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

Epidemiology of circulating human influenza viruses from the Democratic Republic of Congo, 2015.

INTRODUCTION:The establishment of the influenza sentinel surveillance system in Kinshasa, Bas Congo, Maniema, Katanga, and Kasai Provinces allowed generation of important data on the molecular epidemiology of human influenza viruses circulating in the Democratic Republic of Congo (DRC). However, some challenges still exist, including the need for extending the influenza surveillance to more provinces. This study describes the pattern of influenza virus circulating in DRC during 2015. METHODOLOGY:Nasopharyngeal swabs were collected from January to December 2015 from outpatients with influenza-like illness (ILI) and in all hospitalized patients with Severe Acute Respiratory Infection (SARI). Molecular analysis was done to determine influenza type and subtype at the National Reference Laboratory (NRL) in Kinshasa using real time reverse transcription-polymerase chain reaction (rRT-PCR). Analysis of antiviral resistance by enzyme inhibition assay and nucleotide sequencing was performed by the Collaborating center in the USA (CDC, Atlanta). RESULTS:Out of 2,376 nasopharyngeal swabs collected from patients, 218 (9.1%) were positive for influenza virus. Among the positive samples, 149 were characterized as influenza virus type A (Flu A), 67 as type B (Flu B) and 2 mixed infections (Flu A and B). Flu A subtypes detected were H3N2 and H1N1. The Yamagata strain of Flu B was detected among patients in the country. Individuals aged between 5 and 14 years accounted for the largest age group affected by influenza virus. All influenza viruses detected were found to be sensitive to antiviral drugs such as oseltamivar, zanamivir, peramivir and laninamivar. CONCLUSION:The present study documented the possible involvement of both circulation of Flu A and B viruses in human respiratory infection in certain DRC provinces during 2015. This study emphasises the need to extend the influenza surveillance to other provinces for a better understanding of the epidemiology of influenza in DRC. It is envisioned that such a system would lead to improved disease control and patient management

Frameworks for Preventing, Detecting, and Controlling Zoonotic Diseases

Preventing zoonotic diseases requires coordinated actions by government authorities responsible for human and animal health. Constructing the frameworks needed to foster intersectoral collaboration can be approached in many ways. We highlight 3 examples of approaches to implement zoonotic disease prevention and control programs. The first, rabies control in Ethiopia, was implemented using an umbrella approach: a comprehensive program designed for accelerated impact. The second, a monkeypox program in Democratic Republic of the Congo, was implemented in a stepwise manner, whereby incremental improvements and activities were incorporated into the program. The third approach, a pathogen discovery program, applied in the country of Georgia, was designed to characterize and understand the ecology, epidemiology, and pathogenesis of a new zoonotic pathogen. No one approach is superior, but various factors should be taken into account during design, planning, and implementation

Flow chart of the cases selected for each analysis.

<p>Flow chart of the cases selected for each analysis.</p

Accuracy of the minimum number of signs/symptoms in predicting laboratory-confirmed monkeypox cases.

<p>Accuracy of the minimum number of signs/symptoms in predicting laboratory-confirmed monkeypox cases.</p

Association between clinical characteristics and laboratory-confirmed monkeypox (MPX) or varicella (VZV) case classification.

<p>Association between clinical characteristics and laboratory-confirmed monkeypox (MPX) or varicella (VZV) case classification.</p

The number of suspect monkeypox (MPX) cases that were captured by Case Definitions A and B by their MPX laboratory case classification.

<p>The number of suspect monkeypox (MPX) cases that were captured by Case Definitions A and B by their MPX laboratory case classification.</p

Diagnostic measures^a of significant^b clinical signs/symptoms.

<p>Diagnostic measures<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005857#t003fn001" target="_blank">^a</a> of significant<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005857#t003fn002" target="_blank">^b</a> clinical signs/symptoms.</p

Summary of the diagnostic parameters for both case definitions.

<p>Summary of the diagnostic parameters for both case definitions.</p

Characteristics of suspect monkeypox (MPX) cases identified between December 2009 and February 2014 (n = 752).

<p>Characteristics of suspect monkeypox (MPX) cases identified between December 2009 and February 2014 (n = 752).</p