Discussion

The 7-1-7 metric is a relatively recent framework, proposed in 2021, aimed at improving and monitoring progress towards the early detection and rapid control of health threats arising from suspected infectious disease outbreaks or pandemics [8]. However, there is a paucity of research on the extent to which countries have adopted this metric and how they are performing. Our study is one of the few studies that have applied the 7-1-7 framework to assess outbreak preparedness and response performance across countries and outbreaks. Other studies include a retrospective observational study by Bochner et al (2023), which examined implementation of the 7-1-7 target for detection, notification, and response to public health threats in five countries [14], a study by Ssemanda et al (2024) which evaluated the initial response to a cholera outbreak in Elegu Town on the Uganda-South Sudan border using the 7–1–7 timeliness metrics [15], and a mixed-methods study in Cambodia which evaluated adoption, implementation, and operational use of the 7-1-7 framework within the country’s public health system [16]. Other operational applications of the framework include a study by Katumba et al. (2025) which applied the 7-1-7 framework in a prototypical anthrax outbreak to identify missed opportunities for early detection in Southwestern Uganda [13], as well as a study that estimated the impact of decreasing vaccination response times for outbreaks of vaccine-preventable diseases in low- and middle-income countries [17].

In general, the findings on the 7-1-7 performance in our study are lower than previously reported in Uganda, where 52% of the events met the 7-day target for detection, 74% met the 1-day notification target and 45% met the 7-day early response target [10] and Liberia where 71% of events met the 7-day target for detection, 98% met the 1-day notification target and 59% met the 7-day response target [18]. The outbreaks assessed in our study occurred in districts with varying levels of capacity and preparedness, which might have influenced the timeliness of appropriate actions. Moreover, unlike the former assessments, which relied largely on data from national assessments with more diverse samples of public health events, our study focused on a very small sample of six outbreaks, which may introduce methodological differences in comparison and interpretation, hence affecting the external validity of our findings.

Our findings reveal substantial delays across all stages of the 7-1-7 framework, with median times exceeding recommended targets and wide variability across districts. While the capacity to detect and respond to outbreaks exists at the district level, the observed delays point to localised pockets of deficiencies within the system, as well as contextual drivers of delays. This pattern is consistent with evidence from multi-country assessments, where bottlenecks to detection were most often concentrated at the health facility level, while delays in notification and early response were predominantly found at the district level [14]. These findings suggest that persistent gaps in preparedness, coordination and resource availability exist across settings, all of which were identified through the application of the 7-1-7 framework.  Taken together, the findings underscore the need to move beyond assessing system preparedness in isolation, toward strengthening the timeliness and coordination of core outbreak response functions (detection, notification and response) which are critical to containing emerging public health threats.

The target for timely detection in our study was only met by three outbreaks. This is comparable to a wider study which retrospectively assessed detection across 41 public health events in Brazil, Ethiopia, Liberia, Nigeria, and Uganda [19] although our study assessed fewer events.  Having competent health workers to undertake the role of disease surveillance at the frontline plays an important role because they are often the first to detect suspect cases [20]. In this study, marked inter-district variation in detection timeliness was observed. Particularly, yellow fever detection took 155 days in Bundibugyo district compared to 9 days in Ngora district.  These findings suggest that beyond overall system-level gaps, there might be important district-specific constraints which need to be addressed. Secondly, we posit that because yellow fever presents with symptoms similar to malaria, potential for misdiagnosis is high, hence late detection. This suggests a need for clear standard case definitions, as well as capacity building for health workers to quickly detect yellow fever based on differential diagnoses, especially in settings which are prone to yellow fever outbreaks.

The delayed detection of measles in Moroto compounds all the above issues, in addition to the fact that Moroto is a border district with a migrant pastoral community. This may increase the risk of missed detection. Moreover, this migrant population is more likely to patronize multiple health providers including private health providers, leading to poor reporting. Enhanced health worker capacities to detect, and clear reporting lines are critical especially for epidemic prone border districts.

In this study, knowledgeable health workers were instrumental for the quick detection of outbreaks but incapable health workers were also highlighted as a barrier to outbreak detection efforts. Similar studies by Zalwango et al., [21] and Bochner A et al.,[14] corroborate this. Studies conducted in Uganda report that the lack of knowledge among health workers is a major cause of delay in the detection of Sudan Ebola virus disease while in Kenya, Toda et al., [22] report that the knowledge of health workers regarding standard case definitions was not adequate among health workers and their supervisors, which affected the reliability of routine surveillance reports generated from health facilities.  Health worker capacity to diagnose, promptly and appropriately report an outbreak is the lynchpin of outbreak detection and containment, thus underscores the need to build awareness through training a workforce of disease detectives across all levels.

