Sounding the alarm: Defining thresholds to trigger a public health response to monkeypox

Endemic to the Democratic Republic of the Congo (DRC), monkeypox is a zoonotic disease that causes smallpox-like illness in humans. Observed fluctuations in reported cases over time raises questions about when it is appropriate to mount a public health response, and what specific actions should be taken. We evaluated three different thresholds to differentiate between baseline and heightened disease incidence, and propose a novel, tiered algorithm for public health action. Monkeypox surveillance data from Tshuapa Province, 2011–2013, were used to calculate three different statistical thresholds: Cullen, c-sum, and a World Health Organization (WHO) method based on monthly incidence. When the observed cases exceeded the threshold for a given month, that month was considered to be ‘aberrant’. For each approach, the number of aberrant months detected was summed by year—each method produced vastly different results. The Cullen approach generated a number of aberrant signals over the period of consideration (9/36 months). The c-sum method was the most sensitive (30/36 months), followed by the WHO method (12/24 months). We conclude that triggering public health action based on signals detected by a single method may be inefficient and overly simplistic for monkeypox. We propose instead a response algorithm that integrates an objective threshold (WHO method) with contextual information about epidemiological and spatiotemporal links between suspected cases to determine whether a response should be operating under i) routine surveillance ii) alert status, or iii) outbreak status. This framework could be modified and adopted by national and zone level health workers in monkeypox-endemic countries. Lastly, we discuss considerations for selecting thresholds for monkeypox outbreaks across gradients of endemicity and public health resources.Author summary: Most of the scientific literature about statistical thresholds for outbreak detection is biased toward large datasets that derive from wealthy countries. In the scientific community, it is acknowledged that even objectively determined statistical thresholds can produce varied results when applied to the same dataset, but to date, no study has evaluated thresholds for rare but endemic diseases such as monkeypox. We applied three statistical thresholds to a monkeypox surveillance dataset from the Democratic Republic of the Congo (DRC) to identify periods of elevated disease incidence. Each threshold produced markedly different results in terms of the number of aberrant months detected. We conclude that thresholds alone are insufficient to determine when a public health response should be triggered to monkeypox, and instead propose a novel algorithm that combines the use of an objective threshold with contextual information about social and epidemiological links between cases. Similar approaches could be valuable when applied to other, rare tropical infectious diseases where data are limited.