In contrast with studies of timeliness metrics at regional or global levels, findings from this body of work reveal unique country-level strengths and challenges for Uganda during outbreak events. Country-specific analyses can be incredibly important for tracking and improving preparedness and response efforts, while additionally providing insights for other countries in the region planning for similar events. The findings are also useful more broadly because of the detailed data collected by Uganda and the mixed methods approach, which allows for specific insights not often achievable with other datasets.
Analyses of timeliness during multisectoral outbreaks in Uganda occurring between 2018 and 2022 suggest that overall national performance in detection and response time is relatively strong. Uganda performed faster than the 7-1-7 targets for two intervals (i.e., outbreak start to detection and detection to response)14, and compared to timeliness observed during outbreaks occurring between 2017 and 2019 in WHO AFRO member states, Uganda had shorter median times to events for nearly all intervals (Supplementary Table 5)12.
Uganda’s strong performance times were particularly pronounced for outbreaks of diseases with which the country has had previous experience, a quantitative finding supported key informant interviews. Given Uganda has seen repeat outbreaks of several diseases, sometimes even on an annual basis, this finding points to a positive or improved timeliness trend for familiar outbreak types in Uganda.
A convergence of qualitative and quantitative findings additionally illustrates that the perceived threat of VHFs results in heightened preparation for outbreaks of certain diseases (i.e., the development of risk communication materials for Marburg and Ebola virus disease [EVD]), as well as a faster overall response times. This finding is supported by our previous analysis of frequency of One Health milestone reporting, which found that more timeliness data were reported for VHFs compared to outbreaks of non-VHFs16. Preparedness for and rapid response to diseases of a high threat ultimately contributed to the successful control of the 2022 outbreak of Sudan EVD, which was brought under control before it grew into an event of severe enough spread to warrant declaration of a Public Health Emergency of International Concern27.
While timeliness during outbreaks of VHFs may avert morbidity and mortality, perhaps even prevent a pandemic, the comparative lags in response to diseases considered of a lesser threat as well as diseases that countries have less experience with, could limit pandemic preparedness. Non-agnostic approaches may lead to longer delays between key milestones during outbreaks of novel, unknown diseases, including Disease X. Findings from this study provide empirical support for leveraging pre-existing collaborations across sectors and networks to improve response for diseases, regardless of the causative pathogen.
Outbreak response in Uganda was also demonstrated as critically dependent on specific outbreak capacity. Examples include laboratory diagnostics, as highlighted by the observed length of outbreak metrics and key informant discussions of yellow fever (Table 1, Supplementary Data 1, 2). The sub-themes of diagnostic and laboratory considerations and existing infrastructure and health system structures, as well as most other sub-themes emerging from our qualitative analysis, aligned closely with findings from previous studies of barriers to and enablers of outbreak detection and response14,28,29,30. For instance, qualitative work among COVID-19 response teams in Papua New Guinea found similar response barriers, including laboratory considerations (e.g., cold chain availability) and inadequate human resources, while identifying community engagement as a key enabler to testing28. A systematic review of the literature conducted by Swaan et al.29 also identified reporter motivation, availability of resources, and laboratory considerations29.
Based on their qualitative study conducted among experts responding to the zoonotic Middle East respiratory syndrome epidemic, van Roode et al.30 offer a global perspective on systemic challenges on data sharing during zoonotic outbreaks, many of which overlap with our findings at the national level in Uganda30. Key issues identified include suboptimal collaboration, specifically citing coordination challenges across One Health disciplines and sectors, as well as barriers related to technical preparedness such as laboratory infrastructure and capacity. One of the core dilemmas identified related to the dichotomy of needing international assistance and coordination for outbreaks versus recognizing and respecting the sovereignty of countries30. Our findings reflect this dilemma at the national level, with informants describing that international influences (including funding) may sway Uganda’s priorities, thus impeding what should be a wholly nationally driven prioritization process.
van Roode et al. also found that funding and investment disparities affected multisectoral outbreak response30. Slower release of funding to non-human sectors generates bureaucratic bottlenecks, which in turn inhibit responder’s ability to mount a truly coordinated and collaborative One Health response to outbreaks. In some instances, illness or die-off events first observed within animal populations may even serve as a predictive alert of an outbreak of human illness. For example, in 2012, staff trained in wildlife disease surveillance through the USAID PREDICT project discovered six howler monkeys dead near a wildlife sanctuary in Bolivia31. Post-mortem diagnostic tests (RT-PCR) confirmed infection by flavivirus, later confirmed as yellow fever virus. The MoH was immediately notified of the findings, enabling prompt implementation of public health prevention measures such as human vaccination campaigns, education and outreach, and mosquito control measures. Consequently, no human cases of yellow fever occurred during this outbreak32.
