse effects of any epidemic or pandemic. Existing management systems have failed to fulfill requirements for curbing the rapid spread of the virus. This chapter firstly describes the current solutions by giving real-world examples. Then, we propose an epidemic management system (EMS) that relies on unimpeded and timely information flow between nations and organizations to ensure resources are distributed effectively. This system will use mobile technology, blockchain, epidemic modeling, and artificial intelligence technologies. We used the Multiplatform Interoperable Scalable Architecture (MPISA) model that allows the integration of multiple platforms and provides a solution for scalability and interoperability problems. Open data repositories and the MiPasa blockchain are also described. These relevant data can be used to predict the potential future spread of the epidemic. Selecting the correct methods for epidemic modeling is discussed as well. Another challenge is deciding on allocating resources where they are most necessary; we propose deploying automated machine learning and stochastic epidemic model-based decision support systems for such purposes. Citizens should not have privacy concerns about the information systems. These trust issues and privacy concerns can be solved by using decentralized identity and zero-knowledge proof-based mechanisms. These mechanisms will ensure that users are in control of their data. In this chapter, we also discuss choosing the right machine learning method, privacy measures, and how the performance challenges can be addressed. This chapter concludes on a discussion of how we can design and deploy better EMSs and possible future studies.

Design thinking during a health emergency: building a national data collection and reporting system

BACKGROUND

Design thinking allows challenging problems to be redefined in order to identify alternative user-center strategies and solutions. To address the many challenges associated with collecting and reporting data during the 2014 Ebola outbreak in Guinea, Liberia and Sierra Leone, we used a design thinking approach to build the Global Ebola Laboratory Data collection and reporting system.

MAIN TEXT

We used the five-stage Design Thinking model proposed by Hasso-Plattner Institute of Design at Stanford in Guinea, Liberia and Sierra Leone. This approach offers a flexible model which focuses on empathizing, defining, ideating, prototyping, and testing. A strong focus of the methodology includes end-users' feedback from the beginning to the end of the process. This is an iterative methodology that continues to adapt according to the needs of the system. The stages do not need to be sequential and can be run in parallel, out of order, and repeated as necessary. Design thinking was used to develop a data collection and reporting system, which contains all laboratory data from the three countries during one of the most complicated multi-country outbreaks to date. The data collection and reporting system was used to orient the response interventions at the district, national, and international levels within the three countries including generating situation reports, monitoring the epidemiological and operational situations, providing forecasts of the epidemic, and supporting Ebola-related research and the Ebola National Survivors programs within each country.

CONCLUSIONS

Our study demonstrates the numerous benefits that arise when using a design thinking methodology during an outbreak to solve acute challenges within the national health information system and the authors recommend it's use during future complex outbreaks.

Strengthening Applied Epidemiology in West Africa: Progress, Gaps, and Advancing a Regional Strategy to Improve Health Security

The ability to prevent, promptly detect, and appropriately respond to a public health threat is essential for health security. Field epidemiology training has helped increase the quality and quantity of the public health workforce to strengthen disease surveillance, outbreak preparedness and response, and general public health capacity. We conducted a desk review on the status of the Field Epidemiology and Laboratory Training Program model in 16 countries in West Africa. We also developed a questionnaire and shared it with West African Health Organization (WAHO) member states to document their experiences and the status of training in their countries. WAHO organized a regional 3-day consultative meeting with major stakeholders in the region to examine progress, gaps, and challenges, and outline a roadmap to strengthen the Field Epidemiology and Laboratory Training Program. Stakeholders shared their experiences, engaged in discussions to identify strengths and gaps, and made plans on a way forward. Member states are at different levels of implementing field epidemiology and laboratory training programs in their countries, and, therefore, major gaps remain in the number and distribution of trained epidemiologists throughout West Africa. Member states implement different variants of the program and in some instances the same cadre of health workers are trained in different but comparable programs with different funding streams. Two member states had not begun implementing the training program. Developing regional centers of excellence was recommended in the long term while collaboration among member states to train the required number of epidemiologists to fill the acute needs could be helpful in the short and medium term. Curriculum harmonization and expansion, deployment and use of trained epidemiologists, accreditation of training institutions, and generation of indigenous funding streams are recommended to improve the Field Epidemiology and Laboratory Training Program in West Africa.

