Availability of published evidence on coverage, cost components, and funding support for digitalisation of infectious disease surveillance in Africa, 2003-2022: a systematic review.

BACKGROUND: The implementation of digital disease surveillance systems at national levels in Africa have been challenged by many factors. These include user applicability, utility of IT features but also stable financial support. Funding closely intertwines with implementations in terms of geographical reach, disease focus, and sustainability. However, the practice of evidence sharing on geographical and disease coverage, costs, and funding sources for improving the implementation of these systems on the continent is unclear. OBJECTIVES: To analyse the key characteristics and availability of evidence for implementing digital infectious disease surveillance systems in Africa namely their disease focus, geographical reach, cost reporting, and external funding support. METHODS: We conducted a systematic review of peer-reviewed and grey literature for the period 2003 to 2022 (PROSPERO registration number: CRD42022300849). We searched five databases (PubMed, MEDLINE over Ovid, EMBASE, Web of Science, and Google Scholar) and websites of WHO, Africa CDC, and public health institutes of African countries. We mapped the distribution of projects by country; identified reported implementation cost components; categorised the availability of data on cost components; and identified supporting funding institutions outside Africa. RESULTS: A total of 29 reports from 2,033 search results were eligible for analysis. We identified 27 projects implemented in 13 countries, across 32 sites. Of these, 24 (75%) were pilot projects with a median duration of 16 months, (IQR: 5-40). Of the 27 projects, 5 (19%) were implemented for HIV/AIDs and tuberculosis, 4 (15%) for malaria, 4 (15%) for all notifiable diseases, and 4 (15%) for One Health. We identified 17 cost components across the 29 reports. Of these, 11 (38%) reported quantified costs for start-up capital, 10 (34%) for health personnel compensation, 9 (31%) for training and capacity building, 8 (28%) for software maintenance, and 7(24%) for surveillance data transmission. Of 65 counts of external funding sources, 35 (54%) were governmental agencies, 15 (23%) foundations, and 7 (11%) UN agencies. CONCLUSIONS: The evidence on costing data for the digitalisation of surveillance and outbreak response in the published literature is sparse in quantity, limited in detail, and without a standardised reporting format. Most initial direct project costs are substantially donor dependent, short lived, and thus unsustainable.

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.

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.

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.

Health administrative data enrichment using cohort information: Comparative evaluation of methods by simulation and application to real data.

BACKGROUND: Studies using health administrative databases (HAD) may lead to biased results since information on potential confounders is often missing. Methods that integrate confounder data from cohort studies, such as multivariate imputation by chained equations (MICE) and two-stage calibration (TSC), aim to reduce confounding bias. We provide new insights into their behavior under different deviations from representativeness of the cohort. METHODS: We conducted an extensive simulation study to assess the performance of these two methods under different deviations from representativeness of the cohort. We illustrate these approaches by studying the association between benzodiazepine use and fractures in the elderly using the general sample of French health insurance beneficiaries (EGB) as main database and two French cohorts (Paquid and 3C) as validation samples. RESULTS: When the cohort was representative from the same population as the HAD, the two methods are unbiased. TSC was more efficient and faster but its variance could be slightly underestimated when confounders were non-Gaussian. If the cohort was a subsample of the HAD (internal validation) with the probability of the subject being included in the cohort depending on both exposure and outcome, MICE was unbiased while TSC was biased. The two methods appeared biased when the inclusion probability in the cohort depended on unobserved confounders. CONCLUSION: When choosing the most appropriate method, epidemiologists should consider the origin of the cohort (internal or external validation) as well as the (anticipated or observed) selection biases of the validation sample.

Outbreak of human monkeypox in Nigeria in 2017-18: a clinical and epidemiological report.

BACKGROUND: In September, 2017, human monkeypox re-emerged in Nigeria, 39 years after the last reported case. We aimed to describe the clinical and epidemiological features of the 2017-18 human monkeypox outbreak in Nigeria. METHODS: We reviewed the epidemiological and clinical characteristics of cases of human monkeypox that occurred between Sept 22, 2017, and Sept 16, 2018. Data were collected with a standardised case investigation form, with a case definition of human monkeypox that was based on previously established guidelines. Diagnosis was confirmed by viral identification with real-time PCR and by detection of positive anti-orthopoxvirus IgM antibodies. Whole-genome sequencing was done for seven cases. Haplotype analysis results, genetic distance data, and epidemiological data were used to infer a likely series of events for potential human-to-human transmission of the west African clade of monkeypox virus. FINDINGS: 122 confirmed or probable cases of human monkeypox were recorded in 17 states, including seven deaths (case fatality rate 6%). People infected with monkeypox virus were aged between 2 days and 50 years (median 29 years [IQR 14]), and 84 (69%) were male. All 122 patients had vesiculopustular rash, and fever, pruritus, headache, and lymphadenopathy were also common. The rash affected all parts of the body, with the face being most affected. The distribution of cases and contacts suggested both primary zoonotic and secondary human-to-human transmission. Two cases of health-care-associated infection were recorded. Genomic analysis suggested multiple introductions of the virus and a single introduction along with human-to-human transmission in a prison facility. INTERPRETATION: This study describes the largest documented human outbreak of the west African clade of the monkeypox virus. Our results suggest endemicity of monkeypox virus in Nigeria, with some evidence of human-to-human transmission. Further studies are necessary to explore animal reservoirs and risk factors for transmission of the virus in Nigeria. FUNDING: None.

User Evaluation Indicates High Quality of the Surveillance Outbreak Response Management and Analysis System (SORMAS) After Field Deployment in Nigeria in 2015 and 2018.

During the West African Ebola virus disease outbreak in 2014-15, health agencies had severe challenges with case notification and contact tracing. To overcome these, we developed the Surveillance, Outbreak Response Management and Analysis System (SORMAS). The objective of this study was to measure perceived quality of SORMAS and its change over time. We ran a 4-week-pilot and 8-week-implementation of SORMAS among hospital informants in Kano state, Nigeria in 2015 and 2018 respectively. We carried out surveys after the pilot and implementation asking about usefulness and acceptability. We calculated the proportions of users per answer together with their 95% confidence intervals (CI) and compared whether the 2015 response distributions differed from those from 2018. Total of 31 and 74 hospital informants participated in the survey in 2015 and 2018, respectively. In 2018, 94% (CI: 89-100%) of users indicated that the tool was useful, 92% (CI: 86-98%) would recommend SORMAS to colleagues and 18% (CI: 10-28%) had login difficulties. In 2015, the proportions were 74% (CI: 59-90%), 90% (CI: 80-100%), and 87% (CI: 75-99%) respectively. Results indicate high usefulness and acceptability of SORMAS. We recommend mHealth tools to be evaluated to allow repeated measurements and comparisons between different versions and users.