Furthermore, inequities in resource allocation to different health sectors contributes to perceptions of power imbalances across disciplines, as described by informants. These power imbalances are detrimental to efforts to build trust across sectors, which can be devastating to efforts to work across the necessary collaborators required to prevent and control outbreaks. (Supplementary Note).
Discrepancies between when funds and supplies are released to human versus animal health sectors illustrates a qualitative finding that could not be explored in depth through our quantitative analyses. This limitation is due in large part to our sampling strategy for our database, which was linked to the PHEOC under the Ministry of Health. Consequently, our data is biased toward human diseases as opposed to those diseases affecting exclusively the animal, environmental, and plant sectors.
Additionally, we recognize that while COVID-19 was removed from our dataset, the timeframe of outbreaks included in our analysis captures outbreaks occurring against the backdrop of the pandemic. Despite Uganda’s yearly improvements in Start to End timeliness, observed gains in speed for several intervals in 2019 were followed by declines in 2020 and 2022, perhaps reflecting resource and operational challenges posed by the pandemic33. Statistically significant reductions in Detect to Respond intervals in 2020 and 2022, for example, may have been due to disruptions in human and financial resources which were redirected to respond to COVID-1934. Given the hypothesis that we might see faster timeliness following the start of the pandemic due to heightened vigilance and surveillance efforts, these findings underscore the importance of building resilient detection and response systems capable of maintaining performance for outbreak events even during a global health crisis.
Notably, there is also the possibility of biased quantitative findings, given that 36% of outbreaks events occurring during the study timeframe were excluded due to missing documentation. Regarding incomplete data for the 81 outbreaks analyzed here, a secondary study of these timeliness data conducted by a team at Resolve to Save Lives conducted a missingness analysis based on patterns of missing dates. (Kim, S. et al., submitted BMJ Global Health) The analysis found a predominantly random pattern of missing dates across most disease categories, except for undiagnosed illnesses. While this randomness would suggest minimal risk, we cannot exclude the possibility of bias due to missing data. We must also recognize that our assumptions underlying the dates used for imputation in the proportional hazards models may have also led to biased findings.
The limitations of our database underscore broader shortcomings of timeliness metrics as a tool: quantitative snapshots of timeliness risk missing key findings such as the perceived importance of a collaborative One Health approach in increasing speed during outbreaks. Comprehensive insight into the complex processes during outbreaks requires complementary qualitative work, a step which could logically take place during an After-Action Review meeting, when multidisciplinary partners have the opportunity to convene and discuss strengths and challenges encountered. Establishing a systems approach in analyzing bottlenecks and other problems versus blaming of any one individual or sector, is key in establishing the level of trust necessary to constructively review negative, as well as positive factors.
These analyses also must consider that in some instances, timeliness metrics may simply reflect epidemiological characteristics of the disease-causing pathogen, rather than the performance of responders29. For example, foodborne illnesses may naturally have a shorter duration from start to end than VHFs or diseases with longer incubation periods. Furthermore, timeliness in determining the mode of exposure will depend on the causative pathogen and type of outbreak (i.e., a more prolonged process in foodborne outbreaks) and will influence subsequent metrics. Our models may not fully capture such nuances, including within-pathogen variability or contextual factors like outbreak setting. Recent implementation of the 7-1-7 global timeliness targets to tuberculosis found field staff recommended an adaptation of these metrics to ‘3-5-7’ based on the epidemiology and screening process for the bacterium35.
The type of surveillance used to detect the outbreak will also influence timeliness metrics, particularly for indicator-based surveillance depending on proximity to a health facility. As this study did not capture exact geographic coordinates of the outbreak start, we were unable to analyze how location (i.e., rurality) impacted detection times.
We additionally recognize that timeliness metrics may be subject to measurement bias due to interpretation of outbreak milestone dates. Milestone date extraction was conducted by one study team member from the University of California16. This study member built upon previous experience interpreting One Health milestone dates based on a study of thousands of outbreak reports, for which exercises were conducted across three investigators to validate interpretation of dates36.
While useful for identifying trends in timeliness, the One Health timeliness metrics alone are not a panacea and must be utilized alongside other pandemic preparedness and prevention tools to inform epidemic and pandemic policy action. Furthermore, timeliness metrics are premised on an assumption that increased speed between intervals results in improved outbreak outcomes, such as reduced morbidity and mortality in human and animal populations. In our comparison of timeliness metrics calculated for this study in Uganda versus timeliness calculated across WHO AFRO by Impouma et al.12 we saw that despite having faster overall Start to Detect and Detect to Notify times in 2018, Uganda had a longer overall Start to End interval. Additional analyses should explore this assumption that increased speed between all intervals translates to improved outbreak outcomes, as well as other possible explanations for the observed phenomena, such as differences in definitions of outbreak end. Analyses might also explore if certain timeliness metrics, including those with predictive alerts and preventive action, are more or less influential on improved outcomes.