Antimicrobial resistance preparedness in sub-Saharan African countries

BACKGROUND

Antimicrobial resistance (AMR) is of growing concern globally and AMR status in sub-Saharan Africa (SSA) is undefined due to a lack of real-time data recording, surveillance and regulation. World Health Organization (WHO) Joint External Evaluation (JEE) reports are voluntary, collaborative processes to assess country capacities and preparedness to prevent, detect and rapidly respond to public health risks, including AMR. The data from SSA JEE reports were analysed to gain an overview of how SSA is working towards AMR preparedness and where strengths and weaknesses lie.

METHODS

SSA country JEE AMR preparedness scores were analysed. A cumulative mean of all the SSA country AMR preparedness scores was calculated and compared to the overall mean SSA JEE score. AMR preparedness indicators were analysed, and data were weighted by region.

FINDINGS

The mean SSA AMR preparedness score was 53% less than the overall mean SSA JEE score. East Africa had the highest percentage of countries reporting having AMR National Action Plans in place, as well as human and animal pathogen AMR surveillance programmes. Southern Africa reported the highest percentage of countries with training programmes and antimicrobial stewardship.

CONCLUSIONS

The low mean AMR preparedness score compared to overall JEE score, along with the majority of countries lacking implemented National Action Plans, suggests that until now AMR has not been a priority for most SSA countries. By identifying regional and One Health strengths, AMR preparedness can be fortified across SSA with a multisectoral approach.

The Surveillance Outbreak Response Management and Analysis System (SORMAS): Digital Health Global Goods Maturity Assessment

Background

Digital health is a dynamic field that has been generating a large number of tools; many of these tools do not have the level of maturity required to function in a sustainable model. It is in this context that the concept of global goods maturity is gaining importance. Digital Square developed a global good maturity model (GGMM) for digital health tools, which engages the digital health community to identify areas of investment for global goods. The Surveillance Outbreak Response Management and Analysis System (SORMAS) is an open-source mobile and web application software that we developed to enable health workers to notify health departments about new cases of epidemic-prone diseases, detect outbreaks, and simultaneously manage outbreak response.

Objective

The objective of this study was to evaluate the maturity of SORMAS using Digital Square’s GGMM and to describe the applicability of the GGMM on the use case of SORMAS and identify opportunities for system improvements.

Methods

We evaluated SORMAS using the GGMM version 1.0 indicators to measure its development. SORMAS was scored based on all the GGMM indicator scores. We described how we used the GGMM to guide the development of SORMAS during the study period. GGMM contains 15 subindicators grouped into the following core indicators: (1) global utility, (2) community support, and (3) software maturity.

Results

The assessment of SORMAS through the GGMM from November 2017 to October 2019 resulted in full completion of all subscores (10/30, (33%) in 2017; 21/30, (70%) in 2018; and 30/30, (100%) in 2019). SORMAS reached the full score of the GGMM for digital health software tools by accomplishing all 10 points for each of the 3 indicators on global utility, community support, and software maturity.

Conclusions

To our knowledge, SORMAS is the first electronic health tool for disease surveillance, and also the first outbreak response management tool, that has achieved a 100% score. Although some conceptual changes would allow for further improvements to the system, the GGMM already has a robust supportive effect on developing software toward global goods maturity.

Outbreak analytics: a developing data science for informing the response to emerging pathogens

Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control‘. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

Mobile-based and open-source case detection and infectious disease outbreak management systems: a review

In this paper we perform a rapid review of existing mobile-based, open-source systems for infectious disease outbreak data collection and management. Our inclusion criteria were designed to match the PANDORA-ID-NET consortium’s goals for capacity building in sub-Saharan Africa, and to reflect the lessons learned from the 2014–16 West African Ebola outbreak. We found eight candidate systems that satisfy some or most of these criteria, but only one (SORMAS) fulfils all of them. In addition, we outline a number of desirable features that are not currently present in most outbreak management systems.