On the systems side, the strengthening of laboratory diagnostic capacity cannot be over emphasized because it augments health worker capacity to engage in disease surveillance, appropriate sample collection and efficient diagnostics. In this study however, limited laboratory testing capacities and the delay in sample transportation to UVRI emerged as barriers to timely detection. These weaknesses likely contributed to detection times exceeding the recommended 7-day target, suggesting that the delay was not driven solely by the failure of health workers to correctly recognize symptoms, but were also compounded by systemic bottlenecks in laboratory access and sample referral pathways.

Furthermore, in a pluralistic healthcare system like Uganda, where almost 50% are private providers, the risk of missing a suspected case is amplified. This may partly be due to the fact that health worker capacity building for disease and mortality surveillance has been predominantly geared towards workers in government owned (public facilities). Moreover, many private facilities do not make routine reports into the central IDSR system. Private facilities have been identified in several studies as a gap in the prompt detection and response to disease outbreaks. Studies in Uganda highlighted that the patronage of private facilities by suspected cases contributed to the delay in detecting the 2024 Sudan Ebola Outbreak [22]. The same study revealed that 91% of the facilities did not report to DHIS2 while 85% did not have any health worker trained on IDSR [22]. On the contrary, these indicators scored 100% in all participating public facilities. The strengthening of the private sector as a frequent first point of entry for suspected cases should thus be prioritised. Emphasis should be placed on training health workers as well as mandatory reporting into the IDSR system.

Half of the events assessed in our study met the criteria of timely notification. These included Measles in Moroto district, Cholera in Kyotera and mpox in Kasese district. The other three outbreak notifications fell beyond the set timelines, with a range of 5-58 days. Our findings are, however, lower than evidence from Bochner et al [14], who reported that most events, 71% (29/41), had met a target of 1 day to notify, with a range of 0-24. Plausible reasons emerge from the qualitative data: among the facilitators of timely notification were the existence of an e-surveillance system, an SMS alert system, as well as functional and/or proper communication channels between health facilities, sentinel surveillance sites and the DHT. In settings where some or any of these systems are dysfunctional, notification is likely to be delayed.

However, our findings also revealed that there was a tendency for sentinel sites to report exclusively to Uganda Virus Research Institute, neglecting to communicate to the district authorities. This is a distortion of the mandatory reporting of any suspected case via the district authorities and represents a major barrier to timely notification. It also underpins the importance of clear communication structures within the system to enhance surveillance and follow-up of suspected cases.

Other barriers were also noted. For measles, cholera and mpox, delayed diagnosis was evidenced by long turnaround times from laboratories, an important factor contributing to delayed notification. This highlights the need for well-equipped and functional laboratories to support disease investigations for enhanced health security.

Based on the target of timely response within the seven-day timeline, only a third of the outbreaks had prompt response. This is much lower compared to the 49% (20/41) outbreaks which met a target of 7 days to complete all early response actions. However, the median response times were comparable 10 days versus 8 days [19].  This could point towards inadequate cross-sector collaboration, district support and availability of human and logistical resources. The delayed response could also suggest systemic challenges in coordination, logistics or resource mobilization. Similar delays have been reported in other settings [14] and thus suggest the need for targeted investments aimed at strengthening district emergency preparedness and response actions. 

Our study was not without limitations. We relied largely on retrospective data, and some key event dates may have been incompletely or inaccurately documented in the available records. This may have affected the accuracy of the reconstructed outbreak timelines. Importantly, given the small sample size, our study was not powered to make meaningful statistical conclusions, which limits the external validity of our findings. Moreover, analysis was based on unequal outbreak numbers across diseases, introducing a selection bias where diseases with higher event counts might have contributed disproportionately to the aggregate findings. Findings therefore ought to be interpreted with caution. Secondly, the qualitative insights presented may not necessarily capture the full breadth of all contextual facilitators and barriers to timely detection, notification and response to disease outbreaks in Uganda, and therefore, it underscores the need for further research. Thirdly, while we explored barriers to timely detection, notification and response, we did not explore the root causes of these barriers.  Lastly, the reported median timelines were calculated across multiple diseases, which may mask important disease-specific differences. Also, substantial variability across districts further limits the generalizability of the aggregated estimates. The limitations notwithstanding, the primary strength of our study is the triangulation of quantitative outbreak timelines with qualitative insights from key informant interviews which was intended to strengthen interpretation of the 7‑1‑7 matrix with contextual understanding.  In addition, triangulation enhanced the credibility of our findings and enabled interpretations of system bottlenecks and facilitators rather than only relying on timelines. Additionally, our study is among the few evaluations that have applied the 7-1-7 framework to assess the timeliness in outbreak response in Uganda. We believe our findings will inform targeted improvements in Uganda’s public health emergency preparedness and response actions.