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

Background East Africa is home to 170 million people and prone to frequent outbreaks of viral haemorrhagic fevers and various bacterial diseases. A major challenge is that epidemics mostly happen in remote areas, where infrastructure for Biosecurity Level (BSL) 3/4 laboratory capacity is not available. As samples have to be transported from the outbreak area to the National Public Health Laboratories (NPHL) in the capitals or even flown to international reference centres, diagnosis is significantly delayed and epidemics emerge. Main text The East African Community (EAC), an intergovernmental body of Burundi, Rwanda, Tanzania, Kenya, Uganda, and South Sudan, received 10 million € funding from the German Development Bank (KfW) to establish BSL3/4 capacity in the region. Between 2017 and 2020, the EAC in collaboration with the Bernhard-Nocht-Institute for Tropical Medicine (Germany) and the Partner Countries’ Ministries of Health and their respective NPHLs, established a regional network of nine mobile BSL3/4 laboratories. These rapidly deployable laboratories allowed the region to reduce sample turn-around-time (from days to an average of 8h) at the centre of the outbreak and rapidly respond to epidemics. In the present article, the approach for implementing such a regional project is outlined and five major aspects (including recommendations) are described: (i) the overall project coordination activities through the EAC Secretariat and the Partner States, (ii) procurement of equipment, (iii) the established laboratory setup and diagnostic panels, (iv) regional training activities and capacity building of various stakeholders and (v) completed and ongoing field missions. The latter includes an EAC/WHO field simulation exercise that was conducted on the border between Tanzania and Kenya in June 2019, the support in molecular diagnosis during the Tanzanian Dengue outbreak in 2019, the participation in the Ugandan National Ebola response activities in Kisoro district along the Uganda/DRC border in Oct/Nov 2019 and the deployments of the laboratories to assist in SARS-CoV-2 diagnostics throughout the region since early 2020. Conclusions The established EAC mobile laboratory network allows accurate and timely diagnosis of BSL3/4 pathogens in all East African countries, important for individual patient management and to effectively contain the spread of epidemic-prone diseases.

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.

For 20 years, the Global Outbreak Response and Alert Network (GOARN) has been a leader in the coordination of international outbreak response. On the premise that no single institution can provide all capacities required to successfully respond to a complex public health emergency or fulfil all outbreak response training needs, GOARN embarked on a capacity building journey that draws on the unique strengths of more than 250 partner institutions. Through extensive engagement and collaboration, GOARN Partners have created a bespoke, multifaceted, 3-tiered training programme which has evolved over the last 15 years and enhanced the competencies of thousands of multidisciplinary outbreak responders around the world.

The number of diphtheria suspects in Lumajang district was the second highest in East Java province during the diphtheria outbreaks in 2018. The number of diphtheria cases was more than 500% in 2018 compared to 2017. To give diphtheria antitoxin (DAT), the provincial diphtheria expert team consider various suspects’ characteristics for DAT recommendation as DAT supplies are limited. This case report aimed to explore and describe the relationship between diphtheria suspects’ characteristics, including age, gender, symptoms, immunization status, duration of disease, and contact status with other suspects according to the  DAT recommendations from the expert team. This case report was descriptive and used a cross-sectional approach. It was conductedduring the diphtheria outbreaks and involved total samples of all suspects. Based on age, the majority of the suspects (92%)were under 19 years old. Suspects at this age were the target of an outbreak response immunization (ORI) program. Those who were not targetted to receive ORI (aged over 19 years) began to appear in November and mostly in December. The trend of non-ORI targetted age increased after the third round of ORI implementation. According to the report form parents, most suspects (46.7%) had complete immunization status, and only 6.7% of their immunization records were reported on growth chart cards. All suspects with positive diphtheria never had and know routine immunization records. The laboratory tests show only 5% were suspected with positive diphtheria with a cultural type of mitis toxigenic. As many as 32% of the total suspects were recommended for DAT treatment. The use of controlled DAT could save 1,640,000 iU. Keywords: Diphtheria, outbreak response immunization, diphtheria antitoxin.

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.

The response to the novel coronavirus outbreak in China suggests that many of the lessons from the 2003 SARS epidemic have been implemented and the response improved as a consequence. Nevertheless some questions remain and not all lessons have been successful. The national and international response demonstrates the complex link between public health, science and politics when an outbreak threatens to impact on global economies and reputations. The unprecedented measures implemented in China are a bold attempt to control the outbreak – we need to understand their effectiveness to balance costs and benefits for similar events in the future.

Highlights•Timeliness, scope, and quality of cVDPV2 outbreak response campaigns substantially impact global risks. •Countries and GPEI could stop cVDPV2 transmission with existing vaccines if they improve campaigns. •The properties of nOPV2 use in the field remain uncertain, and GPEI needs to manage expectations.

Strengthening health systems and maintaining essential service delivery during health emergencies response is critical for early detection and diagnosis, prompt treatment, and effective control of pandemics, including the novel coronavirus disease 2019 (COVID-19). Health information systems (HIS) developed during recent Ebola outbreaks in West Africa and the Democratic Republic of the Congo (DRC) provided opportunities to collect, analyze, and distribute data to inform both day-to-day and long-term policy decisions on outbreak preparedness. As COVID-19 continues to sweep across the globe, HIS and related technological advancements remain vital for effective and sustained data sharing, contact tracing, mapping and monitoring, community risk sensitization and engagement, preventive education, and timely preparedness and response activities. In reviewing literature of how HIS could have further supported mitigation of these Ebola outbreaks and the ongoing COVID-19 pandemic, 3 key areas were identified: governance and coordination, health systems infrastructure and resources, and community engagement. In this concept study, we outline scalable HIS lessons from recent Ebola outbreaks and early COVID-19 responses along these 3 domains, synthesizing recommendations to offer clear, evidence-based approaches on how to leverage HIS to strengthen the current pandemic response and foster community health systems resilience moving forward.

Using data from 12 US health departments, we estimated mean serial interval for monkeypox virus infection to be 8.5 (95% credible interval 7.3-9.9) days for symptom onset, based on 57 case pairs. Mean estimated incubation period was 5.6 (95% credible interval 4.3-7.8) days for symptom onset, based on 35 case pairs.