Use of Surveillance Outbreak Response Management and Analysis System for Human Monkeypox Outbreak, Nigeria, 2017-2019

In November 2017, the mobile digital Surveillance Outbreak Response Management and Analysis System was deployed in 30 districts in Nigeria in response to an outbreak of monkeypox. Adaptation and activation of the system took 14 days, and its use improved timeliness, completeness, and overall capacity of the response.

Precision Global Health - The case of Ebola: a scoping review

BACKGROUND

The 2014-2016 Ebola outbreak across West Africa was devastating, acting not only as a wake-up call for the global health community, but also as a catalyst for innovative change and global action. Improved infectious disease monitoring is the stepping-stone toward better disease prevention and control efforts, and recent research has revealed the potential of digital technologies to transform the field of global health. This scoping review aimed to identify which digital technologies may improve disease prevention and control, with regard to the 2014-2016 Ebola outbreak in West Africa.

METHODS

A search was conducted on PubMed, EBSCOhost and Web of Science, with search dates ranging from 2013 (01/01/2013) - 2017 (13/06/2017). Data was extracted into a summative table and data synthesized through grouping digital technology domains, using narrative and graphical methods.

FINDINGS

The scoping review identified 82 full-text articles, and revealed big data (48%, n = 39) and modeling (26%, n = 21) technologies to be the most utilized within the Ebola outbreak. Digital technologies were mainly used for surveillance purposes (90%, n = 74), and key challenges were related to scalability and misinformation from social media platforms.

INTERPRETATION

Digital technologies demonstrated their potential during the Ebola outbreak through: more rapid diagnostics, more precise predictions and estimations, increased knowledge transfer, and raising situational awareness through mHealth and social media platforms such as Twitter and Weibo. However, better integration into both citizen and health professionals' communities is necessary to maximise the potential of digital technologies.

Outbreak science: recent progress in the detection and response to outbreaks of infectious diseases

The frequency of reported outbreaks of infectious diseases has increased over the past 3 decades, with predictions that this rise will continue. Outbreak response continues to follow nine basic principles: establish the presence of an outbreak, verify the diagnosis, make a case definition, find cases and contacts, conduct basic epidemiology, test hypotheses, institute control measures, communicate the situation and establish ongoing surveillance. Within each of these areas, significant advances have been made over the past 5 years using progress in digital, laboratory, epidemiology and anthropological equipment or techniques. Irrespective of these, future outbreaks of high-consequence are inevitable, and vigilance and preparation must continue in order to prevent significant mortality, morbidity and socio-economic crisis.

m-Health in Public Health Practice

Beyond the earlier skepticism that greeted its usefulness and applicability in public health practice, m-Health has continued to gain significant support and acceptability among public health practitioners in terms of the ease with which its features and reach have been harnessed to address global disease prevention and health promotion objectives. Research evidence over the past few decades suggest the positive effect of adopting m-Health in improving knowledge dissemination, creating and sustaining motivation for positive health behavior changes or practices, and empowering individuals, communities and populations to take greater responsibility for their health. This chapter examines historical and current literature evidence of m-Health application in public health practice, its impact on global population health and future prospects.

A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks

BACKGROUND

Zoonotic diseases account for a substantial portion of infectious disease outbreaks and burden on public health programs to maintain surveillance and preventative measures. Taking advantage of new modeling approaches and data sources have become necessary in an interconnected global community. To facilitate data collection, analysis, and decision-making, the number of spatial decision support systems reported in the last 10 years has increased. This systematic review aims to describe characteristics of spatial decision support systems developed to assist public health officials in the management of zoonotic disease outbreaks.

METHODS

A systematic search of the Google Scholar database was undertaken for published articles written between 2008 and 2018, with no language restriction. A manual search of titles and abstracts using Boolean logic and keyword search terms was undertaken using predefined inclusion and exclusion criteria. Data extraction included items such as spatial database management, visualizations, and report generation.

RESULTS

For this review we screened 34 full text articles. Design and reporting quality were assessed, resulting in a final set of 12 articles which were evaluated on proposed interventions and identifying characteristics were described. Multisource data integration, and user centered design were inconsistently applied, though indicated diverse utilization of modeling techniques.

CONCLUSIONS

The characteristics, data sources, development and modeling techniques implemented in the design of recent SDSS that target zoonotic disease outbreak were described. There are still many challenges to address during the design process to effectively utilize the value of emerging data sources and modeling methods. In the future, development should adhere to comparable standards for functionality and system development such as user input for system requirements, and flexible interfaces to visualize data that exist on different scales. PROSPERO registration number: CRD42018110466.

Assessing the Concepts and Designs of 58 Mobile Apps for the Management of the 2014-2015 West Africa Ebola Outbreak: Systematic Review

Background

The use of mobile phone information technology (IT) in the health sector has received much attention especially during the 2014-2015 Ebola virus disease (EVD) outbreak. mHealth can be attributed to a major improvement in EVD control, but there lacks an overview of what kinds of tools were available and used based on the functionalities they offer.

Objective

We aimed to conduct a systematic review of mHealth tools in the context of the recent EVD outbreak to identify the most promising approaches and guide further mHealth developments for infectious disease control.

Methods

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched for all reports on mHealth tools developed in the context of the 2014-2015 EVD outbreak published between January 1, 2014 and December 31, 2015 on Google Scholar, MEDLINE, CAB Abstracts (Global Health), POPLINE, and Web of Science in any language using the search strategy: (“outbreak” OR “epidemic”) AND (“mobile phone” OR “smartphone” OR “smart phone” OR “mobile phone” OR “tablet” OR “mHealth”) AND (“Ebola” OR ”EVD” OR “VHF” OR “Ebola virus disease” OR “viral hemorrhagic fever”) AND (“2014” OR “2015”). The relevant publications were selected by 2 independent reviewers who applied a standardized data extraction form on the tools’ functionalities.

Results

We identified 1220 publications through the search strategy, of which 6.31% (77/1220) were original publications reporting on 58 specific mHealth tools in the context of the EVD outbreak. Of these, 62% (34/55) offered functionalities for surveillance, 22% (10/45) for case management, 18% (7/38) for contact tracing, and 6% (3/51) for laboratory data management. Only 3 tools, namely Community Care, Sense Ebola Followup, and Surveillance and Outbreak Response Management and Analysis System supported all four of these functionalities.

Conclusions

Among the 58 identified tools related to EVD management in 2014 and 2015, only 3 appeared to contain all 4 key functionalities relevant for the response to EVD outbreaks and may be most promising for further development.

Contact tracing performance during the Ebola epidemic in Liberia, 2014-2015

BACKGROUND

During the Ebola virus disease (EVD) epidemic in Liberia, contact tracing was implemented to rapidly detect new cases and prevent further transmission. We describe the scope and characteristics of contact tracing in Liberia and assess its performance during the 2014-2015 EVD epidemic.

METHODOLOGY/PRINCIPAL FINDINGS

We performed a retrospective descriptive analysis of data collection forms for contact tracing conducted in six counties during June 2014-July 2015. EVD case counts from situation reports in the same counties were used to assess contact tracing coverage and sensitivity. Contacts who presented with symptoms and/or died, and monitoring was stopped, were classified as "potential cases". Positive predictive value (PPV) was defined as the proportion of traced contacts who were identified as potential cases. Bivariate and multivariate logistic regression models were used to identify characteristics among potential cases. We analyzed 25,830 contact tracing records for contacts who had monitoring initiated or were last exposed between June 4, 2014 and July 13, 2015. Contact tracing was initiated for 26.7% of total EVD cases and detected 3.6% of all new cases during this period. Eighty-eight percent of contacts completed monitoring, and 334 contacts were identified as potential cases (PPV = 1.4%). Potential cases were more likely to be detected early in the outbreak; hail from rural areas; report multiple exposures and symptoms; have household contact or direct bodily or fluid contact; and report nausea, fever, or weakness compared to contacts who completed monitoring.

CONCLUSIONS/SIGNIFICANCE

Contact tracing was a critical intervention in Liberia and represented one of the largest contact tracing efforts during an epidemic in history. While there were notable improvements in implementation over time, these data suggest there were limitations to its performance-particularly in urban districts and during peak transmission. Recommendations for improving performance include integrated surveillance, decentralized management of multidisciplinary teams, comprehensive protocols, and community-led strategies.

Systematic review of electronic surveillance of infectious diseases with emphasis on antimicrobial resistance surveillance in resource-limited settings

BACKGROUND

Electronic surveillance of infectious diseases involves rapidly collecting, collating, and analyzing vast amounts of data from interrelated multiple databases. Although many developed countries have invested in electronic surveillance for infectious diseases, the system still presents a challenge for resource-limited health care settings.

METHODS

We conducted a systematic review by performing a comprehensive literature search on MEDLINE (January 2000-December 2015) to identify studies relevant to electronic surveillance of infectious diseases. Study characteristics and results were extracted and systematically reviewed by 3 infectious disease physicians.

RESULTS

A total of 110 studies were included. Most surveillance systems were developed and implemented in high-income countries; less than one-quarter were conducted in low-or middle-income countries. Information technologies can be used to facilitate the process of obtaining laboratory, clinical, and pharmacologic data for the surveillance of infectious diseases, including antimicrobial resistance (AMR) infections. These novel systems require greater resources; however, we found that using electronic surveillance systems could result in shorter times to detect targeted infectious diseases and improvement of data collection.

CONCLUSIONS

This study highlights a lack of resources in areas where an effective, rapid surveillance system is most needed. The availability of information technology for the electronic surveillance of infectious diseases, including AMR infections, will facilitate the prevention and containment of such emerging infectious diseases.

Assessing the Exposome with External Measures: Commentary on the State of the Science and Research Recommendations

The exposome comprises all environmental exposures that a person experiences from conception throughout the life course. Here we review the state of the science for assessing external exposures within the exposome. This article reviews (a) categories of exposures that can be assessed externally, (b) the current state of the science in external exposure assessment, (c) current tools available for external exposure assessment, and (d) priority research needs. We describe major scientific and technological advances that inform external assessment of the exposome, including geographic information systems; remote sensing; global positioning system and geolocation technologies; portable and personal sensing, including smartphone-based sensors and assessments; and self-reported questionnaire assessments, which increasingly rely on Internet-based platforms. We also discuss priority research needs related to methodological and technological improvement, data analysis and interpretation, data sharing, and other practical considerations, including improved assessment of exposure variability as well as exposure in multiple, critical life stages.

Ebola virus - epidemiology, diagnosis, and control: threat to humans, lessons learnt, and preparedness plans - an update on its 40 year's journey

Ebola virus (EBOV) is an extremely contagious pathogen and causes lethal hemorrhagic fever disease in man and animals. The recently occurred Ebola virus disease (EVD) outbreaks in the West African countries have categorized it as an international health concern. For the virus maintenance and transmission, the non-human primates and reservoir hosts like fruit bats have played a vital role. For curbing the disease timely, we need effective therapeutics/prophylactics, however, in the absence of any approved vaccine, timely diagnosis and monitoring of EBOV remains of utmost importance. The technologically advanced vaccines like a viral-vectored vaccine, DNA vaccine and virus-like particles are underway for testing against EBOV. In the absence of any effective control measure, the adaptation of high standards of biosecurity measures, strict sanitary and hygienic practices, strengthening of surveillance and monitoring systems, imposing appropriate quarantine checks and vigilance on trade, transport, and movement of visitors from EVD endemic countries remains the answer of choice for tackling the EBOV spread. Herein, we converse with the current scenario of EBOV giving due emphasis on animal and veterinary perspectives along with advances in diagnosis and control strategies to be adopted, lessons learned from the recent outbreaks and the global preparedness plans. To retrieve the evolutionary information, we have analyzed a total of 56 genome sequences of various EBOV species submitted between 1976 and 2016 in public databases.

Human-centred design in global health: A scoping review of applications and contexts

Health and wellbeing are determined by a number of complex, interrelated factors. The application of design thinking to questions around health may prove valuable and complement existing approaches. A number of public health projects utilizing human centered design (HCD), or design thinking, have recently emerged, but no synthesis of the literature around these exists. The results of a scoping review of current research on human centered design for health outcomes are presented. The review aimed to understand why and how HCD can be valuable in the contexts of health related research. Results identified pertinent literature as well as gaps in information on the use of HCD for public health research, design, implementation and evaluation. A variety of contexts were identified in which design has been used for health. Global health and design thinking have different underlying conceptual models and terminology, creating some inherent tensions, which could be overcome through clear communication and documentation in collaborative projects. The review concludes with lessons learned from the review on how future projects can better integrate design thinking with global health research.

An ethical assessment model for digital disease detection technologies

Digital epidemiology, also referred to as digital disease detection (DDD), successfully provided methods and strategies for using information technology to support infectious disease monitoring and surveillance or understand attitudes and concerns about infectious diseases. However, Internet-based research and social media usage in epidemiology and healthcare pose new technical, functional and formal challenges. The focus of this paper is on the ethical issues to be considered when integrating digital epidemiology with existing practices. Taking existing ethical guidelines and the results from the EU project M-Eco and SORMAS as starting point, we develop an ethical assessment model aiming at providing support in identifying relevant ethical concerns in future DDD projects. The assessment model has four dimensions: user, application area, data source and methodology. The model supports in becoming aware, identifying and describing the ethical dimensions of DDD technology or use case and in identifying the ethical issues on the technology use from different perspectives. It can be applied in an interdisciplinary meeting to collect different viewpoints on a DDD system even before the implementation starts and aims at triggering discussions and finding solutions for risks that might not be acceptable even in the development phase. From the answers, ethical issues concerning confidence, privacy, data and patient security or justice may be judged and weighted.

Mathematical models for devising the optimal Ebola virus disease eradication

BACKGROUND

The 2014-2015 epidemic of Ebola virus disease (EVD) in West Africa defines an unprecedented health threat for human.

METHODS

We construct a mathematical model to devise the optimal Ebola virus disease eradication plan. We used mathematical model to investigate the numerical spread of Ebola and eradication pathways, further fit our model against the real total cases data and calculated infection rate as 1.754.

RESULTS

With incorporating hospital isolation and application of medication in our model and analyzing their effect on resisting the spread, we demonstrate the second peak of 10,029 total cases in 23 days, and expect to eradicate EVD in 285 days. Using the regional spread of EVD with our transmission model analysis, we analyzed the numbers of new infections through four important transmission paths including household, community, hospital and unsafe funeral.

CONCLUSIONS

Based on the result of the model, we find out the key paths in different situations and propose our suggestion to control regional transmission. We fully considers Ebola characteristics, economic and time optimization, dynamic factors and local condition constraints, and to make our plan realistic, sensible and feasible.

The Role of Spatial Statistics in the Control and Elimination of Neglected Tropical Diseases in Sub-Saharan Africa: A Focus on Human African Trypanosomiasis, Schistosomiasis and Lymphatic Filariasis

Disease control and elimination programmes can benefit greatly from accurate information on the spatial variability of disease risk, particularly when risk is highly spatially heterogeneous. Due to advances in statistical methodology, coupled with the increased availability of geospatial technology, this information is becoming increasingly accessible. In this chapter we describe recent advancements in spatial methods associated with the analysis of disease data measured at the point-level and demonstrate their application to the control and elimination of neglected tropical diseases (NTDs). We further provide information on spatially referenced data sources and software that can be used to create NTD risk maps, concentrating on those that can be freely obtained. Examples relating to three NTDs affecting populations in sub-Saharan Africa are presented throughout the chapter, i.e., human African trypanosomiasis, schistosomiasis and lymphatic filariasis. These three diseases, with differing routes of transmission, control methods and level of spatial heterogeneity, demonstrate the flexibility and applicability of the methods described.